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It's also suffering from hype. And the criticism you note isn't one-directional in the field at large. I'm finding that ML/AI researchers deriding ML/Data engi

by sidlls 10y ago

It's also suffering from hype. And the criticism you note isn't one-directional in the field at large. I'm finding that ML/AI researchers deriding ML/Data engineers and "scientists" as not doing "real" ML or AI is becoming a thing, similar to how some computer scientists deride engineering as not doing real computing.

WhitneyLand 10y ago
I hate this. Remember they did it to Michael Faraday in 1813, deriding him as not a real scientist as he was about to play a part in advancing one of the greatest challenges of mankind (unification).
logicallee 10y ago
It is not suffering from hype. There is too little hype. People are vastly underestimating what is about to happen. See my comments here: https://news.ycombinator.com/item?id=13079598 https://news.ycombinator.com/item?id=13079598 under our recent article " Artificial Intelligence Generates Christmas Song". Basically, if there is no pixie dust that makes humans intelligent, and instead it is a matter of the architecture of the brain and the first few years of supervised sensory input, then neural net breakthroughs (which use a similar architecture/topology) have the potential at any moment in time to break through and match general human intelligence. What I mean is that if someone sent back source code from 80 years from now, but we had to run it on a bunch of Amazon / Google servers in a server farm, we're pretty much guaranteed to have enough computing power to do so! (This is a combination of the number of neurons, their number of connections, and their very slow speed.) We have the hardware. Now: we do not actually have the source code from 80 years from now that we can go ahead and run on those machines. So, we're at like heavier-than-air travel right before the Wright Brothers flew at Kittie Hawk. Except we have like jet engines already -- just no way to design them into something that flies. I think that AI is vastly underrated and I watch with incredible interest every single breakthrough. For the second category above, ML built into products or engineering solutions, Alphago surprised me, because Go had an intractibly large possibility space, it's not in any sense subject to brute-forcing or exhaustive search. Dragon's Natural dictation surprised me in that using its model its able to get basically perfect dictation. I've never worked as a transcriptionist, but a small search reveals it has basically annihilated the industry of medical transcription. These are not the big, general breakthrough. But the big, general breakthrough is right there, somewhere. The results researchers are coming up with are astounding, and they're doing it in many cases with neural nets, quite similar to the wiring of the human mind. The hype is faaaaaaaaaaaaaaar less than warranted for the stage that we're at. At any moment someone can put together something that achieves higher-level intelligence and can be set loose upon the world corpus of culture. True, there are no clear indications that this is about to happen (For example: people do not extract innate language algorithms from the human genome which encodes them), so we are not exactly taking many steps that we could be trying to. As far as I know we're not even genetically engineering people to see what different parts of DNA do -- which is obviously a very, very good thing, and who would allow anyone to bear to term a child made as an experiment to see what DNA does. But despite not going from a human starting point, the results that we are achieving in many cases match and surpass human ability - while we do know that in many cases some of their architecture is similar. I feel quite strongly that we have more than enough hardware for general intelligence - and I see advances every day that could end up going past the ppint of no return on it. -- EDIT: got a downvote, but I would prefer a discussion if you think I'm wrong.
- jpatokal 10y ago
  I didn't downvote you, but the TL;DR of the article is that most ML demonstrations to date have been toys and there are no known real-world applications that would justify the "40% of jobs lost!" hype. And you're trying to rebut this by referencing an AI-generated Christmas jingle. I think the author rests their case...
  - lucidrains 10y ago
    just toys? https://en.wikipedia.org/wiki/Deep_learning#Applications https://en.wikipedia.org/wiki/Deep_learning#Applications
  - logicallee 10y ago
    No, I just meant to reference my discussion from there (i.e. for people to read through my comments there, after clicking.) IOW I meant to transclude that discussion here. (Perhaps within that comment thread a good specific summary comment is: https://news.ycombinator.com/item?id=13090869 https://news.ycombinator.com/item?id=13090869) Obviously it is hard to know when that magic moment will happen that some kind of general AI is created that can learn in some sense similarly to how humans do. My every indication and astonishment at the results that are being produced strongly suggests "at any moment". The results are absolutely astonishing every day and we have vastly more than enough firepower. You might also be interested in this separate thread where I dealt with questions of consciousness and pain. I again reference it here: https://news.ycombinator.com/item?id=13026020 https://news.ycombinator.com/item?id=13026020 and you can click through at the top to follow my reference. We are way past the point of no return here, and in my estimation it is a question of years or at the most decades - not centuries.
    - lucidrains 10y ago
      absolutely dude.. for example, this paper just popped up in the last week https://arxiv.org/abs/1611.02167 https://arxiv.org/abs/1611.02167
      - 10y ago
cma 10y ago
>It's also suffering from hype. I'm imagining someone tweeting at Robin, several years back: "Good Prediction Markets expert says: Most firms that think they want advanced Prediction Markets really just need linear regression on cleaned-up data." ;)
ma2rten 10y ago
It's also suffering from hype. If you mean hype in media and general public, I agree with you. Research is inherently risky and uncertain, often that is not conveyed correctly. Also research results are often oversold. If you mean that big tech companies are overinvesting in Machine Learning then I have to disagree. It's not a coincidence that the companies that invested the most in Machine Learning (Google and Facebook) are companies that have a lot of data and already use Machine Learning in their products. It provides things like better feed ranking and better signals for search. These "invisible" improvements to existing products are often overlooked. And the criticism you note isn't one-directional in the field at large. Yes, like I mentioned I think there is confusion about terminology. Many ML Engineers (in my definition) call themselves Data Scientists. This leads to misunderstandings, when people don't recognize that others have different goals.
- unscaled 10y ago
  You mean, some tech companies understand the difference between Data Science, AI in general, ML and Deep Learning and try to utilize the best tools in hand to handle their data. But what is driving this hype, as well as the Blockchain hype, is not engineer-driven companies like Google, but rather MBA-driven buzzword-friendly tech companies (imagine Balmer-era Microsoft), non-tech companies who want to share the cake, the media and finally the investors who are misled by the rest, but end up creating a capital-based feedback loop for them. As far as the people driving the hype train right now are concerned, Machine Learning is the way you do AI, and Deep Learning is just a more powerful (ahem deeper) version of Machine Learning. This is what AlphaGo used to defeat Lee Sedol, so it's obviously superior, and we should use it to process all data, in the same way we should strive to store everything on a blockchain, which is clearly superior to hash tables and databases.
shas3 10y ago
This is the first time I'm hearing that computer scientists deride engineering as not-real-computing. Any references? It doesn't check out based on my background. In fact anecdotally, I've heard the reverse. I've heard EE and stat algorithms folks criticize CS ML/CV researchers for using algorithms as black boxes compared to the rigorous standards of EE/Stat (aka reviewer standards in IEEE Transactions in Information Theory, IEEE Trans Sig Proc, majority of stat journals). Now obviously there are a vast number of CS researchers who are competent at stat-level rigor. But there are also a large number of ML researchers (applied ML?) in CS departments who use algorithms as black boxes.
- liviu- 10y ago
  My understanding is that the grandparent comment was referring to _software_ engineers.
- swordswinger12 10y ago
  The prof of my applied ML class this semester jokingly refers to ML research's approach to statistics as "punk stats", which I find to be pretty accurate.
c3534l 10y ago
This and the above comment kind of says my thoughts on the matter. I think there is a very real emerging field that combines mathematics, traditional computer science, and AI into a big family resemblance of quantitative methods and research, but that we're still in the early stages and we're still sorting things out. I remember hearing someone on hacker news joke that when he got out of school people called him a statistician, then people started saying he was a data miner, and now he sells himself as a data scientist, but he hasn't actually done anything differently apart from just keeping up with the field. Sometimes it feels as if we're just playing with words and people get excited for advances and what it means for the future, but the reality is people just keep plodding along make incremental progress, finding new ways to leverage the tools they have, and continuing to publish papers. For all of the tech industry's worship of "disruptive innovation" and massive changes, the reality is that we've been developing and refining our quantitative abilities and figuring out automation since the industrial revolution. AI may become less exciting to people 5 years from now, but we will still continue doing what we've been doing.

by sidlls 10y ago

WhitneyLand 10y ago

I hate this. Remember they did it to Michael Faraday in 1813, deriding him as not a real scientist as he was about to play a part in advancing one of the greatest challenges of mankind (unification).

logicallee 10y ago

It is not suffering from hype. There is too little hype. People are vastly underestimating what is about to happen. See my comments here: https://news.ycombinator.com/item?id=13079598 https://news.ycombinator.com/item?id=13079598 under our recent article " Artificial Intelligence Generates Christmas Song". Basically, if there is no pixie dust that makes humans intelligent, and instead it is a matter of the architecture of the brain and the first few years of supervised sensory input, then neural net breakthroughs (which use a similar architecture/topology) have the potential at any moment in time to break through and match general human intelligence. What I mean is that if someone sent back source code from 80 years from now, but we had to run it on a bunch of Amazon / Google servers in a server farm, we're pretty much guaranteed to have enough computing power to do so! (This is a combination of the number of neurons, their number of connections, and their very slow speed.) We have the hardware. Now: we do not actually have the source code from 80 years from now that we can go ahead and run on those machines. So, we're at like heavier-than-air travel right before the Wright Brothers flew at Kittie Hawk. Except we have like jet engines already -- just no way to design them into something that flies. I think that AI is vastly underrated and I watch with incredible interest every single breakthrough. For the second category above, ML built into products or engineering solutions, Alphago surprised me, because Go had an intractibly large possibility space, it's not in any sense subject to brute-forcing or exhaustive search. Dragon's Natural dictation surprised me in that using its model its able to get basically perfect dictation. I've never worked as a transcriptionist, but a small search reveals it has basically annihilated the industry of medical transcription. These are not the big, general breakthrough. But the big, general breakthrough is right there, somewhere. The results researchers are coming up with are astounding, and they're doing it in many cases with neural nets, quite similar to the wiring of the human mind. The hype is faaaaaaaaaaaaaaar less than warranted for the stage that we're at. At any moment someone can put together something that achieves higher-level intelligence and can be set loose upon the world corpus of culture. True, there are no clear indications that this is about to happen (For example: people do not extract innate language algorithms from the human genome which encodes them), so we are not exactly taking many steps that we could be trying to. As far as I know we're not even genetically engineering people to see what different parts of DNA do -- which is obviously a very, very good thing, and who would allow anyone to bear to term a child made as an experiment to see what DNA does. But despite not going from a human starting point, the results that we are achieving in many cases match and surpass human ability - while we do know that in many cases some of their architecture is similar. I feel quite strongly that we have more than enough hardware for general intelligence - and I see advances every day that could end up going past the ppint of no return on it. -- EDIT: got a downvote, but I would prefer a discussion if you think I'm wrong.

jpatokal 10y ago
I didn't downvote you, but the TL;DR of the article is that most ML demonstrations to date have been toys and there are no known real-world applications that would justify the "40% of jobs lost!" hype. And you're trying to rebut this by referencing an AI-generated Christmas jingle. I think the author rests their case...
- lucidrains 10y ago
  just toys? https://en.wikipedia.org/wiki/Deep_learning#Applications https://en.wikipedia.org/wiki/Deep_learning#Applications
- logicallee 10y ago
  No, I just meant to reference my discussion from there (i.e. for people to read through my comments there, after clicking.) IOW I meant to transclude that discussion here. (Perhaps within that comment thread a good specific summary comment is: https://news.ycombinator.com/item?id=13090869 https://news.ycombinator.com/item?id=13090869) Obviously it is hard to know when that magic moment will happen that some kind of general AI is created that can learn in some sense similarly to how humans do. My every indication and astonishment at the results that are being produced strongly suggests "at any moment". The results are absolutely astonishing every day and we have vastly more than enough firepower. You might also be interested in this separate thread where I dealt with questions of consciousness and pain. I again reference it here: https://news.ycombinator.com/item?id=13026020 https://news.ycombinator.com/item?id=13026020 and you can click through at the top to follow my reference. We are way past the point of no return here, and in my estimation it is a question of years or at the most decades - not centuries.
  - lucidrains 10y ago
    absolutely dude.. for example, this paper just popped up in the last week https://arxiv.org/abs/1611.02167 https://arxiv.org/abs/1611.02167
    - 10y ago

cma 10y ago

>It's also suffering from hype. I'm imagining someone tweeting at Robin, several years back: "Good Prediction Markets expert says: Most firms that think they want advanced Prediction Markets really just need linear regression on cleaned-up data." ;)

ma2rten 10y ago

It's also suffering from hype. If you mean hype in media and general public, I agree with you. Research is inherently risky and uncertain, often that is not conveyed correctly. Also research results are often oversold. If you mean that big tech companies are overinvesting in Machine Learning then I have to disagree. It's not a coincidence that the companies that invested the most in Machine Learning (Google and Facebook) are companies that have a lot of data and already use Machine Learning in their products. It provides things like better feed ranking and better signals for search. These "invisible" improvements to existing products are often overlooked. And the criticism you note isn't one-directional in the field at large. Yes, like I mentioned I think there is confusion about terminology. Many ML Engineers (in my definition) call themselves Data Scientists. This leads to misunderstandings, when people don't recognize that others have different goals.

unscaled 10y ago
You mean, some tech companies understand the difference between Data Science, AI in general, ML and Deep Learning and try to utilize the best tools in hand to handle their data. But what is driving this hype, as well as the Blockchain hype, is not engineer-driven companies like Google, but rather MBA-driven buzzword-friendly tech companies (imagine Balmer-era Microsoft), non-tech companies who want to share the cake, the media and finally the investors who are misled by the rest, but end up creating a capital-based feedback loop for them. As far as the people driving the hype train right now are concerned, Machine Learning is the way you do AI, and Deep Learning is just a more powerful (ahem deeper) version of Machine Learning. This is what AlphaGo used to defeat Lee Sedol, so it's obviously superior, and we should use it to process all data, in the same way we should strive to store everything on a blockchain, which is clearly superior to hash tables and databases.

shas3 10y ago

This is the first time I'm hearing that computer scientists deride engineering as not-real-computing. Any references? It doesn't check out based on my background. In fact anecdotally, I've heard the reverse. I've heard EE and stat algorithms folks criticize CS ML/CV researchers for using algorithms as black boxes compared to the rigorous standards of EE/Stat (aka reviewer standards in IEEE Transactions in Information Theory, IEEE Trans Sig Proc, majority of stat journals). Now obviously there are a vast number of CS researchers who are competent at stat-level rigor. But there are also a large number of ML researchers (applied ML?) in CS departments who use algorithms as black boxes.

liviu- 10y ago
My understanding is that the grandparent comment was referring to _software_ engineers.
swordswinger12 10y ago
The prof of my applied ML class this semester jokingly refers to ML research's approach to statistics as "punk stats", which I find to be pretty accurate.

c3534l 10y ago

This and the above comment kind of says my thoughts on the matter. I think there is a very real emerging field that combines mathematics, traditional computer science, and AI into a big family resemblance of quantitative methods and research, but that we're still in the early stages and we're still sorting things out. I remember hearing someone on hacker news joke that when he got out of school people called him a statistician, then people started saying he was a data miner, and now he sells himself as a data scientist, but he hasn't actually done anything differently apart from just keeping up with the field. Sometimes it feels as if we're just playing with words and people get excited for advances and what it means for the future, but the reality is people just keep plodding along make incremental progress, finding new ways to leverage the tools they have, and continuing to publish papers. For all of the tech industry's worship of "disruptive innovation" and massive changes, the reality is that we've been developing and refining our quantitative abilities and figuring out automation since the industrial revolution. AI may become less exciting to people 5 years from now, but we will still continue doing what we've been doing.