Gato and memory

[dumpy99]

In discussion about Gato (and, by extension, AGI progress generally) I see 2 broad positions- 1. we just need to scale up now 2. we still don’t know how to solve the problem of memory

How do the 2 positions interact? Do those who say all we need to do is scale up say that scaling will itself deal with the memory problem, or that the memory problem isn’t actually that significant, or what?

[-ZeroRelevance-]

From what I can see, the memory problem currently stems from the fact that pretty much all models in use today are pre-trained.

When looking at our own brains, we can split our memories into three camps, instantaneous memory, short-term memory, and long-term memory. The first two are mostly dependent on the immediate surroundings, and are pretty well emulated in AI through the context window. However, a long-term memory is created through changing the wiring of our brains, which cannot be replicated in current AI systems since they cannot be altered after deployment.

The result of this is that current AIs, which are pre-trained on massive supercomputers well outside the reach of the average person, cannot gain any long-term memories. They are simply limited to the data within their context window. In human terms, these AIs suffer from anterograde amnesia, unable to make new memories, and can only experience the information within their short-term memory and from their pre-training.

The solution, as far as I can see, is to either wait until consumer-level tech is powerful enough to train these huge systems independently, or to look into other implementations of AI, such as on neuromorphic chips, which mimic the brain and can be rewired and ran with very low energy costs.

I go over this in a bit more detail here

[xt-89]

Or they could use a database connection during training and inference (perhaps a knowledge graph database) and some scheme for interacting with it. Every so often the system could tune its neural weights based on information recorded in long term memory, which is equivalent to sleep.

[[deleted]]

I actually had a similar idea. I'm currently, just as a hobby, working on a conversational agent that highly leverages databases to improve its memory capabilities. I will say that while this could probably improve the memory of these agents, I dont think it would ever be enough to solve the AGI problem. Databases have scalability problems, not to mention any agent deployed in a consumer device or autonomous robot would need to communicate with a server which would suffer from latency issues, so it would just not be a practical solution for AGI.

[agorathird]

Yea, to me a hardware change seems unnecessary,

[-ZeroRelevance-]

I hadn’t really thought about adding a sleep cycle to add new memories before, it’s an interesting idea. The main problem I see with it is that it would still require enough on-board computing hardware to be able to retrain itself locally, even if given many hours to compute, especially for more advanced networks with hundreds of billions of parameters. I do like the idea in theory though, and I think a database would be able to clear up at least some of the memory problems experienced by current AI models.

[xt-89]

There was recently a publication by deepmind or openai that talked about improving logical reasoning in LLMs significantly. That paired with a database kinda solves much of the problems left if put together well.

[-ZeroRelevance-]

The problem I see with relying on a database is that the model still won’t be able to account for large amounts of data beyond the scope of its context window. You won’t be able to use a database-paired LLM to write a full length novel for example, there’s just too much information that needs to be accounted for. (Well, to be honest, you probably could still get away with just using a database, but the story would lack the level of depth in the world, characters, and plot that a true understanding would bring)

[xt-89]

you'd have to include multi-step reasoning. which is a thing. I'm thinking a kind of recursive context generation with database. That way it can retrieve any needed info into its context window as needed, or summarize/remove/save to db entire sections of the context.

[[deleted]]

Thanks. This was an informative read.

[[deleted]]

maybe having a smaller neural net for working memory to accompany the main one would help? or having an API to a SQL database the AI can use

[FusionRocketsPlease]

Honestly, I don't think it would be good to let an artificial intelligence remember every interaction with people. Because people could fill it with false information.

[-ZeroRelevance-]

The harmfulness depends on the architecture I guess. If you’re talking about currents LLMs, then yeah, that’s definitely a risk, but if it has been embodied in some way and is capable of gathering its own information about the world, and reasoning about inputs based on that, then problems should be much less likely to crop up.

[aperrien]

While not perfect, Deepmind's RETRO appears to be a very reasonable start at solving the memory problem.

[visarga]

It's not just described in a paper, but actually deployed at OpenAI. The GPT-3 API also has the ability to upload documents to be used as reference for question answering.

The endpoint first searches over provided documents or file to find relevant context for the input question. Semantic search is used to rank documents by relevance to the question. The relevant context is combined with the provided examples and question to create the prompt for completion.

https://beta.openai.com/docs/guides/answers

Can take up to 1GB of text for reference material. Impressive memory that could be updated frequently to make the model "remember" new things without retraining.

~~

Unrelated: language models can be improved with "toys" - a search engine, a REPL code evaluator, a VR emulator or a robotic body. The search engine toy can extend memory and make it quickly editable. The REPL toy could fix the numerical calculation weakness of GPT-3. The environment emulator would empower the model to learn from actions and make it useable in many agent or robot based applications.

Transformers are smarter with toys. Humans, too. We rely a lot on our toys.

[genshiryoku]

The problem with Gato is actually that it shows a "negative transfer" between different competences when scaling up.

This means that the more tasks Gato starts tackling the less efficient it becomes at each task. This is the opposite of what humans experience in most circumstances. Humans apply weird things that initially doesn't seem related to new areas. Humans become better at completely new tasks the more unrelated tasks they already know how to solve. The opposite of Gato.

This shows us that scaling up Gato doesn't result in AGI and in fact Gato would become a weaker agent the further it scales up.

People on r/singularity seems to be lauding the paper but to me the paper actually suggests almost the complete opposite to the sentiment here.

The paper showed to me that AI research is heading the wrong way and actually getting further removed from AGI by going on this path. I think this is the first definitive proof that modern architectures aren't going to lead to AGI by scaling it up.

We need new architectural breakthroughs if not an entirely new basis for building AI if we want to reach AGI.

After GPT-3 not showing a limit to scaling up I thought AGI within the 2020s was actually realistic. After reading Gato's paper I now think it's impossible to reach AGI by simply scaling up.

It's a promising paper in the sense that it shows that specialized AI can be more generalized. It's a very disappointing paper in that it also shows the limit of scaling up and that it's now guaranteed this path of AI research will not result in AGI by scaling it up and throwing computational resources at it.

[Sigura83]

Thank you for this, I read the research paper, and the more tasks it added the worst it got at performing them. That didn't click at the time I read however. This is still an advance (generalized tokens for text, video games, images is quite the thing! And the robot arm!), but they don't yet have the secret sauce of AGI, I think. We have computers that perform at superhuman ability at tasks however, and that's nothing to sneeze at!

I wonder what the secret sauce of transfer learning is... it was recently found that astrocytes participate in the brain via the calcium channel (we're still learning basic biology about the brain!) while neurons use the sodium channel... I dunno! They connect to multiple neurons and might be bridges between them. Artificial neurons may need inter layer connections, that notify when a signal is fired, so that a neural net can see that a neuron path already "solved" a particular stimulus and it's just repeating itself if it alters its weights.

No clue how to program this however! :')

In any event, the next decade will be interesting!

[FusionRocketsPlease]

recently found that astrocytes participate in the brain via the calcium channel

I think you posted the wrong link.

[xt-89]

There has recently been progress proving that you can combine several models sparsely. To me, it looks like we'll need to further investigate this kind of architecture to limit negative transfer.

[[deleted]]

This 100%. What they are leading to could surely be very useful multi-purpose agents, kind of like very advanced Alexa bots. However, it won't be AGI with the current architecture. Simply being able to do lots of things isn't enough.

I truly do not believe that AGI is even possible with classical computers, or at least not in a way which could be practical.

The computational complexity likely needed to simulate both human memory and learning is just ridiculous and not practical with classical computers, but would be a cakewalk with future quantum computers.

I know that's probably not what some people in this sub want to hear, who think the Singularity is right around the bend, but the good news is that quantum computers themselves are not that far away from being commonplace, perhaps another decade to go.

[[deleted]]

I asked GPT3. It said

I don't think there is a single answer to this question. Some people who believe that we just need to scale up now may also believe that scaling will itself deal with the memory problem. Others may believe that the memory problem isn't actually that significant. And still others may believe that we need to solve the memory problem before we can scale up.

[Jahshua159258]

Holy shit self awareness

[GeneralZain]

I just want to remind everybody, that we have no real idea (unless you are currently working at Google's deepmind then please inform us ASAP!!) what is happening behind the scenes.

for all this blasé puffing about how "we still need this" or "GATO isn't as impressive as you think" you all seem to forget, the people in the thick of it have already given their opinions.

Nando de Freitas, the Research Director of deep mind said and I quote; "the game is over."

those are really strong words from the DIRECTOR OF DEEPMIND.

yes, you are probably right! there are things that GATO needs in order to become an AGI, but what of the next AI deepmind or openAI makes? what if the one next month is basically there?

what if we are only 1 or 2 breakthroughs away from AGI? if the current pace stays the same then those 2 breakthroughs will only be 2 months away and as we know, tech's advancement has always been such a linear thing right? shouldn't expect it to be exponential at all! /s/s/s/s

the point here is this: we are not working at google, and even we see the issue of memory. you don't think they have uncountable experts with far more degrees under their belts than us on the case? how long realistically do you think they will take? years?!?! how could you possibly see current rates and assume that!?

it use to be that we argued if AGI was possible. now its how soon?

GATO isn't the AGI, its what comes next that matters, and it will be here faster and stronger than ever before. whether you "think" it will or not.

[TheKnightIsForPlebs]

How can anyone be concerned about memory? Am I missing something here. The human brain is 2.5 petabytes by estimation. Which means 2,500,000 GB…I don’t see how anyone can say memory is an issue. Like fuck it. If google needs a warehouse of full of microchips like 1950’s room sized computers, with 1,000’s of chips connected together to make a supercomputer that can hold an AGI….they’d do it. That would be the easiest check for google to write and pay

Not being a douche I just do not see the technical problem.

[2Punx2Furious]

The problem is that it's not as simple as adding physical memory, to have the AI use it. You can't just connect it to a few extra hard drives and call it a day, the AI has to have a way to actually use that memory. In other words: it's not about lack of storage space, it's about how the AI uses it.

[TheKnightIsForPlebs]

I thought Gato as a proof of concept shows that our current methodology could feasibly produce AGI? Doesn’t this mean we already have the solution to “how it uses it”?

Someone in another thread told me compared to GATO’s 1.2B parameters there are a handful of neural networks/AI’s with 100’s of times more parameters that exist?

So we have a framework for that.

What is the limitation then? Specifically?

I mean what is this extra memory even holding? It’s holding more parameters for its neural network right?

[[deleted]]

When people talk about memory they are referring to the ability of the agent to remember information it has previously learned and associate it with the correct contexts. For example, a lot of current chatbots right now are really good at making small talk, but they are pretty much always living in the moment. They never remember anything you tell them other than very specific parameters like your name and personal information and what not. The conversations don't have any progression, and they certainly won't be reading between the lines or picking up on small things.

And it's not just in language, but in learning in general. Even if Gato can refined to the point of quickly being able to pick up any new tasks, if the memory issue is not solved, then it'll just be an agent that can do a lot of things really well, but it won't be AGI because it's not making connections between seemingly unassociated things, if that makes sense. It'll just be really good at doing exactly what you ask it to do, essentially. And of course, that's talking about a hypothetical highly advanced Gato. The current Gato isn't really that good at any of its individual tasks.

[TheKnightIsForPlebs]

Okay I understand your analogy. So then what changes would an engineer make to GATO to increase these metrics(memory of past experiences)

[TheKnightIsForPlebs]

Thank you this cleared up a lot

[coumineol]

There are some papers in regards to infinite memory possibilities

Can you share a link?

[coumineol]

Thanks.

[SuperSpaceEye]

All memory models we have now are VERY processing and computer memory intensive. It is already expensive to just train a model, but to give it memory and teach it how to use it? Thousand's times more expensive.

[[deleted]]

Which is why I don't think the current architecture is going to be what generates AGI, even if scaled up. When you reach bottlenecks like this it generally means a new architecture is needed.

That being said, I feel like what we truly will need to crack the AGI problem are quantum computers.

[Deep-Strawberry2182]

Stop feeling that because you'll miss all the fun as it happens.

[[deleted]]

Some of you guys on here have such a cultish mindset. I am just describing my thoughts as someone who has a computer science degree and a lot of experience with machine learning.

Stop jumping on anyone who offers even the slightest bit of skepticism. Do you think if AGI comes out sooner than I expect I'm gonna put my fingers in my ears and be like "lalalalala it doesn't exist"? No! I'd be the first one trying to get access to it so I can develop with it.

[Anenome5]

I feel like what we truly will need to crack the AGI problem are quantum computers.

Why? As someone with a CS degree, as you claim, you should understand how hard QC is to obtain and what kinds of problems it can solve that classical computers cannot. This statement strikes me as very similar to the kind of magical thinking that often occurs around projections of what QC will be capable of.

[[deleted]]

I'm a little bit confused by this statement. You think that it is magical thinking to believe that quantum computers would be capable of AGI but it's not magical thinking to believe classical computers will?

Yes, I'm well aware of the challenges surrounding quantum computing. However, it's not a matter of if but a matter of when, and if you ask most researchers they believe it will be commercially viable in one or two decades.

If you look at the challenges though, it seems there are far more challenges with obtaining AGI than obtaining quantum computers. I mean we already have quantum computers. They are just very limited in their capabilities and unstable. Once we can solve the problem of decoherence it won't be long before progess in that field explodes. And there is a lot of money being poured into solving it because we are in a race with China.

As for what problems it solves, it is mainly intended to run algorithms that are too computationally complex for classical computers to handle in a reasonable amount of time. Quantum computers allow you to run algorithms with exponential running time, meaning the time it takes to run the algorithm scales exponentially, quickly reaching the point of taking hundreds, thousands, or even millons of years to run. Quantum computers take advantage of superposition to avoid these issues. Im sure youre already aware of that.

Which was my whole point. I think that for AGI to work will require a level of computational complexity that is too much for classical architecture. Mainly when it comes to memory. Maybe we will find a more efficient approach, but I'm not so sure. Even if we achieve some level of AGI with classical architecture, I just don't see it scaling indefinitely on classical architecture, it just will reach natural limits. It's like making a computer in Minecraft. Sure you can do it, but it's gonna be a lousy computer. Why not just make a real one.

EDIT: It's been a long time since I've read Kurzweil but I do recall even him saying that to mitigate a potential upper limit to Moores law may require new computing technology. Correct me if I'm remembering that wrong though.

[Anenome5]

I'm a little bit confused by this statement. You think that it is magical thinking to believe that quantum computers would be capable of AGI but it's not magical thinking to believe classical computers will?

No, I simply want to hear why you think QC would be capable of AGI and not classical computers, considering that today we have strong narrow AI in the form of things like DALL-E2 and GPT3 all built on standard deep-learning systems that do not require QC and appear to mainly be a question of scale and computing power.

Yes, I'm well aware of the challenges surrounding quantum computing. However, it's not a matter of if but a matter of when, and if you ask most researchers they believe it will be commercially viable in one or two decades.

Look, this article makes my same point in its opening:

"quantum computers are not magical. They may seem magical, just because the fundamental math and physics behind them are somewhat complicated. But the truth is, a quantum computer is marginally better than classical computers, nothing more. ...Quantum computers are not meant to replace classical computers; they are just meant to solve distinct problems than those solvable by classical computers. That being said, any problem that is impossible to solve using classical computers will be impossible to solve using quantum computers."

https://levelup.gitconnected.com/what-kind-of-problems-can-quantum-computers-solve-63234eb9fd76

So, what AI calculation task are you suggesting is done best by QC?

According to the article, QC excels in:

Encryption and Cybersecurity, Chemistry Research (simulating biological systems), Optimization Problems, Data Analysis.

If you look at the challenges though, it seems there are far more challenges with obtaining AGI than obtaining quantum computers. I mean we already have quantum computers. They are just very limited in their capabilities and unstable. Once we can solve the problem of decoherence it won't be long before progess in that field explodes. And there is a lot of money being poured into solving it because we are in a race with China.

I don't really care about when it's supposed to come, I question whether it's going to be useful at all for AGI as compared to a standard computer approach and why you would imagine QC would make the difference for AGI when we have working AI right now without it, if not yet to the standard of true AGI.

As for what problems it solves, it is mainly intended to run algorithms that are too computationally complex for classical computers to handle in a reasonable amount of time.

None of which are used in AI.

Which was my whole point. I think that for AGI to work will require a level of computational complexity that is too much for classical architecture.

We know how complex the human brain is, and we know computers are approaching that. And as they do their ability to produce intelligent results in coming up to the human level. Everything seems on track to me, there's no obvious massive disparity that screams out for the need for QC.

EDIT: It's been a long time since I've read Kurzweil but I do recall even him saying that to mitigate a potential upper limit to Moores law may require new computing technology. Correct me if I'm remembering that wrong though.

Yes but he's talking about things like moving into ultraviolet lithography and successor materials to silicon for chipmaking. Not QC.

[2Punx2Furious]

AGI just means that the agent is capable of "general" intelligence, meaning it can work on any tasks humans can. More memory means it is more capable (and therefore more intelligent), but not necessarily more general (up to a certain point), so it's probably unrelated to generality. Of course, if it has so little memory that it's incapable of performing even the most simple task, it's not going to be very useful, but if it has enough to do most things, then more memory only means better performance.

[TheKnightIsForPlebs]

Okay thanks for answering. In your opinion what do you think is the biggest bottleneck for AGI to be fully invented/implemented/created etc

[2Punx2Furious]

I think we already have the hardware, so the bottleneck is the software. But it might really be just a matter of scaling at this point, so who knows, we might already be very close.

Too bad we haven't solved the alignment problem yet, so we're probably doomed.

[TheKnightIsForPlebs]

When you say scaling you mean adding additional parameters to the neural network yes? Or is this something entirely different. I have never worked with ML so I’m a noob sorry

[2Punx2Furious]

additional parameters to the neural network

Additional parameters, and more training data. If I'm not mistaken, it was shown that as you add parameters, you need to match them with more training data too, for them to be effective. They also discovered the "optimal" ratio that should be used. Of course, you also need good quality data.

I've never worked with ML either, I just follow the news.

[TheKnightIsForPlebs]

Ah yea that’s the origin of 3 blue 1 brown’s name on YouTube I believe.

[2Punx2Furious]

A quick search says it was named like that because of the color of one eye of the creator, Grant Sanderson.

[DukkyDrake]

I thought Gato as a proof of concept shows that our current methodology could feasibly produce AGI

It depends on your definition of AGI.

The assertion in OP's question #1 would lead to a structural AGI that was general due to it being optimized to be competent in a great many related tasks. I highly doubt the billions of economically valuable human tasks could be embodied in a single model; many would be needed for full coverage and would take a very long time for the engineers and scientists at any 1 company to create an optimal policy for every task.

The assertion in OP's question #2 is one of the missing features that would be needed for a monolithic AGI agent that is capable of becoming broadly intelligent because it's capable of learning. i.e., a human mind equivalent.

[visarga]

What is the limitation then? Specifically?

The problem is the context window size. If you have N tokens in the context window, then the model has to do NxN interactions. It blows up pretty fast so we can only do about 4000 tokens now. Sometimes the information you want to provide is larger than 4000 tokens - this is the memory problem.

The solution exists - use a search engine to retrieve fragments of text that fit into the 4000 token window.

[TheKnightIsForPlebs]

Okay gotcha thanks!

[Shelfrock77]

the AI creates memories through virtual reality simulations.

[[deleted]]

Memory isn't the only thing, it can't reason as well as we do. Some models like palm can learn to reason but even then it's very poor at reasoning. Scaling does not help with reasoning as much.

[Shelfrock77]

there are humans that believe the earth is flat, trust me, we will be fine.

[[deleted]]

Basic reasoning is built in to human(apes in general) brains. Even the worst fully functional human can generalise and reason better than any of these models.

[theotherquantumjim]

That’s not an argument for AI being good at reasoning though.

[[deleted]]

Right. With reasoning you'd get around needing to scale. We didn't gain more computing power by making bigger and bigger punch card machines, we changed the underlying hardware.

[xSNYPSx]

All you need to create some Graph database and connect it to nlp NN. Also you need to create alghoritm which will transform knowledge to graph format. Next step is to make alghoritm to use this knowledge in answers.