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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

#11

Post by jmole »

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/

Yes, and text can be written in non-causal ways, which means even if you parse things in order, you can easily end up with a non-causal understanding.

“I just saw a huge fish, but then I realized I was out of worms. I should’ve bought more, but I spent $20 on gas instead.”

Here the causal order goes from last to first (minus the first clause)

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

#12

Post by contravariant »

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…

That said the interesting thing about language AIs is that they are by design able to communicate, which leads to interesting results, even though it is increasingly obvious that they don't really have any 'awareness' as such.

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

#13

Post by ninjin »

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 have questions and see if the first author is interested in joining when he wakes up as he really did all the legwork for this one.

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

#14

Post by mrfusion »

Isinlor wrote: post #6
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 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?

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

#15

Post by mellosouls »

Isinlor wrote: post #6
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 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…

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 [...] GPT-3 understanding of physical world is similarly bad.

No, GPT-3 has no "understanding" whatsoever - we at least have a conscious engagement with the topics you cite and a genuine understanding that we lack a directly relatable perspective.

Our understanding in many circumstances - like those you mention, may be severely constrained, but it exists as part of a sentience that there is no reason to believe GPT-3 or any other current AI tool - possesses.

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

#16

Post by m0zg »

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 skeptic because of it. I think perceptual tasks work reasonably well, NLP barely works for anything, and any sort of cognition doesn't work at all, as of August 2020. I will be the first to admit that we don't have real "AI" yet, and we won't have it in the foreseeable future. But to say that real intelligence is not possible with the current tool set is simply incorrect. It may be impractical. It may take 100+ years to figure out how to build nets this large and train them successfully before sun burns out, but it is _theoretically_ provably doable.

In a way it's kind of like fusion: you have a proof that it can work right above your head during the day. We just don't know how to do it at our "human" scale with our knowledge and capabilities. Our knowledge and capabilities, however, are not a constant - they improve exponentially over time, although most people don't perceive this process as exponential.

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

#17

Post by LdSGSgvupDV »

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 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, there are lots of "for loop" in our code/machine. But, in real world, we could not observe any "for loop". It is fast and continuous in the macro scale.

In order to reach strong AI, it is time to focus on quantum computing.

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

#18

Post by sudosysgen »

LdSGSgvupDV wrote: post #17
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 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.

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

#19

Post by sdenton4 »

mellosouls wrote: post #15
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…

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 [...] GPT-3 understanding of physical world is similarly bad. No, GPT-3 has no "understanding" whatsoever - we at least have a conscious engagement with the topics you cite and a genuine understanding that we…

Do we really understand? What's the mechanism for that?

My thinking is that we humans have some ability to do things by rote until we have a good statistical understanding, at which point we use the statistical understanding mostly, and fall back to rote where we have to be careful. But, mostly, we're stats machines too.

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

#20

Post by nl »

valgaze wrote: post #7
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…

The parameter/dataset arms race producing emergent intelligence rather than just bigger models is a fantasy. However without a good/better explanation in the meantime , really interesting capabilities emerge at higher parameters Specifically to your point of arithmetic— here is GPT3 performing “zero shot” (ie no examples) arithmetic: https://photos.app.goo.gl/pf6fHcrio7QFtU4S8 The “completion” includes an answer to t…

That's pretty impressive.

Google's Pegusus summarizer learnt to count to 5: https://ai.googleblog.com/2020/06/pegasus-state-of-art-model...

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