3 ms·

"What should we think about the experts? Projections of failure were made by eminent, respectable, serious people. They spoke in considered tones of why AI hype

"What should we think about the experts? Projections of failure were made by eminent, respectable, serious people. They spoke in considered tones of why AI hype was excessive and might trigger an “AI winter”, and the fundamental flaws of fashionable approaches and why brute force could not work. These statements were made routinely in 2014, 2015, 2016… And they were wrong. I am aware of few issuing a mea culpa or reflecting on it. It is a puzzling failure, and I’ve reflected on it before." Who are these experts? Where are records of these routine statements? Seriously, I am an AI researcher, who said this?

p1esk 6y ago
Gary Marcus?
- andreyk 6y ago
  I mean, he and one or two other AI-research adjacent people basically have a hobby of bringing up limitations of Deep Learning etc, sure. But if that's all that is meant by this post, this is a weak point indeed...
  - p1esk 6y ago
    After IBM Watson won Jeopardy! game Noam Chomsky said: "Watson understands nothing. It's a bigger steamroller". I wonder if he still holds this view after reading GPT-3 samples.
nl 6y ago
Here's a good example from 2018: "Deep learning (does not) scale... We can't just scale up AlexNet and get respectively better results - we have to fiddle with specific architectures, and effectively additional compute does not buy much without order of magnitude more data samples, which are in practice only available in simulated game environments." https://blog.piekniewski.info/2018/05/28/ai-winter-is-well-on-its-way/ https://blog.piekniewski.info/2018/05/28/ai-winter-is-well-o... But Gary Marcus is the gold standard. Check out his 2018 take-down attempt, which is clearly mostly wrong: https://arxiv.org/ftp/arxiv/papers/1801/1801.00631.pdf https://arxiv.org/ftp/arxiv/papers/1801/1801.00631.pdf
Seabiscuit 6y ago
These predictions often surely make most sense and are most prevalent in a particular field of NLP research, like dialogue (open-domain, non-task oriented). Believable dialogue seems to require some kind of world knowledge, certain forms of 'common sense' (e.g. arithmetic reasoning), and the ability to track belief states and be coherent over long periods. GPT-2 was critiqued for its inability to deal with simple arithmetic questions, as well as contradicting itself over long periods [1]. GPT-3 has similar deficiencies in terms of sensible real-world knowledge and displaying coherence both with its own previous answers and with a more general real-world knowledge: 'Within the domain of discrete language tasks, we have noticed informally that GPT-3 seems to have special difficulty with “common sense physics”, despite doing well on some datasets (such as PIQA [BZB+19]) that test this domain. Specifically GPT-3 has difficulty with questions of the type “If I put cheese into the fridge, will it melt?”.' [2]) of the type that would obviously torpedo attempts at open domain dialogue over long periods. Take a typical two paper pointing out the failings of brute force methods for dialogue. They trot out a classic example: 'Without getting into the specifics of how these systems are constructed, it is apparent that such an approach [brute force] provides a method for imitating a dialogue but not for participating in one. We can see the limitations of the approach from examples like these: Human: what is your job ? Machine: i ’m a lawyer . Human: what do you do ? Machine: i ’m a doctor (Vinyals & Le, 2015).' [3] Huge models can provide plausible, but not consistently coherent, dialogue inputs. In the dialogue domain, the 'winter' will come (/has come) when it becomes clear that Meena, BlenderBot etc need a little help when it comes to coherence over an arbitrary number of turns, displaying 'common sense physics', and so on. [1] https://thegradient.pub/gpt2-and-the-nature-of-intelligence/ https://thegradient.pub/gpt2-and-the-nature-of-intelligence/ [2] https://arxiv.org/pdf/2005.14165.pdf https://arxiv.org/pdf/2005.14165.pdf [3] https://arxiv.org/pdf/1812.01144.pdf https://arxiv.org/pdf/1812.01144.pdf
a_throwaway_4 6y ago
Yann LeCun has warned of hype causing an AI winter: Spectrum: Hype is bad, sure, but why do you say it’s “dangerous”? LeCun: It sets expectations for funding agencies, the public, potential customers, start-ups and investors, such that they believe that we are on the cusp of building systems that are as powerful as the brain, when in fact we are very far from that. This could easily lead to another “winter cycle.” https://spectrum.ieee.org/automaton/artificial-intelligence/machine-learning/facebook-ai-director-yann-lecun-on-deep-learning https://spectrum.ieee.org/automaton/artificial-intelligence/... To clarify, that's not to the credit of the article. The author is basically taking the piss off people like LeCun, sarcastically describing them as "eminent, respectable, serious people" who "spoke in considered tones" (as if that's a bad thing) and wondering why they haven't issued a "mea culpa". At least, I find it a bit conceited to expect the people who built up a field of research from nothing to apologise for being worried that the field might be in danger from overhyping by a flood of newcomers who don't understand it.

p1esk 6y ago

Gary Marcus?

andreyk 6y ago
I mean, he and one or two other AI-research adjacent people basically have a hobby of bringing up limitations of Deep Learning etc, sure. But if that's all that is meant by this post, this is a weak point indeed...
- p1esk 6y ago
  After IBM Watson won Jeopardy! game Noam Chomsky said: "Watson understands nothing. It's a bigger steamroller". I wonder if he still holds this view after reading GPT-3 samples.

nl 6y ago

Here's a good example from 2018: "Deep learning (does not) scale... We can't just scale up AlexNet and get respectively better results - we have to fiddle with specific architectures, and effectively additional compute does not buy much without order of magnitude more data samples, which are in practice only available in simulated game environments." https://blog.piekniewski.info/2018/05/28/ai-winter-is-well-on-its-way/ https://blog.piekniewski.info/2018/05/28/ai-winter-is-well-o... But Gary Marcus is the gold standard. Check out his 2018 take-down attempt, which is clearly mostly wrong: https://arxiv.org/ftp/arxiv/papers/1801/1801.00631.pdf https://arxiv.org/ftp/arxiv/papers/1801/1801.00631.pdf

Seabiscuit 6y ago

These predictions often surely make most sense and are most prevalent in a particular field of NLP research, like dialogue (open-domain, non-task oriented). Believable dialogue seems to require some kind of world knowledge, certain forms of 'common sense' (e.g. arithmetic reasoning), and the ability to track belief states and be coherent over long periods. GPT-2 was critiqued for its inability to deal with simple arithmetic questions, as well as contradicting itself over long periods [1]. GPT-3 has similar deficiencies in terms of sensible real-world knowledge and displaying coherence both with its own previous answers and with a more general real-world knowledge: 'Within the domain of discrete language tasks, we have noticed informally that GPT-3 seems to have special difficulty with “common sense physics”, despite doing well on some datasets (such as PIQA [BZB+19]) that test this domain. Specifically GPT-3 has difficulty with questions of the type “If I put cheese into the fridge, will it melt?”.' [2]) of the type that would obviously torpedo attempts at open domain dialogue over long periods. Take a typical two paper pointing out the failings of brute force methods for dialogue. They trot out a classic example: 'Without getting into the specifics of how these systems are constructed, it is apparent that such an approach [brute force] provides a method for imitating a dialogue but not for participating in one. We can see the limitations of the approach from examples like these: Human: what is your job ? Machine: i ’m a lawyer . Human: what do you do ? Machine: i ’m a doctor (Vinyals & Le, 2015).' [3] Huge models can provide plausible, but not consistently coherent, dialogue inputs. In the dialogue domain, the 'winter' will come (/has come) when it becomes clear that Meena, BlenderBot etc need a little help when it comes to coherence over an arbitrary number of turns, displaying 'common sense physics', and so on. [1] https://thegradient.pub/gpt2-and-the-nature-of-intelligence/ https://thegradient.pub/gpt2-and-the-nature-of-intelligence/ [2] https://arxiv.org/pdf/2005.14165.pdf https://arxiv.org/pdf/2005.14165.pdf [3] https://arxiv.org/pdf/1812.01144.pdf https://arxiv.org/pdf/1812.01144.pdf

a_throwaway_4 6y ago

Yann LeCun has warned of hype causing an AI winter: Spectrum: Hype is bad, sure, but why do you say it’s “dangerous”? LeCun: It sets expectations for funding agencies, the public, potential customers, start-ups and investors, such that they believe that we are on the cusp of building systems that are as powerful as the brain, when in fact we are very far from that. This could easily lead to another “winter cycle.” https://spectrum.ieee.org/automaton/artificial-intelligence/machine-learning/facebook-ai-director-yann-lecun-on-deep-learning https://spectrum.ieee.org/automaton/artificial-intelligence/... To clarify, that's not to the credit of the article. The author is basically taking the piss off people like LeCun, sarcastically describing them as "eminent, respectable, serious people" who "spoke in considered tones" (as if that's a bad thing) and wondering why they haven't issued a "mea culpa". At least, I find it a bit conceited to expect the people who built up a field of research from nothing to apologise for being worried that the field might be in danger from overhyping by a flood of newcomers who don't understand it.