Hi all,
since I now know that the Qnap AI is inferencing / pre-trained, I wonder how often that AI improves simply said - or how often there are updates, considering the fast development of AI in general.
I honestly have to say that I am not very stunned by the object recognition (it's a pity because i like the UI and handling of Qumagie). It barely helps me with my work, and I mostly find relevant pictures only when someone tagged the photos beforehand or the word is in the title. e.g. we have 580.000 photos atm and it only finds one picture with ice cream - and we definitely have more ^^ And I can't tell him to search for more ice cream damn it :D
Yes, I am an impatient guy, so I started object recognition again with all of the photos already in the index. But the outcome was pretty much the same again. Some categories are simply filled so wrong or don't contain a lot of photos, despite me knowing there must be thousands. e.g. we have soooo many football (soccer) photos, and only 88 of them are categorized xD I remember once testing DigiKam for our company, and it even recognized that it's a group photo or portrait (combined with other search terms - awesome!), and also the object recognition worked well (that was even b4 the new AI wave). Unfortunately it wasn't really suited/practical for us.
Am I doing something wrong, or can I do something about it? How often is there an update, or do I have to manually look for one every now and then? Any info appreciated, since we invested lots of working hours (and budget for the server setup) in that project. Thank you.
Sorry for the long post (I like to write) and for another "pleb" question, but our IT company can't help either, so it's Reddit I go to once again ^^
Cheers

leaders #science #opensource #tool #major_players #event #paper #dataset #startups #api #feature #release #update #hardware #opinions #scheduled

Yann LeCun, a renowned AI expert, shares his perspective on the nature of concepts and abstractions, stating that they do not necessarily involve language and that his speech is an approximate representation of his mental models [1][2]. He suggests that the difference in bandwidth between the visual system and the language understanding system may explain certain phenomena [3]. LeCun also believes that Large Language Models (LLMs) do not manipulate mental models and therefore will not achieve human-level intelligence on their own [4]. He clarifies that when he refers to "mental pictures," he is not talking about literal images but rather abstract mental models that can be manipulated, some of which may resemble pictures [5]. LeCun also highlights that while humans have more factual knowledge and linguistic abilities than dogs, dogs surpass humans in their understanding of the physical world and their ability to reason and plan [6]. He uses a common plot template found in novels and movies to illustrate the idea that learning a skill requires hands-on experience, not just theoretical knowledge [8].
Meta and IBM are advocating for openness and goals aligned with academia, startups, VCs, and the open source community. They feel that their voices are not being heard sufficiently by policy makers in the US, EU, and Canada. Individual EU countries like France, Germany, Italy, and Denmark support open platforms for AI for sovereignty and economic development. However, the EU commission and parliament are less favorable towards this approach [7][9].
Andrej Karpathy shares their experience of facing challenges with achieving optimal memory bandwidth on Nvidia GPUs due to limitations in the compiler and stack. Despite trying various optimization techniques, they are unable to surpass 80-90% memory bandwidth on certain kernels [10].
Google AI researchers Tomas Pfister and Sercan Arik will be hosting a live Q&A session at the #ICLR2024 Google booth today at 9:30 AM to discuss the latest in Cloud AI Research [11][12]. Google AI has made advancements in differential privacy research to enable private training of Gboard language models [13]. Google AI also presented at #ICLR2024 on using deep neural networks to potentially improve early detection of fetal hypoxia and enhance maternal/neonatal care [14]. A discussion on measuring and expressing uncertainty in language model predictions will also take place at the #ICLR2024 Google booth [15].
The Google Research Connectomics team, in collaboration with Harvard University, has published a 1.4 petabyte human brain connectome in Science Magazine. This groundbreaking work reveals new structures within the human brain and marks the 10th anniversary of the team's formation. The dataset is available for researchers to explore and refine, opening up new avenues for further discoveries in neuroscience [16][17][18][19].
Cohere has introduced Command R Fine-Tuning, a new powerful model that offers superior performance on enterprise use cases at a fraction of the cost compared to larger models in the market. This advancement enables enterprises to efficiently scale AI to production. The model consistently produces top-notch results in various industries such as finance and scientific research. The Cohere Command R with fine-tuning is available on the Cohere platform, AWS SageMaker, and will soon be accessible on additional platforms [20][21][22][23][24]. Cohere is also hosting a Build Day event in San Francisco where participants can create innovative solutions using Command R and R+ to build enterprise-grade knowledge systems and multilingual customer chatbots [25][26].
NVIDIA AI Developer shared free courses in generative AI for learning AI [27]. Groq Inc announced that Marcin Komorowski has joined their team to work on their next generation chip for AI Inference solutions. They will also showcase the LPU Inference Engine at ISC24 [28][29]. NVIDIA is a dominant player in the AI chip market, with loyal customers and strong market dominance. However, other tech companies like Groq are starting to challenge Nvidia's position with new chip offerings [30].
Stability AI has announced Stable Artisan, which allows users to access advanced models like Stable Diffusion 3, Stable Video, and Stable Image Core to create high-quality media directly within Discord [31]. Amdocs collaborated with NVIDIA to enhance customer experiences in telecom contact centers by using an LLM-based solution for billing inquiries [32]. DocuSign is utilizing NVIDIA Triton and Azure to leverage contract information, converting agreement data into actionable insights, speeding up contract review processes, and enhancing productivity [33]. NVIDIA Morpheus, an AI-driven cybersecurity framework, can be used to enhance anomaly detection in Linux audit logs [34].
Andrew Ng recently spoke at an event at the U.S. Capitol about AI and regulations, highlighting the importance of protecting open source technology. OpenAI has been licensing news archives from publishers like Financial Times to train its models. A new algorithm called Deja Vu has been proposed to accelerate the inferencing of large language models. A computer vision system developed by Safe Pro Group called Spotlight AI is being used to identify landmines and... (incomplete)
1. Yann LeCun @ylecun https://twitter.com/ylecun/status/1788446833640874115
2. Yann LeCun @ylecun https://twitter.com/ylecun/status/1788448916435841532
3. Yann LeCun @ylecun https://twitter.com/ylecun/status/1788450011929284773
4. Yann LeCun @ylecun https://twitter.com/ylecun/status/1788450332386722156
5. Yann LeCun @ylecun https://twitter.com/ylecun/status/1788451367645774276
6. Yann LeCun @ylecun https://twitter.com/ylecun/status/1788453799566233980
7. Yann LeCun @ylecun https://twitter.com/ylecun/status/1788464185921130617
8. Yann LeCun @ylecun https://twitter.com/ylecun/status/1788473350177599888
9. Yann LeCun @ylecun https://twitter.com/ylecun/status/1788553995755110788
10. Andrej Karpathy @karpathy https://twitter.com/karpathy/status/1788528061027152221
11. Google AI @googleai https://twitter.com/googleai/status/1788458770386800655
12. Google AI @googleai https://twitter.com/googleai/status/1788459202681205195
13. Google AI @googleai https://twitter.com/googleai/status/1788482498751525279
14. Google AI @googleai https://twitter.com/googleai/status/1788493400800366830
15. Google AI @googleai https://twitter.com/googleai/status/1788530862868349215
16. Google AI @googleai https://twitter.com/googleai/status/1788634055400903138
17. Google AI @googleai https://twitter.com/googleai/status/1788635682799280217
18. Google AI @googleai https://twitter.com/googleai/status/1788636515150512178
19. Google AI @googleai https://twitter.com/googleai/status/1788682690172260693
20. cohere @cohere https://twitter.com/cohere/status/1788581173645500712
21. cohere @cohere https://twitter.com/cohere/status/1788581175222583683
22. cohere @cohere https://twitter.com/cohere/status/1788581180201123861
23. cohere @cohere https://twitter.com/cohere/status/1788581186257748251
24. cohere @cohere https://twitter.com/cohere/status/1788581193258050037
25. cohere @cohere https://twitter.com/cohere/status/1788716407309705456
26. cohere @cohere https://twitter.com/cohere/status/1788717270614904905
27. NVIDIA AI Developer @NVIDIAAIDev https://twitter.com/NVIDIAAIDev/status/1788630020128206918
28. Groq Inc @GroqInc https://twitter.com/GroqInc/status/1788559302170480784
29. Groq Inc @GroqInc https://twitter.com/GroqInc/status/1788631206537359803
30. Groq Inc @GroqInc https://twitter.com/GroqInc/status/1788676335251910810
31. Stability AI @stabilityai https://twitter.com/stabilityai/status/1788634334145945648
32. NVIDIA AI @NVIDIAAI https://twitter.com/NVIDIAAI/status/1788569619336224787
33. NVIDIA AI @NVIDIAAI https://twitter.com/NVIDIAAI/status/1788645113465311330
34. NVIDIA AI @NVIDIAAI https://twitter.com/NVIDIAAI/status/1788660217028231563
35. Andrew Ng @AndrewYNg https://twitter.com/AndrewYNg/status/1788648531873628607
36. Greg Brockman @gdb https://twitter.com/gdb/status/1788694574149337336

This is the second of two posts. If you haven't read the first one, you'll want to here.
The recording pauses for another two hours. When it resumes, Jiyuuila and the small Arxur have resumed their trip, as inferenced from the regularly spaced booms of her footsteps echoing off the walls. The microphone shakes more than usual with each one, and poor positioning causes Jiyuulia’s voice to be slightly out of focus. A light mechanical hum comes from somewhere nearby.
Hey! So we’re back, and this time… Haagghhh… we’re making progress!
*Slowly.*
With some caveats. But we are getting somewhere!
…Which… Uugh… I suddenly realize that I’ve yet to specify where somewhere is, exactly.
*Squishy!*
I know, I know. But there was the chase, and the injuries, and — agh, no, that’s getting off into the weeds again when I really should be—
*We’re going to the gear!*
Yeah! That! Thanks, Kyrix.
*You needed it.*
…As my charge has so succinctly put it, Kyrix isn’t randomly directing us through these danger-filled tunnels just for the fun of it.
*Hey!*
The main goal right now is twofold: One, we need to find the rest of the crew and figure out how many survived the initial attack. Two, we left vital equipment and supplies behind when we fled, equipment that we’ll need if we want to survive down here long enough to figure out our next steps. Both problems are best solved if we can find our way back to where we started, before everything went to hell. Even just the mapping tools and the data collected by the scouting teams represent weeks worth of work for one individual to cover by themselves, not to mention any possible weapons, food, medicine, or other miscellaneous gear that might still be there. It’s likely that any of the other surviving crewmembers have had a similar idea, and there was at least one hunting party’s worth of Arxur out at the time that would’ve come back to an empty cavern and are probably still wondering where everyone went. Which I’m yet again realizing is another thing I was supposed to get to…
*And you’re supposed to move faster. Use your magic again!*
Jiyuulia huffs.
Oh, if it was just that simple…
Things would’ve been different, that’s for sure.
…
I believe I’d just finished telling the Great Hunters about how our group had gotten back together after its initial little disaster with the resident crazies, yeah? Kyrix and I being relegated to the front on account of being the ones with the light and all that.
Which, I mean, I did try to get out of — I feel I perform better when I’m in a supporting role myself — but as it turns out, Arxur are taller than Kolshians, even magic ones. The tunnel’s width was usually such that we were kinda forced into a single-file line—
*No we weren’t?*
…correction, forced into a side-by-side formation where I just so happened to count as two separate people, and lighting up someone’s back rather than the tunnel ahead was rather less-than-helpful, so I had to be up there.
*I thought it was because you kept getting stuck.*
Jiyuulia mutters something unintelligible.
Aside from being the optimal position for a variety of reasons, the front came with an important responsibility: Kyrix and I were mostly responsible for deciding which route looked best to take whenever the tunnel forked into multiple paths.
*So mostly the wider ones that went downhill. And you’re leaving me out!*
I was just — ugh, fine.
Alright, so this time around, Kyrix didn’t get out of the difficulties involved in forging new paths through unfamiliar territory with me. In fact, he actually had several special duties that he alone was able to perform, some of which were absolutely vital to our travel plan!
Starting with the most simplistic, and yet still one almost deceptively useful in both its innocuousness and specificity to him, is his newfound title of “Mortal Technology Weakness Alleviator.” Because while as the pad I carry remains both something that I alone can operate and harbors a level of usefulness that’s near-impossible to overstate, the unfortunate nature of the life-saving device is that, at the end of the day, it’s still a lowly common model that’s seen a few too many charge cycles in its lifetime, and that the battery life of the thing is nothing short of dismal.
Sure, Kyrix steals it away from me to play AR games for a few minutes here and then, but there’s a good reason I make him charge it afterwards, and that’s because that’s about all it’s got left in it anymore. And while it’s no game, the ceaseless draw of the flashlight on the back would’ve normally been far too much for the thing to handle; the twenty-minute battery life and similarly lengthy recharging time far too severe of a limitation on our group’s ability to make any sort of reasonable progress into the innermost depths of the tunnels. And in fact it was for a time, halting us almost as often as my own lackluster stamina and generally making the itself a great annoyance in our trip forward, but thankfully it did not take more than two angry sessions in the dark before a solution was drafted and implemented.
In an effort not to go stumbling down one of the many pitfalls of both the literal and metaphorical varieties that our journey presented, Kyrix himself came forward with a powerful idea: Utilizing his unique abilities and numerous qualifications stemming from his many hours of using the thing whilst he was further erasing any hopes I may have had for ever again seeing my initial upon the high scores board of any of my AR games, Kyrix made himself out to be a most unusual, and yet most excellent choice, warranting his immediate promotion to the position of lightbearer.
*All I did was point the light and turn the charger crank.*
Around and around did the charger crank turn, and only further ‘round does it go yet. Unprompted and entirely at the insistence of his own brilliant mind, Kyrix quickly settled himself into a more appropriate position over my left shoulder, and, barring a small number of awkward positional readjustments best left unsaid, the dedicated lever-puller utilized his immense determination and crank-turning prowess to ensure the continued operation of the low-grade mortal construct throughout the grueling fifteen-minute sessions we had between breaks as our party trekked through the foreboding bowels of the underworld. Truly, he was an inspiration to us all.
*At least I always lasted the full fifteen minutes.*
Jiyuulia chokes.
…You’ve got a way with words, you know that?
*You’re the one who’s spent two minutes talking about a crank.*
Fair point. Moving on, then.
More than just a generator, Kyrix’s portable size opened up a litany of other useful tasks he alone could perform. Considering that his locomotive abilities still fall short of even my own, (legs are rather important when it comes to movement, it seems) several of these were limited to theory rather than practice, but there was one in particular that did not require any locomotive abilities at all.
Poison gas detection.
It might not be what first comes to mind, but by far the most insidious trap down here has nothing to do with swarms of killer bugs or suboptimal tunnel width variations. Oxygen on this hellscape of a planet is already poor enough to begin with, and while airflow in these caves is usually more than enough to make up for it, that’s not the case everywhere. In the deeper bends, the stranger corners, and even sometimes in the odd and otherwise unassuming place for what seems to be for no discernible reason at all, airflow will halt for long stretches, leaving pockets of still air that can get to upwards of a mile in length. Worse, these air pockets weren’t just stuffier than an unwashed mammal’s bedroom — they actively facilitated the buildup of noxious and deadly gasses that served to forever silence any who foolishly strayed into their silent chambers.
Now, it wasn’t as bad as it could have been. We weren’t a ramshackle band of primitive Yotul stoneheads swinging flaming sticks around our heads for light, and our total lack of equipment meant we lacked the clinking, sparking metals required for a more traditional mining accident.
Fiery death aside, there was still the risk of losing consciousness for the last time in some dark, dank tunnel somewhere as our minds were addled beyond the ability to recognize our impending doom, our perfectly-preserved corpses lying facedown in that same tunnel forever, never to be reclaimed by either nature nor fellow sapient…
Jiyuulia shudders. The Arxur yelps, scrabbling to hold on.
Less than appealing, to say the least. Such a fate would not exactly have been considered a warrior’s death under your eyes, Great Hunters, and given the whole nature of this trial thing we’re not really here to die at all if we can help it, so we needed a better option. And we had one! I’ll let him do the honors.
*Hah! Get ready!*
*So the idea was that sometimes the gas pockets were small, like me, so we could go through them if we had to and be okay. But because each trap was of different badness and we didn’t know how long we could spend in each one, we needed something to tell us when the trap was getting too long and we needed to turn around.*
*So, after the first time we nearly all died, Khogue came up with a really good idea! If I’m small, which I am, then my lungs are too, and that means I pass out faster. So if we just turned around whenever I passed out, then we could get out of the gas pocket before anybody else fell asleep too, and then we’d all be okay!*
More or less that way, at least. And the first time was very nearly the last time too, it wasn’t just you we had to haul out, Kyrix. Had it been Giznel or the Pilot on the floor, I don’t think we would’ve all made it out. Also, “Khogue?”
*Squishy! You forgot already? Khogue came up with the idea after he woke up. It wasn’t you, I passed out first and was busy waking up, Giznel was huffing too hard to speak, Kyrix was being mad again, and Selkasthithmerkzalgilnashzim is too dumb to think of good ideas like that.*
…I’m beginning to remember something about carbon monoxide poisoning being cumulative.
*What?*
Exactly.
*That’s not helpful.*
Jiyuulia exhales loudly.
Never mind.
*Squishy—*
Anyway, despite any possible misgivings some people may have had about using a living thing as an indicator valve, it was a “good” idea, one of the few that have arisen so far. Which really says a lot about our situation, most of the discussions I’d overheard from before our little trip down the river didn’t go nearly so well. I guess there’s something to be said about small groups.
That doesn’t mean that there weren’t, like, several dozen times a fight almost broke out between various parties over nothing, and at least five or so of those times something really did happen and I had to sit and watch four Arxur warriors stage a pitched battle maybe ten or so feet from my easily pierced flesh before, after a few minutes, somebody would finally surrender their “argument,” and we could move on.
Thankfully, those five were concentrated towards the beginning. Not because of any flagging sense of comradery, or even practical realizations about how to best allocate our resources, but because as it turns out, I can have ideas too, even if only unintentionally and as a result of exasperation rather than any true planning. Maybe I just have a talent for mediation, but most fights broke up pretty quickly after I just refused to bother stopping for them anymore. Art’s hard to do in the dark, after all.
*Except it only worked when your legs weren’t on break.*
I don’t see you walking on your own.
*I don’t need to. Yours are big enough for two people.*
…Thanks, Kyrix.
*Maybe three! Or four! It’s hard to tell.*
I’m flattered. Moving on.
*Yes, move faster.*
Jiyuulia huffs.
Back to the other crewmembers, I’ve been perhaps a bit too harsh on them. They weren’t all bad, after all. Call me an optimist, but I don’t think the Dominion would’ve kept them around if that were the case. And I’m not just talking about Giznel, either. Each and every one of them served a purpose, even if Kyrix… okay, I admit defeat. Kyrix, is that really his name?
*Kyrix is Kyrix. Also-Kyrix is also Kyrix, Also-Also-Kyrix is also also Kyrix, Also-Also-Also Kyrix is also also also Kyrix. It’s easy.*
FOUR?
*Yeah? I know like fifteen Kyrixes! It’s a good name. Strong leader. Very famous.*
But—
*I don’t really get why you’re confused. Kyrix the pilot. Kyrix the hunter. Kyrix the farmer. Kyrix the me!*
…Of course. How silly of me to expect anything different.
Well then. I kinda had something to say about Also-Kyrix, but whatever. He’s target practice.
Jiyuulia sniffs.
So the Butcher, Selkas… help me out again?
*Selkasthithmerkzalgilnashzim.*
You’re joking.
*No! I would never lie to the Great Hunters!*
But that’s like eight syllables! She never speaks more than ten!
*It’s not a joke!*
Jiyuulia’s breath hitches for just a moment.
Alright, alright, calm down. I believe you. Can you spell her name for me?
*What’s spelling?*
Jiyuulia goes silent, her heavy footfalls ceasing instantly as she comes to a rapid halt. She says nothing, remaining still for just over eleven seconds.
…I see we both have some things to learn later.
*You’re gonna teach me magic?!*
N— you know what, sure, why not? It’s totally magic.
*I’m gonna blow up mountains!*
You’ve already done that, if I recall. But I digress.
So the Butcher, Selkasthithmerkzal…
*Selkasthithmerkzalgilnashzim.*
Ugh, can’t I just call her Selkas? Or maybe Thith?
*Oh no, don’t do that. She doesn’t like people doing that. Lyrylef tried it once, and she stole her tongue!*
Ooof course she did. I should’ve figured.
Jiyuulia shuffles. Her heavy footsteps resume their pounding down the tunnel.
Alright, so with that history lesson out of the way, the Butcher, who is apparently named Selkasthithmerkzalgilnashzim — because of course she is — deserves the most credit after Giznel. For all she lacks in basic dignity, beauty, cognitive ability, geniality, kindness, personality, mental stability, or in fact any other sort of normally redeeming traits at all, actually, she’s still a hulking 7’1” Arxur warrior with the shoulders to match. And while she might not exactly have been capable of being left to her own devices, a hastily shouted order was more than enough to get her to take action.
Take, for instance, the first gas trap. Our resident artist only came back with the rest of us because she dragged his half-dead corpse out at a full walking pace. Which is a bit surprising, considering her mouthbreathing tendencies would’ve had me expect her to be the second out after Kyrix. Then again, drug effects are relative to body mass, and — I’m getting off track again.
Either way, he lived, which was neat. We were pretty sure he wasn’t going to, for a time. I still haven’t heard a thank you for all that CPR I had to give him, but, well, I can’t say I’m all that surprised. I don’t think anyone involved much enjoyed it.
*Don’t forget how she helped you too!*
Ah, right, yeah. Credit’s due where it’s due. The tunnels get a bit thin sometimes, and a bit of assistance through the occasional not-so-tight squeeze was much appreciated. She was certainly… efficient, when needed. There’s something to be said for that, even if it did result in a few more new rips and tears in my clothes than I’m particularly comfortable admitting.
Don’t get me wrong here, I’m still not exactly a proponent for the idea of having to get close to, much less touch a creature that’d tear my throat out without shame or remorse in two seconds flat. But when compared to the alternative of being stuck in some small section of the tunnel for the next few days, getting to experience the whole of dehydration all over again… well. I can’t say I didn’t appreciate the assistance.
…I did say thank you, by the way.
…
I probably should cover him too, yeah?
Jiyuulia sighs.
Alright.
The far more reasonably named, yet somehow even less reasonable Khogue wasn’t purely a drag either. While not a one of us had a clue as to where we’d ended up after the whole river debacle, and navigating a 3D underground environment is not exactly on the list of things many people are good at — again, what I wouldn’t do for a Gojid — but he wasn’t as far gone as the rest of us. Khogue’s… taste (literally) for art has left him with quite the perceptive eye, and I hate to say it, but a flair for creativity as well.
Aside from being the “genius” who came up with our new air quality test, he’s always the first to notice things about the environment around us. Striated rocks in the walls turning a slightly different shade of yellow, strange smells at the very edges of Arxurian perception, unusual drops and shifts in air currents… he’s no geologist, but there’s a reason he got sent out on scouting missions with everyone else, despite his proclivities. We’d have gotten even more lost a long time ago if it weren’t for him.
Oh, Kyrix and I still jointly held the navigator position. Somebody’s ought to be the judge of which ones look traversable, and who better than me to decide just how tight we wanted to go? Not to mention we still had the light, which was definitely a deciding factor. But that doesn’t mean his sense of direction didn’t have us soundly beaten when it came to forks with multiple viable options, and hence why the general plan for getting back involved something along the lines of “wander around until he recognizes where we’re at, and then just follow him back to camp.”
*The plan wasn’t that bad!*
Really? Do explain what it was, then.
…
Yeah. Not really all that great.
Besides, it hardly matters now anyway. As mentioned earlier, you guys may have noticed that plan’s become a little hard to follow as of late. While that means the crew isn’t here to hear me berate their existences — which is very good for my expected lifespan — that also means that Kyrix and I are totally alone and lost in some dark tunnel thousands of feet underground with no way out, which has rather the opposite effect. As for where they’ve gone, and why they’re not here with us right now… I, aheh, may have had something to do with that.
Hey hey hey, I had a good reason! Dissolution of the group is still preferable to dismemberment of the group.
*Squishy hit a rock. I helped.*
Mmhmm. I’m sure you remember the swarm of bugs, Great Hunters? That unassailable cloud of death that heralded the coming of yet another change in how we’ve conducted this adventure, and the further threat of which has kept us on high alert ever since?
Well, as I’m sure you’re not surprised to hear, (you are primarily responsible for this whole test, after all) and something that really shouldn’t have surprised us given the intended lesson last time, but we’re not alone down here.
We were taking a break in a more open section of the tunnels at the time, dirtying ourselves in some damp, dirty room, spanning maybe twenty or thirty feet across and about that many wide. The walls around us were beginning to show oddly familiar signs of wear, and Khogue had reasoned that while he may not have seen these exact patterns of chips and cracks before, he had seen something similar enough during his scouting expeditions to warrant checking it out.
I’m not one to complain when people want to take a break, and the foundations were strange and oddly weak-looking, so a quick stop to survey them was far from out of the question, at least to most of us. Also-Kyrix the pilot was off yelling about something again, and suffice it to say I didn’t really care any more by that point, so I can’t exactly tell you what it was all about beyond him having some sort of misgivings over our laziness, but just because I wasn’t paying attention doesn’t mean something else wasn’t.
Such as, for example, a gigantic eyeless beast the size of a large hovercar.
*A true hunt, like in the stories!*
Of course, it had to be covered from head to tail in a solid coat of those same hardened plates everything else down here’s had, so it was already basically invulnerable, but I’m going to assume you added the four-inch claws and rock-crushing maw on as a joke. I don’t know what kind of combat capabilities you expect us to have, Great Hunters, but I’d like to remind you that we’re a duo consisting of a lamed Arxur four-year-old and a supernaturally unfit Kolshian specializing in utility spells. As it stands, we couldn’t take on a regular monster, much less deal with nature’s equivalent of a tank.
That thing came crashing through the wall with a deafening roar, evidently a creature so strong that it had no need for stealth or subtlety. For whatever unknown reason — though I think I may have a compelling guess — it decided to use those same claws it’d just used to burst through stone against us.
It went for the pilot first, obviously. Not even five seconds in, and two tons of an even worse kind of predatory monster came lunging through the middle of the room straight for him. I think it wanted him to shut up just as badly as anyone else. Can’t blame it, really.
And hey! That just means we can pin all the blame on him. Not like anyone (else) will contest it.
“Luckily,” Also-Kyrix managed to dodge at the last moment, barely avoiding evisceration in what was honestly a pretty impressive move. And for all their faults, the team of warriors was able to put aside their bickering pretty quickly once a greater danger presented itself, and the four Arxur were up and roaring to go before it could get another swipe in. Not bad, all things considered. I’d seen far worse from them before.
Unfortunately, however, beyond the initial phase, the battle went about as well as you would expect from pitting four unarmed infantry against an armored vehicle. That is to say, poorly.
If one was being generous, they might raise the argument that the monster this time around gave us the mercy of actually being something somebody could feasibly attempt to defend themselves against, but in all honesty, it really wasn’t. While it wasn’t quite on the same level as the bug swarm, still more of a monster than an environmental hazard — though it was doing its level best to change that — that doesn’t mean that it wasn’t a physical incarnation of some dread hellbeast straight from a cheesy Harchen horror film.
Battle strategies came and went, but it was hopeless from the start. With the thing coated in that same strange material as everything else the hunting teams had come across in the caves had, it was no more vulnerable to swinging Arxur claws than your typical concrete bunker. The most damage anyone managed to deal through a traditional attack involved Selkthith— Selkasfifth— the Butcher jamming a claw up its nose and whirling it around up there. And while the strategy certainly punctured its defenses, the gush of reddish black oozing down its face a more-than-inspiring sight to those of us with more violent aptitudes, the new wall decoration that immediately formed thereafter when it charged forward and headbutt her into flying across the room for her achievement probably had somewhat of a mitigating effect. The three sets of spare ribs I heard snap after it swung its tail around into the rest of the crew, even more so.
Before I go on: Kyrix, you have anything to add? You’re kinda silent up there.
*No, you’re doing good. It was very exciting!*
Alright, then.
So, before you go laughing at the crew for their failure, I’d like to dispel any notions you might be getting about their combat prowess. The truth is, the four Arxur fought valiantly, each and every one a true predator. I wouldn’t even call their strategy all that flawed, seeing as how it was all they had. Flight wasn’t an option, and their backs were against the wall. While it might not have been the greatest showing of force — stars, it was a total loss even — they put up enough of a fight to keep the thing distracted enough to avoid having it charging the two least combat-oriented members of the group and instantly snuffing out our non-lives, so there’s certainly not as much to complain about as there could have been. Not that I’m sure how I’d complain if I’d ceased to exist, but you get the picture.
But at the same time, no matter how impressive the Butcher’s rage may be or how thankful I am that the crew managed to buy me a whole fifteen seconds before getting taken out, the reality is that they never stood a chance against the monster. Not with their claws alone.
Good thing those of us still standing — or straddling the shoulders of those still standing — didn’t limit ourselves so tightly, then.
*We used big rocks!*
Kyrix deserves all the credit for the idea. I was, uh, “not in the necessary headspace at the time” to come up with the more, ahem, creative solutions, but luckily, I didn’t have to this time. Sometimes the part where your charge is an actual predator who’s used to watching people being torn apart every day and not thinking anything of it helps!
Other times it gives you extremely dysfunctional ships’ crews and whole civilizations teetering on the brink of collapse, but hey. Us blasphemous herbivores ought to be good at something, huh?
Anyway, you remember how we got off the station, right Great Hunters? Well, call him unoriginal all you want, but given its previous effectiveness and his list of resources starting and ending with that of one half-catatonic Kolshian wrecking ball he could yell at, not to mention that Kyrix classic “I’m-about-to-die” desperation, poking and yelling at me until I ran headlong into the least stable-looking load-bearing wall he could find was really probably the best plan he could’ve made. And, seeing as how we’re both still alive, it was a highly effective one too!
The room we were in was old and unstable to begin with, and the removal of a critical support column instantly collapsed the cavern in a shower of falling debris. A few dozen tons of falling rock proved too much for even the monster’s hardened carapace, and suddenly it was no more.
*It went squish! Or squelch. I don’t know what the difference is.*
So, in other words, I lived up to my name, (again) and we were insanely lucky to survive. While the monster’s your fault, I should probably thank you for your generosity with the miracles as of late; not being flattened into a thin paste as a result of my cleithrophobic tendencies is certainly appreciated.
That being said, I’d also appreciate it if you’d stop setting me up for needing so many. Please don’t take this the wrong way, I’m fully aware of the journey of a Hunter Potentiate and his Spirit of Bounty is a perilous one, the hunter’s worthiness must be without question, but hooo does this seem a bit excessive sometimes.
I mean, the shining blue light stemming from a brand new hole in the wall, tastefully placed on the other side of the new rubble wall that split our group in twain? The fresh opening to a new, healthy moss cavern to the four suddenly ecstatic Arxur warriors who got to go explore a new paradise while we got left behind, those we saved completely unable to do more than wish us well as they strut off to safety? A glimpse of salvation, forever beyond our reach, our souls damned to return to the infinite darkness of the blackest corners of the world in our continued search for an exit to this never-ending labyrinth?
Absolutely, devastatingly, soul-crushingly cruel. Let it not be said that you do not test the Potentiate’s emotional fortitude with even but an ounce less harshness than you do his practicals.
…
As for us now, well, that’s the latest of it. It’s been a few hours since then, us having long since left behind the site of our near-demise and all those who accompanied us. Us, trudging through the bowls of the underworld, darkness only held back by the light of a single pad, almost completely alone and defenseless, all conversation exhausted long ago in favor of saving my breath for progress…
*Hah!*
I am not usually one to criticize the plans of the Great Hunters, but overall? The experience so far has been less what I would call “predatory” and more what I would define as “bleak.” You would know best as to what he needs, but it’s been tough all the same. I even understand the irony of my complaining, especially in the face of your previous generosity, and yet…
Ah, well. It’s only been a few hours, and you did bless us with the knowledge that new moss caves are both extant and accessible using the tactics we have utilized thus far. Our goals need not seem as impossible as they did before, and for that, I thank you. Even if we can’t go the same way as everyone else, you’ve granted us all the power in the world to ensure we may find our own way forward, true to the hunter’s creed. I may not be a hunter, but I have proven myself a weapon, and Kyrix himself has proven a competency and worthiness in wielding me. The time-honored status of a Hunter’s weapon is not one I take lightly, even in the highly unusual circumstances we find ourselves in.
Jiyuulia snickers under her breath. It’s clear she’s having fun with this. The Arxur does not seem to notice.
Admittedly, it has been nice to not have to worry about being shanked in the back by an irate lunatic. Even if that means that I must take extra care with the squeezes, the threat of getting stuck and all but guaranteeing a slow and painful death weighing heavily on my mind every time my sides brush the walls, being allowed to bear the challenge without having to listen to a squabbling horde behind me as I do almost makes up for it. Almost.
…
Really, I guess I’m just asking for something to happen. The whole “defenseless in a featureless cave” thing is kinda starting to drag on a bit. The tunnels are starting to all blend together.
The Kolshian goes mum for about three minutes, the resounding thud of her footsteps and whooshing gusts and grunts of her breathing the only sounds she makes as she continues her way down the tunnel. The Arxur remains quiet as well, apparently having nothing to add. The whirring of the charger crank persists through it all, uninterrupted.
Huh?
Jiyuulia speeds up, huffing madly as she begins to approach her limit. The pace of her steps is faster than it has ever been before during an entry.
You can’t be serious.
Her pace continues to speed up. The Arxur can’t seem to decide whether to breathe or not, switching between overwhelmingly fast hyperventilation to not at all seemingly at random.
It wasn’t even five minutes ago!
Lack of records and Jiyuulia’s unusual physical condition make it difficult to predict the length of her strides, consequentially rendering the act of making any estimates on her exact speed impossible. Whatever their length, the interval between her feet lifting and falling continues to shorten, falling further and further before suddenly, a foot is lifted before the other is allowed to fall, and the thud of her footfalls amplifies exponentially.
WHAHAHAHA!
The run only lasts a few seconds before it breaks down, one penultimate footfall crashing through something brittle before sliding on something that sounds like loose, rough gravel. The rest of the Kolshian comes down shortly thereafter, the deafening slam sending the tiny fragments scattering every which way as her body plows a new trench through the gravel. The Arxur, and consequently the microphone it’s holding, are sent flying through the air, coming down in a much lighter secondary crash that does not seem to harm either it or the device. For whatever reason, despite the abrupt and violent stop, Jiyuulia’s exuberant behavior only heightens further, breaking into an impressive belly laugh that only further serves to disrupt the position of the stones coating the floor. The Arxur joins her in expressing delight. Its laughter is somewhat chittery, clicking at regular intervals as its breath tightens and loosens rapidly.
*You did the magic! You did the magic!*
Lasting nearly two and a half minutes, computer estimates suggest that the raw, animated display of sheer mirth is at least 62% driven by hysteria, with an error margin of 17% given the unusual vocal registers and/or poor reference data available for both subjects. Several times does the laughter begin to calm slightly, only for one of the subjects to break out in a new wave of laughter that inevitably causes the other to collapse into a fit of their own. Even after it finally stops, neither party seems willing to speak, Jiyuulia herself refraining from speech for another thirty-eight seconds. She drags herself into a seated position somewhere around the sixteen second mark, gravel bouncing as she lifts herself up and reaches for the pad. When she finally does speak, her voice is exhausted, but jubilant.
Okay, I might have to convert.
Ahem, I meant, wow! Not quite what I was expecting.
*Thank you thank you thank you thank you—*
Jiyuulia stands. It’s a laborious process, requiring far more effort than such an act ever should, but she stands. Gravel crunches oddly beneath her feet as her feet plow through more of the substance. Eventually, the Arxur finds its way back onto her shoulders, seating itself without pausing in its ceaseless chanting.
Wow. That’s the only word for it.
*Blue light! Moss! A dead glowstick on the ground! We’ve found it!*
Jiyuulia begins walking forwards. She’s faster than usual.
*Go faster, go faster!*
I mean, what are the odds? Us, finding another moss cavern within hours of being separated? And one that’s been inhabited recently? Burn marks everywhere, and an old, used-up glowstick in the middle? We’ve found the lost hunting party!
*Rope! Guns! Meat! Safety!*
It’s perfect! It’s ideal! It’s the best possible outcome!
Still a bit hysteric, Jiyuulia breathes in deeply, letting the breath out slowly. The Arxur continues to spout meaningless drivel.
There are literally no downsides! We can just follow the glowsticks, turn where they say to, and we’ll easily—
The Arxur goes silent.
Oh.
Oh no.
…
T-t-that’s a lot of bodies.
I. I see. See, uh, uhm.
…Wow.
Okay, okay. Calm. Rational. They want a description. A description. Uhh…
Okay, so. Bones scattered everywhere. Everywhere. More skeletons than I can count. Piles of bones taller than I am. All yellowed. Ancient. Arxur and… something else.
Gigantic stone vault door. Forty, maybe fifty feet in diameter. Massive hole in the bottom. Blue light shining through. Free of skeletons, except for one. Arxur. Slumped against the door. One arm missing.
Bullet wound through the skull. Ballistic.
What… what happened here?
Jiyuulia pauses. Neither she nor the Arxur say anything for nearly two minutes. She is not still. Bones clatter. Suddenly, she lets out a gasp, then grunts of exertion. Hard ceramics scrape against rough stone.
They were prey.
The bodies? The ones who aren’t Arxur? Their eye sockets are on the sides.
They were prey, and Listener? This was their final stand.
Jiyuulia fiddles with something she’s holding. It clicks, then begins to hum with a low note.
They did not survive.
Jiyuulia lifts the device to her shoulder. It’s not built for her physiology, slipping a few times in her haste, and even when she does figure it out, the positioning is still awkward. It’s heavy, but she’s stronger. The hum reaches a crescendo.
All… they ever did… was for naught. They… were forgotten.
Time seems to stop with the roar of the plasma rifle. It doesn’t sound like a modern-day Arxur raid rifle, but judging by the popping sounds of sizzling granite in the background, it isn’t supposed to. Jiyuulia flips a switch. The hum stops. The sizzle does not.
I don’t plan on making the same mistake.
File “Entry 8 – 00:57, January 14th, 2137.mp3” ended.
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A/N:
Hey now! It was was technically a whole three days shy of three months! Not three months at all!
...Now that that's out of the way, the story. The second arc finally gets to the point I had tried to get to waaay back in Entry 6, and oh boy is it time! But where is that point? Where has the fat squid found herself, deep in the bowels of the desert caves? And what of the rest of the crew, where'd they end up? I guess you'll just have to wait for Entry 9 to find out.
The chapter's long. Really long. 11,267 words long. 84 pages and nearly 30,000 words of planning done in the Entry 8 Content Google Doc long. I'm well aware that it's so long that it's not helping my Reddit metrics, and people aren't finding my story as a result. Which is really too bad; I think they'd enjoy themselves reading about the adventures of the obese squid and her emaciated lizard! But I'm also afraid it's likely to keep happening. I try, but this story has a mind of its own sometimes, and Jiyuulia demands the lengths she does — it just doesn't feel right otherwise. And with my responsibilities as they are, that just means the release cycle is gonna continue to be very slow. I'm going to keep trying to speed it up a little, but as always, I make no promises as to this fic other than that it will either be finished, or barring that, its outline posted. Hopefully it doesn't come to that, I'm as motivated as ever, but I feel it's worth saying.
Extra assistance came during the writing of this entry, so please give thanks to my editor Edmond Johansson and proofreaders u/Cummy_Wummys and u/kabhes, authors of Curing Malpractice and From Drugs to Meat, respectively! I have no doubt you likely already have, but if you haven't, you should go check their stories out too, they're pretty good! I should know, I help edit them.
I know it may be a bit atypical for Kolshfic writers, but I am not banned and still active on the official NOP Discord! There's a thread in the creator library on there for all your AH questions and desires, so please, if you have anything you need my attention for, please fire off a question over there! Otherwise, I await all of your silliest comments below. The orange arrows are admittedly nice too. Your validation and my happiness are one and the same!

"AGI may already be here. We just haven't woken it up yet."
That's my current operating hypothesis after snooping around the world of agentic AI and reading up some more methods that have yet to take.
The path to "first generation AGI" seems clear to me now, and if it's clear to me, it certainly should've been clear to the big labs.
Hot takes at the start (feel free to attack these points)

• AGI is imminent. Labs may already have operational AGI at this exact moment. Definitions of AGI are loose and fluid, but my own has remained "an AI model capable of universal task automation and the ability to autonomously carry out tasks." Human-level intelligence notwithstanding, but it would be helpful. This early type of AGI is not going to be the "Positronic Brain and Sapient Artificial Human in a Computer" some use as shorthand for AGI, but will likely have spooky abilities.
  
• Counterintuitively, labs that have not sought these methods likely will have reached the point of diminishing returns. Despite scaling laws holding up, cost of compute, limits of available data, and various other slowdowns means that those relying on foundational models and scaling alone will have maxed out soon enough, if they haven't already. GPT-5 might be as good as a static foundational model can get before improvements become difficult or even meaningless to discern.
  
• AI winter will never happen. However, bad luck and desperate over-hype can certainly cause an "AI Autumn." My definition of an AI winter relies on lack of valuable results leading to reduced funding, not just funding being reduced by itself. The AI bubble we're in very well could (and ought to) pop, and I would still deny that is an "AI winter", because GPT-4-class models can actually provide material value. In 1974 and 1988, GOFAI and expert systems provided no/outrageously minuscule value. The last time I felt an AI winter was possible was after IBM Watson's failure to provide any meaningful or useful benefits to users or companies in the mid-2010s, had "Attention Is All You Need" never been published. For an AI winter to occur would require not just funding to drop but for material advancements and papers to all but cease publishing for months or years at a time, and that would require all AI outputs be completely and utterly useless, which is absurdly obviously not the case. Though perhaps an "AI Nuclear Winter" could occur if world governments clamped down hard on AI research and forced data scientists to cease publishing anything new.
  

First, about First-generation AGI and a "universal task automation machine"
First-generation AGI (or weak AGI) is one of those terms I made up a while ago (alongside artificial expert intelligence, frozen AGI, and proto-AGI) to navigate that bizarro peripheral area between ANI and AGI that had long gone unexplored and ignored to describe a type of AI that possessed the universal capabilities of AGI without some of the more sci-fi concepts of artificial personhood.
Then I was reminded of Isaac Arthur and his explanation that automation is thought of wrongly, which is why we keep misinterpreting it. AI and robots don't automate jobs, they automate tasks. Consider this: since 1900, how many jobs have actually been fully automated? Not that many. Elevator bellhops, phone operators (to an extent), human computers, bank tellers (to an extent), and a few others. Yet how many tasks have been automated? Exponentially more, to the point we often don't notice it. Think of cashiers— money counting and physically scanning items has long been automated, but the job itself still remains. Self checkout and cashless stores only have had limited success. They might have more with new advancements, but that's not the point: the point is that mechanization and automation impact tasks rather than whole jobs, which is why the Automation Revolution seems to simultaneously be nonexistent and constantly affecting jobs at the same time.
Running with this led me to consider the invention of a Universal Task Automation, or a UTA, machine, as an alternative interpretation of an AGI.
Think of the UFO phenomenon and how it was recently rechristened to "UAPs" to take the phenomenon more seriously and reduce the connotations of alien life and New Age American mythology attached to "UFO." Perhaps UTA machine could have been that for AGI, if I felt there was enough time for it. UTA machines in my head have all the predicted capabilities of AGI without having to also factor in ideas of artificial consciousness or sapience, reverse engineering the brain, or anything of that sort.
Generally, foundational models match what I expected out of a UTA machine. But they are still limited at the moment. People have said that GPT-3, GPT-4, Gemini Ultra, and most recently Claude 3 Opus are AGI, or have debated upon it. I say they both are and aren't.
The phenomenon people are describing as AGI is the foundational model architecture— which indeed can be considered a form of "general-purpose AI." However, there's a few things they lack that I feel would be important criteria in order to jump from "general-purpose AI" to "artificial general intelligence."

Foundational models + Concept Search + Tree Search + Agent Swarms is the most likely path to AGI.

Concept search involves techniques for efficiently searching and retrieving relevant information from the vast knowledge captured in foundational models. It goes beyond keyword matching by understanding the semantic meaning and relationships between concepts. Advanced methods like vector search, knowledge graphs, and semantic indexing enable quick and accurate retrieval of information relevant to a given query or context. That said, a "concept" within an AR-LLM is a stable pattern of activation across the neural network's layers, forming a high-dimensional numerical representation of what humans understand as an "idea" or "concept." This representation is a projection of the original thought or concept, which is encoded in human language, itself a lower-dimensional projection of the underlying ideas.
Multi-modal models, which can process and generate information across different modalities (text, images, audio, etc.), have the capability to transfer information between these lower and higher-dimensional spaces. The process of crafting input tokens to guide the model towards desired outputs, is often referred to as "prompt engineering."
The capacity of a neural network (biological, digital, or analog) to maintain and access multiple coherent numerical representations simultaneously, without losing their distinct meanings or relationships, is what we perceive as "problem-solving" or "general intelligence." The more "concepts" or "ideas" a network can handle concurrently, the more accurately it models the mechanisms of problem-solving and intelligence, including social intelligence.
Tree search algorithms explore possible action sequences or decision paths by constructing a search tree. Each node represents a state, and edges represent actions leading to new states. Techniques like depth-first search, breadth-first search, and heuristic search (e.g., A*) navigate the tree to find optimal solutions or paths. Tree search enables planning, reasoning, and problem-solving in complex domains.
Demis Hassabis has said that tree search is a likely path towards AGI as well:
https://www.youtube.com/watch?v=eqXfhejDeqA
Agent swarms involve multiple autonomous agents working together to solve complex problems or achieve goals. Each agent has its own perception, decision-making, and communication capabilities. They coordinate and collaborate through local interactions and emergent behavior. Swarm intelligence enables decentralized problem-solving, adaptability, and robustness. Agent swarms can collectively explore large search spaces and find optimal solutions.
Andrew Ng recently showcased how important agents are towards boosting the capabilities of LLMs:
https://twitter.com/AndrewYNg/status/1770897666702233815

Today, we mostly use LLMs in zero-shot mode, prompting a model to generate final output token by token without revising its work. This is akin to asking someone to compose an essay from start to finish, typing straight through with no backspacing allowed, and expecting a high-quality result. Despite the difficulty, LLMs do amazingly well at this task!

...

GPT-3.5 (zero shot) was 48.1% correct. GPT-4 (zero shot) does better at 67.0%. However, the improvement from GPT-3.5 to GPT-4 is dwarfed by incorporating an iterative agent workflow. Indeed, wrapped in an agent loop, GPT-3.5 achieves up to 95.1%.

Again, necessarily, we are ill prepared for the convergence of these methods.
Agentic AI alone is likely going to lead to extraordinary advancements.
Take this AI-generated image of an apple. A friend sent this to me, and I personally am deeply skeptical of all the details of it (a lot of "anonymous, as of yet, unannounced" things in it), but the benefit of the doubt explanation is that this apple was fully drawn by an AI.
But not by diffusion, or by GANs, or any prior method. Rather, the anonymous researcher who had this drawn had instructed an experimental agent workflow powered by an as-of-yet unannounced LLM to generate an image of an apple ("give me a picture of an apple" allegedly), assuming the agent would utilize Midjourney to do so (see: https://www.youtube.com/watch?v=_p6YHULF9wA) as you actually can use early autonomous agents right now to do things such as using Midjourney or ChatGPT.
Instead, this particular agent interpreted the researcher's command a bit literally, and rather searched up what apples looked like, then proceeded to open an art program and manually draw the apple, paintbrush tool and fill-in tool and all. That image is the final result of which.
Now again, I'm skeptical of the whole story and none of it is verified, but it also tracks closely to what I've been expecting out of agentic AI for some time now. In a "Trust, but Verify" sort of way, I don't fully believe the story because it seems to match my expectations too closely, but nothing mentioned is explicitly beyond our capabilities.
Indeed, "agent-drawn AI art" is one of the things I've been passingly anticipating/fearing for months, as it almost completely circumvents every major criticism with contemporary diffusion-generated AI art, including the fact that it was allegedly manually drawn, and even drawn after the agents autonomously Googled the appearance of an apple. It just seems too humanlike, too "good," (and too convenient, because that also completely circumvents the "it's not learning like humans, it's illegally scraping data" argument) but again, that only seems unrealistic to those who don't follow the burgeoning world of AI agents.
Again, see this:
https://www.youtube.com/watch?v=Xd5PLYl4Q5Q
Single-agent workflows are like the "spark of life" for current models, and agent swarms are going to be what causes some rather spooky behaviors to emerge.
And that belies the larger point: current expectations of AI are driven by historical performance and releases. Most people are expecting GPT-5-class AI models to essentially be GPT-4++, but with magical "AGI" powers, as if prompting GPT-5 will give you whole anime and video games without really knowing how. We're used to how LLMs and foundational models work and extrapolate that into the future.
In fact, GPT-3 (as in the original 2020 GPT-3) with a suitably capable agent swarm may match a few of the capabilities we expect from GPT-5. Perhaps there is a foundational model "overhang" that we were blinded to due to a lack of autonomous capabilities (plus the cost of inferencing these agents makes it prohibitive for the larger models).
This is what I believe will lead to AGI, and likely in very short order. We are not at all prepared for this, again, because we're expecting the status quo (as changing and chaotic as it already is) to remain. The rise of agentic AI alone is going to hit those unprepared and unknowing like a tsunami as it will likely feel like AI capabilities leapt 5 years overnight.
This is a major reason why I say AI winter is not likely to happen. The claims that AI winter are about to happen are largely based around the claims that foundational models have reached a point of diminishing returns and that current AI tech is overhyped. I still feel the ceiling for foundational model capabilities is higher than what we see now, and that there's at least another generation's worth of improvement before we start running into actual diminishing returns. Those saying that "the fact no one has surpassed GPT-4 in the past year is proof GPT-4 is the peak" forget that there was a time when GPT-3 had no meaningful competitor successor for three years.
Generally what I have noticed is that no one seems to be interested in genuinely leapfrogging OpenAI, but rather catching up and competing with their latest model. This has been the case since GPT-2: after 2's release in early 2019, we spent an entire year seeing nothing more than other GPT-2-class models trickling out, such as Megatron and Turing-NLG, which technically were larger but not much more impressive, right up until GPT-3's launch eclipsed them all. And despite a three year gap between 3 and 4's release, few seemed interested in surpassing GPT-3, with even the largest model (PaLM) not even seeing a formal release and most others sticking to within the size of GPT-3. Essentially when GPT-4 was released, everyone was still playing catch-up with GPT-3, and have done the same thing with 4. Claude 3 surpassing GPT-4 is not different to that time when Turing-NLG surpassed GPT-2— it's all well and good, but ultimately GPT-5 is the one that's going to set the standard for the next class of models. Even Gemini 1.5 Pro and Ultra don't seem materially better than GPT-4, rather possessing much greater RAG and context windows but otherwise still within the 4-class of reasoning and capability. If nothing else, it seems everything will converge in such a way that GPT-5 will not be alone for long.
This is why I'm not particularly concerned about an AI winter as a result of any sort of LLM slowdown.
As a result of LLMs tapping out, that would only be a concern if GPT-5 came out and was only marginally better than Claude 3 Opus. We won't know until we know.
And again, that's only talking about the basic foundational models with their very limited agency. If OpenAI updated GPT-4 so that you could deploy an autonomous agent(s), we'd essentially have something far better than a model upgrade to GPT-4.5 (this is what I originally assumed the Plug-Ins and the GPT Store were going to be, which is why my earlier assumptions about these two things were so glowingly optimistic).
Point is, I simply feel AI has crossed a competency threshold that prevents any sort of winter from occurring. My definition of an AI winter relies on a lack of capability causing a lack of funding. In the 1960s and early 70s, researchers were promising AIs as good as we have now with computers that were electric bricks and total digital information that could fit inside of a smartphone charger's CPU. The utter lack of power, data, and capability meant that AI could not achieve even the least impressive accomplishments besides raw calculations (and even those required decent builds). If the researchers had accomplished 1% of their goals, that would have been enough for ARPA to not completely eviscerate all of their funding, as at least something could have been used as a seed to sprout into a useful function or tool.
In the 80s, things were different, in that computers were powerful enough to accomplish at least 1% of the aims of the 5th generation computer project, and the resulting winter did not completely kill the field as had occurred. The promise then wasn't even for AGI necessarily, but rather for AI models that bear a strong resemblance to modern foundational models. Again, something not possible without vastly more powerful computers and vastly more data.
Here, now, in the 2020s, the feahope of an AI winter is essentially that the general-purpose statistical modeling AIs we have now that have been widely adopted and used by millions, and whose deficiencies are more or less problems of scale and a lack of autonomous agency, are not superintelligent godlike entities promised by Singularitarians, and that will magically cause the entire field to evaporate once investors wise up, and then everyone currently using or even relying on GPT-4 will realize how worthless the technology is and cease using it and the entire suite of AI technologies available now entirely. While I think something akin to an "AI autumn" is very much possible if companies realize that expectations do outstrip current capability, I feel those saying AI winter is imminent are more hoping to validate their skepticism of the current paradigm.
This is dragging on too long, so reread the hot takes at the top if you want a TLDR.

tl;dr Direction is hard to call, she's seeing a near term peak and selling a) if the stock achieves her $545-575 PT, or if it breaks down after earnings. Support and re-accumulation at ~$435-420 levels. (Long term she sees NVDA with a larger MC than APPL.) Great detail and discussion in the whole long article. More analysis coming from her next week post ER.

Conclusion [moved up from bottom]:

Nvidia’s earnings outcome is not easy to read in the tea leaves. This is because the fundamentals are the best in the S&P 500 and the CFO has been clear that she has strong visibility for this quarter and into next year. It’s possible the company misses, but not probable (outside of something China related). Rather, Nvidia’s issues are sector-wide as semiconductor indexes and the bellwether TSM are looking weak on technicals. This would signal even if Nvidia beats/raises and the stock goes up, that its peer group may weigh on the company’s price action in the near-term. There’s also immense pressure that Nvidia raises, which may not be realistic given constraints on supply.
We’ve been crystal clear in both August and September that Nvidia has a move to $545 to $570 and this could mark the top. We continue to believe this is the price target where our firm will again take gains. If we don’t get there this evening, and price breaks down, then we will also take gains. In our opinion, this is the only way to procure a win-win scenario with a stock that holds a leading allocation in a portfolio that has extended 200% in one year.
source

Nvidia’s Fiscal Q3 Earnings Preview: The Pressure Is On

This article was originally posted on Forbes on November 21, 2023.
Nvidia has surged this year with 241% gains YTD, which has more than doubled the returns of the FAANGs. This is no small feat considering it’s widely understood Big Tech is holding up the broader market. Valuations are stretched and leadership is only narrowing; to say there’s pressure going into Nvidia’s report this evening is an understatement.
The outsized demand for the H100 has led to historic moments as Nvidia is expected to exit this fiscal year with quarterly data center revenue of $14 to $15 billion compared to $3.6 billion per quarter at FY2023 exit. Should these estimates be correct (we will get the official guide this evening), Nvidia will end the year with a bang with approximately 300% growth in the final fiscal quarter.
Wow, what a year. Investors may not truly appreciate what Nvidia accomplished given a global pandemic and shelter-in-place orders fueled triple digit growth in tech stocks three years ago. Yet, what Nvidia accomplished was entirely due to product-market fit and design prowess with no end of the world scenario needed. It’s rare what Nvidia did, which was to ignite demand of enormous magnitude.
It’s well known my firm was early to this move in Nvidia with a bold analysis that claimed Nvidia will surpass Apple in valuation by 2026. You can look forward to my firm updating the long-term thesis in the coming weeks with details on how Nvidia will close-in on the next trillion in market cap. But in the near-term, Nvidia investors face what makes or breaks a portfolio, which is the inevitable moment of when Nvidia will top and sell off, how to handle these enormous gains, and if Nvidia can surprise the market again now that it was the defacto leader in the Nasdaq’s historic rally this year.
My firm strongly believes that simply picking a stock is akin to playing a fantasy sport, whereas discussing how to manage the stock is what separates fantasy from the live game. On Nvidia, we’ve been quite clear that we were net buyers in 2022 and we have been trimming the position to take gains in 2023. Meanwhile, Nvidia has remained our largest position until very recently when we put a different stock as first place and Nvidia as second place. Although we typically reserve our trades for our research members, we’ve been open about our strategy of active portfolio management with this spectacular, winning position. Judging by filings by famous hedge fund managers, we are in good company with this strategy.
Going into this highly anticipated report, I’d like to provide my readers with more information on how we are managing our Nvidia position and what to expect from the earnings report. This is a near-term analysis whereas our long-term thesis that Nvidia will surpass Apple in valuation is still firmly intact.

Neck-Breaking Release Cycle: H200 is Hopping Ahead

Nvidia has a near-monopoly in data center GPUs, and one of its strategies to protect its moat is to upgrade GPUs quickly to where it’s hard for AMD, Intel or custom silicon to catch up. The release cycle from the H100 to H200 is neck-breaking, as a typical cycle is two years whereas the H200 will ship in volume one year following the H100. The B100 based on the Blackwell architecture is expected to hit the market at the end of calendar year 2024 with the X100 following soon after.
📷 Source: Nvidia Investor’s Presentation
If 2023 was the year AI accelerators made their importance known to Wall Street, then 2024 will be the year that memory and HBM3/HBM3E makes its importance known as the competition is going head-to-head at memory capacity and bandwidth per GPU rather than compute performance. This further translates to mean the AI race is more focused on inference for the next generation of GPUs as the neural network can be run entirely in memory without the need to move data back-and-forth with the external memory. The H200 is the first GPU with HBM3e for 141 GB of memory and 4.8 TB/s bandwidth. This will result in 1.6X to 1.9X better inferencing performance than the H100.
To drive the point further as to how important memory will be in the next generation of GPUs, the compute performance from the H100 to the H200 is not changing much. According to what the industry has seen so far from Nvidia’s GPU HGX 200 systems, there will be “32 PLOPS FP8” performance, which would be achieved through eight H100s with 3,958 teraflops of FP8 each. The translation is that Nvidia’s H200 upgrade is strategically focused on memory, which also translates to Nvidia feeling pressure from AMD as the MI300X will be the first GPU to hit the market with the memory capacity and bandwidth for full utilization to increase LLM inferencing performance.
By adding HBM3 and HBM3e memory, the compute engines get a performance boost, albeit at a higher cost as HBM3 costs 5-6 times more than typical DRAM. Fewer GPUs will be needed so the cost does not translate to an equal increase in total cost of ownership. GPUs with HBM3 and HBM3E will run compute-intensive large language models with fewer GPUs than is required with the H100s due to offering roughly double the memory. The need for fewer GPUs is accomplished by running LLMs in the memory. The H200 with 141GB of memory compared to the H100’s 80GB will reduce the number of GPUs required for running popular large language models.
If you read between the lines on the H200, then Nvidia is a bit nervous about AMD’s MI300X with the H200 serving as an attempt to bridge the H100 and the B100. AMD’s design more than doubles the memory of the H100 with 192GB HBM3 memory and 5 TB/s of bandwidth, and most importantly, will be out a few months prior to the H200. The MI300X was the first to run a 40B parameter large language model on a single GPU.
AMD should feel satisfied that it forced the near-monopoly leader to hurry toward releasing the H200 with HBM3e as an answer to the MI300X. We covered this in a deep dive for our premium members in July and reiterated it again in August when we covered our favorite memory stock.

What to Expect in the Upcoming Earnings Report:

The very quarter that Nvidia began reporting double digit negative revenue growth of (-16.5%) was the best buying moment. Near the bottom a year ago, our firm wrote for Forbes that Nvidia Was Ready to Rumble with the RTX 40 Series and the H100 GPUs. Notably, Nvidia is up 200% YTD yet is up over 300% since the October low, which is why timing matters.
One year later, and Nvidia is unrecognizable from where the company was exactly one year ago. For the October quarter, Nvidia is expected to report YoY growth of 169.6% for $16 billion and growth of 190.6% YoY growth for the December quarter. According to current estimates, the December quarter is peak growth.
📷
Pictured Above: The very quarter that Nvidia bottomed in fiscal Q1 was the quarter that the stock was had its highest short interest since the Covid low as the product thesis was little understood at the time.
A beat is very important for Nvidia given the spotlight on this company. Demand is certainly there, and what instead is in question (into the foreseeable future) is supply.
Here is what the CFO stated on the last earnings call:
“We expect supply to increase each quarter through next year” and also “Demand for our Data Center platform where AI is tremendous and broad-based across industries on customers. Our demand visibility extends into next year. Our supply over the next several quarters will continue to ramp as we lower cycle times and work with our supply partners to add capacity.”
Where the market was a tad disappointed last quarter was when the CFO declined to elaborate on what percentage increase in supply she was expecting to see. The translation is that these are hard comps to compete with, and without a substantial increase in supply, the growth rate may have an inherent constraint given supply has already increased triple digits YoY.
The soaring demand for GPUs is evident in Nvidia’s growth rate. Per the Financial Times, Nvidia is planning to ship 1.5M to 2M GPUs next year compared to a target of 500,000 this year. Given this outsized demand, the hiccup is more likely to happen on the supply side. For this reason, we detail Taiwan Semiconductor’s chart below.
When you strip out data center revenue, what you have is an even higher growth rate for the data center segment of 226% to $12.5 billion expected this quarter. So, the question remains —- can supply continue to grow at these elevated percentages?
📷 Source: Nvidia IR
The data center segment is clearly the thesis but it doesn’t hurt that gaming has rebounded, as well, with 22% growth last quarter.
📷 Source: Nvidia IR
Last quarter, the gross margin improved significantly to 70.1% compared to 64.6% in Q1 and 43.5% in the same period last year. This was the best gross margin in Nvidia’s history due to higher average sales prices and some contribution from the increased mix of software.
Per the CFO: “software is a part of almost all of our products, whether they're our Data Center products, GPU systems or any of our products within gaming and our future automotive products.” Separately, the standalone software business is worth “hundreds of millions of dollars annually.” As seen with our note on the H100 release from last year, its important investors are early to a tipping point. This is why we’ve been adamant that Nvidia’s true AI moment was in 2020 with the A100. If you bought the stock for the H100, you likely missed this year’s power move. The same will be true for Nvidia’s software revenue.
Regarding this quarter’s gross margin, management expects it to expand to 71.5% in the upcoming quarter. The operating income grew by an incredible 1,263% YoY to $6.8 billion, which shows the cyclical nature of semiconductors. The operating margin was 50.3% compared to 7.4% in the same period last year. The management guidance for the next quarter is 53.1%. Typically, Nvidia’s operating margin is in the 30% range.
📷 Source: Nvidia IR
This has flowed through to the bottom line with Nvidia’s adjusted EPS up 429% YoY for $2.70 compared to 481.3% growth expected this quarter for EPS of $3.37.
📷 Source: Seeking Alpha
Nvidia has the strongest cash flow margins among mega cap stocks. The operating cash flow margin is 47% with a free cash flow margin of 44.8%. In addition to higher revenue helping the cash flow, there was also $1.25 billion in customer payments received ahead of the invoice date.
📷 Source: YCharts
The company has cash and marketable securities of $16.02 billion with debt of $9.7 billion. Last quarter, there was $3.28 billion shares repurchased. The Board of Directors approved an additional $25 billion in stock purchases with $4 billion authorized remaining at the end of Q2.

Data Center Assumptions

Internal Analyst Notes from the I/O Fund on Nvidia’s Data Center Segment
The magnitude of Q4 guidance will be very important given heighted expectations. Assuming Nvidia meets its Q3 guidance of $16B +/- 2%, we’ve put together a simple scenario analysis to parameterize the different outcomes anticipated based on Nvidia’s potential Q4 guidance.
+/- indicates anticipated stock positive or negative price performance on the next trading day based on that scenario.
📷 At $40,000 per H100, that equals $28B in H100 sales alone, and when you add the A100 and other data center sales at a current run rate of $14B, the Data Center segment could report total revenue of $42B in FY24 (CY23). When you equal this out across the upcoming quarters, it looks something like this based on our estimates and Piper Sandler estimates. 📷 We believe the market will reactive negatively if Nvidia provides F4Q24 (Jan-Q) guidance that is in-line or lower than consensus growth of 11% Q/Q for the Jan-Q.
On the flip side, Nvidia will likely need to provide guidance of at least greater than 20% Q/Q growth for a significant positive reaction. This is because consensus will need to make upward revisions to their earnings for the remainder of FY24 (CY23) and FY25 (CY24). This is critical to support the current valuation with NVDA trading at ~45x NTM Non-GAAP P/E in-line with its 5 year average of ~45x NTM Non-GAAP P/E as of Monday November 21, 2023.
Our base case assumption is that Nvidia’s F4Q24 (Jan-Q) guidance will estimate Q/Q growth of at least +20%. Recall, H100 was only introduced to the market toward end of CY22. The Apr-Q was the very first quarter when Nvidia was beginning to see the impact of AI and demand for the H100. Piper Sandler believes Nvidia will close out the year with data center revenue of $42B and 2H23 Data Center revenue ~88% greater than 1H23 revenue.
Furthermore, we believe if Nvidia maintains its ~35% beat that it had for the Jul-Q for the rest of FY24 (CY23), Nvidia can potentially do $52B in Data Center revenue for FY24 (CY23).
Looking ahead to FY25, we believe Nvidia can do ~$92B in Data Center revenue based on our estimates for % beats for Actual Data Center revenue vs. Estimates for Data Center Revenue (Piper Sandler). 📷

Exploring Scenarios for the Upcoming Earnings Report:

The neutral-case scenario is that Nvidia reports in line, but can’t give the Street what it wants, which is a raise on already impressive growth to help sustain the market leader’s gains this year.
If investors are being realistic, a raise is best left to next quarter when the company typically offers a fiscal year outlook. The question is not whether Nvidia is a top AI stock, and has a promising future (of course it does). The question at hand is whether Nvidia can produce a report that pushes buyers off the sidelines. These are two different matters, and are often in opposition after a large run-up in price.
The best-case scenario is that Nvidia’s been downplaying its supply (just a touch) and there will be a beat for the fiscal Q4 guide. Nvidia’s story is quite clear, which is that the data center segment is producing historic growth and the bottom line is so beautiful, you have to squint to make sure it’s real. If this happens, we could see the price go into the mid-$500s before technicals are predicting that buyers will be exhausted. As a reminder, that’s only a 7% move from where the stock is trading now.
Piper Sandler has a data center estimate for fiscal year 2024 of $42 billion, which translates to $14.9 billion in data center revenue if we assume $12.5 billion this quarter. We detail below the price targets we are eyeing to take more gains should Nvidia report a beat on Q4FY24.
The topping-out scenario is that Nvidia’s buying is exhausted, and there isn’t one fundamental analyst on earth that can help investors figure out when this will happen. That is best left to somewhat-esoteric technical analysis. As you’ll note, I am not calling this the bear-case as there is not a bear case for Nvidia. Even if the company loses China entirely due to restrictions, it’s likely that demand gets absorbed. However, there is a bear case for the semiconductor sector, of which Nvidia is exposed to, and I detail this for you below.
Regarding the topping-out scenario, it’s unlikely Nvidia has a major negative surprise to the downside as semiconductors have strong visibility compared to, say, an ad-tech company. The management team should be going to great lengths to be consistent and accurate with Wall Street given the long golden roadway in front of them. Therefore, the topping-out scenario is aptly named as a 200% gain means you’ve got to impress the Street to keep those gains, and Nvidia may need to refuel for a quarter or so until we can get to a new fiscal year guide next quarter.

The Red Scare

What’s not to be forgotten in the excitement of the product road map is China, which has been the predominant risk for semiconductor stocks dating back to 2018. Last year, the government restricted Nvidia from selling its two most powerful chips to China, the A100 and H100. To circumvent these restrictions, Nvidia designed slightly less powerful chips called the A800 and H800. As reported by Reuters, the H800 has as much computing power as the H100 in certain settings. For the United States, these chips are important to block as they strengthen China’s military.
Last month, the U.S. Department of Commerce announced updated rules focuses on computing performance by removing the bandwidth parameter and focusing exclusively on how powerful a chip is, as well as performance density, which will prevent companies from working loopholes. According to an official who spoke to Reuters, “the U.S. will require companies to notify the government about semiconductors whose performance is just below the guidelines before they are shipped to China.”
Although this is a medium-term issue for Nvidia, analysts believe the demand is high enough today that the company shouldn’t have any issues absorbing the 20% to 25% loss in its data center segment from tighter export restrictions to China. Looking further out for FY2025, Keybanc sees a $5 impact to Nvidia’s $25.62 EPS estimate, and up to a $20B impact to its data center segment with current estimates at $101B for the data center in FY2025.
Eventually, demand may settle – especially as more competitors step up – and investors should pencil-in losing China revenue as a risk that is materializing now, with the revenue impact likely to be felt in FY2025.

The Topping Out Scenario

Nvidia (NVDA)

Nvidia continues to push to all-time highs, which is a scenario that was outlined in our prior free report on NVDA in September of this year. In the last analysis, the I/O Fund Portfolio Manager stated: “as long as we hold $340, Nvidia has the potential for one more swing higher into year-end/early next year.”
The primary scenario presented had the $545-$574 region as the target for the next swing higher. As of today, we are about 7% away from this target in what appears to be the final 5th wave in an uptrend off the October 2022 low.
I have laid out two scenarios that I continue to see playing out in the coming weeks-months:
​

• The topping-out scenario has NVDA in a complex topping pattern. We would see a sharp reversal from current levels that would ultimately break below $435-$419 support region. This would signal that the top is in, and we would then setup our downward targets to start accumulating again.
• The bull scenario has us already in the final swing higher. Our targets are between $545 - $575 for this move. If we end up seeing a gap and continuation higher from the earnings report, then we would get a direct path to these targets. We would use this strength to continue to trim. The other scenario is that we see a slight pullback that holds the $435-$419 region, which would set us up for a push into higher targets in the coming months.

📷

Semiconductor Industry May Be the Achilles Heel

Nvidia could certainly miss, yet it’s less likely given the company has outsized demand and visibility on supply. Within this context, it is easier to see the level of risk with interrelated stocks. One chart that is quite concerning, which has ramifications for all of tech, is Taiwan Semiconductor (TSM)
📷
The bounce from the October, 2022 low is clearly an overlapping and messy move higher. This is common of B waves. What’s concerning is that the drop from the July high is a 5 wave pattern that broke through the major trendline. This would be wave 1 of the larger (C) wave.
What followed is a 3 wave retrace, so far, which would be wave 2. If the next drop is a 5 wave pattern that takes us below $89, it will be a strong warning. On the other hand, if we can see a vertical move over $104, then it will shift the odds away from the red count above, and suggest that we could see a larger swing higher into early 2024, which would be the green count.
We do not own TSM as we closed this position, yet one reason we are watching this chart is to help manage our semiconductor positions as a break below $89 is concerning enough to have a read-through to our other positions. In this case, we will likely hedge the semiconductor stocks that we have identified as those we want to own in a downturn.
A break below $89 could also be concerning given TSM is in the crosshairs with China, and the United States recently tightened export restrictions to effectively cutoff AI chips. China has made it known they are pursuing domestic silicon, and if so, TSM may become stuck in a tug-o-war on which country gets 3nm, 4nm and 5nm supply.

The Broader Semiconductor Sector (PHLX)

The PHLX Semiconductor Index is a popular index of the broader semiconductor sector. It currently has the same downside setup that we are seeing in TSM. However, it is moving up into major resistance and into a cycle that suggests a reversal is likely to follow.
📷
The fan placed at the October, 2022 low represents a series of important angles that the PHLX has been using in its push higher. The red 1x1 line is a true 45 degrees off the low, and is the most important angle in defining an uptrend. Note how price broke below it and is now testing this angle as resistance.
Furthermore, those symbols above price represent cycles that we see within the PHLX. Note how price tends to reverse the trend that is moving into them. So, regarding these cycles, how we trend into them is the most important thing. We are currently trending up, into the current cycle, while testing the major angle in red.
It is likely that the broader semi sector sees a reversal soon, and until the PHLX can retake the red 1x1 angle, the pressure and risk remain to the downside. (conclusion moved to top of post)
​
​

>**What will you be doing with this PC? Be as specific as possible, and include specific games or programs you will be using.**
Voice inferencing with ai, a cloud server to store pictures/images, slight gaming (not important)
>**What is your maximum budget before rebates/shipping/taxes?**
1k usd
>**When do you plan on building/buying the PC? Note: beyond a week or two from today means any build you receive will be out of date when you want to buy.**
In a week or so
>**What, exactly, do you need included in the budget? (ToweOS/monitokeyboard/mouse/etc\)**
Just the pc
>**Which country (and state/province) will you be purchasing the parts in? If you're in US, do you have access to a Microcenter location?**
I prefer to keep my country undisclosed, there's no microcenter here. I'll try to search for alternatives by my own if the parts isnt available in my country
>**If reusing any parts (including monitor(s)/keyboard/mouse/etc), what parts will you be reusing? Brands and models are appreciated.**
I've heard used gpus are great deal so I'm open to that option (other parts should be new)
>**Will you be overclocking? If yes, are you interested in overclocking right away, or down the line? CPU and/or GPU?**
Yes, down the line. Most likely will be overclocking gpu
>**Are there any specific features or items you want/need in the build? (ex: SSD, large amount of storage or a RAID setup, CUDA or OpenCL support, etc)**
minimum 4tb storage space, prefabably ssd, but if its not possible then 2tb ssd + 2tb hdd. Would also like nvdia gpu 12gb vram as a lot of people mentioned it works better in deep learning. 64gb ram would be great but 32gb is also acceptable as i can upgrade it down the line.
>**Do you have any specific case preferences (Size like ITX/microATX/mid-towefull-tower, styles, colors, window or not, LED lighting, etc), or a particular color theme preference for the components?**
Dont care about aesthetics nor sizes
>**Do you need a copy of Windows included in the budget? If you do need one included, do you have a preference?**
Not needed
>**Extra info or particulars:**
Thanks in advance

1. Researchers introduce ‘Swift’, the first autonomous vision-based drone that beat human world champions in several fair head-to-head races. This marks the first time that an autonomous mobile robot has beaten human champions in a real physical sport [Details].
2. Generative AI updates from Google Cloud Next event:
   1. General availability of Duet AI in Google Workspace .
   2. SynthID - a tool for watermarking and identifying AI images generated by Imagen (Google’s text-to-image diffusion model). It embeds a digital watermark directly into the pixels of an image, making it invisible to the human eye, but detectable for identification, without reducing the image quality.
   3. AlloyDB AI for building generative AI applications with PostgreSQL.
   4. Vertex AI’s Model Garden now includes Meta’s Llama 2 and TII’s Falcon — and pre-announcement of Anthropic’s Claude 2..
   5. Model and tuning upgrades for PaLM 2, Codey, and Imagen. 32,000-token context windows and 38 languages for PaLM 2.
   6. Style Tuning for Imagen - a new capability to help customers align their images to their brand guidelines with 10 images or less.
   7. Launch of fifth generation of its tensor processing units (TPUs) for AI training and inferencing.
3. Meta AI released CoTracker - a fast transformer-based model that can track any point in a video.
4. WizardLM released WizardCoder 34B based on Code Llama. WizardCoder-34B surpasses GPT-4, ChatGPT-3.5 and Claude-2 on HumanEval Benchmarks.
5. Meta AI introduced FACET (FAirness in Computer Vision EvaluaTion) - a new comprehensive benchmark dataset for evaluating the fairness of computer vision models for protected groups. The dataset is made up of 32K images containing 50,000 people, labeled by expert human annotators.
6. Allen Institute for AI launched Satlas - a new platform for exploring global geospatial data generated by AI from satellite imagery.
7. A new generative AI image startup Ideogram, founded by former Google Brain researchers, has been launched with $16.5 million in seed funding. Ideogram's unique proposition lies in reliable text generation within images.
8. a16z announced a16z Open Source AI Grant program and the first batch of grant recipients and funded projects.
9. Runway AI announced Creative Partners Program - provides a select group of artists and creators with exclusive access to new Runway tools and models, Unlimited plans, 1 million credits, early access to new features and more.
10. OpenAI has released a guide for teachers using ChatGPT in their classroom—including suggested prompts, an explanation of how ChatGPT works and its limitations, the efficacy of AI detectors, and bias.
11. DINOv2, a self-supervised vision transformer model by Meta AI which was released in April this year, is now available under the Apache 2.0 license.
12. Tesla is launching a $300 million AI computing cluster employing 10,000 Nvidia H100 GPUs.
13. Inception, an AI-focused company based in the UAE unveiled Jais, a 13 billion parameters open-source Arabic Large Language Model (LLM).
14. Google announced WeatherBench 2 (WB2) - a framework for evaluating and comparing various weather forecasting models.
15. Alibaba launched two new open-source models - Qwen-VL and Qwen-VL-Chat that can respond to open-ended queries related to different images and generate picture captions.
16. OpenAI disputes authors’ claims that every ChatGPT response is a derivative work.
17. DoorDash launched AI-powered voice ordering technology for restaurants.
18. OpenAI launched ChatGPT Enterprise. It offers enterprise-grade security and privacy, unlimited higher-speed GPT-4 access, longer context windows for processing longer inputs, advanced data analysis capabilities and customization options.
19. OpenAI is reportedly earning $80 million a month and its sales could be edging high enough to plug its $540 million loss from last year.

If you like this news format, you might find my newsletter, AI Brews, helpful - it's free to join, sent only once a week with bite-sized news, learning resources and selected tools. I didn't add links to news sources here because of auto-mod, but they are included in the newsletter. Thanks

News provided by aibrews.com

1. Researchers introduce ‘Swift’, the first autonomous vision-based drone that beat human world champions in several fair head-to-head races. This marks the first time that an autonomous mobile robot has beaten human champions in a real physical sport [Details].
2. Generative AI updates from Google Cloud Next event:
   1. General availability of Duet AI in Google Workspace [Details].
   2. SynthID - a tool for watermarking and identifying AI images generated by Imagen (Google’s text-to-image diffusion model). It embeds a digital watermark directly into the pixels of an image, making it invisible to the human eye, but detectable for identification, without reducing the image quality [Details].
   3. AlloyDB AI for building generative AI applications with PostgreSQL [Details].
   4. Vertex AI’s Model Garden now includes Meta’s Llama 2 and TII’s Falcon — and pre-announcement of Anthropic’s Claude 2 [Details].
   5. Model and tuning upgrades for PaLM 2, Codey, and Imagen. 32,000-token context windows and 38 languages for PaLM 2 [Details].
   6. Style Tuning for Imagen - a new capability to help customers align their images to their brand guidelines with 10 images or less [Details].
   7. Launch of fifth generation of its tensor processing units (TPUs) for AI training and inferencing [Details].
3. Meta AI released CoTracker - a fast transformer-based model that can track any point in a video [Hugging face GitHub].
4. WizardLM released WizardCoder 34B based on Code Llama. WizardCoder-34B surpasses GPT-4, ChatGPT-3.5 and Claude-2 on HumanEval Benchmarks [Details].
5. Meta AI introduced FACET (FAirness in Computer Vision EvaluaTion) - a new comprehensive benchmark dataset for evaluating the fairness of computer vision models for protected groups. The dataset is made up of 32K images containing 50,000 people, labeled by expert human annotators [Details].
6. Allen Institute for AI launched Satlas - a new platform for exploring global geospatial data generated by AI from satellite imagery [Details].
7. A new generative AI image startup Ideogram, founded by former Google Brain researchers, has been launched with $16.5 million in seed funding. Ideogram's unique proposition lies in reliable text generation within images [Details].
8. a16z announced a16z Open Source AI Grant program and the first batch of grant recipients and funded projects [Details].
9. Runway AI announced Creative Partners Program - provides a select group of artists and creators with exclusive access to new Runway tools and models, Unlimited plans, 1 million credits, early access to new features and more [Details].
10. OpenAI has released a guide for teachers using ChatGPT in their classroom—including suggested prompts, an explanation of how ChatGPT works and its limitations, the efficacy of AI detectors, and bias [Details].
11. DINOv2, a self-supervised vision transformer model by Meta AI which was released in April this year, is now available under the Apache 2.0 license [Details Demo].
12. Tesla is launching a $300 million AI computing cluster employing 10,000 Nvidia H100 GPUs [Details].
13. Inception, an AI-focused company based in the UAE unveiled Jais, a 13 billion parameters open-source Arabic Large Language Model (LLM) [Details].
14. Google announced WeatherBench 2 (WB2) - a framework for evaluating and comparing various weather forecasting models [Details].
15. Alibaba launched two new open-source models - Qwen-VL and Qwen-VL-Chat that can respond to open-ended queries related to different images and generate picture captions [Details].
16. OpenAI disputes authors’ claims that every ChatGPT response is a derivative work [Details].
17. DoorDash launched AI-powered voice ordering technology for restaurants [Details].
18. OpenAI launched ChatGPT Enterprise. It offers enterprise-grade security and privacy, unlimited higher-speed GPT-4 access, longer context windows for processing longer inputs, advanced data analysis capabilities and customization options [Details].
19. OpenAI is reportedly earning $80 million a month and its sales could be edging high enough to plug its $540 million loss from last year [Details].

🔦 Weekly Spotlight

1. How 3 healthcare organizations are using generative AI [Link].
2. The A.I. Revolution Is Coming. But Not as Fast as Some People Think [Link].
3. LIDA by Microsoft: Automatic Generation of Visualizations and Infographics using Large Language Models [Link].
4. Curated collection of AI dev tools from YC companies, aiming to serve as a reliable starting point for LLM/ML developers [Link].
5. Beating GPT-4 on HumanEval with a Fine-Tuned CodeLlama-34B [Link].

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Self-promo is allowed in these weekly discussions. If you want to make a separate post, please read and go by the rules or you will be banned.
Previous Megathreads & Subreddit revamp and going forward

1. Researchers introduce ‘Swift’, the first autonomous vision-based drone that beat human world champions in several fair head-to-head races. This marks the first time that an autonomous mobile robot has beaten human champions in a real physical sport [Details].
2. Generative AI updates from Google Cloud Next event:
   1. General availability of Duet AI in Google Workspace .
   2. SynthID - a tool for watermarking and identifying AI images generated by Imagen (Google’s text-to-image diffusion model). It embeds a digital watermark directly into the pixels of an image, making it invisible to the human eye, but detectable for identification, without reducing the image quality.
   3. AlloyDB AI for building generative AI applications with PostgreSQL.
   4. Vertex AI’s Model Garden now includes Meta’s Llama 2 and TII’s Falcon — and pre-announcement of Anthropic’s Claude 2..
   5. Model and tuning upgrades for PaLM 2, Codey, and Imagen. 32,000-token context windows and 38 languages for PaLM 2.
   6. Style Tuning for Imagen - a new capability to help customers align their images to their brand guidelines with 10 images or less.
   7. Launch of fifth generation of its tensor processing units (TPUs) for AI training and inferencing.
3. Meta AI released CoTracker - a fast transformer-based model that can track any point in a video.
4. WizardLM released WizardCoder 34B based on Code Llama. WizardCoder-34B surpasses GPT-4, ChatGPT-3.5 and Claude-2 on HumanEval Benchmarks.
5. Meta AI introduced FACET (FAirness in Computer Vision EvaluaTion) - a new comprehensive benchmark dataset for evaluating the fairness of computer vision models for protected groups. The dataset is made up of 32K images containing 50,000 people, labeled by expert human annotators.
6. Allen Institute for AI launched Satlas - a new platform for exploring global geospatial data generated by AI from satellite imagery.
7. A new generative AI image startup Ideogram, founded by former Google Brain researchers, has been launched with $16.5 million in seed funding. Ideogram's unique proposition lies in reliable text generation within images.
8. a16z announced a16z Open Source AI Grant program and the first batch of grant recipients and funded projects.
9. Runway AI announced Creative Partners Program - provides a select group of artists and creators with exclusive access to new Runway tools and models, Unlimited plans, 1 million credits, early access to new features and more.
10. OpenAI has released a guide for teachers using ChatGPT in their classroom—including suggested prompts, an explanation of how ChatGPT works and its limitations, the efficacy of AI detectors, and bias.
11. DINOv2, a self-supervised vision transformer model by Meta AI which was released in April this year, is now available under the Apache 2.0 license.
12. Tesla is launching a $300 million AI computing cluster employing 10,000 Nvidia H100 GPUs.
13. Inception, an AI-focused company based in the UAE unveiled Jais, a 13 billion parameters open-source Arabic Large Language Model (LLM).
14. Google announced WeatherBench 2 (WB2) - a framework for evaluating and comparing various weather forecasting models.
15. Alibaba launched two new open-source models - Qwen-VL and Qwen-VL-Chat that can respond to open-ended queries related to different images and generate picture captions.
16. OpenAI disputes authors’ claims that every ChatGPT response is a derivative work.
17. DoorDash launched AI-powered voice ordering technology for restaurants.
18. OpenAI launched ChatGPT Enterprise. It offers enterprise-grade security and privacy, unlimited higher-speed GPT-4 access, longer context windows for processing longer inputs, advanced data analysis capabilities and customization options.
19. OpenAI is reportedly earning $80 million a month and its sales could be edging high enough to plug its $540 million loss from last year.

If you like this news format, you might find my newsletter, AI Brews, helpful - it's free to join, sent only once a week with bite-sized news, learning resources and selected tools. I didn't add links to news sources here because of auto-mod, but they are included in the newsletter. Thanks

1. Researchers introduce ‘Swift’, the first autonomous vision-based drone that beat human world champions in several fair head-to-head races. This marks the first time that an autonomous mobile robot has beaten human champions in a real physical sport [Details].
2. Meta AI released CoTracker - a fast transformer-based model that can track any point in a video.
3. WizardLM released WizardCoder 34B based on Code Llama. WizardCoder-34B surpasses GPT-4, ChatGPT-3.5 and Claude-2 on HumanEval Benchmarks.
4. Meta AI introduced FACET (FAirness in Computer Vision EvaluaTion) - a new comprehensive benchmark dataset for evaluating the fairness of computer vision models for protected groups. The dataset is made up of 32K images containing 50,000 people, labeled by expert human annotators.
5. Allen Institute for AI launched Satlas - a new platform for exploring global geospatial data generated by AI from satellite imagery.
6. Generative AI updates from Google Cloud Next event:
   1. General availability of Duet AI in Google Workspace .
   2. SynthID - a tool for watermarking and identifying AI images generated by Imagen (Google’s text-to-image diffusion model). It embeds a digital watermark directly into the pixels of an image, making it invisible to the human eye, but detectable for identification, without reducing the image quality.
   3. AlloyDB AI for building generative AI applications with PostgreSQL.
   4. Vertex AI’s Model Garden now includes Meta’s Llama 2 and TII’s Falcon — and pre-announcement of Anthropic’s Claude 2..
   5. Model and tuning upgrades for PaLM 2, Codey, and Imagen. 32,000-token context windows and 38 languages for PaLM 2.
   6. Style Tuning for Imagen - a new capability to help customers align their images to their brand guidelines with 10 images or less.
   7. Launch of fifth generation of its tensor processing units (TPUs) for AI training and inferencing.
7. A new generative AI image startup Ideogram, founded by former Google Brain researchers, has been launched with $16.5 million in seed funding. Ideogram's unique proposition lies in reliable text generation within images.
8. a16z announced a16z Open Source AI Grant program and the first batch of grant recipients and funded projects.
9. Runway AI announced Creative Partners Program - provides a select group of artists and creators with exclusive access to new Runway tools and models, Unlimited plans, 1 million credits, early access to new features and more.
10. OpenAI has released a guide for teachers using ChatGPT in their classroom—including suggested prompts, an explanation of how ChatGPT works and its limitations, the efficacy of AI detectors, and bias.
11. DINOv2, a self-supervised vision transformer model by Meta AI which was released in April this year, is now available under the Apache 2.0 license.
12. Tesla is launching a $300 million AI computing cluster employing 10,000 Nvidia H100 GPUs.
13. Inception, an AI-focused company based in the UAE unveiled Jais, a 13 billion parameters open-source Arabic Large Language Model (LLM).
14. Google announced WeatherBench 2 (WB2) - a framework for evaluating and comparing various weather forecasting models.
15. Alibaba launched two new open-source models - Qwen-VL and Qwen-VL-Chat that can respond to open-ended queries related to different images and generate picture captions.
16. OpenAI disputes authors’ claims that every ChatGPT response is a derivative work.
17. DoorDash launched AI-powered voice ordering technology for restaurants.
18. OpenAI launched ChatGPT Enterprise. It offers enterprise-grade security and privacy, unlimited higher-speed GPT-4 access, longer context windows for processing longer inputs, advanced data analysis capabilities and customization options.
19. OpenAI is reportedly earning $80 million a month and its sales could be edging high enough to plug its $540 million loss from last year.

If you like this news format, you might find my newsletter, AI Brews, helpful - it's free to join, sent only once a week with bite-sized news, learning resources and selected tools. I didn't add links to news sources here because of auto-mod, but they are included in the newsletter. Thanks

I’m a CF starting out, and we use the PLS-5 quite frequent - anyone have any idea how to target this goal from the PLS that goes “Anna got hurt, show me what picture shows how you think she got hurt” as it relates to the goal “X will make inferences by pointing to pictures stimuli” tried to look up inferencing pics on TpT but nothing is similar to how to shows it on the PLS, any advice helps

Hey everyone!
Been fully focused on the Pockit project’s developments for the last couple of months, as well as manufacturer meetings in recent weeks. I wanted to take this moment to share a bunch of good news 🔔 especially as it's been a while.
(Sidenote: A few people mentioned that they were unable to make posts due to an automatic spam-filter. I should have fixed this earlier, but it should be good to go now, as far as I can tell from the subreddit settings, so post away!)
So, below is a breakdown of all that is up with Pockit. I’ll expand some of these items into a series of posts+pictures over the next couple of weeks.

Production planning in advance

• Since late Spring, I have met with a few companies to get quotes and organize production options, and contacted several component suppliers to confirm about stock + lead-times.
• I have also made formal reservation requests for some important components that have been in short supply due to the pandemic.
  • Making frequent modifications to the board designs based on component availability has been exhausting (presumably even for consumer-device manufacturers), so this being resolved will be a huge relief.

Pockit into everyone’s hands

• The most important conclusion of my recent work pertains to the long-standing question of whether Pockit units will be available to the community. At this point, I can say yes with greater confidence 👍
• An early-access/preorder campaign will go live in November -- possibly earlier if things go well. I know a lot of you guys are waiting for the release of Pockit, and I'm doing my best to make sure that I have the pieces in order so as to make a confident + successful launch.

Hardware developments

• I’ve wrapped up the design+implementation of several new Blocks, many of which are based on requests from group members + emails, etc.
• Among these are a new ultra-low-power Display Block (based on Sharp's Memory display technology), an updated Wireless Charging Block, and a "Giant" Battery Block for extended-runtime applications as well as mobile-robotics. For the latter, I've also completed extensive testing and refinement of easy-to-plug Geared Motor Blocks, which I hope to show in an upcoming video.

Firmware + Apps

• There have been numerous firmware improvements. The most significant of these is a re-implementation of the Block<>Pockit interface to create faster + more robust detection of Block attachment/detachment, and of the Bridge protocol for higher-throughput communication between multiple Pockit Cores.
• Also added 9 new apps to the collection, and a dashboard functionality (this is in alpha state) that lets you merge two or more apps, depending on your use case, to run a custom hybrid-app on the device.

nCode (natural language programming for Pockit)

• Made much progress on this, and I like where it's going, but still contemplating (based on two community members’ thoughtful feedback) whether this should be postponed to after the device release. I’ll put up a snapshot in the coming days and take into account you guys’ opinions.

Enclosure generator:

• Implemented a tool (for use within the dashboard) that auto-generates and exports design files for those who wish to 3D-print/laser-cut/CNC-route your own shells (housings) customized for a particular Pockit device, i.e., your choice and arrangement of Blocks.

Working with others:

• Multiple organizations (primarily hardware-related but also two that deal with software) have offered collaboration. Interest in Pockit from these various teams/companies has been encouraging and once the device is launched, we can together explore ways to open up access for everyone to various state-of-the-art technologies using Pockit as a prototyping platform.
• Also, the Coral (Google AI) team has been in touch, thanks to one of their managers who is part of our community ✨. They provided several Edge TPU (an ASIC for high-speed tensor processing/inferencing) modules to help finalize the 1-unit-size ML Accelerator Block that I proposed in the last video.

Pockit real-life base:

• Considering all of the above progress and the goal of bringing Pockit units out faster, I'm planning to soon assemble a team and set up a larger Pockit lab (i.e., an office for prototyping + quality-testing, etc.). I’m expecting to start on it this Fall.
• An important goal for the resulting team, including myself, will be to actively engage with everyone in the community, especially once Pockit production begins. This includes helping with questions, taking feature requests, facilitating collaboration, and developing the Blocks-and-Apps ecosystem. The resulting feedback loop will boost what Pockit can do, and more interestingly, what people do with Pockit.
  • At that time, let's set up a Discord server for real-time communication, as u/Cute_Principle81 and u/Worried_Lawfulness43 and others have requested.

So, that's the summary of the updates (more details in the coming days).
As always, looking forward to questions and comments from you guys! 🙌

Table Of Contents

1. Imaging Complex Geometries2. Challenging Surfaces3. A Large Number of Variants4. Complex Material Handeling5. Working Distance Constrains6. High Resolution Necessities

Background of Industrial Image Acquisition Challenges:

According to Fanuc, Image Acquisition contributes to 85% of Machine Vision solution success. The workflow of a typical AI based Machine Vision system includes four steps. First, the image acquisition is done through a Plug n Play camera setup that optimizes the acquisition process. Second, data preparation is done by configuring and collecting training images. Third, specialized deep learning architecture trains are used to optimize models using cloud infrastructure. Finally, the optimized model is deployed for inferencing in an edge device and monitored for accuracy. The first step of the workflow, image acquisition is a complex process and has a lot of challenges.
However, a regular industrial setting comes with many challenges that make it increasingly hard to acquire perfect images for conducting visual process or quality inspections
Also read: Image Acquisition Components
Let’s look at some real world scenarios:

Imaging of complex geometries –

The manufacturing industry builds a lot of parts with complex geometries. These parts have multiple faces, complex shapes, and small detailing that can be seen from some angles. Such objects consist of design structures including undercuts, hollow space, or intricate internal details. To capture the images of such parts for the training of the machine vision system, the parts either need to be rotated and moved or multiple cameras need to be deployed to capture all the intricacies of the object so that defects that come in the production line can be detected with minimal errors.

Challenging surfaces –

Surfaces that are either transparent or reflective pose a challenge for image acquisition systems. Even in uniform lighting conditions, these surfaces cannot be captured adequately by the vision system for proper training and inspection. There have to be specialized lighting techniques so that useful training images can be taken from such surfaces. These can include dome light, diffused lighting, or any such lighting which makes sure that illumination is spread evenly across the entire surface. The use of polarised light can also reduce lighting hotspots that make it difficult to capture images.

A large number of variants –

In any industrial business, there are many variants of parts and materials being manufactured at a large scale. All such SKUs or stock-keeping units must be thoroughly inspected for defects. They differ in size, shape, color, geometry, and the production volume and the AI needs to comprehensively inspect them for errors. A robust cataloguing system can help the design of a good training dataset that has an even distribution of all such SKUs in terms of the errors that need to be addressed in them in the ratio of their production volumes. This avoids any bias in the training data due to unbalanced and skewed numbers.

Harsh environments –

The manufacturing industry has a lot of operations that have to be performed in harsh environments with dust, smoke, or heat. Some environments are also hazardous for people working in them. In harsh environments, image acquisition systems need to work and deliver results. Enclosures with correct IP ratings can protect the hardware. Other kinds of enclosures are used for explosive environments. Harsh environments can affect the hardware of the system and hazards can damage the product altogether. Continuous and correct use of protective layers over acquisition equipment safeguards the system and allows it to function correctly.

Complex Material Handling –

Harsh environments also affect the materials that are being inspected by the machine vision system. During inspection processes, materials should not be compromised for any reason. Sometimes, customized material handling systems need to be developed to cater to these materials. Better material handling translates into better image acquisition, and in turn higher accuracy from the machine vision system due to clarity of images. For instance, fasteners that come in bulk need to be provided feeding systems so that cameras can localize each object and take a picture at the required angle and orientation.

Working Distance Constraints –

Inspections of existing materials might need to be done in conjunction with other tasks that involve human workers or machine vision systems might need manual interventions at certain points of their operations. The positioning of cameras and the movement of humans must not cause any blockage for one another. There can be a minimum working distance constraint in larger factories or maximum working distance constraints in smaller, tighter spaces. Cameras need to be at a proper distance to the assembly line to inspect the parts. The usage of specialized optics can account for such working distance constraints.

High-Resolution Necessities –

While dealing with parts that have intricate details and designs and sub-parts that are very small and packed into the parent part, there is a necessity of using high-resolution image acquisition systems. To capture the tiny features, multiple cameras at different working distances can be used or a single high-resolution camera must be chosen. High-resolution imagery has challenges related to camera systems as traditional cameras like area scan cameras cannot support such image capture due to the smaller number of pixels. Line scan imaging is a new technology that can create high-resolution images for better visibility of features by the acquisition system.

Summary –

Thus, the challenges of image acquisition are numerous but they can be managed through good technology and intelligent design. Qualitas overcomes many such image acquisition challenges along with other hurdles that come in the workflow. Having Qualitas as a partner for machine vision technology can extend a company’s network of technologies, increase revenue and margins through profitable integration of machine vision into the inspection process of various parts getting manufactured, and gain an edge over the competitors with the use of advanced and up-to-date technology to automate all possible processes in the domain of manufacturing.
​
AI in Machine Vision Machine Vision Software Machine Vision Case studies

Table Of Contents

1. Imaging Complex Geometries2. Challenging Surfaces3. A Large Number of Variants4. Complex Material Handeling5. Working Distance Constrains6. High Resolution Necessities

Background of Industrial Image Acquisition Challenges:

According to Fanuc, Image Acquisition contributes to 85% of Machine Vision solution success. The workflow of a typical AI based Machine Vision system includes four steps. First, the image acquisition is done through a Plug n Play camera setup that optimizes the acquisition process. Second, data preparation is done by configuring and collecting training images. Third, specialized deep learning architecture trains are used to optimize models using cloud infrastructure. Finally, the optimized model is deployed for inferencing in an edge device and monitored for accuracy. The first step of the workflow, image acquisition is a complex process and has a lot of challenges.
However, a regular industrial setting comes with many challenges that make it increasingly hard to acquire perfect images for conducting visual process or quality inspections
Also read: Image Acquisition Components
Let’s look at some real world scenarios:

Imaging of complex geometries –

The manufacturing industry builds a lot of parts with complex geometries. These parts have multiple faces, complex shapes, and small detailing that can be seen from some angles. Such objects consist of design structures including undercuts, hollow space, or intricate internal details. To capture the images of such parts for the training of the machine vision system, the parts either need to be rotated and moved or multiple cameras need to be deployed to capture all the intricacies of the object so that defects that come in the production line can be detected with minimal errors.

Challenging surfaces –

Surfaces that are either transparent or reflective pose a challenge for image acquisition systems. Even in uniform lighting conditions, these surfaces cannot be captured adequately by the vision system for proper training and inspection. There have to be specialized lighting techniques so that useful training images can be taken from such surfaces. These can include dome light, diffused lighting, or any such lighting which makes sure that illumination is spread evenly across the entire surface. The use of polarised light can also reduce lighting hotspots that make it difficult to capture images.

A large number of variants –

In any industrial business, there are many variants of parts and materials being manufactured at a large scale. All such SKUs or stock-keeping units must be thoroughly inspected for defects. They differ in size, shape, color, geometry, and the production volume and the AI needs to comprehensively inspect them for errors. A robust cataloguing system can help the design of a good training dataset that has an even distribution of all such SKUs in terms of the errors that need to be addressed in them in the ratio of their production volumes. This avoids any bias in the training data due to unbalanced and skewed numbers.

Harsh environments –

The manufacturing industry has a lot of operations that have to be performed in harsh environments with dust, smoke, or heat. Some environments are also hazardous for people working in them. In harsh environments, image acquisition systems need to work and deliver results. Enclosures with correct IP ratings can protect the hardware. Other kinds of enclosures are used for explosive environments. Harsh environments can affect the hardware of the system and hazards can damage the product altogether. Continuous and correct use of protective layers over acquisition equipment safeguards the system and allows it to function correctly.

Complex Material Handling –

Harsh environments also affect the materials that are being inspected by the machine vision system. During inspection processes, materials should not be compromised for any reason. Sometimes, customized material handling systems need to be developed to cater to these materials. Better material handling translates into better image acquisition, and in turn higher accuracy from the machine vision system due to clarity of images. For instance, fasteners that come in bulk need to be provided feeding systems so that cameras can localize each object and take a picture at the required angle and orientation.

Working Distance Constraints –

Inspections of existing materials might need to be done in conjunction with other tasks that involve human workers or machine vision systems might need manual interventions at certain points of their operations. The positioning of cameras and the movement of humans must not cause any blockage for one another. There can be a minimum working distance constraint in larger factories or maximum working distance constraints in smaller, tighter spaces. Cameras need to be at a proper distance to the assembly line to inspect the parts. The usage of specialized optics can account for such working distance constraints.

High-Resolution Necessities –

While dealing with parts that have intricate details and designs and sub-parts that are very small and packed into the parent part, there is a necessity of using high-resolution image acquisition systems. To capture the tiny features, multiple cameras at different working distances can be used or a single high-resolution camera must be chosen. High-resolution imagery has challenges related to camera systems as traditional cameras like area scan cameras cannot support such image capture due to the smaller number of pixels. Line scan imaging is a new technology that can create high-resolution images for better visibility of features by the acquisition system.

Summary –

Thus, the challenges of image acquisition are numerous but they can be managed through good technology and intelligent design. Qualitas overcomes many such image acquisition challenges along with other hurdles that come in the workflow. Having Qualitas as a partner for machine vision technology can extend a company’s network of technologies, increase revenue and margins through profitable integration of machine vision into the inspection process of various parts getting manufactured, and gain an edge over the competitors with the use of advanced and up-to-date technology to automate all possible processes in the domain of manufacturing.

*Edit: which accent SOUNDS the dumbest. Can't edit titles.
I realize there is variety among these listed also. Black vernacular in Georgia would sound different than in New York. Kentucky sounds different from Texan. I actually have a bit of a rural accent from Ontario (picture the show Letterkenny to some degree) and I hate it. I think I sound dumb. I realize you can't actually make inferenced based on accent.
View Poll

Part 1
Diana is a girl known for her medical enigmas that require a constant dosage of prescription drugs, and for a long time, I chopped our experience at open house as due to a mental breakdown.
“Hi! Welcome to ********* University!”
There are moments in your life where your heart beats around in your chest like a parakeet flapping around in a cage. The sensation tends to spread in your stomach. A suggestion that if someone were to look at you through thermal lens, they would see splotches of yellow and green all over.
I totally stole that metaphor from some poet girl I used to date in high school, but that’s not the point. In spring of 2018, we stepped off the tour bus and Counselor Burlesque greeted us at this ‘too good to be true’ academic institution. The background morning sunlight was at her side as if it were of upmost intention to be as picturesque as possible. I can speak for everyone when I say that there was a contagious warmth.
Well, everyone except Brittany Flowers - or who I liked to call - “The Fireball”. “Okay, so where’s the guy with the striped rainbow shoes and the makeup?”
Counselor Burlesque – whose fuckin sexy as heck, by the way - furled her brow at Brittany’s comment like someone had told a funny joke and she tried to maintain professionalism. "I don't quite understand."
I guess Brittany continued trying to decipher the monkey's paw. "The fucking clown. He's gonna come up at any second, throw pie at our faces and then say we're all on some TV prank show. I'm with whatever. Any credit on my acting resume is fine with me."
We stood in midst of a wide campus. Whiff of mowed grass hit my nose; it gave me a sense of… nostalgia? Kind of nature scent that pressures you in soft places. Around her, around all of us, were houses. Most of which were in groups. Each carried the same color palette of orange brick and white outlined windows. At first, I thought the building in front of us was a church because the windows were almost too damn large. From the tiled concrete floor to the triangular rooftops that gave off impressions of castles, I inferenced the red carpet was on royal territory.
So organized, so far spread out, like a really ostentatious neighborhood. I remember nudging my bud Tommy and going: “This is like a suburb or some shit.”
Tommy goes: “Like Disney land, for nerds.”
“Come on, baby, let’s get this show on the road,” My future roommate, Don, shouted as he got out the vehicle with the other alumni candidates.
When ready, we all followed our Counselor from behind, marveled at the fountain in the middle of the grass plain, and she took us through a straight-forward path that led us to the building ahead. It was called "Residence Hall". There were a bunch of venues of different host professors. Majority of which were intended to represent different majors. So, for example: if you were a Computer Science major, your first stop of this open-house tour would be to the electrical science houses. I think, at least. Don’t know because I didn’t have an idea on what my major was going to be between computer science and creative writing. Those who didn’t have a major continued with Counselor Burlesque. We spent the next few hours touring the most relevant aspects of the college. The main halls, classrooms, cafeterias, recreation centers – which basically means gyms, sports and other leisure activities. Eventually, we all got split up for different things. Some went to go check out the dorm tenements. Others – like myself - went to go explore the sceneries and nature aspects, like that really awesome lake. I got to snap as many pictures as I wanted with someone else who, I guess, was just as much of a camera-freak.
When we got back inside the main Residence building, everyone was directed to an auditorium, but not everyone would follow suit in immediate haste. Most took their sweet time still roaming the venue area, and I did the same. I mean, I felt like this was candy-land. After such an exciting day, the thought of sitting down to listen to some boring ass speech didn’t carry any appeal.
In my roam, I notice two security men standing at an elevator, which was the coolest thing ever because it was my first time seeing an elevator in a school before. Behind them, I spot these two guys walking down the hallway. They were huddled together as if they were semi-intelligent rats planning to steal cheese. I’ve seen the looks on their delinquent faces a million times. Know exactly what it is. They’re about to do something idiotic.
The two bozos stick their hand out of their hoodies and start smacking down these little pebbles that create a spark and "pop" when they hit the floor. I thought it was bonkers someone could try that in a place like this on opening day.
It confirmed my suspicion: this school must have no real standards on who they accepted. I’m amazed that myself was able to make it. The two men were already in the car, but in response to the pop, probably fearing the worse, they hop out with a: “Hey, hey!”
The nitwits aren’t cunning enough to haste together a “I ain’t do nuffin’” act in time. I scoff and turn around to go head back toward the auditorium.
“Psst, yo, bro,” I heard Don behind me seconds later.
He had spotted me from the crowd and had called me over to the same elevator. He held it and scurried me with a gesture. I scamper to get in with him. Sorry, to get in with them – because there was another person who hopped in. Me, Don, and a girl named Diana.
“Yeah, alright, let’s get it,” Don announced while pressing the “C” button.
For that, he had to lift up a slip that said: “Floor currently not in use, for staff only.”
It didn’t glow, the door wouldn’t shut. Diana noticed that there was a key slot with an actual key left in it. She twisted it, pressed “C” herself, and down we went.
Good thinking. Amazing, in their haste they must have forgotten to take the key out.
“Wait, what?” I spoke. “We can’t just jack this. What about the auditorium?”
Don laughed. “It’ll just be a second. Just doing some venturin’. Y’know, some extended touring.”
“Yeah, don’t be such a wuss-ass,” Diana said. “Look at your dumb face, you know you wanna.”
Out of everyone, Don and I were probably eating this up the most. The grins on our faces told it all. This college, institution, whatever, was in our backyards. You know what I mean? No, you probably don’t. Look, what I’m trying to say, is that we lived in an urban area where things aren’t exactly easy to get by. Spent a lot of time getting around every inch of it in my adolescence. Yet, I’ve never even heard of this place and it’s only a couple hours off the outskirts. Was never on the map, our high-school teachers never brought it up – and you know those lunatics never shut the fuck up about college around Junior & Senior years. Though, I could have sworn – maybe once or twice in my lifetime – my parents drove through this area in ************, but I don’t ever remember seeing this damn behemoth of a place. I have a vague memory of driving by a giant metal-dome structure and was always curious as to what could be inside. Though, I could be thinking about the wrong place. While I’m on this tangent, I wanna preface that when we arrived at the C-floor, something happened that was just…well, in a phrase, downright bizarre. Of course, I hadn’t thought too much about it at the time. However, it’s easier to pick it out in memory as something that fits right-the-hell in with all the other seriously confusing crap I’ve had to deal with. It’s especially easy to remember since it’s the only real strange thing that happened that entire first year. An instance of abnormality, if you will.
We step out of the elevator, and it’s quiet. Don and I get giddy at this like dumb children. A juxtaposition from the upstairs, barely audible mess. Most of all, no one knew we were there, and it didn’t seem like anyone else was there either. It was like when the lights turned off in a Grade School classroom, you just can’t help yourself.
In a moment of brilliance, Diana pressed the “2” on the elevator input so it would go back to the floor we were on. In the hopes that it would have gone back up before the security guys were done with the issue.
To our left, there’s a room. It had see-through glass with the title “ID-Check-in,” which will be an office for identification cards. I say “will”, because from looking outside-in, it didn’t really seem like the insides were fully installed. The desks were clear, the paint on the walls were in patches, and there was some loose…stuff…hanging from the ceiling, like loose yellow cotton or something. Diana called it gross, but its condition made sense to me. This college was new, after all. To our right was a room with no name. While Diana and Don went to check it out, I became distracted by a hum underneath my feet. Surprised the other two hadn’t said anything about it. There was a vibration. An engine, or something in works underneath me. While I’m staring at my feet, I’m also subconsciously picking at some wrinkled piece of wall at my side because I can’t help myself but to do shit like that. My nails scratched through and made contact with some cold surface. It looked like some metal interior through the paint. I look back at the office, at the parts of the wall that didn’t have paint over it, and then I realize that it isn’t paint at all, but rather an incomplete exterior.
Before I can further invest, the vibration beneath my feet energized to an earthquake. It didn’t last further than a second. I stumble, lights above my head flicker before they shut off all together. I’m suspended in pitch darkness for no longer than a millisecond before a screech pierces the silent underground. It was Diana. It came from whatever room her and Don had gone into. Then a loud crash happened that made me jump out of my shoes.
At that moment, in pitch blackness, instant regret and instant karma were in locked arms, prancing around like ghouls feasting off the idea of me currently pissing my pants. I turn on my phone’s flashlight and I Shaggy-it. With jittering legs but with some unreasonable source of courage, I yell for them both but receive no answer, just continued cries from Diana. Through the room, I shine around and get a grasp on what type of area I’m in. It’s some sort of electrical, power junk room. Shelves upon shelves of laptops, cords scribbled across the floor, dangled from the ceiling. Advancement towards them was like trudging through a forest of wire. This place was a hazard on steroids. It definitely wasn’t the type of room you stumble across and venture. So many twists and turns, so dark, little space, forcing humid air into my lungs, it doesn’t help that I feel like I’m playing that old Ju-On Grudge game for the Wii, if anyone remembers that.
I get fed up and call out Don to say anything. Fucking anything at all.
“Hear me? I’m right here. Dude…she’s…hurry,” as Don said that, it seemed like he was also trying to “shh” Diana as well, while managing his own tone.
I get a better sense of direction from him and made it.
I see Don, but I don’t see Diana. Next to him, there’s a big ass breach, a hole. My flashlight spots it all, but there’s hardly even a need for it. Because from the hole emanated a turquoise hue that gave some light to our surroundings. Due to the rumble, Diana had toppled over onto some precarious area of floor and collapsed through – sheesh.
“Damn, yo, can you calm the hell down, now? You’re gonna get us into some bull,” Don said. He was on his knees, peered down through the breach in the floor.
I did the same.
Diana stood up, hands on her hips in a pouty position, covered in debris. "Then get me the fuck out of here."
I can tell from the distance of the fall that she must have been at least a little hurt. For the most part, she seemed fine, just obviously stuck. In brighter ambiance, I probably would have laughed.
There’s still a problem. The distance is way too large for either of us to reach and grab her. ‘What now?’
We brainstorm ideas while Diana kept insisting that we just go and get some help. Me and Don look at each other.
Neither of us wants to do that shit. That’s college suicide. Not a great first impression. We’d rather not risk all three of our parents going to town on us.
Even so, the longer we stay down there, a foreboding in this dark, claustrophobic space gave me so many spinal chills. We cave. Don was to stay as I went and grabbed help, but as I turn myself to leave, my flashlight caught a miracle.
By some oracle, by some gift from the gods, I spotted a ladder propped right before a vent near the ceiling. Perfect.
Don helped me get it.
Diana gets quiet. Like, real quiet. ‘She must have calmed down,’ was my thought. With the retrieved ladder, we get back to the breach and look down. Diana isn’t moving. Faced opposite direction of before, her thighs are together like someone’s desperate attempt to not pee themselves.
She’s looking at something. I make the connection in my head: the direction she’s looking in is also the source of the turquoise. Then her behavior gets odd. Her head titled to the right, then to the left. She puts her right hand up, then drops it. All this while staring in the same direction. This behavior repeated.
Don puts down the ladder and places it next to her. No reaction. We’re wasting time.
I call out. “Dia-“
Another scream. Like, high-pitched: she’s the woman in the shower who just got their curtains pulled off by a psycho killer in some horror flick. It’s so loud, I’m convinced that whatever she’s looking at is coming to get her. Me and Don lose our shit, we panic with her. I’m not ashamed to admit I felt like running away. We both yell for Diana to get on the ladder, but she kept yelling that she’s seeing some sort of figure.
“Oh, my goodness, oh my fu-, oh god, help, please get me out, get me out, what is this thing?”
That’s my best translation of her gibberish through sobs.
I never heard Don beg before. “Diana, Diana, please. The ladder is right here. Just take it, please, it’s okay.”
“Diana, just close your eyes, okay? Close your eyes, listen to the sound of our voices. Climb up the ladder,” I said.
Diana is able to stop screaming, then turned to us at snail’s pace, like a wet kitten. Trying to keep her eyes closed but can’t resist the tiny peaks at whatever has got her shook. After some time, she’s able to climb all the way. In my arms, she’s stiffed up like she witnessed the eye of medusa. We sat there, and I try to make out Diana’s rapid, jumbled attempts at conveying what she witnessed…and from what I could make out, I couldn’t believe it.
You might be thinking: “well, now make a break for it, asshats!”
I would’ve loved to, but it’s not like we can drag or even carry her out of this deathtrap.
We then heard something that gave us enough wind to even knock Diana out of her shock. The sound of keys from outside the room, footsteps, and voices from two men.
“We know someone’s in there. Come out. Whoever you are, believe me, you’re gonna be in a world of shit within the next two minutes.”
My goodness. I knew we technically trespassed, but I would’ve never expected such venom in his cadence. Whatever our consequences were for this, it felt like it wasn’t merely gonna be a stern talking to or simple demand to never come back all together.
I look at Don’s face in the little light. It’s scrunched up, a lion whose competitor in the food chain entered his territory.
I understood it. Heck, I felt it. That man who approached from outside sounded no different to the superiority complex from some type of cops in our hometown. Always looked to escalate a situation before it was called for.
When the two men busted in, Don hid near the hole. I hid with Diana further away behind trays. We covered our mouths. Their flashlights zig-zagged. We were break-out prisoners in avoidance of searchlights. One of their line-of-sights managed to go right above my eyes. The two men were audibly puzzled by the floor’s breach. Stupidly, they both peered in together.
Although I hated Don at that moment for what he did next, I’m actually grateful for it nowadays. I have to admit, he has titanium balls.
Don snuck behind and pushed them both in the hole’s direction. He spared them the amount of force required to have them crashing through. Instead, they were able to catch themselves. It bought us enough time to start making our escape.
I wanted to yell something like: “you idiot, why would you do something like that?”
There was no time to complain, however. Don’s logic was we were already deep in shit, he didn’t mind gambling an extra layer of trouble for himself if it meant getting away scot-free. The lights were off so they wouldn't see our faces clear, there probably weren’t any cameras installed yet as I hadn’t remember seeing any. We all could’ve gotten away free as birds.
Could’ve.
So, we’re booking it through this hazard zone. Shelves, trays are knocked over, tech is collapsing, I get scraped on my legs and arms. I held Diana’s hands, but her foot must have gotten caught on some bundle of wire she stepped on. My first instinct is to help her get up, but they’re on us. There’s no way I can get her up. We’re screwed. I feel Don’s grip on my shoulder, pulling me in his direction against my own will.
I didn’t want to leave Diana. Understand this when I say it’s beyond difficult to shrug off fight or flight responses. My legs moved in accordance to survival. I hear Diana get caught and I feel like the biggest asshole in the world, she’ll never forgive us for that.
We make it. The hallway is still dark. Need a way out. I shine at a staircase, but I figured it would be locked since we weren’t supposed to be down there anyway. Bolting through a straight corridor as one man chased us, we could either make a right or a left in front of the office from earlier. I chose left, and lucky us, there’s a bright sign that said “Exit". We pray, push through the panic bar, and it opens. Fresh, outside air. We’re then in a garage area. There isn’t a fraction of rest in our haste. After an upward slope out of the garage, we’re greeted to clear streets. We cross one and hide behind trees. Strangely, the security guard never appeared. Next thirty minutes is spent of me panting, legs felt like fresh boiled noodles. Thinking about Diana made me want to slap myself.
Don had this asshole grin on his face. “Just like the old days, huh?”
“Shut up.”
Through front entrance of Residence Hall, a crowd emerged. Open house came to a close. It’s disjointed at first, so we’re able to inch ourselves as apart of everything in seamless fashion. The busses pulled up, and before too long, everyone was ushered on. There was a head count, and of course, there was one person missing. My stomach pulled a titanic.
“She’ll be here in just a second,” Counselor Burlesque said with a wink that sparked glitter.
5 minutes later, and there she was. Diana, with two security guards behind her, had arrived. On board, she stood by the bus driver, peering over available seats. I happened to be the only one with an empty chair. Probably the most bizarre thing that day, she sat next to me with a smile. In no time at all, everyone was on their way home.
Don’t get me twisted, before we departed, I did wanna gesture the Counselor and ask for information. Not to patronize Diana, I could have asked her, but if she were in big trouble, I also wanted to confess that I had all to do with it as well. It wouldn’t have been fair for only her to be punished. Of course, I wouldn’t have ratted Don out.
I didn’t do that, and I didn’t have to. Because it was actually Don who ended up asking before I could say a word. It also turned out that he did for the exact same reason.
“Oh, everything's fine,” I overheard Burlesque say to Don. “She had stumbled upon some of our art gallery works. Saw something she didn’t like and had a bad time. We sedated her accordingly.”
Diana didn’t have anything to say about that comment and didn’t seem to care. It truly had been the case, then. She must’ve fell into some art room and tripped out. That’s a logical explanation.
Though, you'd think there would be concern about someone pushing two members of security.
“Oh, baller,” He said. “Oh, well, she’s not in trouble, is she? Like, she can still go to this school?”
“Well of course she can, silly. If anything, we welcome daring students who are willing to take risk and wander. Nothing ever wrong with a little, Y’know, venturin’… some extended touring. We have a no-wuss policy zone, if you will.”
When she said that, she turned, sought out my where I was seated, and gave me a wink before turning to head out. Weird, but whatever.
I guess that’s the end of this tale.
So, I’ll answer some of your questions.
For one, Diana wasn’t putting up a front. On the bus, she was genuinely happy. Later on, I asked about what she saw down there that triggered her panic. She said it was some weird sculptures and paintings.
In her words: “Weird stuff, but nothing horrifying.”
Another thing I want to note is that she wouldn’t shut up about how ‘great’ the University was. Like, on and on, until we all separated.
For second, you might be thinking: “why did you continue going back to that school for freshman year, onwards?”
To that I say: why not? Sure, it was a weird day for an open house, and yeah, it’s kind of ridiculous that such a weak floor design was even possible. We could have probably put up a case to sue.
Then again, we were in an area that wasn’t supposed to be available for entry. Look, my point is that from our perspective, and factoring in Diana’s anecdote, there’s no reason to believe that we hadn’t stirred up most of the trouble we got into that day. An accident happened, that was about it.
Not to mention, the benefits here are far too good to leave over one little incident.
Nowadays, I spend a lot of nights reflecting back on open house day. Can’t help myself but to force dots together.
I try not to abide by things that can’t be proven. If something can be explained by hallucination, then so be it.
I try to cater towards normality.
Despite this, in jaws of unexplainable, events of my sophomore year at that college – that you may or may not know about – I find myself doing the opposite. I find myself not able to sleep, because there are certain words that rattle in my head that stem from that day. I often lay with lights on passed midnight, feeding myself the suggestion that those words may be relevant now, more than I ever imagined.
Diana’s explanation, of course, differed from when I pulled her up from the hole, and from when I asked her on the bus.
Through the frantic staccato of her breathing, I managed to make out what she was trying to say.
Not verbatim. From what I can make out, she was describing some sort of humanoid, white figure that she witnessed. It was inside a bubbling tank. It was alive, active, with: “bulging, yellow eyes.”
I did make out one complete statement. It’s the reason I’m writing this story.
It’s the phrase she repeated the most.
“It’s copying me. It copied my every movement.”
Part 3

What is FPGA
For those who don't know what a FPGA is, here is a very simple washed down explanation. Computers are simply made of gates, AND and OR gates. Instead of generic CPU (like AMD, Intel and ARM), FPGA allow users to run custom logic gates which are programmed to run only a certain dedicated task. Therefore, its called Field Programmable Gate Array.
Why do you use FPGA over CPU?
Performance and Energy consumption. As you can see here:
https://imgur.com/a/ErWxKxB
Also, some example of ASIC is the Google TPU (Tensor Processing Unit), which is design to run tensorflow optimally. However, FPGA allow you to optimise it for Tensorflow and you can reprogram it to run and optimise something else the next day. For example, it might be only optimise for Tensorflow version 2 today, you can also update it to run optimally for Tensorflow version 5 later on. FPGA is like a reprogrammable dedicated accelerator for applications.
What are some of FPGA application?
Satellite communications, FPGA consume very little power and can be remotely updated to run an optimize program later on.
Communication Towers, remotely update the FPGA and for real time applications.
Industrial Controllers, real-time control applications, motor drivers and etc.
What does it do with a CPU?
Back to tensorflow FGPA again as an example. CPU will be responsible for anything else other than running Tensorflow. A CPU can handle connection to a camera, capture a photo, apply filters and etc, finally pass it to the FPGA, and get the result from it again. In other words, CPU and FPGA can work together in applications that require real-time high performance applications. Of course, theoretically you can program FPGA to do what a CPU does, but it will be too tedious to do, and you might not need that sort of performance.
How will AMD incorporate Xilinx?
The main application that I see is Machine Learning - While Nvidia GPU is good for training Machine Learning models, FPGA is very good for inferencing. If AMD do it right, we should be able to train on a AMD CDNA and deploy it automatically on Xilinx
https://imgur.com/a/48rUP3P
https://blogs.nvidia.com/blog/2020/09/03/what-is-mlops/
The picture above is from nVidia. With Xilinx, AMD can not only complete the whole MLops and also have it ready to deploy on dedicated inference accelerator.
Another is automobiles, not just on autonomous side, but also for control critical components such as accelerators, emergency braking and etc. Its going to be either AMD with Xilinx or nVidia with ARM.
Benefits
Higher Total Addressable Market
AMD and Xilinx technology stack compliment each other
Higher margin in data centres and more enterprise customers
if the deal go through, Dr. Lisa will have stay even longer with AMD to execute it, and i take that as a win. ;)
Risk
Machine learning is just a hype.
No demand for edge computing.
Most importantly, lack of software stack and support from AMD and Xilinx
Can they execute it?
I am not sure about this, but I believe in Dr. Lisa and her team. If not, why are we even investing in AMD. Thats all I can say.

ok so I gotta friend we both girls and like for the last 2 months or so we be hardcore simping for girls together. She like send me picture “Look how hot she is” and I’m like “so hot”. Or just gushing over over female characters voices.
She’s never said her sexuality and I haven’t either and yeah. Like I know I’m not straight idk if I’m bi, pan, or just lesbian I just know girls <33. Also she sent meme about being gay and liking girls and yeah.
I lowkey wanna come out but idk how because idk what I am. we never rly stated that either of us liked girl we just like u know.
And like if I come out to her I wanna ask her how she feels. Like if she knows her sexuality but I dont want to force her to come out or anything.
So what I do? I just wanna know if my friend likes girls too. Like from context clues it’s pretty obvious she does but neither of us said anything concrete and yeye im kinda shit at inferencing I don’t wanna fuck up and come to the wrong conclusion.
Also how do i come out if i don’t know exactly what I am yet?