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I fully agree that there is an ongoing reckoning with the limitations of deep learning, but that is the sign of a thriving community not a failing one. Also, A

I fully agree that there is an ongoing reckoning with the limitations of deep learning, but that is the sign of a thriving community not a failing one. Also, AGI is a misplaced milestone, even as OP explores a more interesting one: as incremental advances in AI empower non-human entities (corps and governments) with unique powers of surveillance and autonomous action, the impacts can be just as important as if it were a machine intelligence. The uptick in articles is just a sign that it's trendy to poke holes in hype. Expecting AI to go away is like expecting the Internet (another fundamental shift that was announced with an annoying hype bubble) to go away. It's just becoming a part of life, and creating new power structures while it does.

aalleavitch 8y ago
I’ve long believed the first true “AGI” will be a corporation in which all of the individual jobs have just been automated out of existence one by one over time. Eventually, many companies will essentially just become autonomous entities under the direction of shareholders. It may even have protocols put in place to determine when to hire outside consultants to come in and improve itself.
nerdponx 8y ago
I fully agree that there is an ongoing reckoning with the limitations of deep learning I would go a step even further. I don't think this even justifies being called a "reckoning", we are just finding the limits of something that we have not yet found the limit I would go a step even further. I don't think this even justifies being called a "reckoning", we are just learning its limits.
YeGoblynQueenne 8y ago
Has there been a misunderstanding of my comment? I didn't mean to say that I expect "AI to go away". For one thing, I certainly do not equate all of AI to deep learning, or even machine learning. My point is that deep learning has generated a lot of excitement in the last few years, but as we're hitting inevitable diminishing returns the excitement is dying down and people are starting to think about how to move beyond current techniques. As usual, the last to catch on to the reversal of the old trend, or the new trend if you prefer, are the people who pay the money- industry investors or the military etc, and then the general public. My money is on using deep learning for "low-level" sort of sensory tasks and then GOFAI, symbolic techniques, for high-level reasoning. There has been some recent activity on trying to marry symbolic reasoning and deep learning (e.g. differentiable ILP, from Evans and Grefenstette in DeepMind [1] etc) and it's a promising line of research that has the potential to yield "best-of-both-worlds" results. But - "AI going away"? Not any time soon! ________ [1] https://arxiv.org/abs/1711.04574 https://arxiv.org/abs/1711.04574 Although note that their assertion that ILP can't address noisy or ambiguous data is plainly wrong :)
- sangnoir 8y ago
  > Has there been a misunderstanding of my comment? I didn't mean to say that I expect "AI to go away" The "it" in your original comment makes the subject ambiguous - your comment can be interpreted as saying AI itself (not the arms-race) will "fizzle out". I agree that AI's hype cycle is nearing the "trough of disillusionment" stage, but after that will come a wider and deeper application of AI in a multitude of fields and industries instead of the narrow applications today. Even the diminished returns are worth chasing if it increases your turnover by 1-2%.
  - YeGoblynQueenne 8y ago
    >> I agree that AI's hype cycle is nearing the "trough of disillusionment" stage, I'm sorry but I never said anything like that. AI is not the same as deep learning. It's deep learning that has been hyped in the last few years, not AI in general. Of course, in the lay press, there is a great confusion between AI, machine learning and deep learning- but I don't see why there should be an assumpion that I, too, am equally confused about those terms. In any case, it should be easy to dispel any misunderstandings by a quick look at my profile- I'm an AI PhD research student. I would hardly claim that AI is about to fizzle- or even deep learning. What is being discussed in the quote from the original article is the purported arms race to AGI. And how could AI "fizzle" now- when it has been growing as a field for the last 60+ years? Honestly, I don't see how any other interpretation of the "ambiguity" in my comment can be justified, assuming good faith. I sure had to squint really hard to see any ambiguity at all.
    - evrydayhustling 8y ago
      It sounds like we (in this thread) are all on the same page about the future of AI-writ-large. Regarding good faith, the quotation pattern you used didn't mention deep learning - you referred to either "the arms race" or "classifiers on large datasets" as fizzling out, and my reply resolved them to "AI" as a spanning term, since that was the context of the original post you quoted. For what it's worth, I would have written the same response about deep learning specifically for the same reasons you point out later in the thread that AI will remain useful. The specific profile of opportunities opened by DL is finding plenty of valuable homes in corporate processes where other kinds of automation fill in the gaps between it and AI. At this point, I'm not sure whether you disagree with that, or just think that some hype could afford to fade (no argument!). Since you are a grad researcher, I'll throw in some of my context. I did my PhD focused on probabilistic graphical models, back when it was easy to ignore NNs (and when we expected to hit some of the same perceptual milestones). As a grad student, a big part of your job is to filter fads and find ideas that will stay true. Because of that, I was slower than I could have been to recognize the feedback loops in what is "true" about applied AI. Deep learning's initial fit for some architectures and problems has attracted attention that made it much cheaper and easy to experiment, and therefore useful for more and more - even gobbling up adjacent techniques and giving them new names (over any manner of academic protest). That feedback loop isn't unbounded, but I guess I'm just sharing the perspective that hype, while annoying, isn't something that even a grad student can afford to disdain.

aalleavitch 8y ago

I’ve long believed the first true “AGI” will be a corporation in which all of the individual jobs have just been automated out of existence one by one over time. Eventually, many companies will essentially just become autonomous entities under the direction of shareholders. It may even have protocols put in place to determine when to hire outside consultants to come in and improve itself.

nerdponx 8y ago

I fully agree that there is an ongoing reckoning with the limitations of deep learning I would go a step even further. I don't think this even justifies being called a "reckoning", we are just finding the limits of something that we have not yet found the limit I would go a step even further. I don't think this even justifies being called a "reckoning", we are just learning its limits.

YeGoblynQueenne 8y ago

Has there been a misunderstanding of my comment? I didn't mean to say that I expect "AI to go away". For one thing, I certainly do not equate all of AI to deep learning, or even machine learning. My point is that deep learning has generated a lot of excitement in the last few years, but as we're hitting inevitable diminishing returns the excitement is dying down and people are starting to think about how to move beyond current techniques. As usual, the last to catch on to the reversal of the old trend, or the new trend if you prefer, are the people who pay the money- industry investors or the military etc, and then the general public. My money is on using deep learning for "low-level" sort of sensory tasks and then GOFAI, symbolic techniques, for high-level reasoning. There has been some recent activity on trying to marry symbolic reasoning and deep learning (e.g. differentiable ILP, from Evans and Grefenstette in DeepMind [1] etc) and it's a promising line of research that has the potential to yield "best-of-both-worlds" results. But - "AI going away"? Not any time soon! ________ [1] https://arxiv.org/abs/1711.04574 https://arxiv.org/abs/1711.04574 Although note that their assertion that ILP can't address noisy or ambiguous data is plainly wrong :)

sangnoir 8y ago
> Has there been a misunderstanding of my comment? I didn't mean to say that I expect "AI to go away" The "it" in your original comment makes the subject ambiguous - your comment can be interpreted as saying AI itself (not the arms-race) will "fizzle out". I agree that AI's hype cycle is nearing the "trough of disillusionment" stage, but after that will come a wider and deeper application of AI in a multitude of fields and industries instead of the narrow applications today. Even the diminished returns are worth chasing if it increases your turnover by 1-2%.
- YeGoblynQueenne 8y ago
  >> I agree that AI's hype cycle is nearing the "trough of disillusionment" stage, I'm sorry but I never said anything like that. AI is not the same as deep learning. It's deep learning that has been hyped in the last few years, not AI in general. Of course, in the lay press, there is a great confusion between AI, machine learning and deep learning- but I don't see why there should be an assumpion that I, too, am equally confused about those terms. In any case, it should be easy to dispel any misunderstandings by a quick look at my profile- I'm an AI PhD research student. I would hardly claim that AI is about to fizzle- or even deep learning. What is being discussed in the quote from the original article is the purported arms race to AGI. And how could AI "fizzle" now- when it has been growing as a field for the last 60+ years? Honestly, I don't see how any other interpretation of the "ambiguity" in my comment can be justified, assuming good faith. I sure had to squint really hard to see any ambiguity at all.
  - evrydayhustling 8y ago
    It sounds like we (in this thread) are all on the same page about the future of AI-writ-large. Regarding good faith, the quotation pattern you used didn't mention deep learning - you referred to either "the arms race" or "classifiers on large datasets" as fizzling out, and my reply resolved them to "AI" as a spanning term, since that was the context of the original post you quoted. For what it's worth, I would have written the same response about deep learning specifically for the same reasons you point out later in the thread that AI will remain useful. The specific profile of opportunities opened by DL is finding plenty of valuable homes in corporate processes where other kinds of automation fill in the gaps between it and AI. At this point, I'm not sure whether you disagree with that, or just think that some hype could afford to fade (no argument!). Since you are a grad researcher, I'll throw in some of my context. I did my PhD focused on probabilistic graphical models, back when it was easy to ignore NNs (and when we expected to hit some of the same perceptual milestones). As a grad student, a big part of your job is to filter fads and find ideas that will stay true. Because of that, I was slower than I could have been to recognize the feedback loops in what is "true" about applied AI. Deep learning's initial fit for some architectures and problems has attracted attention that made it much cheaper and easy to experiment, and therefore useful for more and more - even gobbling up adjacent techniques and giving them new names (over any manner of academic protest). That feedback loop isn't unbounded, but I guess I'm just sharing the perspective that hype, while annoying, isn't something that even a grad student can afford to disdain.