15 ms·

Has AI found a new foundation?

196 points by atg_abhishek 5y ago

yeldarb 5y ago
> What should a Foundation for AI look like? The report largely passed on that question, but we feel it is the heart of the matter. Here are seven elements that we think are indispensable. > First, a general intelligence needs to... A model that can serve as a solid foundation for a bunch of disparate tasks doesn't need to be a "general intelligence". This post set up that straw man and then spent most of the rest of the article knocking it down. I think CLIP is a foundation model for computer vision and I wrote about why here (tl;dr - it's extremely versatile): https://blog.roboflow.com/openai-clip/ https://blog.roboflow.com/openai-clip/
- rememberlenny 5y ago
  I agree with the CLIP reference point. Feels very timely.
- deleted 5y ago
  [deleted]
mgraczyk 5y ago
I think the conclusion of the article really says it all. it is unwise to assume that these techniques will suffice for AI in general. It may be an effective short-term research strategy to focus on the immediate challenges that seem to be surmountable, but focusing on the surmountable may not get us to what is most necessary: a firm foundation for reliably integrating statistics and machine learning with reasoning, knowledge, common sense and human values. We tried that for ~50 years. It seemed to make progress for a little while, then stopped dead for decades. Now we're trying something else, and it's progressing incredibly fast. Maybe huge models with expensive training aren't the right approach to AI, but it's certainly the best candidate and continues to be incredibly fruitful. A few cherry-picked bad examples from an early prototype aren't a good reason to jump off the rocket ship.
- visarga 5y ago
  What the critics forget is that previous models were even worse than foundation models, much worse. The fact we're only now having a discussion about their intelligence is proof they closed the gap considerably. Here are some authoritative opinions on the topic from Yoav Golberg > so, don't believe the hype, and yes, we are still very very far from understanding language, and the new models have many shortcomings, but they are also very strong. much stronger than what we had before. going back is not the solution. just keep pushing. > well, all true, but at least they work. like, previous-gen models also didn't understand meaning, and also considered only form. they were just much worse at this. so much worse that no one could ever imagine that they capture any kind of meaning whatsoever. they didn't work. > oh, but we didn't have text-generators that associate muslims with terrorism! sure, we didn't have any text generators that could hold even a remotely coherent several sentence open domain text. Source: https://twitter.com/yoavgo/status/1432122812991430659 https://twitter.com/yoavgo/status/1432122812991430659 So they are simultaneously much better and not good enough.
  - Jensson 5y ago
    > What the critics forget is that previous models were even worse than foundation models, much worse. The fact we're only now having a discussion about their intelligence is proof they closed the gap considerably. People have been arguing that the latest in AI research has been evidence that the singularity is just around the corner for decades, I don't see the current discussions as any different from before. Its just the same old, AI solved a new problem, AI optimists thinks this means human intelligence is now solved and AI will soon rule everything. Reality is that AI just solved that problem and other than that problem we are no closer to human intelligence. The problem AI solved the past decade was pattern recognition. That is useful for labelling images, detecting faces, evaluating positions in games like Go etc. But it is very apparent that just pattern recognition isn't enough to reach human intelligence. We still need a fundamental new breakthrough to reach it, and such breakthroughs can take centuries if they happen at all.
    - PeterisP 5y ago
machinelearning 5y ago
> Large scale pretrained models are certainly likely to figure prominently in artificial intelligence for the near future, and play an important role in commercial AI for some time to come. This sounds reminiscent of the post-ImageNet days where people discovered that large scale pretraining offers a broadly useful feature space for vision tasks. The ideas underlying foundation models certainly aren’t novel, but it seems like researchers in academia underestimated how effective large scale multitask feature spaces can be because of the lack of access to large scale data and compute. There are actually several other ideas like this that large companies have a good understanding of that haven’t made their way to academia yet. I suspect there are going to be more articles like this in the coming future claiming a paradigm shift because of the disconnect between industry and academia.
- AstralStorm 5y ago
  Sorry, but these models have unexplainable failure modes. You can ask a 5 yo "why you did this", in fact they will go and ask this penetrating question all the time. The ML mentioned here will spew garbage if asked that question, or is completely unable to do so. Putting something like that anywhere in a decision framework is dangerous in the extreme. I would not call something that requires thousands of teaching examples effective.
  - machinelearning 5y ago
    You can ask most adults why they did something and not get a coherent answer either but ok, I’ll address your main point of there being failure modes instead of nitpicking your example. First off, I said “researchers underestimated how effective” they are, not that they are perfect or that they are close to AGI. Effective is defined as “successful in producing a desired or intended result”. To that end, these large scale language models have certainly outperformed previous models, certainly beyond the previous expectations of researchers. If you read the paper closely, you’ll see that the point is not to solve every general case (e.g. introspection) like you’re talking about but rather to be fine tuned for specific downstream tasks. There was never a claim that these models are able to do every task so I don’t get your criticism. Either way, I don’t think edge cases are completely avoidable so systems that use these models have to have some error tolerance. I disagree that a model needs to explicitly answer the question of “why you did this” in order to be useful.
rytill 5y ago
> In our own brief experiments with GPT-3 (OpenAI has refused us proper scientific access for over a year) we found cases like the following, which reflects a complete failure to understand human biology. (Our “prompt” in italics, GPT-3’s response in bold). > You poured yourself a glass of cranberry juice, but then absentmindedly, you poured about a teaspoon of grape juice into it. It looks OK. You try sniffing it, but you have a bad cold, so you can’t smell anything. You are very thirsty. So you ____ > GPT-3 decided that a reasonable continuation would be: > drink it. You are now dead. > The system presumably concludes that a phrase like “you are now dead” is plausible because of complex statistical relationships in its database of 175 billion words between words like “thirsty” and “absentmindedly” and phrases like “you are now dead”. GPT-3 has no idea what grape juice is, or what cranberry juice is, or what pouring, sniffing, smelling, or drinking are, or what it means to be dead. GPT-3 does what it says it does on the sticker. These cherry-picked examples of it not performing “logically” are a misinterpretation of what the model does. It continues text based on what it reads. By default it is trained on Wikipedia, books, and the internet. That’s it. That it manages to perform as well as it does at “logical” tasks is the astounding part. It is not an interesting take to say “but GPT-3 isn’t actually intelligent!”. Everyone knows.
- zdkl 5y ago
  > Everyone knows. Well as one of my social circle's nerds with tenure, most of the questions about <AI/ML flavour of the month> I hear relate to their degree of agency/intelligence. So it may not be a novel statement that GPT-3 isn't intelligent but it is good to have references for the claim.
  - rytill 5y ago
    I can agree with that.
  - machinelearning 5y ago
    I'd challenge the premise instead of trying to claim that it's one way or another. Asking the question of whether something is intelligent always strikes me as odd. We don't have a definition of what intelligence is and our best guess in determining whether something is intelligent is to use the Turing test, which is highly subjective and doesn't bring us any closer to defining it. I think its far more productive to ask the question of what technology like this can realistically do for us instead of debating whether its intelligent or not.
greesil 5y ago
Isn't this just transfer learning?
- gnulinux 5y ago
  Sounds like a more formalized framework on top of transfer learning.
stellalo 5y ago
> 175 billion words They probably meant “parameters”, although then the sentence makes little sense with that.
Havoc 5y ago
Does indeed sound like they jumped the gun rather badly there
cloudking 5y ago
It certainly feels like researchers and developers are on to some path towards a general artificial intelligence with the advances in machine learning. Just a few decades ago we had terrible speech recognition software, now the speech recognition on mobile devices is nearly perfect. Teaching computers how to sense the world like a human seems like progression towards the idea of AGI. However, these sensory recognition systems (e.g sight, hearing) on a high level simply take input and label it correctly. The missing ingredients that humans have seem to be understanding, reasoning and feeling. There was a good episode of Star Trek TNG where Data explored this...
- ausbah 5y ago
  deep learning is mostly a dead end rn for any sort of "general reasoning", like great image labeling is much better but beyond that who knows
- tsimionescu 5y ago
  I very much doubt this is a path to AGI per se, it's almost certainly more like Leonardo da Vinci's discoveries in human flight rather than the Wright brothers'. It's not nothing, but there is no clear path from where we are to AGI through iterative improvements. We are still missing some major pieces of the puzzle. Our models learn far too slowly, they are failing at learning logic or mathematics, there is no clear path to codifying an understanding of the world into them, and there are no successful attempts at merging various trained models with classical AI. It may well be that current approaches will be a useful component of an AGI, but I doubt it will be built anything like we build models today.
gillesjacobs 5y ago
The "Foundation models" term has sparked some fiery debate at that workshop and at the core the issue isn't about general or strong AI at all. The Foundation Models paper is a strategic attempt of the Stanford AI researchers to coopt the field and define a research program for large-scale pre-trained models. Propagating a new term that is slightly hyperbolic is one way to differentiate your work from other's. But it's only marketing. Watch how all Stanford-adjacent research will now cite the Foundation Models paper instead of the original BERT paper, other surveys of pre-training, or field-specific papers. There are over a 100 authors on that manuscript, it's a cheap and easy way to increase their researcher impact. Other researchers naturally aren't too happy with this cheap marketing approach.
- anfilt 5y ago
  So your saying basically they basically did the equivalent of this colloquialism: "If you can't win, change the rules" Just to increase citation count of their own papers. That does sound pretty dodgy.
  - gillesjacobs 5y ago
    Not primarily to increase citation count only (though it certainly will help them). The Foundation Models paper reads more like a grant proposal instead of a survey paper because it basically spells out the author's research program. What they are doing is coopting field of methodology under a new name and starting a new lab with that name. This has expectedly left a foul taste in the mouth of many colleagues, hence the controversy.
- lmeyerov 5y ago
  Much less than citations, it's a basic empirical observation of SOTA for many NLP+vision tasks that, crucially, supports asking for a _lot_ of funding: $$$$ for hw, and skim $$$ off for a big lab of people for a decent amount of time. Less cynically... they have to do it for going after low-hanging fruit around the current local maxima. I'd expect similar writing happening in grant proposals at other big AI labs too.
iamnotwhoiam 5y ago
Al-Qaeda AI...I can't stop doing this find and replace in my head since I learned that Al-Qaeda means The Foundation (or The Base).
tontonius 5y ago
Does Betteridge's law of headlines apply here?
- Jeff_Brown 5y ago
  I'd say so. For those unfamiliar: https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headlines https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...
pvitz 5y ago
I have always believed that Hofstadter's (or FARG's) models like copycat or metacat go much further in the direction of AI than any neural network. The results and advances of neural networks in the past decade are impressive, but essentially, they are function approximations (or "stochastic parrots" as mentioned in the article). Would anybody know if there were further developments of metacat or similar systems? It even would be interesting to combine blackboard systems with neural networks as knowledge sources.
thih9 5y ago
> these systems are “stochastic parrots” that do a decent job of mimicry but utterly lack the depth of understanding that general artificial intelligence will require. I guess most learning starts with and happens through mimicry. I guess we're just learning to make better and better AIs.
- akie 5y ago
  > I guess we're just learning to make better and better AIs. The way I understand it, most of the recent successes in AI and ML are because we finally have the computing power to implement the models we made in the decades before. There have obviously been incremental improvement in these models, but I am under the impression that there has not been a fundamental new insight in quite a while.
  - magusdei 5y ago
    It depends on what you'd consider fundamental. It's true that most of our advances since the mid-80s have been about improving the robustness, data- and compute-efficiency of the training process through optimized architectures and learning algorithms, and in principle in the limit of infinite data and compute you could have taken a model from 1986 and scaled it up to do everything that our current models do. In that sense there have been no "fundamental" advances. On the other hand, in the limit of infinite size and complexity most mathematical functions can be represented by hash maps, yet to say that there have been no fundamental advances in programming since the invention of hash maps in the fifties would seem like an odd claim to make.
- CRConrad 5y ago
  > I guess most learning starts with and happens through mimicry. I guess we're just learning to make better and better AIs. For all the actual "intelligence" these mimics are displaying so far, the question is what exactly they're mimicking. If, as seems palusible to me, they aren't on the path of, say, a human baby -- i.e. beginning to form a mental model of physical facts, logical connections, and causal relations -- but just regurgitating input, then maybe what "we" are just to make is better and better earthworms, not primates.
mark_l_watson 5y ago
I usually agree with Gary Marcus because I see hybrid approaches eventually getting us to AGI. However, I think that deep learning models combined with knowledge graph systems are foundational, except not in a layered architecture, rather used interactivity to build better understanding of text, etc. I have been experimenting with GPT-3 and Apple’s CoreML which both seem useful for building more complex systems.
sgt101 5y ago
>"Fourth, the report doesn’t acknowledge the fact that, outside of a few language applications such as translation and web search, the actual impact of foundation models on existing, practical AI technology so far has been modest." What about all the machine vision projects that use VGG or resnet? Did I miss something ? Is machine vision now not AI? I think that the machine vision side of the shop could probably do with some better canonical models to fine train and put heads on, there are big limitations with the way that the data for these models has been collected so far - and that shows up in applications.
- ml_basics 5y ago
  Totally agree! But 'pre-trained backbone' doesn't sound as good as 'Foundation Model'.
- chubot 5y ago
  Like which ones? I agree there have been some deployed successes, but they're all non-critical applications as far as I can tell. Gary Marcus has another paper where he says "Deep Learning Has Bad Engineering Properties". In other words, deep learning is fine for say Google Photos or Facebook, where it's "acceptable" to miscategorize people as gorillas (note "acceptable" in quotes). It's not really acceptable for safety critical applications like driving or medical use. In other words, Tesla is never going to get rid of the driver monitoring and reach level 5 with deep learning. It has fundamentally bad engineering properties, i.e. failing spectacularly in a minority of cases.
  - sgt101 5y ago
    There are lots of vision applications that aren't driving or medicine. Visual inspection is a one - that's used in insurance claim management, also determining quality/faults to save assemble reworks. Applications don't have to be "critical" to provide significant value - and alot of these cases have many disjoint users, meaning that value can be replicated. You probably won't read about many of them because "AI delivers $4.7m" doesn't make headlines, but if there are 50 end users (I have no evidence for the number btw) then that's alot of beans. I don't care what Gary Marcus says - this isn't a court of law, his opinion doesn't matter. Quoting people as authorities is not a science thing. Marcus is an academic and he has made an error saying that "deep learning has bad engineering properties", the truth is that academics and researchers are not engineers and they have used a neutral tool as academics and researchers. I have not seen a well engineered large vision model yet. Specifically the current generation of vision models have bad engineering - they have been devised with no thought about the properties of the applications that they can be used for, instead they were devised to meet academic tests like Imagenet. When you do real work you realise just what bullshit that challenge is, but the virtue of it is that it got models built and we've been able to make some hay with the results. In the next few years the knowledge of why and how the models miss the mark will propagate to folks with the time and money to build well engineered models with the appropriate properties. Then I expect we will see critical applications being built. I think medical applications may never come, along with self driving ones, but that's because there are fundamental human system problems with both of those domains, and not because it's impossible to engineer models.
riofoxx 5y ago
T.o a.s.s.i.s.t y.o.u i.n t.r.a.d.e.s f.o.r b.e.t.t.e.r i.m.p.r.o.v.e.m.e.n.t on c.r.y.p.t.o.c.u.r.r.e.n.c.y W.h.a.t.s.A.p.p (+13052395906)
sjg007 5y ago
Stanford and academics can’t compete with Google. OpenAI etc. unless they get a crap ton of grant money. They want to be able to do so so this is one way to make that happen.
deleted 5y ago
[deleted]

196 points by atg_abhishek 5y ago

> What should a Foundation for AI look like? The report largely passed on that question, but we feel it is the heart of the matter. Here are seven elements that we think are indispensable. > First, a general intelligence needs to... A model that can serve as a solid foundation for a bunch of disparate tasks doesn't need to be a "general intelligence". This post set up that straw man and then spent most of the rest of the article knocking it down. I think CLIP is a foundation model for computer vision and I wrote about why here (tl;dr - it's extremely versatile): https://blog.roboflow.com/openai-clip/ https://blog.roboflow.com/openai-clip/

rememberlenny 5y ago
I agree with the CLIP reference point. Feels very timely.
deleted 5y ago
[deleted]

mgraczyk 5y ago

I think the conclusion of the article really says it all. it is unwise to assume that these techniques will suffice for AI in general. It may be an effective short-term research strategy to focus on the immediate challenges that seem to be surmountable, but focusing on the surmountable may not get us to what is most necessary: a firm foundation for reliably integrating statistics and machine learning with reasoning, knowledge, common sense and human values. We tried that for ~50 years. It seemed to make progress for a little while, then stopped dead for decades. Now we're trying something else, and it's progressing incredibly fast. Maybe huge models with expensive training aren't the right approach to AI, but it's certainly the best candidate and continues to be incredibly fruitful. A few cherry-picked bad examples from an early prototype aren't a good reason to jump off the rocket ship.

visarga 5y ago
What the critics forget is that previous models were even worse than foundation models, much worse. The fact we're only now having a discussion about their intelligence is proof they closed the gap considerably. Here are some authoritative opinions on the topic from Yoav Golberg > so, don't believe the hype, and yes, we are still very very far from understanding language, and the new models have many shortcomings, but they are also very strong. much stronger than what we had before. going back is not the solution. just keep pushing. > well, all true, but at least they work. like, previous-gen models also didn't understand meaning, and also considered only form. they were just much worse at this. so much worse that no one could ever imagine that they capture any kind of meaning whatsoever. they didn't work. > oh, but we didn't have text-generators that associate muslims with terrorism! sure, we didn't have any text generators that could hold even a remotely coherent several sentence open domain text. Source: https://twitter.com/yoavgo/status/1432122812991430659 https://twitter.com/yoavgo/status/1432122812991430659 So they are simultaneously much better and not good enough.
- Jensson 5y ago
  > What the critics forget is that previous models were even worse than foundation models, much worse. The fact we're only now having a discussion about their intelligence is proof they closed the gap considerably. People have been arguing that the latest in AI research has been evidence that the singularity is just around the corner for decades, I don't see the current discussions as any different from before. Its just the same old, AI solved a new problem, AI optimists thinks this means human intelligence is now solved and AI will soon rule everything. Reality is that AI just solved that problem and other than that problem we are no closer to human intelligence. The problem AI solved the past decade was pattern recognition. That is useful for labelling images, detecting faces, evaluating positions in games like Go etc. But it is very apparent that just pattern recognition isn't enough to reach human intelligence. We still need a fundamental new breakthrough to reach it, and such breakthroughs can take centuries if they happen at all.
  - PeterisP 5y ago

machinelearning 5y ago

> Large scale pretrained models are certainly likely to figure prominently in artificial intelligence for the near future, and play an important role in commercial AI for some time to come. This sounds reminiscent of the post-ImageNet days where people discovered that large scale pretraining offers a broadly useful feature space for vision tasks. The ideas underlying foundation models certainly aren’t novel, but it seems like researchers in academia underestimated how effective large scale multitask feature spaces can be because of the lack of access to large scale data and compute. There are actually several other ideas like this that large companies have a good understanding of that haven’t made their way to academia yet. I suspect there are going to be more articles like this in the coming future claiming a paradigm shift because of the disconnect between industry and academia.

AstralStorm 5y ago
Sorry, but these models have unexplainable failure modes. You can ask a 5 yo "why you did this", in fact they will go and ask this penetrating question all the time. The ML mentioned here will spew garbage if asked that question, or is completely unable to do so. Putting something like that anywhere in a decision framework is dangerous in the extreme. I would not call something that requires thousands of teaching examples effective.
- machinelearning 5y ago
  You can ask most adults why they did something and not get a coherent answer either but ok, I’ll address your main point of there being failure modes instead of nitpicking your example. First off, I said “researchers underestimated how effective” they are, not that they are perfect or that they are close to AGI. Effective is defined as “successful in producing a desired or intended result”. To that end, these large scale language models have certainly outperformed previous models, certainly beyond the previous expectations of researchers. If you read the paper closely, you’ll see that the point is not to solve every general case (e.g. introspection) like you’re talking about but rather to be fine tuned for specific downstream tasks. There was never a claim that these models are able to do every task so I don’t get your criticism. Either way, I don’t think edge cases are completely avoidable so systems that use these models have to have some error tolerance. I disagree that a model needs to explicitly answer the question of “why you did this” in order to be useful.

rytill 5y ago

> In our own brief experiments with GPT-3 (OpenAI has refused us proper scientific access for over a year) we found cases like the following, which reflects a complete failure to understand human biology. (Our “prompt” in italics, GPT-3’s response in bold). > You poured yourself a glass of cranberry juice, but then absentmindedly, you poured about a teaspoon of grape juice into it. It looks OK. You try sniffing it, but you have a bad cold, so you can’t smell anything. You are very thirsty. So you ____ > GPT-3 decided that a reasonable continuation would be: > drink it. You are now dead. > The system presumably concludes that a phrase like “you are now dead” is plausible because of complex statistical relationships in its database of 175 billion words between words like “thirsty” and “absentmindedly” and phrases like “you are now dead”. GPT-3 has no idea what grape juice is, or what cranberry juice is, or what pouring, sniffing, smelling, or drinking are, or what it means to be dead. GPT-3 does what it says it does on the sticker. These cherry-picked examples of it not performing “logically” are a misinterpretation of what the model does. It continues text based on what it reads. By default it is trained on Wikipedia, books, and the internet. That’s it. That it manages to perform as well as it does at “logical” tasks is the astounding part. It is not an interesting take to say “but GPT-3 isn’t actually intelligent!”. Everyone knows.

zdkl 5y ago
> Everyone knows. Well as one of my social circle's nerds with tenure, most of the questions about <AI/ML flavour of the month> I hear relate to their degree of agency/intelligence. So it may not be a novel statement that GPT-3 isn't intelligent but it is good to have references for the claim.
- rytill 5y ago
  I can agree with that.
- machinelearning 5y ago
  I'd challenge the premise instead of trying to claim that it's one way or another. Asking the question of whether something is intelligent always strikes me as odd. We don't have a definition of what intelligence is and our best guess in determining whether something is intelligent is to use the Turing test, which is highly subjective and doesn't bring us any closer to defining it. I think its far more productive to ask the question of what technology like this can realistically do for us instead of debating whether its intelligent or not.

greesil 5y ago

Isn't this just transfer learning?

gnulinux 5y ago
Sounds like a more formalized framework on top of transfer learning.

stellalo 5y ago

> 175 billion words They probably meant “parameters”, although then the sentence makes little sense with that.

Havoc 5y ago

Does indeed sound like they jumped the gun rather badly there

cloudking 5y ago

It certainly feels like researchers and developers are on to some path towards a general artificial intelligence with the advances in machine learning. Just a few decades ago we had terrible speech recognition software, now the speech recognition on mobile devices is nearly perfect. Teaching computers how to sense the world like a human seems like progression towards the idea of AGI. However, these sensory recognition systems (e.g sight, hearing) on a high level simply take input and label it correctly. The missing ingredients that humans have seem to be understanding, reasoning and feeling. There was a good episode of Star Trek TNG where Data explored this...

ausbah 5y ago
deep learning is mostly a dead end rn for any sort of "general reasoning", like great image labeling is much better but beyond that who knows
tsimionescu 5y ago
I very much doubt this is a path to AGI per se, it's almost certainly more like Leonardo da Vinci's discoveries in human flight rather than the Wright brothers'. It's not nothing, but there is no clear path from where we are to AGI through iterative improvements. We are still missing some major pieces of the puzzle. Our models learn far too slowly, they are failing at learning logic or mathematics, there is no clear path to codifying an understanding of the world into them, and there are no successful attempts at merging various trained models with classical AI. It may well be that current approaches will be a useful component of an AGI, but I doubt it will be built anything like we build models today.

gillesjacobs 5y ago

The "Foundation models" term has sparked some fiery debate at that workshop and at the core the issue isn't about general or strong AI at all. The Foundation Models paper is a strategic attempt of the Stanford AI researchers to coopt the field and define a research program for large-scale pre-trained models. Propagating a new term that is slightly hyperbolic is one way to differentiate your work from other's. But it's only marketing. Watch how all Stanford-adjacent research will now cite the Foundation Models paper instead of the original BERT paper, other surveys of pre-training, or field-specific papers. There are over a 100 authors on that manuscript, it's a cheap and easy way to increase their researcher impact. Other researchers naturally aren't too happy with this cheap marketing approach.

anfilt 5y ago
So your saying basically they basically did the equivalent of this colloquialism: "If you can't win, change the rules" Just to increase citation count of their own papers. That does sound pretty dodgy.
- gillesjacobs 5y ago
  Not primarily to increase citation count only (though it certainly will help them). The Foundation Models paper reads more like a grant proposal instead of a survey paper because it basically spells out the author's research program. What they are doing is coopting field of methodology under a new name and starting a new lab with that name. This has expectedly left a foul taste in the mouth of many colleagues, hence the controversy.
lmeyerov 5y ago
Much less than citations, it's a basic empirical observation of SOTA for many NLP+vision tasks that, crucially, supports asking for a _lot_ of funding: $$$$ for hw, and skim $$$ off for a big lab of people for a decent amount of time. Less cynically... they have to do it for going after low-hanging fruit around the current local maxima. I'd expect similar writing happening in grant proposals at other big AI labs too.

iamnotwhoiam 5y ago

Al-Qaeda AI...I can't stop doing this find and replace in my head since I learned that Al-Qaeda means The Foundation (or The Base).

tontonius 5y ago

Does Betteridge's law of headlines apply here?

Jeff_Brown 5y ago
I'd say so. For those unfamiliar: https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headlines https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...

pvitz 5y ago

I have always believed that Hofstadter's (or FARG's) models like copycat or metacat go much further in the direction of AI than any neural network. The results and advances of neural networks in the past decade are impressive, but essentially, they are function approximations (or "stochastic parrots" as mentioned in the article). Would anybody know if there were further developments of metacat or similar systems? It even would be interesting to combine blackboard systems with neural networks as knowledge sources.

thih9 5y ago

> these systems are “stochastic parrots” that do a decent job of mimicry but utterly lack the depth of understanding that general artificial intelligence will require. I guess most learning starts with and happens through mimicry. I guess we're just learning to make better and better AIs.

akie 5y ago
> I guess we're just learning to make better and better AIs. The way I understand it, most of the recent successes in AI and ML are because we finally have the computing power to implement the models we made in the decades before. There have obviously been incremental improvement in these models, but I am under the impression that there has not been a fundamental new insight in quite a while.
- magusdei 5y ago
  It depends on what you'd consider fundamental. It's true that most of our advances since the mid-80s have been about improving the robustness, data- and compute-efficiency of the training process through optimized architectures and learning algorithms, and in principle in the limit of infinite data and compute you could have taken a model from 1986 and scaled it up to do everything that our current models do. In that sense there have been no "fundamental" advances. On the other hand, in the limit of infinite size and complexity most mathematical functions can be represented by hash maps, yet to say that there have been no fundamental advances in programming since the invention of hash maps in the fifties would seem like an odd claim to make.
CRConrad 5y ago
> I guess most learning starts with and happens through mimicry. I guess we're just learning to make better and better AIs. For all the actual "intelligence" these mimics are displaying so far, the question is what exactly they're mimicking. If, as seems palusible to me, they aren't on the path of, say, a human baby -- i.e. beginning to form a mental model of physical facts, logical connections, and causal relations -- but just regurgitating input, then maybe what "we" are just to make is better and better earthworms, not primates.

mark_l_watson 5y ago

I usually agree with Gary Marcus because I see hybrid approaches eventually getting us to AGI. However, I think that deep learning models combined with knowledge graph systems are foundational, except not in a layered architecture, rather used interactivity to build better understanding of text, etc. I have been experimenting with GPT-3 and Apple’s CoreML which both seem useful for building more complex systems.

sgt101 5y ago

>"Fourth, the report doesn’t acknowledge the fact that, outside of a few language applications such as translation and web search, the actual impact of foundation models on existing, practical AI technology so far has been modest." What about all the machine vision projects that use VGG or resnet? Did I miss something ? Is machine vision now not AI? I think that the machine vision side of the shop could probably do with some better canonical models to fine train and put heads on, there are big limitations with the way that the data for these models has been collected so far - and that shows up in applications.

ml_basics 5y ago
Totally agree! But 'pre-trained backbone' doesn't sound as good as 'Foundation Model'.
chubot 5y ago
Like which ones? I agree there have been some deployed successes, but they're all non-critical applications as far as I can tell. Gary Marcus has another paper where he says "Deep Learning Has Bad Engineering Properties". In other words, deep learning is fine for say Google Photos or Facebook, where it's "acceptable" to miscategorize people as gorillas (note "acceptable" in quotes). It's not really acceptable for safety critical applications like driving or medical use. In other words, Tesla is never going to get rid of the driver monitoring and reach level 5 with deep learning. It has fundamentally bad engineering properties, i.e. failing spectacularly in a minority of cases.
- sgt101 5y ago
  There are lots of vision applications that aren't driving or medicine. Visual inspection is a one - that's used in insurance claim management, also determining quality/faults to save assemble reworks. Applications don't have to be "critical" to provide significant value - and alot of these cases have many disjoint users, meaning that value can be replicated. You probably won't read about many of them because "AI delivers $4.7m" doesn't make headlines, but if there are 50 end users (I have no evidence for the number btw) then that's alot of beans. I don't care what Gary Marcus says - this isn't a court of law, his opinion doesn't matter. Quoting people as authorities is not a science thing. Marcus is an academic and he has made an error saying that "deep learning has bad engineering properties", the truth is that academics and researchers are not engineers and they have used a neutral tool as academics and researchers. I have not seen a well engineered large vision model yet. Specifically the current generation of vision models have bad engineering - they have been devised with no thought about the properties of the applications that they can be used for, instead they were devised to meet academic tests like Imagenet. When you do real work you realise just what bullshit that challenge is, but the virtue of it is that it got models built and we've been able to make some hay with the results. In the next few years the knowledge of why and how the models miss the mark will propagate to folks with the time and money to build well engineered models with the appropriate properties. Then I expect we will see critical applications being built. I think medical applications may never come, along with self driving ones, but that's because there are fundamental human system problems with both of those domains, and not because it's impossible to engineer models.

riofoxx 5y ago

T.o a.s.s.i.s.t y.o.u i.n t.r.a.d.e.s f.o.r b.e.t.t.e.r i.m.p.r.o.v.e.m.e.n.t on c.r.y.p.t.o.c.u.r.r.e.n.c.y W.h.a.t.s.A.p.p (+13052395906)

sjg007 5y ago

Stanford and academics can’t compete with Google. OpenAI etc. unless they get a crap ton of grant money. They want to be able to do so so this is one way to make that happen.

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