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Natural language benchmarks don’t measure AI models’ general knowledge well

venturebeat.com

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Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#21

Post by nl »

ninjin wrote: post #13

Second author here with a link to the arXiv paper: https://arxiv.org/abs/2008.02637 Although I have to say, VentureBeat did much better than most media outlets I have seen writing about current research and what they write is not only accurate but also largely devoid of hype. Perhaps we actually managed to “keep the hype down” as we intended when writing this piece? I will check in on this post now and then if you ha…

Nice paper and important work.

Given that nearest-neighbor outperforms on closed book, is it reasonable to suspect the model is doing NN itself internally (which would explain the good performance on close duplicates?)

And if this is the case do you think training-time processing of data to attempt to move convert it to question/answer form data rather than raw QA would be a reasonable approach towards tackling this?

Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#22

Post by IfOnlyYouKnew »

tsimionescu wrote: post #3

I'm always amazed that some people think performing statistical analysis (training neural networks) on text can lead to actual intelligence and knowledge about the world, if only we increase the number of model parameters by a few more orders of magnitude (GPT-3 and the likely strategy for GPT-4). Of course, all this without any model of how likely it is that the knowledge is embedded in the text, such as trying to t…

It amazes me people think their brain works differently.

It's pretty close to believing in body/mind dualism, the only thing in neuroscience more outdated than Freud.

Your brain works within the same laws of physics as the outside world. We don't know how the brain works exactly. But once we understand it, it is unlikely to be qualitatively different from a neural network.

On the other side of the equation, the emergent behaviour of deep networks make them fundamentally different from the sort of statistics that came before. So even if the building blocks are old and boring, the larger system isn't.

Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#23

Post by [deleted] »

m0zg wrote: post #16
tsimionescu wrote: post #3

I'm always amazed that some people think performing statistical analysis (training neural networks) on text can lead to actual intelligence and knowledge about the world, if only we increase the number of model parameters by a few more orders of magnitude (GPT-3 and the likely strategy for GPT-4). Of course, all this without any model of how likely it is that the knowledge is embedded in the text, such as trying to t…

I don't see why you're "amazed". Neural networks are _provably_ an universal approximator. As such, they can approximate _any_ function to an arbitrary degree of precision. It just might require an impractically large/complex neural net to do so, and you might not be able to train it yet, but it's _provably_ doable in the limit, if you remove size/training constraints. I'm a practitioner in the field, and an AI skept…

[deleted]

Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#24

Post by MarkPNeyer »

nextos wrote: post #2

Judea Pearl has been bringing up the lack of causal knowledge in ML very often. He has even posted lots of interesting comments in Andrew Gelman's blog, e.g.: https://statmodeling.stat.columbia.edu/2009/07/05/disputes_a... I tend to think that lots of solutions could come from topics like those discussed in this book, with a lot of further development: http://probmods.org/

This sounds like a possible analog to someone who is intelligent but doesn't really understand math or science. This person might be able to BS their way through certain fields, but could never pass a calculus exam.

Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#25

Post by LdSGSgvupDV »

sudosysgen wrote: post #18
LdSGSgvupDV wrote: post #17

Earlier quoted context omitted.

It seems they are on the right direction. But, the number of parameters hit the physical barrier of silicon-based device. To make the neuron network be a strong AI, we may need the parameters in scales of 10^19~10^44 (uncertainty limit and Planck time) estimated from how many frames per second in real world. I personally got a feeling that this is bound to how nature calculate the physical phenomenon. For simulation,…

For loops in AI are actually only there to implement continuous mathematical operations such as matrix multiplications in a single core. Modern AI chips and implementations use very little to no for loops.

Sorry, maybe it is a wrong analogy. What I want to emphasize is that there is no calculating time in nature. At least, we could not feel "lag" in our "Nature Server".

For the part of chips, did you mean that it runs the "for loop" as parallel in each micro-chip (or gate or whatever tiny structure it is)?

Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#26

Post by visarga »

IfOnlyYouKnew wrote: post #22
tsimionescu wrote: post #3

I'm always amazed that some people think performing statistical analysis (training neural networks) on text can lead to actual intelligence and knowledge about the world, if only we increase the number of model parameters by a few more orders of magnitude (GPT-3 and the likely strategy for GPT-4). Of course, all this without any model of how likely it is that the knowledge is embedded in the text, such as trying to t…

It amazes me people think their brain works differently. It's pretty close to believing in body/mind dualism, the only thing in neuroscience more outdated than Freud. Your brain works within the same laws of physics as the outside world. We don't know how the brain works exactly. But once we understand it, it is unlikely to be qualitatively different from a neural network. On the other side of the equation, the emerg…

What we need here, I think, is a GAN-like approach. Use a generator + a discriminator. The generator could be like GPT-3, but it needs another model to filter out the nonsense. We work the same way - come up with crazy ideas, then check them out to see if they hold ... 'I had an idea, but nah, it wouldn't work'. It's a process supported by different skills.

Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#27

Post by sudosysgen »

LdSGSgvupDV wrote: post #25
sudosysgen wrote: post #18

Earlier quoted context omitted.

For loops in AI are actually only there to implement continuous mathematical operations such as matrix multiplications in a single core. Modern AI chips and implementations use very little to no for loops.

Sorry, maybe it is a wrong analogy. What I want to emphasize is that there is no calculating time in nature. At least, we could not feel "lag" in our "Nature Server". For the part of chips, did you mean that it runs the "for loop" as parallel in each micro-chip (or gate or whatever tiny structure it is)?

>Sorry, maybe it is a wrong analogy. What I want to emphasize is that there is no calculating time in nature. At least, we could not feel "lag" in our "Nature Server".

Yes, but the reality is that there is lag in our projection of what nature is and that we just ignore it. Different parts of our perception actually don't exist at the same instant.

>For the part of chips, did you mean that it runs the "for loop" as parallel in each micro-chip (or gate or whatever tiny structure it is)?

Pretty much, the for-loop is unfolded and ran in parallel.

Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#28

Post by Animats »

Veedrac wrote: post #10

The article doesn't give examples, but the paper does. Here are a few examples of question overlap, for context. Test Question: who plays max voice in a goofy movie Train Question: who does max voice in a goofy movie Answer: Jason Marsden Test Question: when will the 2018 oscar nominations be announced Train Question: when are the oscar nominations for 2018 announced Answer: January 23 2018 Test Question: who has sco…

That's useful for chatbots, where you have a big FAQ and people ask questions. That's exactly what RASA does, using TensorFlow. It's reasonably good at matching user questions with stored questions. But that's all the AI part does.

Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#29

Post by snowwrestler »

IfOnlyYouKnew wrote: post #22
tsimionescu wrote: post #3

I'm always amazed that some people think performing statistical analysis (training neural networks) on text can lead to actual intelligence and knowledge about the world, if only we increase the number of model parameters by a few more orders of magnitude (GPT-3 and the likely strategy for GPT-4). Of course, all this without any model of how likely it is that the knowledge is embedded in the text, such as trying to t…

It amazes me people think their brain works differently. It's pretty close to believing in body/mind dualism, the only thing in neuroscience more outdated than Freud. Your brain works within the same laws of physics as the outside world. We don't know how the brain works exactly. But once we understand it, it is unlikely to be qualitatively different from a neural network. On the other side of the equation, the emerg…

The issue isn't whether neural networks work, the issue is the dataset. Look again at what the parent wrote:

> I'm always amazed that some people think performing statistical analysis (training neural networks) on text can lead to actual intelligence and knowledge about the world

Emphasis mine. The human brain does not develop what we know as human intelligence by analyzing text.

Re: Natural language benchmarks don’t measure AI models’ general knowledge well

#30

Post by ccozan »

mrfusion wrote: post #14
Isinlor wrote: post #6

Earlier quoted context omitted.

It does lead to intelligence and knowledge of the world. It leads to somewhat similar understanding of the world as our understanding of 4D, quantum mechanics or environment close to a black hole. Pretty bad one, mostly based on repeating whatever we have read about in popular science literature without any intuitive understanding. GPT-3 understanding of physical world is similarly bad. It's just simply a modality no…

So you’re saying ai needs to be grounded to the real world to be effective?

Yes. Specifically the way human brain interacts with environment, the same way any AGI will need to sense and react to an environment, the more real the better.

An interesting example would be if the NN learns something from a book, also fed it with the all the critique on that book ( the environment reaction ). So it can possibly learn from a human what is good or bad in that book, etc.

Then both AI and humans can speak the same language ( on terms.of "feeling"/sensing ) and eventually learn the proper context, so less parameters are needed.

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