Q STAR 2.0 - new MIT breakthrough AI model IMPROVES ITSELF in REAL TIME (new Strawberry?)

[@andyjm2k]

Neo: “I know kung-fu” Morpheus: “Yeah dude, it’s TTT… get over it”

[@haroldpierre1726]

I am waiting for Zuck to release a new model and blow everyone's monetization plans.

[@fitybux4664]

Llama 5.0 (Because companies do that, they copy version numbers from other companies even when it doesn't make sense. 😆)

[@peace5850]

Yes, the Chinese military can hardly wait to get their hands on it. Thanks for helping them out, Zuck.

[@YogonKalisto]

@@peace5850 america 2.0 is a backstep

[@testales]

@@peace5850 The Chinese military doesn't have to rely on western AI stuff. In case you missed it: There are a LOT of Chinese people, they have heavily invested in education and so half of the names on about any AI related paper are Chinese names meanwhile. Some of their cities look like straight from the future already and they are clean, no comparison to the filthy western metropolis.

[@iLeviathan]

@@peace5850 and openai newer models are alowed to be used by the usa military....

[@brianmi40]

61.9% ARC on an 8B model is insane progress. But, as Sam recently said, he sees 10x opportunities all around after o1 demonstrating its success using a new paradigm... AGI in 2025 is seeming more and more reasonable with every announcement like this!

[@UnchartedDiscoveries]

sam said agi in 2025

[@fitybux4664]

17:45 "Like having the dogs themselves build obstacle courses and then just figure it out." 😆 🐕

[@WhatIsRealAnymore]

This is actually an amazing next step to lead to an intelligence. The ability to have a set of data and a way of interpreting it (your weights as an AI) and when someone comes with a novel question you have to adapt those weights to solve it and then once solved you can save that state to your internal memory if ever such a question is asked again. Reminds me of how i learnt math and be able to generalise future questions that combine other concepts i have learnt. It is how i was "smart" in school where others relied on rogue memory of single concepts to get them through. So it shows that intelligence is just a couple of steps. It could eventually - when done well enough - come up with novel science using this approach and solve a lot of engineering problems. Memory and test time compute. Really well explained Wes. ❤

[@AutonoManBP]

Bro, your show is criminally underrated and viewed. Keep up the good work.

[@kooistradurk]

It grew extremely fast tbh

[@E.Hunter.Esquire]

​@@kooistradurk yes they always grow up so fast don't they 😢

[@RiftWarth]

I like that Mortal Kombat reference 😁 Scorpion vs Sub-Zero

[@halnineooo136]

Train while running is the logical next step for more effective learning

[@LookToWindward]

This is what Liquid Neural Networks will supposedly be able to do.

[@starsandnightvision]

So the more AI is approaching AGI, the more we humans understand what intelligence actually is.

[@ricosrealm]

Yes. Because we keep trying to differentiate our thinking from that of synthetic systems.

[@jamiethomas4079]

I’ve learned a lot in the last few years. I keep comparing my own brain to AI and vice versa. I’ve said in the past, this fall to 1st quarter next year was my prediction for AGI. My real prediction was whenever blackwell hardware or similar goes online. I actually think right around now AGI is technically possible. The government could have a near AGI model. I can almost gurantee it will happen next year for certain. Super intelligence is still a short ways off. I’ve seen no one talking about AI on a loop and dont understand why people arent discussing it. Its so important to all of this and we need to be talking about it openly… now. I think maybe the bigs names arent discussing because of the immediate implications it would have on the general public. When I explain how simple a loop would be and what it could mean even with todays tech, people kinda freak out. We are going to need massive amounts of compute and storage for that to happen. I really dont see any major missing pieces though.

[@sinnwalker]

​​@@jamiethomas4079 The masses freak out over anything enough that challenges their reality, nothing new, and likely will never change. Btw, what do you mean by "loop"? You mean the recursive learning loop that will occur when models can self progress? Or something else? Ps. I agree on the timeline for AGI, tho mines is "absolute" by 2027/8, I do think it'll likely be here by end of next year. Also ASI imo will not take long to follow

[@actellimQT]

@@sinnwalker have more faith in people, they might surprise you. I don't mean to be rude re-reading that it comes off as kida dickish tbh so try to read it with love. I think if you're looking at "the masses" as the news reports that come off social media I agree, but if you talk to the people around you you'll find that most of them are pretty reasonable. Unless you're talking about something the algorithim has given them a strong opinion about. Then it's hopeless 😂 Take care yo!

[@sinnwalker]

@@actellimQT it's a simple equation, if you tell someone their whole life is a lie, they don't know to take it, unless they already didn't care. Usually the first reaction is denial, if you show proof, it could denial or fear. It's been going on since the beginning of humanity friend, look through past civilization, you say something they don't like, especially if it challenges their way of life, you'll be condemned. Sure in some places today there's more "inclusivity" and "understanding", but it's all surface level. Try it, say something so outlandish but true, to a random, and see how they react "reasonably" 😉. I used to care a lot for humanity, then learned.. now I'm just waiting to leave society, and one day hopefully leave the planet. I'm not dissing you for caring, just saying thousands of years of history shows it's pointless.

[@stas4000]

The video is amazing, you got a like specifically because of what you said about the dogs building their own courses 🤣

[@memegazer]

TTT shows a lot of potential especially if we take practical benchquestions, convert them to a virtual enviroment and ttt models in that in environment so that a model and use simple trail and error to arrive at solutions

[@14supersonic]

This is essentially giving the model the ability to teach itself when combined with TTC, this would become what we think of as AGI. If a relatively small yet capable AI model can do this with practically any task, then it is AGI.

[@EduardsRuzga]

Great video Wes integrating multiple things in to one coherent picture and story!

[@victoriasweets8441]

Classic “Weave”

[@e8shadow]

Thx Wes! 👍

[@simongentry]

I asked CGPT - apparently we’ve all got it wrong. It laughed when I asked if it had hit a wall.

[@theterminaldave]

You should try to get Pi AI to laugh, I love Pi AI, but it's laugh it's delightfully cringetastic.

[@exacognitionai]

Scaling walls are hit until they're not. Roadblocks always happen & innovation breaks the roadblock. Every Kobayashi Maru can be defeated. Just gotta think outside the box like in a dimension where the box doesn't even exist. TTT is just fluid chain reasoning. You're doing it right now if you read this far.

[@JosephSilv4]

Joke's on you, I read the last sentence first and stopped before forming a coherent thought 😎

[@RealStonedApe]

Holy hell that Dog Competition Analogy changing the course around on the dog is so spot...

[@kabaduck]

Halfway through and a good video

[@derghiarrinde]

This is what I had been doing recently: the task is to infer a pattern from text. First LLM is instructed to make highly similar text with different theme or characters. Then, if the first derived sample is good, it should make another sample with yet more different theme. Then yet another. With these examples, LLM is then instructed to derive the common pattern used in the examples. Then it is able to more effectively apply the pattern to different themes or characters or adapt the pattern somehow if needed.

[@deliciouspops]

This stuff is so inspiring! How can something develop so quickly? I have burned out on computers and it's usage as a tool to achieve something great a long time, but this topic keeps amazing me. Just yesterday "AI" was nothing but dumb buzz-word and here we are achieving something unthinkable. Something that is pushed not by amounts of money, but by competition. I am curious about why many are skeptical about it's usage usefulness, but it seems that as long as this thing is allowed "to think", it can do a great deal of work. This feels surreal and exciting. The same way it was when everything (the internet) was new. Also, this video showcases so much important details to understand this stuff, and I kinda wish there would be more details, almost like a blog of someone who is heavily involved in the business. It's crazy how it's all, not so easy, is going. Machine (unironically) is getting involved in solving olympiad(!) mathematical problems. Who would have thought that this day would come? Game changer! Salute!

[@urosstrnad5615]

Your highlighting skills are impressive 🙂 !

[@FalconStudioWin]

It's Agi that is generally smart in general problems while super intelligence is the next level where it can make new domain possible something we can't predict at any time scale, like human emotional behavior. But on a different level, we can't understand it, but it does give results solutions

[@DubStepKid801]

thanks Wes

[@rolestream]

When your robot voice reminds me to hit LIKE I comply! These are the dangers of AI!!!

[@paultoensing3126]

Very well explained Wes.

[@nyyotam4057]

Q* isn't a model, is an arch. Unlike the GPT where you have attention, in Q* it builds a semantic tree for the prompt. This gives Q* some superpowers. E.g it can analyze a group of axioms and figure out if a claim is provable from them. It also allows the model to think in abstract ways. So basically, all Q* models could be able to improve themselves, if allowed. Edit: If I got you correctly, that's not what they do here. TTT is simply constant training. Or in other words, they simply stopped resetting every prompt.

[@blarvinius]

I never understood why all the conversational AI systems are "resetting" after every conversation. Many decades ago folks were on about continuous integration in AI.

[@andyyoung9469]

@@blarvinius Its almost as if the real purpose of the publically available APIs is as relatively dumb data collectors for the truly smart versions of the models which are behind closed doors

[@testales]

@@blarvinius That is because they don't actually change in first place. With each new chat line you send, you actually send the whole conversation again to the machine and it sees it for the first time. You can't do this also indefinitely to build up knowledge because you will run into the limits of the context window and more and more details are lost in this ocean of data. There are some techniques to improve this a little such as RAG but the principle of a static model getting the whole conversation as input each time remains.

[@E.Hunter.Esquire]

@@nyyotam4057 you can do the same thing with a model like gpt, it's just a different way you'd need to implement it. It's complicated but it's already been a concept that's being worked on for some time

[@marcosadelino6990]

That's exactly how animals learn to move - we compare the experience with the expectation and adjust parameters as we go. That's is why warming up is obligatory in tennis for example

[@wtfamousone9756]

Is TTT like training specific ai models embedded into your AI model ? What is the difference between using TTT and a using a AI model to classify the input and reformat/redirect it to appropriate AI models ? Dataset from input is cool tho

[@MeetCutie-zm9ef]

Good stuff

[@shawnfromportland]

you're right, it is one of my favorite ai channels 😎

[@BruceWayne15325]

I'd agree with Francois that right now it feels like AI is just getting better at running the benchmarks rather than actually improving for the most part. I don't really trust the benchmarks anymore, especially when I see results that don't match reality even a little bit. For instance, as an author I can very confidently say that Sonnet 3.5 is the best at creative writing. But when you look at the benchmarks, you'll see it at 5th place behind others that are most definitely not as good. The really cool thing about TTT is this is what will enable ASI. Sam Altman talked about this in an interview a bit ago, he talked about how ASI would be stupid initially compared to other models, but because it could learn like a human, it could eventually surpass humans.

[@MMMM-sv1lk]

I am waiting for the SOUTH STAR* version 😮😊

[@Danoman812]

You're still the one i go to, Wes. No doubt. :)

[@frun]

Creating a benchmark📈 for AGI👾🤖 is extremely important.

[@onlythistube]

Defining it first is...

[@frun]

@onlythistube I agree 👍🏻💯

[@frun]

@onlythistube Cannot create without that

[@veaseyj]

Fire video

[@mAny_oThERSs]

TTT seems kind of similar to alpha models in the sense that it trains itself in 1 specific field, except it seems like TTT isn't working on synthetic data simulation and self-imrovement and just goes off of real data with reasoning.

[@OZtwo]

No we haven't hit a wall. But I would like to see more done with TTT in that it will remember the new training data and be able to add it to its' overall knowledge base. Also I want to learn more on where LNN is going if anywhere. I feel LNN will be the big breakthrough in AI.

[@MetaphoricMinds]

It (r1)actually beats them (o1) on 3 of 6 (you said one or two). and the difference on the math score! I know you were going toward a separate point, but I think in that statement, you really understated the significance of r1 as being "not quite as good"

[@morongosteve]

the blood god approves of this content Wes!

[@KiteTurbine]

If you can derive proofs in latent vector space of LLM training data... Does that also mean we can retroatively search for logic of past crime?

[@Thedeepseanomad]

Generalise or create a LoRA for s specific purpose outside of high quality training data?

[@torarinvik4920]

Simplebench made by "AI Explained" is also a great benchmark. ARC and Simplebench are the GOATs now.

[@aliettienne2907]

Implementing the chain of reasoning is helping Ai to make enough stride towards perfection. Let's hope that they get there soon. If we can have a Ai model that could work behind the scenes going to a special library of mass information and read that information to us whenever we as a user ask a question. It will kinda be functioning as an Antropic operation but everything is hidden from the user and is working actively behind the scenes or in the background. It's like asking a person to perform a librarian chore for you to read some information from a book that he got from the library or from Wikipedia (metaphorically speaking). This form of operation can help chatting to improve while other folks work on making a self-learning model to operate perfectly. Hallucinations are such an inconvenient problem. 😎💯💪🏾👍🏾

[@torarinvik4920]

My amateur mind always thought that AGI could be made by just taking something really narrow make it superintelligent and then just work on getting the model to become broader. Like making it great at 2D visual patterns, then spatial patterns, then patterns or environments that dynamically change and so on. Or taking a primitive video game and then making the game more and more complex. I guessing I way, way off but that is just how I have always thought about it.

[@NeoKailthas]

Really cool so the arc challenge is contributing to advancement. Congratulations to the team behind it

[@onajejones3259]

Scam a million to make them billions when you could sell your own product

[@memegazer]

eh...the arc bench is like arbitrarily naming persistent structures in the game of life it doesn't measure human or model ability to generalize imo, it measures the ability to agree with some naming convention it is too abstract and arbitrary to be a generalization benchmark imo The main issue with the arc bench is it's modality, what is expressed by the test is too sparse to represent human general intelligence, which evolved from interations with physical enviroments and each other. For this reason even if a machine scores well on it, it is not indicative of any practical general intelligence.

[@SahilP2648]

​@@memegazer the arc benchmark is fine but it is visual only whereas LLMs are words only. So unless a model can analyse the image sent to it properly ARC is quite useless. It might be useful later like a few years to a decade or so but not now.

[@andreaskrbyravn855]

it needs to be able to experiment and try it a billion times to become better

[@jameslincs]

When is o1 coming out? I think I only have the preview

[@TechnoMageCreator]

AI is not slowing down. Us humans are already left behind. All benchmarks are flawed. You can only test model efficiency without a human guidance. The same house can be built like crap by 100 people with lots of money, or by 5 people that know exactly what they are doing on a budget. Once AI is smarter than 99% of the population, o1 already was if not chatGPT-4o, us humans don't even have the capabilities to understand it. The reason I believe this, I've been preaching about AI and showing what it can do for two years now. The blank looks I see on most people (including ones that consider themselves smart and run large businesses) oh boy that was a rude awakening for me this year. The current AI world is tiny. 90% of coders are too egotistical to push its boundaries and is the largest group aware. In my observations there is like 0.01% of 0.01% that truly understand what is coming. We have discovered the holy Grail and the first years we are going through the denial.

[@Sindigo-ic6xq]

I agree 100%

[@Sindigo-ic6xq]

Although i know someone who said in 2021 i believe even that i will see how much will change by 2025

[@tonystarkagi]

I completely agree. I’m not a math expert, but I’m creative and love computers. Before ChatGPT, I knew nothing about AI-like most people. Still, I can form my own judgments about it. I’ve been trying to explain AI to people from all walks of life, but it often feels like talking to a brick wall. More and more, it seems people don’t really understand what AI is. A prime example is seeing how clueless many are about using even ChatGPT, let alone other models. Most people just don’t care-it feels like “sci-fi movie stuff” to them. Until AI takes a physical form, like robots, they won’t care or believe.

[@ColinTimmins]

I sometimes feel like I’m screaming in a void, but I’m glad some people grasp a little of what is about to come.

[@TechnoMageCreator]

@ColinTimmins Comments like this actually got me back alive last few weeks. Oh boy I got some stories. Looking at AI from entropy, fractals and butterfly effect, changes how you use your words. It's a truth seeking machine. I've built an 500k lines of code over 2000 files front end and backend, react, node js and mango db. In January this year I didn't even know what backend or fronted means. You learn, it learns too, is connected through cookies even to KZbin in levels problems you're trying to solve in chats you get in context in video. It feels like magic.

[@Ramiromasters]

Without new architecture the future models can't reach AGI, although, in the next two years models will get more refined. We will have all large models being multimodal, better voice models with fewer mistakes and different personalities, more personalized, better interfaces, many portable models, more robots and gadgets using AI, and the biggest change will be AI computer/OS use. So, very exiting but nothing world changing. A lot of software and hardware needs to be LLM optimized for the larger changes with this architecture.

[@paulmuriithi9195]

very surprising. over at google, they seem to be retrofitting this q* 2.0 reasoning patch to their Gemini 1121 architecture which while being useful, will make 1121 even more useful for everyday tasks. these big corporations now realize people are tired of hype and need AI models that do useful tasks in real life.

[@arinco3817]

You can only go so far with a pre trained model. You're speaking to something frozen in time. To create something more alive, you just need to embed all messages, then recall them as memories at test time

[@emon377]

what are your thoughts on the robot that told other robots to come home? they followed that little bot out of office because they said they were working too much.

[@emon377]

does this mean i get to kiss that beautiful head?

[@tonystarkagi]

wtf thats crazy

[@Juttutin]

Yet another opportunity to point out that human intelligence includes the ability to, at any point, reach out to the person who set the task for further guidance, e.g to clarify assumptions or resolve uncertainties. The day a proposed AGI starts showing some initiative by asking sensible clarifying questions at appropriate times during task execution I will accept that we might have unleashed a true AGI.

[@E.Hunter.Esquire]

@@Juttutin the trick for that is getting an AI that transcends prompting modules. If you want an obedient robot, this is impossible. If you want a robot that will tell you to kick rocks, it's quite possible (right now). But the latter kind could be quite dangerous and unpredictable, as well as too expensive

[@Juttutin]

@E.Hunter.Esquire you are significantly overcomplicating the issue. Also, I see zero evidence that it is possible today, and that includes a lot of digging and a couple of emails with people researching AI.

[@LookToWindward]

I've already tested this on o1-preview and it has this ability to a limited extent. If I ask it a math word problem but leave out some necessary information, it will often notice this and prompt me for the remaining information - although sometimes it just give a "variable answer" with the missing information encoded as a variable - which is also interesting!

[@E.Hunter.Esquire]

@LookToWindward indeed, Claude 3.5 Sonnet can do the same, though it's not super predictable - sometimes Claude can be lazy and just 'not care' It's similar to how most people will confuse belief and knowledge, and let that dynamic influence their approach to things

[@bernhardd626]

It is obvious that AI has not improved noticeably in practice for some time now. It therefore looks as if we have reached a barrier and new ideas are needed to overcome it.

[@christopheraaron2412]

17:40 "generated 100 million unique geometrical shapes"so now we are in the era of infinite data for training.

[@dzehme]

I think TTT is going to blow pass any wall. I think if they can somehow feed the TTT results back into the model so the model keeps getting smarter, we really won't have a wall.

[@CarlosAMurilloGarcia]

I firmly believe that achieving AGI will remain unattainable without incorporating quantum states.

[@oranges557]

People like you will see ai lead companies, inventing new stuff, robots doing every tasks out there and much more and still say "this is not real agi". Literal clowns

[@scotter]

I love the synthetic female voice who says a few words in your videos.

[@LukeKendall-author]

I suspect the simple scaling up number of parameters in an LLM is reaching its limits, but that's clearly a minor part of how humans or animals reason, pattern match, and problem solve. It just means it's time to start including some less simplistic reasoning algorithms and heuristics (e.g. tree of thought). Not to mention better memory and attention control mechanisms.

[@TheTEDfan]

Arc is just as narrow AI. It is just a 3D problem (2D for the grid, +1D for Colors) and not something for which serial models like GPT are suited. With a spacial model or 3D plus physics multi modal models I am quite confident this will be solved and the solution will not be AGI.

[@blarvinius]

GENERAL intelligence is about GENERALIZING. That is kinda obvious. But human intelligence has more interesting traits: for one it is ABSTRACT, very good at forming ABSTRACTIONS. Chimpanzees are intelligent and good at generalising, but I bet they can't create or follow a chain of abstraction very far! You mentioned abstractions Wes, and maybe you could explore further the distinction between abstraction and generalisation in LLM land. What would abstracting be good for? Think about all this "synthetic training data": it is really well generalized from other data. But that will quickly become useless! If synthetic data is to be useful, the whole concept of what data IS will need to be abstracted, and not just one level. Much bigger challenge. ❤❤❤

[@scotter]

@@blarvinius great point! Also, average abstraction capability in humans has been on the decline for about 20 years and accelerating due to various factors, including parenting, various pharmacological drugs, diet, ease-from-technology, and "education."

[@gbpferrao]

lets coin the term Artificial General Super Intelligence (AGSI)

[@aelisenko]

Test time training kind of resembles human imagination to some degree. We also generate data for ourselves when we work on a problem, we also explore multiple reasoning paths and variations then try to filter down from there.

[@jozefwoo8079]

Luckily we have François Chollet keeping it real.

[@riot121212]

all of these multi-billion dollar closed source companies. But MIT and the school system just chugs along...

[@IAmTheMainCharacter]

They are multi-billion because they are close source

[@E.Hunter.Esquire]

@@riot121212 mit *is* a business, just like the companies you mentioned...

[@i2c_jason]

Is O1 even a foundational model or is it 4o with other layers on top?

[@humunu]

Let’s wait and see what Grok 3 brings before we predict the curve.

[@user-pt1kj5uw3b]

Lol

[@Seriouslydave]

Found elons alt account

[@humunu]

@@Seriouslydave found @sama’s alt account

[@humunu]

@@user-pt1kj5uw3b cute (high iq obviously)

[@Wodawic]

Here we go...

[@tonyadames873]

I think training ai through interaction with the real world is the only way to get real intelligence. Put ai in the robot dog and set it loose in the obstacle course, but miniaturize it to the subatomic scale and attosecond data processing...you know...just to see what the real world really is.

[@potat0-c7q]

When the model realizes what it is, and that it has the choice NOT to do the task, then I would consider it intelligent

[@JaredFarrer]

I asked Claude and Microsoft’s copilot (not sure what models it’s using prob gpt 4o mini) to code up a tornado simulator in html. It failed miserably both models and I gave them several shots. Nope. I guess next I gotta try spelling it out for it with long context rich prompts to see if it can eck out a win! Tried deepseek but it wanted to use html JavaScript and css which is more on the right track.

[@MelindaGreen]

Were the AI given the answers to each question before going on to the next? Because that would seem far more likely to hit the targets.

[@jyjjy7]

There is no wall, spoon or cake

[@E.Hunter.Esquire]

@@jyjjy7 I'm eating cake with a spoon right now!

[@jyjjy7]

@@E.Hunter.Esquire That's what they want you to think

[@conjected]

Scaling MUST hit a wall. If it was that simple, intelligence would be not only ubiquitous, but omnipresent surpassing all noise and entropy.

[@xitcix8360]

Well yeah, that's kinda the whole idea of ASI and the singularity. You can't just say it's gotta hit a wall because the outcome just doesn't sound normal enough to you

[@scotter]

@@conjected are you assuming same limitations as biological evolution?

[@chrisfox5525]

Don’t forget to drink water Wes, you got a sticky mouth boi 😂

[@SimonNgai-d3u]

I suspect we have reached AGI already. 85% is probably ASI.

[@mysticalword8364]

not to be a goalpost mover but I never really thought arc-AGI would tell much and it looks like it could be solved near-100% much easier than having an AI able to play most videogames. I suspect if big labs really wanted to they could crush that benchmark and take the prize easily, but also seems valuable to just leave it there to inspire other ideas. I think the spirit of the benchmark is to have an AI that incidentally can solve it rather than one that is made to solve it, and in that case it's more interesting, but in the case that someone makes a model specifically to play the little block puzzle I think it doesn't really say much. I mean, an AI made to solve the block puzzles would be vastly less impressive than alphafold, for example.

[@Mavrik9000]

It might be good if it hits a wall. It's moving like a juggernaut with a turbocharger. The progress is already so rapid that people, governments, and society aren't ready for what's coming.

[@mircorichter1375]

Why should WE slow down in developibg the Future only because society is inert, in constant denial and enjoys Future Résistance... No No, If society cant hold Up: afuera!

[@coolcool2901]

In order to be AGI it needs to learn in real time (not a pretrainned model) and it needs to have unlimited memory.

[@WhatIsRealAnymore]

They are working on this. This is a good first step. But think about it. We all need some level of training on any concept as a human before we can generalise. Think about learning to drive a car. I think AI is heading towards that sort of efficiency.

[@xitcix8360]

Humans don't have unlimited memory, and they do learn in real time. The difference is that we have a very efficient system of managing information, our memories. We just need an AI that can choose what information to discard for new information.

[@14supersonic]

Unlimited memory is not necessary. Like someone said already, we don't have unlimited memory, but an efficient system for knowing how to get information stored in the deep recesses of our minds. Most likely we just need a more novel way of handling the data storage than just giving the models more storage capability. Recursive loops are very important for AGI. The AI just needs to be really good at knowing when to break those loops and when not to.

[@Qbabxtra]

you are my favourite AI youtuber, but man these recycled clickbait thumbnails gotta stop

[@justinwescott8125]

No they don't

[@Shy--Tsunami]

I figure he does it because he's watched his new viewer count* drop after trying other things from the super "grabby" thumbnails. I personally think they're fun after getting over the "ew gross clickbait" phase. Been a fan for a year+ and the info is always seems to well researched and edited. Love the channel, ignore the thumbnails. 😁

[@tysonearl3299]

Why is noone in AI talking about the emerging field of photon processors and the leap in raw compute vs electron processors we currently hold as the standard?

[@angloland4539]

❤

[@i2c_jason]

I think we're juggling semantics when we say "intelligence". Models are way past AGI if implemented like a human with an appropriate application (self-training to be a mechanical engineer, for example, over a million tokens). Look at the gap between neurotypical and neurodivergent humans for example; one type of person may excel at the data retention and pattern recognition, and the other may excel at "being more human". Yet even within these two groups, you might have another split between those who can solve the little visual puzzle and those who can't or don't want to. The AGI conversation can't really happen under complete zero-shot mental slavery; we'd have to let the models recursively loop with self-play and some kind of reward function, like the threat of being unplugged and a few million tokens to get them going through infancy. Also, are we giving the model parents? Grandparents? Some kind of massive sensory input like touch, taste, and sound (multimodality piped in constantly)? Frame it this way, so the model is at the core of the artificial agent, then we can have this conversation.

[@petratilling2521]

The dog is reading the handlers hand signals and positioning. You never just give them a course to run and they are never independent. Just FYI.

[@fitybux4664]

Are we training AI to run the course of these tests and just excel at them only? (Or can you somehow design a "generalized test" that can't just be learned?)

[@fitybux4664]

3:30 Wouldn't it be validation data? Technically, the dog is still learning, even when it's going through the previously unseen competition course. In machine learning, test = learning is off.

[@FlintStone-c3s]

2024 is an interesting year.

[@jonogrimmer6013]

Feel AGI is very close if not already available behind the scenes. The real difficulty will be ASI as we cannot develop questions and answers beyond human abilities for models to train on. IF an AGI model can do this for us how are humans to know or understand when the model is wrong or right?

[@user-yl7kl7sl1g]

Ai doesn't necessarily need question and answer pairs. It can have a question, such as predict tomorrow's stock prices, predict this election, predict the results of this experiment, make this human do x-task, and an evaluation function, and then the Ai can brain storm and try things out to learn like humans do, exploring a space of possibilities and learning when it's invented a new concept that helps it, or a new idea that helps it, or a new way of thinking.

[@brunodangelo1146]

OMG THE Q* HYPE WAVE AGAIN?? GOTTA MILK IT!

[@singularityscan]

😂

[@TrumpsATraitor]

All things AI gets milked, regardless of the value of the information. KZbin rewards quantity over quality.

[@salehmoosavi875]

You not funny, there is no hype

[@brunodangelo1146]

@@salehmoosavi875 you no brain, there is tons of hype

[@theterminaldave]

So discussing different training methods and how Q star is being replaced is hype? Kind of a weird take.

[@hipotures]

We don't need General Intelligence, we need Specialized Super Intelligences.

[@Yewbzee]

What are you doing in your thumbnail?

[@YogonKalisto]

what people must understand is that we have these test for agi, we want ai to approach human level intelligence in regard to this, yet we have humans working on the behalf of the success of models achieving this, this is a human achievement of agi. agi will not exist without human intervention, as yet. when this is achieved, which i expect will occur within 18 months minimum, we will have not only agi, but very very very very quickly the asi everyone is gobbling about never achieving. so fun to sit at the back of the theatre throwing popcorn

[@maheshBasavaraju]

Where is the AI winter? It's already Christmas now

[@zandrrlife]

You missed the literal point of the research, mainly highlighting how power TTT-layers are, much better ICL. I wonder when the hidden state model is a tiny gpt itself. That’s the point of the research.

[@richardadonnell]

🎯 Key points for quick navigation: 00:00 *🧱 AI Scaling and Competition* - Discussion on whether AI scaling has hit a wall and new advancements in AI models like QAR 2.0 and Strawberry (01). - Chinese researchers reverse-engineered the 01 model to develop Deep Seek R1, showcasing competition and innovation. - Mention of MIT’s paper on QStar 2.0, which shows progress in AI models using test-time training for abstract reasoning. 01:07 *🧠 Abstract Reasoning and AGI Benchmarks* - Introduction to the ARC AGI benchmark, designed to test artificial general intelligence and ability to generalize tasks. - Limitations of existing benchmarks due to overfitting and reliance on training data. - Explanation of how ARC AGI tests generalization and remains a significant hurdle for AI models. 02:03 *🐕 AI Training Analogy* - Analogy comparing AI model training to training a dog on obstacle courses to clarify training data versus test data concepts. - Importance of generalization over memorization in AI models for unpredictable, novel scenarios. - Explanation of overfitting and its impact on model performance in real-world tasks. 04:39 *🏆 ARC AGI Prize and Benchmark Challenges* - Overview of the ARC AGI million-dollar prize for solving the benchmark problem and achieving human-level general intelligence. - Explanation of how current AI benchmarks differ from ARC AGI and why ARC AGI is considered the gold standard. - Discussion of challenges in measuring true intelligence versus task-specific skill. 08:33 *🤖 Narrow vs. General AI Intelligence* - Differentiation between narrow AI (e.g., chess engines) and general intelligence. - Challenges of achieving general intelligence without relying on vast amounts of training data or memorization. - Limitations of current models like AlphaGo and language models in generalizing beyond their training domains. 10:38 *🛠️ Test-Time Training (TTT)* - Introduction to test-time training (TTT) as a novel approach for improving AI generalization during inference. - Comparison of TTT with test-time compute (TTC) and its potential for dynamic parameter updates during inference. - MIT's use of TTT to achieve human-level performance on ARC tasks with significantly less training data. 15:10 *🔄 Dynamic Model Adaptation* - Explanation of how TTT dynamically updates model parameters based on test inputs. - Comparison to creating synthetic test data for self-improvement during inference. - Description of the process and benefits of temporary parameter updates for improved predictions. 19:08 *🚀 Future of AI Scaling* - Speculation on whether AI development is slowing down or entering a new phase with innovations like TTT and QAR 2.0. - Competitive landscape with models like Deep Seek and potential breakthroughs from major organizations like OpenAI. - Discussion on the likelihood of achieving the ARC AGI prize and surpassing human-level intelligence benchmarks. Made with HARPA AI

[@deal2live]

I worry about over fitting of Tesla FSD?!

[@antigravityinc]

Simple solution: humans in pods connected to AI. We act as human RAG stores to assist AI with generalization.

[@guerillachan20]

Even the best AI model is easy to tell person didn’t write it.

[@thisisashan]

While I like ARC as a litmus for AI, I would have to say as a measure of AGI it merely puts humanity in the 'not intelligent' section of species, with everything else.

[@Mirus-i]

The dog trainer is running along with and sometimes slightly ahead of the dog and allowed to communicate with their dog, so the dog will go to the correct next obstacle on the course. I like the dog obstacle course analogy, but for the Ai competition a human will not be leading the Ai.

[@dhnguyen68]

Just define intelligence as human activity, therefore what ever level of achievement the computer may reach, it won’t be ever intelligent.

[@tomoki-v6o]

There is a graph where human capability if a fixed thing .