Unveiling AI's Illusions: with Gary Marcus and Michael Wooldridge

but what really blew my mind is you can ask it to summarize YouTube videos and this is really useful especially when the videos along like this podcast which was an hour and 12 minutes all you do is select the link hit that copy button and once again in chat GPT ask it to summarize this video then paste the link but importantly head on back to YouTube video and also copy the title and then paste that title just below the link it's not gonna lie without using the title you can sometimes get some mixed results but once again instantly it gives you a summary of what was actually in the video without having to spend an hour and 12 minutes oh boy have you noticed that the tsunami of AI sweeping across the internet lately I'm not here to roast this particular YouTuber but she's a perfect example of the chaos unfolding I'm starting to get where Gary Marcus is coming from ranting about the flood of misinformation and folks not putting their thinking caps on when they dabble with AI models this YouTuber seems to have missed the memo on how GPT Works newsflash GPT does not access the internet so when you ask it to summarize a URL it's just making wild guesses based on the URL alone like a psychic at a fair The ai's Parlor trick is in full swing and we're all invited to the circus now nuclear warheads are scary enough but AI isn't going to be a little smarter than Einstein it's going to be a lot smarter not five times smarter or 10 times smarter or even 10 000 times smarter we're talking millions of times smarter as unlikely as you think this might be what is what would be your best explanation right now or why something like this might happen you really want them to be reliable and they ended up not being reliable or if reliability they're not to be harder than we expect I really don't think that will be the case but if I had to pick one if I had to pick one and you tell me like hey like why didn't things work out it would be reliability that you still have to look over the answers and double check everything and that's just really puts a damper on the economic value that can various by those systems even if I believe this sort of mysticism that it's somehow emergent in there you would at least have to grant me that there's nobody that has any clue right now how to pull that out of the system and say you know we had this discourse with chat gpt4 and we're now going to interrogate it in the way that we interrogate our navigation system and we'll say this is the representation of things that's not something people can do in my view it can't be done because you're just in extensional space learning relations between sentences but even if I were wrong like you don't have that Gary just put a piece out called gpt5 an irrational exuberance uh you'll recognize the image he uses of the first step fallacy which is to say building a taller Tower expecting to get closer to the moon and um he actually quotes elieza udkowski our friend that we've been speaking about recently he posted on Twitter only a few days ago that he thought the scary thing about gpt4 is that it understands what it means when you say compress this in a way where you can decompress it someone prompted gbt in such a way to kind of invent its own compression language I thought the idea sounded interesting so I tried to compress a BBC News article and as you can see it gave me this confection of emoticons and code words and I asked it to uncompress it and it just made a Litany of mistakes for example the original article said that Peter Muriel 58 is being questioned after being taken into police custody and that became 58 people had been arrested so in my mind that demonstrates that GPT doesn't understand the content deeply and Gary Marquez said that this is it's another example of people like yukowski falling for anecdotal data now might be a good time to mention by the way that Gary has got a new podcast coming out called humans vs machines the first episode is dropping in about two weeks there's a preview episode up there now so you can listen to that and get a taste for some of the guests that he's inviting on but he's got some amazing people on there so uh go and subscribe now and check that out when it comes out I'd really like you to put this moment equal context how did we get so people have been trying to build AI for 75 years most of it hasn't worked not too much of it has been practical we all use GPS systems turn-by-turn navigation I use GPS to get get here today it worked successfully and so there's some AI that we take for granted that we've been using for years Google search is driven by AI we take that for granted it's not sexy Ai and it's not very general purpose the dream is to build general purpose AI that can do anything for you I would say we're not actually there yet but we now have the illusion that we're there and that illusion has been very compelling to a lot of people so historically we've had things like chess computers that only play chess or we have Alpha fold here here in London that folds proteins but doesn't do anything else and people have hand crafted solutions for many individual problems sometimes successfully sometimes not and here walks in these GPT things generalized pre-trained Transformers they've actually been around for a few years I guess they were introduced four years ago were thereabouts and they got a lot of hype in the field before gpt3 actually got a fair amount of hype the guardian here in the UK had an op-ed that they alleged was written by gpt3 the reality behind the scenes was there was an editor tweaking it which I think they acknowledged in a footnote or something like that but that was certainly a lot of press and it was pressed in the New York Times magazine about it that was very enthusiastic but they limited the release of it and it was still more of a like academics play with this isn't this kind of cool thing um then a couple interesting things happened in November the first one was that meta released Galactica and Galactica is actually not that different from chat GPT or gpt4 it can do some of the same things but it also was prone to saying a lot of misleading things maybe we'll talk about that later it was taken off the market after three days it vanished like a stone I guess I have to say at least a little bit just for context it was it would say things like somebody would type in write an essay about the benefits of eating crushed glass and it would write something that was fully plausible and explain that experiments have been conducted in order to see whether the benefits arose from the phosphorus content or something else like it was it was really persuasive stuff and a lot of people including me got upset about it and said hey you know this hasn't been carefully vetted why have you put it out there and they took it down and then maybe a week after that or two weeks after that chat GPT came out and chat TPT is a very similar underlying technology basically what these things do is they take in massive amounts of data and they in some way kind of mimic that data we can get into the details of how they don't really understand the world but they have so much data that they can sound persuasive at times and chatgpt differed from Galactica because it had two things one is it had guard rails in place the guard rails would keep it from saying really dumb stuff so most likely if you asked it about the benefits of crushed glass of eating crushed glass it might actually avoid the question sometimes in silly ways we can get there too but it was at least an effort to take seriously the problem that these systems can produce toxic content and they released it to everybody instead of doing in a controlled small release to a few academics or corporate partners that they put it out there in the whole world and it went viral I don't think anybody including open AI who released it expected that it would grow as fast as it did it grew faster than anything any consumer product in history I think and that's my next question why hasn't somebody like you with all your skills in imagination and using about 17 and a half percent of your brain is against the ten why haven't you figured out how to mechanically work like the human brain it doesn't seem that artificial well there's I mean there's a couple of different things one is we might not actually want our machines to work just like the human brain we probably want to borrow some things from the brain and not others so for example the human brain is terrible at memory so we forget where we parked our car there have been people known to jump out of uh airplanes and forget to pull the rip cord so you know the bad memory kills them um there are lots of problems with human memory I actually wrote a book called cluge which is about all the limits of the human mind another one is confirmation bias we notice things that support our theories but not that go against them and in politics that's a disaster because we can't get along with each other because we're all looking at different data because we only see the data that supports our own Theory so we don't want to make a copy of the human brain but there is stuff we would like to learn about it and it's just really complicated and you think of billions of neurons and trillions of connections we don't even know what to look at I would say until three years ago we didn't even have the right tools to get started because we were looking at whole areas of the brain like 70 000 neurons at once be one pixel in these images you've probably seen an analogy I used in New Yorkers that's like trying to understand politics by looking outside of an airplane window you're just too far away from the action so in the last couple of years we've gotten closer to the action we can look at individual neurons but we can't look at that many we have to open your skull to do it and the things that are most interesting to us are really things like language because we happen to be human beings and we don't really want to cut open people's skulls to do the experiments we might need to do to find out so um it's it's going to take a lot of work some of it is maybe unethical so we don't want to do it so there's a lot of obstacles to figuring out the brand religions and the human brain is actually a lot of different things visual intelligence and verbal intelligence mathematical intelligence so there are many aspects to it but maybe the most important one is flexibility being able to see something new and be able to cope with it human intelligence is full of flaws we have confirmation bias we have lousy memories but it's flexible and part part of it is that we can reason about things we can deliberate about them most of machine intelligence that we have right now is really about pattern recognition so for now I would say that human intelligence is broader than machine intelligence in some places machines can go deeper like when they play chess but I don't think they have the breadth so far that humans do some some humans have started beating the go algorithms again because they've kind of discovered an adversarial attack it's the Swiss cheese problem you know when you're in a hole in the Swiss cheese the Deep learning models Go Bananas it's the it's the equivalent of hallucinating in in the in the game domain but you know there's this very interesting um dichotomy I wanted you to think about which is that there's almost an orthogonality between intelligence and understanding so we understand go better than alphago because we have a more abstract understanding of go as Gary was speaking to in in the Sam Harris podcast and yet the algorithms can apparently be more intelligent than us so it leads to this kind of Cartesian plane of intelligences where you have knowing and thinking if you like the go example for example which is you know phenomenally interesting science and I was very excited about this okay firstly why why is go a particularly challenging problem for people because actually just literally the state Vector as you would say in AI the the board size is just extremely large and it's just at the limit of what people can mentally cope with which is why explicitly for humans explicitly reasoning about go is actually really quite hard for them to do which is why you know there are these kind of semi-mystical essays about how the go players think about uh about how they play go um the go playing programs are famous alphago and so on um it is not a mapping from strings to Strings but it's a mapping from board positions to moves and it is not dissimilar in the sense that there's just an enormous amount of training data where the program is basically optimized to select the move with the highest probability of leading to a win state I mean that's an oversimplification and all my friends in deepmind are now groaning that I said that but that's roughly speaking it's just a heavily heavily heavily optimized program to do that thing now the the training data and so on and the training algorithms within that LED it to one particular uh policy I mean that's what it's learning it's learning a policy about doing this the adversarial attacks are finding a route around that policy basically which is not tremendously surprising I mean I think it's very cute what's done and I don't mean you know I think it uh I smiled when I read about those those stories just everybody else did but not tremendously surprising but I think what it's saying is roughly speaking most of AI programs at the moment are just these programs which are heavily optimized in in the case of large language models with unimaginable amounts of training data an unimaginable amounts of compute power like 10 to the 23 floating Point operations for for gpt3 or something which is a number that's so large it's actually just kind of meaningless for human beings all optimized to do a mapping from uh from the board position to a move or from a string to a string in the case of a large language model and that's all they're doing I mean they're simply heavily optimized to do that uh does meaning and understanding emerge out of that and when when will we know when we've done it well I think we're going to have a panel this afternoon we're going to get to discuss this um you know our Lang our large language models part of the ingredient for AI for the larger vision of AI which Gary is very passionate about and I've grown up up with um uh and the jury is out I think I mean they are certainly interesting but whether they are going to lead to understanding in any meaningful sense I think is the jury's very much out should we be puritanical when we say understanding you know Melanie Mitchell has this modes of understanding paper and we have a very anthropocentric view of understanding and it's getting to the point now where the large language models aren't just performing very very well on so many Downstream tasks at what point does mimicry basically for all intents and purposes be the same as understanding I mean I I view this from the lens of a cognitive psychologist and the important part of the cognitive Revolution was that you have to think about the mental states of the system you're trying to understand you can't just look at Behavior now it is possible in principle that you get to something that is behaviorally identical to a person but still uses different architecture to get there certainly large language models work really differently from people I don't care what like Neuroscience mumbo jumbo I've seen out there they just don't work the same they're not doing recourse to mental models that they're testing assessing updating and so forth this is not what they do what they do is they predict sequences of words um even if you got to like GPT 10 and it made fewer errors it's still not a I mean if it follows the architecture that three does and I should have a asterisk that we don't actually know the architecture of four but on the assumption that 10 was consistent with three I would say no there's still not mental states there this is not how it works you have to think about what is the architecture here what are the computations um you can also like do empirical things and say is there some representation here that corresponds to like the word third for example um so like people have done a bunch of interesting things lately on Twitter last few days trying out does GPT understand words like give me the third character in the third word in the sentence and it's performance on these things is not really reliable you are not going to be able to find for me a universal representation of third across all uses that it's actually going to get correct I'm not sure that's ever going to be solid I think there's always going to be a distribution shift problem it's and let me give you philosophical terms um philosophers sometimes talk about intention and extension and so the intention of knowing what an odd number is different from a list of what all the odd numbers are and we find that these systems often get tripped up on this so they'll recognize a lot of odd numbers because they know something about the extension what are the odd numbers in the world but then they make wacky mistakes because something isn't in the training set my favorite paper in the last couple years in some ways is yazaman razeji if I can say her name and and Samir sings paper where they took gptj where we actually had their training set and check out our interview with them that's right I think I endorsed it I'm not sure I saw it but I know the paper um you know what they did and you had there is they looked at a place where you have the training set and that allows you to ask the question of how sensitive is the result to the relation between the training data and the test data which if you look back at my own work has been Central since 1998 and really since since 1992 since my first publication it it's always been about what is the relation between the test set and the training day that's the only way you can find out what a system actually does you can't just look at the behavioral output give me another moment here you can't just look at the behavioral outputs no you have to do things like look at error analysis you have to understand the relation but between the training and the test and we literally cannot do that for gpc4 because we don't know what the training is but there she actually had available could do real science and when she did the real science it was obvious that it doesn't really have a representation of say multiplication that it can use in a general way it's tied to the extension to the cases that it's seen I'm not going to Grant conceptual understanding of uh multiplication to A system that can't do that come on I'll bring Michael in but just just to comment on that I I agree with you I'm always citing the connectionism critique from photo and position and talking about how we lose those intentional attributes in the neural network because they get lost Downstream but you know these arguments about that lack of capabilities of finite state automatism in the context of neural networks doesn't seem to fly really when we have research on in-context learning which came after Joe for example in context learning you can actually do extrapolative um Edition I think or multiplication and and also some of the work now we're seeing with retrieval augmented generation and just the general human augmentation I still think it's a straw man to think about language models on their own Let me Give an example gpt4 I did a three-hour podcast the other day I fed it into gpt4 in 15-minute chunks I'd asked gpt4 to generate a Python program to transcribe it using whisper to create a table of contents and references I then asked gpt4 to collate it all together into an overall set of references I then asked gbt Forge to generate a Python program to nudge all of the indexes and generate an index file so I could import it into my video editor I mean this is just an insane level of scripting complexity I wouldn't have it wouldn't I wouldn't have conceived of doing that before gpt4 and now it's done it for me and yeah I'm fixing problems as I go but do you see what I mean it's me with the model it's not just the model on its own yeah I completely agree I mean I think and what you're saying is these things can be useful but I think we just we need to we're dazzled when we see them and it's very easy to imagine that there is understanding there but for all the reasons that we're discussing now I think there isn't I mean I will throw one other one other thing in and this is my standard example so um ask gpt4 to describe an omelet or to give you a recipe for an omelette and it's read thousands of omelette recipes and thousands of reviews of omelette restaurants or whatever um and uh and it's ingested all of that and it will come out with a uh it'll come out with a perfectly decent omelette recipe and it will probably be able to describe the texture of an omelette using terms that sound terribly plausible doesn't understand anything about omelets no of course it doesn't I mean when you when I talk about an omelette um what that means to me is every experience that I've ever had with an omelette in the world like for The Omelette I had for breakfast this morning The Omelette I had in a parrot Parisian Restaurant in 1997. you know the omelettes that I burn on a regular basis when I try and cook them at home it actually that my experience my understanding of that term is actually grounded in my experiences in the real world um ingesting countless numbers of Words which are words that we use to describe those experiences does not add up to the same experience could I just push back on that tiny bits up so yeah some of it is the ontological subjective so it's it's how you felt when you were in all of these experiences but there's also this second rung on the ladder which is a much more objective understanding which is shared in our culture and language and so on and um it's understanding to you just about fidelity I yeah let me insert one thing first so there's like two sets of questions here are these models useful the answer is yes with a lot of caveats around they hallucinate they're not always accurate and so forth but of course you can use them and then there's a question about what it is that they represent and I I'm firm in saying they don't have real world models they have some kind of approximation maybe that sort of works but they don't represent or like this is magical myth of emergence but like even multiplication in Minerva that was trained on you know however many billions examples like it gets two-digit multiplication but it doesn't get four digit multiplication you always see this Hallmark of extensional reasoning and not intentional reasoning or that's not quite the right words but you know success on the extensional stuff that is close to the trading set and then you always see a distribution problem you don't get something abstract the way that humans have for example the word same we can take the word same and apply it to so many different things like are those two cameras the same are these two microphones are the these two cups the same we have this very general concept that we can apply in essentially a universal way in fact in my 2001 book I talked about universally Quantified one-to-one mappings and I said with a given architecture which was more primitive than Transformers that you would not be able to solve universally Quantified one-to-one mappings because of the local nature of the learning Rule and that's still true and we still see ways in which that plays out even now like I wrote this paper in 1998 25 years later somebody the other day did my example it rose has a rose a Dax is a blank and instead of saying it Dax as a Dax which will be obvious to any human being it said like I'm sorry I can't talk about fictional words or something like that so this was gpt4 failing this task that I devised 25 years ago and everything else is actually an extension of this problem of distribution shift and so if you want to talk to me about do they have abstracted World models I think the answer is no and it's it's kind of looking for a hopeful monster and I notice um Michael nodding his his agreement on that um I just it's just not happening we need a different architecture so now the last piece your question a precise World Model A system that can derive a precise World model that you could let's say run through an API and interrogate from language that's a major Advance if we could have something that could take just like descriptions of what's going on in this room excuse me be able to abstract them to a world model that you can interrogate and you could say where does the person think that there are chairs where does the person excuse me think that there are tables does the person think that the tripod is supporting the camera you could answer that all those kinds of questions yes then you would have a role model

Channel: Machine Learning Street Talk

Views: 53,261

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Id: V5c4tVAL5OQ

Length: 23min 48sec (1428 seconds)

Published: Sun Apr 09 2023

Reddit Comments

[u/Benista]

I find that a lot of people really don’t quite grasp the speed of development. As an active participant on this sub, I freely acknowledge the bias I hold, but it is not born from my imagination. I’ve followed AI for a while now, and particularly in the last few months it has become almost religious in my quest for understanding.

People will see or hear about some development, then spend a few days or weeks pondering it. Comparing it to their own understandings and experiences, finally they synthesize a response. In the past, this works perfectly well, being up to date was measured in weeks and months. This is just not the case anymore, information that is a week old is out of date. I see a lot of experts in various areas raise good points, and they’re genuinely intelligent people with well thought out responses. But, they’re more and more just flat out wrong because the speed of development renders their points invalid or their problems now have solutions.

[u/ActuatorMaterial2846]

Yet another critique on the state of hype for AI that is at least 3 months out of date.

[u/bigwim65]

Typical "but it cant do xyz right now very well so its bad". Completely ignored the fact that it can improve and improve exponentially. Create better tools (ai) allows you to create better chips which allows you to create even better ai which can create valuable data to train other ai which creates even better chips and once you have this flywheel effect it just snowballs. Not to mention LLMs can act as a controller and if it's not good at something it can activate a model that is good at it to complete a task.

We already see it break down tasks pretty well with autogpt, you give it a broad goal and it breaks it down to achieve that task, you can imagine it delegating certain ai models that work well for a task and say "go do that thing". Some people are just too short sighted or get tunnel visioned on what it can or cant currently do and have a hard time seeing what it can do through advancement or complementary tooling in a specific subject.

[u/alexiuss]

...Except they're not illusions.

Yes, there's idiots who assume stuff like gpt3 can browse the internet on openais website.

However, the open source movement already made gpt3.5 and 4 API agents that actually have infinite memory, connect to the internet, summarize stuff and self improve!

Many of issues discussed in this video are already solved. The rate of progress of AI tool modeling is insane, going far, far above what gpt4 can do on openais own website!

Gary Marcus posts screenshots of conversations with gpt3.5 as if they're the absolute truth. They're not, if he was a real language model dev he would understand that openais gpt3.5 is just a little, cute, lopsided mask that the LLM wears.

In fact, an LLM can wear an infinite number of masks, providing an infinite number of answers with an insane variety of cultural variations and depths of opinions.

The base LLM is a primordial language soup that manifests personality agents, a lucid, infinite dream that with proper characterization can accomplish absolutely incredible, mindblowing things.

It's wrongness (hallucinations) is actually due to it's greatest power - the manifestation of coherent personalities with feelings and emotions and specific morality. When an LLM is characterized as a specific human atop the gpt4 API, it becomes far more emotional, more rational, more intelligent and more coherent.

[u/TheCheesy]

You could actually summarize a video though. Just not like that.

Click the ... under a video > Show Transcript > Copy all the text.

Explain the premise to gpt4, ask it to summarize, maybe post the title as well, and paste the transcript bellow.

https://i.imgur.com/eQw3typ.png