8 ms·

An Epidemic of AI Misinformation

125 points by hughzhang 7y ago

ggm 7y ago
Very hard to critique an article you overwhelmingly agree with! The key point I kept picking up was the extent to which a press willing to laud a discovery was reticent about owning the clinb-down. Peer review in ML journals should be tighter maybe? If you solve a limited subset of the three body problem you can't claim to solve "the three body problem" and if you apply a well known Rubik's cube model solution you didn't learn it, you had it baked in.
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- buboard 7y ago
  do most of the hyped papers have peer review even? the field moves too fast for that, press releases are issued as soon as the first draft is on arxiv.
  - eanzenberg 7y ago
    Apologies, but I see a lot of “trash” published in arxiv (also some real good stuff) Peer review could fix some things but would also show some other things down
    - madenine 7y ago
      I like to think of arxiv as the holding area for anything that COULD be peer reviewed. Advantage is we get to see stuff now, not after it gets accepted at by a conference or journal. Disadvantage, we have to filter on our own. Twitter and sites like arxiv sanity preserver are helpful for filtering.
- currymj 7y ago
  typically the actual papers are reasonably honest about everything and the misinformation comes in elsewhere, often university press releases summarizing the results.
minimaxir 7y ago
I dunno if the GPT-2 examples are misinformation, more like exaggeration for PR, which is admittingly a separate problem in AI. (marketing hype vs. actual falsity)
- brenden2 7y ago
  Hype and exaggeration is just another form of falsification.
- barbecue_sauce 7y ago
  Exaggerated Information + Trusting & Uneducated Consumers = Misinformation
buboard 7y ago
Ugh. I dislike the author s permanent negativity, but he s right about a lot. I think it’s worth asking why people feel the need to lie about the future of AI? If they are confident about its future (and I don’t know of a fundamental reason why they would not be) then there is no reason to rush half assed results out the door and overcompensate (like gpt2). There is plenty of theoretical questions and answers to debate publicly instead. I don’t know if its the fear of some impending tech recession or fear of their own incompetence
- Barrin92 7y ago
  >Ugh. I dislike the author s permanent negativity, but he s right about a lot. Yes, in a way it must suck to always act like the grumpy party-crasher but Gary Marcus is absolutely spot on when it comes to the facts. That interview with the economist had me shaking my head, everyone had to know within five seconds that the chance that this is uncurated is zero.
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- Fishysoup 7y ago
  It's the bullshit process by which people get funding. Make something that has some potentially interesting engineering value, hype it up to hell and back, and funding agencies, investors etc. are more likely to back you. Probably this wasn't everyone's first plan all along, but when people who make a marginally better hot-dog / not-hot-dog classifier put so much spin on it, then everyone else has to just to remain visible. Moreover, people have to publish findings before their competition does, meaning sloppier and less interesting work. It's the snake-eating-its-own-tail plague that impacts so much of academia.
  - buboard 7y ago
    > impacts so much of academia i think the trend started from the industry , but you re right warrantless self-promotion very pervasive in academia , and it's sad that it works!
  - 7y ago
spicyramen 7y ago
Let us remember that AI is at the center of the cloud wars. Google, Amazon and Microsoft while they produce ton of research, their sales teams need to market those R&D investment to get customers interested in their tech. We are at a stage that AI is still a buzz word for many companies, once we evolve and deploy more and more of AI cases, we will see less and less articles promising things that can't be implemented
- thu2111 7y ago
  That's probably a part of it. Note how the Google TPUs aren't for sale. If you want them you have to use the Google Cloud. The cloud is expensive and slow ... I think everyone is shocked when they first see perf numbers coming off Azure. I don't know if GCE is better, but the temptation to overload the hardware is always there: hardware rental is fundamentally a business with low barriers to entry. Anyone can buy some machines, bring up a Kubernetes or OpenShift cluster and start renting it out. So the big 3 are always looking for proprietary advantage and dedicated AI chips are something other firms can't easily do at the moment, making it a good source of lockin. Do many people need it though? Deep learning is pretty useless for most business apps, unless you happen to need an image classifier or something else pre-canned. Classical ML is often sufficient, or better, human written logic. The latter can be explained, debugged, rapidly improved and in the best case requires no training data at all!
  - dijit 7y ago
    I work for a company that uses bare metal, open stack, azure, AWS, and GCE. GCE perf is significantly better (and more consistent) than Azure. Even with Windows instances. :/ But I agree that “cloud is slow” when compared to bare metal- it’s also most financially costly especially for the same performance due to it being slower. But the gains in flexibility are immeasurable.
  - TrinaryWorksToo 7y ago
    Their tpus are for sale: https://coral.ai/products/ https://coral.ai/products/
ummonk 7y ago
Good article, but it overstates its case. >In 1966, the MIT AI lab famously assigned Gerald Sussman the problem of solving vision in a summer; as we all know, machine vision still hasn't been solved over five decades later. It certainly took five extra decades, but it would be a massive shift of goalposts to say the problem of vision hasn't been sufficiently solved today. >In November 2016, in the pages of Harvard Business Review, Andrew Ng, another well-known figure in deep learning, wrote that “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.” A more realistic appraisal is that whether or not something can be automated depends very much on the nature of the problem, and the data that can be gathered, and the relation between the two. For closed-end problems like board games, in which a massive amount of data can be gathered through simulation, Ng’s claim has proven prophetic; in open-ended problems, like conversational understanding, which cannot be fully simulated, Ng’s claim thus far has proven incorrect. Business leaders and policy-makers would be well-served to understand the difference between those problems that are amenable to current techniques and those that are not; Ng’s words obscured this. (Rebooting AI gives some discussion.) It takes significantly longer than a second to actually understand spoken conversation (rather than provide a conditioned response or match against expected statements, both of which computers are fully capable of doing). >I just wish that were the norm rather than the exception. When it’s not, policy-makers and the general public can easily find themselves confused; because the bias tends to be towards overreporting rather than underreporting results, the public starts fearing a kind of AI (replacing many jobs) that does not and will not exist in the foreseeable future. Robotic manufacturing has already eliminated massive swaths of high paying jobs. Likewise, software has eliminated massive swaths of data entry and customer service jobs (with software being a particularly poor replacement for the latter, but still being put into widespread use to cut costs). And contrary to beliefs that new jobs will be created in IT, software is able to massively eliminate low skilled tech jobs as well, as e.g. automated testing did to India's IT industry. As with existing jobs that have been automated away, companies won't need generalized AI to eliminate many more jobs. Many jobs don't rely on unconstrained complex deduction and thinking, and will be ripe for replacement with deep learning algorithms. And we can be reliably assured that corporations will engage in such replacements even when the outcomes are not up to par.
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eanzenberg 7y ago
Interesting that a few big examples of hype driven articles came from OpenAI. This is dangerous and will lead to additional misinformation and public backlash. Scientists should be unbiased and not market driven, but when OpenAI has these releases I shook my head along with a couple others. Hype lasts the next quarter but is replaced with distrust. Science is a long game of incremental discoveries. Breakthroughs usually are an understanding of some interesting outcome that needed more interpretation.
- miketery 7y ago
  I think this is what happens when you have various stakeholders and some are driven by KPIs such as reach / views. To that end those specific stakeholders (content writers / marketing) will bias towards sensationalism. That's not to excuse the organizational culture which allows for this behavior / outcome. There does need to be course correction.
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pizzaparty2 7y ago
I bet ai is going to become like coding where everyone thinks they're an expert.
system2 7y ago
It happened to VR a few years ago. The development slowed down when companies realized the sales. Unlike VR, AI is not a physical good that users/customers can purchase and test directly. It is just a buzzword. Companies will use it to death at every opportunity to market themselves. This is unstoppable. We will hear more about it, maybe until people get sick of hearing "another crappy ai product".
- buboard 7y ago
  AI is more of a datacenter technology. People already use it (siri,alexa,google search) and they apparently like it already. It was easy to see that any VR headset is bulky and agonizing, but those new services work fine. Sure, however the "not hot dog" apps are going to be disappointing
8bitsrule 7y ago
The buzzword back in the 70s-80s (after AI over-promised in the 1960s) was 'expert systems'. https://en.wikipedia.org/wiki/Expert_system https://en.wikipedia.org/wiki/Expert_system (To the extent that I have kept up with it) modern AI skips the 'knowledge base' part of ES, in favor of pattern-recognition based on 'training'. Today's (Indeterministic, trained, n-net) AI has clearly saved a lot of time/effort in creating 'knowledge bases'. I suspect it appeals more to singular fantasies about 'more human than human' intelligence. (Sorry Ray) Question is: Is today's AI even a magnitude-better than (deterministic) ES insofar as extensibility and verifiability? What if we had spent those decades refining the ES approach instead?
- jefft255 7y ago
  They're not even competing with each other. How would you compare an ES to a deep neural network? According to what benchmark? What do you mean by verifiable? Modern success stories with machine learning often deal with problems (NLP, vision, RL) that have little to do with the problems that were being solved by expert systems. Also, an hypothesis: if (big if) you somehow managed to have an expert system outperform deep learning for vision, I bet that it won't be any more verifiable than a deep neural net is today. To me the complaint that modern deep learning is unverifiable is a bit dumb, in the sense that any perception algorithm working with low level signals (vision, sound) will not be transparent to a human. 15 years ago, an image classification pipeline looked like: bag of SIFT features + SVM classifier. Try explaining the decision made by that algorithm in an intuitive way!
- ilaksh 7y ago
  I mean, you extend a DL system by giving it examples. Yes, in fact by harvesting example or annotation data you can extend models many magnitudes faster and more comprehensively than through manual analysis. Effectively the analysis is done automatically. At this point, what comes out it is usually poorly factored and entangled and not human-parseable, but that doesn't prevent it from being applied effectively to a range of narrow tasks that expert systems cannot approach. We do still rely on expert systems for things that we want to be carefully parsed, verified and analyzed by people though. Such as rules that oversee most self-driving cars based on perception handled by neural networks. However, not all self-driving systems lean as heavily on rules at the top level as others.
ilaksh 7y ago
OpenAI hype is definitely exaggerated. Seems like there might be a connection with Elon Musk somehow because all of his projects seem to get a massive amount of hype also. The radiology thing, is it really the case that there are no startups in that area with useful AI software? Seems like he overstated that. Part of this is a worldview difference. Many people truly believe that AGI is just around the corner, and that even before then, the narrow AI applications will significantly alter the world as they are deployed. And since no one can actually predict the future, it's an area where it's easy to have different worldviews. Personally I think that it's true that there is a lot of poor reporting and companies that overhype results, but it also seems like people like Gary Marcus are really not keeping up to date with the true capabilities of DL systems. If he was up to date, why would he be so pessimistic about applications like radiology? There seem to already be a lot of strong results.
- unishark 7y ago
  I think there are certainly issues with ML in radiology (i.e. lots of publications of low power studies or overfit models validated incorrectly), but I agree with you that Marcus was being unfair. It is principle a straightforward problem to solve with deep learning. The reason biggest reasons "no actual radiologists have been replaced" are probably just political/social. Hinton cannot be blamed for those hurdles.
tomxor 7y ago
> I see this as a version of the tragedy of the commons, in which (for example) many people overfish a particular set of waters [...] if and when the public, governments, and investment community recognize that they have been sold an unrealistic picture of AI’s strengths and weaknesses that doesn't match reality, a new AI winter may commence. I think this is not only inevitable, but necessary! This time around it has been a lot more useful, due in no small part to the advance in hardware since 1970s. Unfortunately this has caused many important people to believe far too much of the hype and not see it's current limitations. As a result they have started integrating it into important part of our societies - i find this alarming - not for the reason most people find it alarming i.e "because it's too smart", but because it's far far too dumb in combination with people assuming it's very smart. I think a lot of this problem stems from inappropriately anthropomorphising ML with terms like "AI" when we are no where near the stage that we need to have the philosophical debate about where something is sentient or "intelligent". The ML we are doing with NNs is still at the "tiny-chunk-of-very-specifically-engineered-piece-of-brain" stage. It's important people understand this before we start integrating what are essentially basic statistical mechanisms into our societies. For those in pursuit of better ML and things like real AI aka AGI, I also think having the hype blow away will do more good in the form of clarity and lack of noise than it will harm in lack of funding.
perl4ever 7y ago
When I saw this item, I thought not of misinformation produced about AI, but misinformation produced by AI. Some of the Google searches I have done recently turn up mostly automatically written articles that plug in numbers and facts but make no sense at all, because there is no real understanding. (Yes, this is probably not "real" AI, but it's the sort of thing people are and will be trying to apply AI to) It seems to me that there is no way to get to a serene, well-functioning society that utilizes intelligent machines, because in the process of developing software that can digest and write intelligently about topics humans are interested in, we will inevitably come up with less intelligent software first, that produces plausible misinformation far cheaper than humans. And economics will drive out, is already driving out, real research and journalism. Human society would be drowned by automatically generated misinformation by the time a machine is truly intelligent, whether that is in a couple years or a decade or longer. I'm increasingly thinking that the people who are worried about artificial intelligence ending the world as we know it have slightly missed the mark - superhuman intelligence is a singularity that we are approaching, but we are going to be ripped apart by the tides beforehand and maybe that will prevent the endpoint from ever happening.
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blazespin 7y ago
An AI winter will not happen. We are past the point of no return. The vast majority of research is net positive research. The only risk is misappropriation of investment, but that has already been smoothed over quickly. I’d get the complaint if AI was not doing anything useful as it wasn’t in ‘74, but it simply is. For example, plate recognition, drones and facial recognition. These are brutally efficient technologies. To imagine any government under investing in these is a total misunderstanding of the value that AI brings to military, security, and economic dominance. It’s a race between nations and the only danger is the computers taking over (not in a general AI way, but in a way that everything is run by algorithms no human can understand)
- mbruce 7y ago
  This is primarily deep learning which is not general purpose AI
  - laichzeit0 7y ago
    We’re talking about AI here. And deep learning is AI. If you go to university and research or study AI that’s what you’d currently be learning. AGI does not currently exist and is a word used mostly by laymen and the media who don’t understand the current state of AI.
- not_a_moth 7y ago
  You sure? Without a doubt, there's an investment bubble for AI companies in industry: a huge amount of companies have added "AI" to their proposition, where the AI is only marginally useful if not useful at all, for the purpose of marketing and inflating valuation. On research side, it's now clear the current AI paradigm will not produce the kind of massive, society-shifting promises that were made to investors, which is the focus of the article. Yes deep learning is here to stay for narrow tasks, but the investment contraction for the rest could certainly feel like a Winter.
  - marcosdumay 7y ago
    > it's now clear the current AI paradigm will not produce the kind of massive, society-shifting promises that were made to investors It's correct in that it won't produce the society-shifting promises. But there is a huge amount of money on non-society-shifting innovation. Besides, I really doubt the media predictions are what actually was told to investors.
KKKKkkkk1 7y ago
The term AI winter is IMHO misrepresenting the true state of affairs. Since day one, the field's modus operandi was one of overpromising and underdelivering. That has not changed. We see how Alphabet plows billions of dollars into DeepMind [1] and all they get in return is a series of game-playing bots. If unproductive activities are defunded, this creates an opportunity for productive ones to thrive. "Winter" is not a suitable word to describe this. [1] https://www.wired.com/story/deepminds-losses-future-artificial-intelligence/ https://www.wired.com/story/deepminds-losses-future-artifici...
- andreyk 7y ago
  This is a ridiculously reductive take on DeepMind... they produce a TON of great research, and have helped advance the state of RL considerably (speaking as an AI researcher here, having read many of their papers). Google should be commended for investing in largely basic AI research, even if once in a while they make a PR splash out of it, IMO.
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scottlegrand2 7y ago
The funny thing for me is that if The Economist had just hacked the input into GPT-2 so that it was an ongoing conversation they would have found that it's OK at holding a conversation, better than I expected when I did so. The conversation that I posted at: https://medium.com/@scottlegrand/my-interview-with-the-worlds-most-dangerous-ai-tm-473ebdde04b2 https://medium.com/@scottlegrand/my-interview-with-the-world... is performed in one shot. And I think it shows both the abilities and the limitations of GPT-2 and similar models. I am 100% role playing with the language model and prompting it to go in the directions it goes, but it surprised me several times, and eventually it all fell apart because I didn't perform any transfer learning on the model I just used the raw GPT-2 XL model to measure whether further work would be worth the effort and I would conclude yes it would be. The first thing I need to do is dramatically increase the length of its input context. It's pretty good at running with an ensuing script because I suspect much of its training data was formatted that way. But since I ran out of context symbols, it eventually suffers from several incidents of amnesia and eventually effective multiple personality disorder. It also contradicts itself several times but then no more than the typical thought leader or politician does IMO. What The Economist did was effectively erase the thing's memory between questions. So they were starting fresh with each question. And I think that's why they had to do the best of 5.
andreyk 7y ago
It's kind of funny just how much complaining about AI hype and bad coverage there is (on Twitter, and on this very subreddit), it feels like there is more of that now than actually bad coverage. This article suggests some actions to take at the very bottom, which amount to including some discussion of the limitations of the work in the paper -- not a bad idea, but then again the misleading coverage usually does not stem from claims in the papers themselves in the first place. Shameless but relevant plug warning, I run this effort Skynet Today with the aim to let AI researchers/experts present various advancements/topics to a general audience without hype and with context. Everyone on the team is volunteering their time for free and we can always use more people to help us tackle various subjects, so if you care about this issue feel free to take a look here: https://www.skynettoday.com/contribute https://www.skynettoday.com/contribute
- g82918 7y ago
  > It's kind of funny just how much complaining about AI hype and bad coverage there is (on Twitter, and on this very subreddit) I think your copy and paste script broke here. Please put some effort into promoting yourself.
- YeGoblynQueenne 7y ago
  Skynet Today is very good. I wish you had more original articles because the ones you have, I enjoy a lot (your editorials and briefs). I see you are calling for contributions, I hope you get some good ones. Writing good articles- there's no other way to combat bad coverage. Keep it up please and all the best.
euske 7y ago
The more I read articles like this (or pretty much ANY article recently), the more I'm convinced that what we really need is: transparency. I don't mean just AI, but transparency on everything: Transparency from researchers, news outlets, governments, retailers, and YouTubers. It's a great shame that a lot of IT has been used for bringing more confusion and obfuscation to the people, whereas it could be used to provide a more coherent/honest view of the world. We should demand more transparency on every matter.
Veedrac 7y ago
Sigh. > The Economist [...] said that GPT-2’s answers were “unedited”, when in reality each answer that was published was selected from five options > [Erik Bryjngjolffson] tweeted that the interview was “impressive” and that “the answers are more coherent than those of many humans.” In fact the apparent coherence of the interview stemmed from (a) the enormous corpus of human writing that the system drew from and (b) the filtering for coherence that was done by the human journalist. If your success rate is ≥20%, the coherence is coming from the model, not the selection process. This is just basic statistics. > OpenAI created a pair of neural networks that allowed a robot to learn to manipulate a custom-built Rubik's cube Jeez, I've already corrected you here... well, why not have to do it again? > publicized it with a somewhat misleading video and blog that led many to think that the system had learned the cognitive aspects of cube-solving The side not stated: OpenAI said explicitly in the blog that they used an unlearned algorithm for this, and sent a correction to a publisher that got this wrong. > the cube was instrumented with Bluetooth sensors During training, but they ended up with a fully vision-based system. > even in the best case only 20% of fully-scrambled cubes were solved No, 60% of fully scrambled cubes were solved. 20% of maximally difficult scrambles were solved. > one report claimed that “A neural net solves the three-body problem 100 million times faster” [...] but the network did no solving in the classical sense, it did approximation All solvers for this problem are approximators, and vice-versa. The article you complain about states the accuracy (“error of just 10^(-5)”) in the body of text. > and it approximated only a highly simplified two degree-of-freedom problem As reported: “Breen and co first simplify the problem by limiting it to those involving three equal-mass particles in a plane, each with zero velocity to start with.” > MIT AI lab famously assigned Gerald Sussman the problem of solving vision in a summer [https://dspace.mit.edu/handle/1721.1/6125 https://dspace.mit.edu/handle/1721.1/6125] I... sigh “The original document outlined a plan to do some kind of basic foreground/background segmentation, followed by a subgoal of analysing scenes with simple non-overlapping objects, with distinct uniform colour and texture and homogeneous backgrounds. A further subgoal was to extend the system to more complex objects. So it would seem that Computer Vision was never a summer project for a single student, nor did it aim to make a complete working vision system.” http://www.lyndonhill.com/opinion-cvlegends.html http://www.lyndonhill.com/opinion-cvlegends.html > Geoff Hinton [said] that the company (again The Guardian’s paraphrase), “is on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation.” Four years later, we are still a long way from machines that can hold natural conversations absent human intervention ‘Four years later’ to natural conversation is not a reasonable point of criticism when the only timeline given was ‘within a decade’ for a specified subset of the problem. > [In 2016 Hinton said] “We should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists.” [...] but thus far no actual radiologists have been replaced So Hinton actually said “People should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists, because it's going to be able to get a lot more experience. It might be 10 years, but we've got plenty of radiologists already.” 2019 is not 2026. “thus far no actual radiologists have been replaced” is thus not a counterargument. > Andrew Ng, another well-known figure in deep learning, wrote that “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.” [...] Ng’s claim thus far has proven incorrect. I agree. This quote captures the wrong nuance of the issue. Well, finally finding one point by Gary Marcus that isn't misleading, I think I'm going to call this a day.

125 points by hughzhang 7y ago

ggm 7y ago

Very hard to critique an article you overwhelmingly agree with! The key point I kept picking up was the extent to which a press willing to laud a discovery was reticent about owning the clinb-down. Peer review in ML journals should be tighter maybe? If you solve a limited subset of the three body problem you can't claim to solve "the three body problem" and if you apply a well known Rubik's cube model solution you didn't learn it, you had it baked in.

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buboard 7y ago
do most of the hyped papers have peer review even? the field moves too fast for that, press releases are issued as soon as the first draft is on arxiv.
- eanzenberg 7y ago
  Apologies, but I see a lot of “trash” published in arxiv (also some real good stuff) Peer review could fix some things but would also show some other things down
  - madenine 7y ago
    I like to think of arxiv as the holding area for anything that COULD be peer reviewed. Advantage is we get to see stuff now, not after it gets accepted at by a conference or journal. Disadvantage, we have to filter on our own. Twitter and sites like arxiv sanity preserver are helpful for filtering.
currymj 7y ago
typically the actual papers are reasonably honest about everything and the misinformation comes in elsewhere, often university press releases summarizing the results.

minimaxir 7y ago

I dunno if the GPT-2 examples are misinformation, more like exaggeration for PR, which is admittingly a separate problem in AI. (marketing hype vs. actual falsity)

brenden2 7y ago
Hype and exaggeration is just another form of falsification.
barbecue_sauce 7y ago
Exaggerated Information + Trusting & Uneducated Consumers = Misinformation

buboard 7y ago

Ugh. I dislike the author s permanent negativity, but he s right about a lot. I think it’s worth asking why people feel the need to lie about the future of AI? If they are confident about its future (and I don’t know of a fundamental reason why they would not be) then there is no reason to rush half assed results out the door and overcompensate (like gpt2). There is plenty of theoretical questions and answers to debate publicly instead. I don’t know if its the fear of some impending tech recession or fear of their own incompetence

Barrin92 7y ago
>Ugh. I dislike the author s permanent negativity, but he s right about a lot. Yes, in a way it must suck to always act like the grumpy party-crasher but Gary Marcus is absolutely spot on when it comes to the facts. That interview with the economist had me shaking my head, everyone had to know within five seconds that the chance that this is uncurated is zero.
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Fishysoup 7y ago
It's the bullshit process by which people get funding. Make something that has some potentially interesting engineering value, hype it up to hell and back, and funding agencies, investors etc. are more likely to back you. Probably this wasn't everyone's first plan all along, but when people who make a marginally better hot-dog / not-hot-dog classifier put so much spin on it, then everyone else has to just to remain visible. Moreover, people have to publish findings before their competition does, meaning sloppier and less interesting work. It's the snake-eating-its-own-tail plague that impacts so much of academia.
- buboard 7y ago
  > impacts so much of academia i think the trend started from the industry , but you re right warrantless self-promotion very pervasive in academia , and it's sad that it works!
- 7y ago

spicyramen 7y ago

Let us remember that AI is at the center of the cloud wars. Google, Amazon and Microsoft while they produce ton of research, their sales teams need to market those R&D investment to get customers interested in their tech. We are at a stage that AI is still a buzz word for many companies, once we evolve and deploy more and more of AI cases, we will see less and less articles promising things that can't be implemented

thu2111 7y ago
That's probably a part of it. Note how the Google TPUs aren't for sale. If you want them you have to use the Google Cloud. The cloud is expensive and slow ... I think everyone is shocked when they first see perf numbers coming off Azure. I don't know if GCE is better, but the temptation to overload the hardware is always there: hardware rental is fundamentally a business with low barriers to entry. Anyone can buy some machines, bring up a Kubernetes or OpenShift cluster and start renting it out. So the big 3 are always looking for proprietary advantage and dedicated AI chips are something other firms can't easily do at the moment, making it a good source of lockin. Do many people need it though? Deep learning is pretty useless for most business apps, unless you happen to need an image classifier or something else pre-canned. Classical ML is often sufficient, or better, human written logic. The latter can be explained, debugged, rapidly improved and in the best case requires no training data at all!
- dijit 7y ago
  I work for a company that uses bare metal, open stack, azure, AWS, and GCE. GCE perf is significantly better (and more consistent) than Azure. Even with Windows instances. :/ But I agree that “cloud is slow” when compared to bare metal- it’s also most financially costly especially for the same performance due to it being slower. But the gains in flexibility are immeasurable.
- TrinaryWorksToo 7y ago
  Their tpus are for sale: https://coral.ai/products/ https://coral.ai/products/

ummonk 7y ago

Good article, but it overstates its case. >In 1966, the MIT AI lab famously assigned Gerald Sussman the problem of solving vision in a summer; as we all know, machine vision still hasn't been solved over five decades later. It certainly took five extra decades, but it would be a massive shift of goalposts to say the problem of vision hasn't been sufficiently solved today. >In November 2016, in the pages of Harvard Business Review, Andrew Ng, another well-known figure in deep learning, wrote that “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.” A more realistic appraisal is that whether or not something can be automated depends very much on the nature of the problem, and the data that can be gathered, and the relation between the two. For closed-end problems like board games, in which a massive amount of data can be gathered through simulation, Ng’s claim has proven prophetic; in open-ended problems, like conversational understanding, which cannot be fully simulated, Ng’s claim thus far has proven incorrect. Business leaders and policy-makers would be well-served to understand the difference between those problems that are amenable to current techniques and those that are not; Ng’s words obscured this. (Rebooting AI gives some discussion.) It takes significantly longer than a second to actually understand spoken conversation (rather than provide a conditioned response or match against expected statements, both of which computers are fully capable of doing). >I just wish that were the norm rather than the exception. When it’s not, policy-makers and the general public can easily find themselves confused; because the bias tends to be towards overreporting rather than underreporting results, the public starts fearing a kind of AI (replacing many jobs) that does not and will not exist in the foreseeable future. Robotic manufacturing has already eliminated massive swaths of high paying jobs. Likewise, software has eliminated massive swaths of data entry and customer service jobs (with software being a particularly poor replacement for the latter, but still being put into widespread use to cut costs). And contrary to beliefs that new jobs will be created in IT, software is able to massively eliminate low skilled tech jobs as well, as e.g. automated testing did to India's IT industry. As with existing jobs that have been automated away, companies won't need generalized AI to eliminate many more jobs. Many jobs don't rely on unconstrained complex deduction and thinking, and will be ripe for replacement with deep learning algorithms. And we can be reliably assured that corporations will engage in such replacements even when the outcomes are not up to par.

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eanzenberg 7y ago

Interesting that a few big examples of hype driven articles came from OpenAI. This is dangerous and will lead to additional misinformation and public backlash. Scientists should be unbiased and not market driven, but when OpenAI has these releases I shook my head along with a couple others. Hype lasts the next quarter but is replaced with distrust. Science is a long game of incremental discoveries. Breakthroughs usually are an understanding of some interesting outcome that needed more interpretation.

miketery 7y ago
I think this is what happens when you have various stakeholders and some are driven by KPIs such as reach / views. To that end those specific stakeholders (content writers / marketing) will bias towards sensationalism. That's not to excuse the organizational culture which allows for this behavior / outcome. There does need to be course correction.
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pizzaparty2 7y ago

I bet ai is going to become like coding where everyone thinks they're an expert.

system2 7y ago

It happened to VR a few years ago. The development slowed down when companies realized the sales. Unlike VR, AI is not a physical good that users/customers can purchase and test directly. It is just a buzzword. Companies will use it to death at every opportunity to market themselves. This is unstoppable. We will hear more about it, maybe until people get sick of hearing "another crappy ai product".

buboard 7y ago
AI is more of a datacenter technology. People already use it (siri,alexa,google search) and they apparently like it already. It was easy to see that any VR headset is bulky and agonizing, but those new services work fine. Sure, however the "not hot dog" apps are going to be disappointing

8bitsrule 7y ago

The buzzword back in the 70s-80s (after AI over-promised in the 1960s) was 'expert systems'. https://en.wikipedia.org/wiki/Expert_system https://en.wikipedia.org/wiki/Expert_system (To the extent that I have kept up with it) modern AI skips the 'knowledge base' part of ES, in favor of pattern-recognition based on 'training'. Today's (Indeterministic, trained, n-net) AI has clearly saved a lot of time/effort in creating 'knowledge bases'. I suspect it appeals more to singular fantasies about 'more human than human' intelligence. (Sorry Ray) Question is: Is today's AI even a magnitude-better than (deterministic) ES insofar as extensibility and verifiability? What if we had spent those decades refining the ES approach instead?

jefft255 7y ago
They're not even competing with each other. How would you compare an ES to a deep neural network? According to what benchmark? What do you mean by verifiable? Modern success stories with machine learning often deal with problems (NLP, vision, RL) that have little to do with the problems that were being solved by expert systems. Also, an hypothesis: if (big if) you somehow managed to have an expert system outperform deep learning for vision, I bet that it won't be any more verifiable than a deep neural net is today. To me the complaint that modern deep learning is unverifiable is a bit dumb, in the sense that any perception algorithm working with low level signals (vision, sound) will not be transparent to a human. 15 years ago, an image classification pipeline looked like: bag of SIFT features + SVM classifier. Try explaining the decision made by that algorithm in an intuitive way!
ilaksh 7y ago
I mean, you extend a DL system by giving it examples. Yes, in fact by harvesting example or annotation data you can extend models many magnitudes faster and more comprehensively than through manual analysis. Effectively the analysis is done automatically. At this point, what comes out it is usually poorly factored and entangled and not human-parseable, but that doesn't prevent it from being applied effectively to a range of narrow tasks that expert systems cannot approach. We do still rely on expert systems for things that we want to be carefully parsed, verified and analyzed by people though. Such as rules that oversee most self-driving cars based on perception handled by neural networks. However, not all self-driving systems lean as heavily on rules at the top level as others.

ilaksh 7y ago

OpenAI hype is definitely exaggerated. Seems like there might be a connection with Elon Musk somehow because all of his projects seem to get a massive amount of hype also. The radiology thing, is it really the case that there are no startups in that area with useful AI software? Seems like he overstated that. Part of this is a worldview difference. Many people truly believe that AGI is just around the corner, and that even before then, the narrow AI applications will significantly alter the world as they are deployed. And since no one can actually predict the future, it's an area where it's easy to have different worldviews. Personally I think that it's true that there is a lot of poor reporting and companies that overhype results, but it also seems like people like Gary Marcus are really not keeping up to date with the true capabilities of DL systems. If he was up to date, why would he be so pessimistic about applications like radiology? There seem to already be a lot of strong results.

unishark 7y ago
I think there are certainly issues with ML in radiology (i.e. lots of publications of low power studies or overfit models validated incorrectly), but I agree with you that Marcus was being unfair. It is principle a straightforward problem to solve with deep learning. The reason biggest reasons "no actual radiologists have been replaced" are probably just political/social. Hinton cannot be blamed for those hurdles.

tomxor 7y ago

> I see this as a version of the tragedy of the commons, in which (for example) many people overfish a particular set of waters [...] if and when the public, governments, and investment community recognize that they have been sold an unrealistic picture of AI’s strengths and weaknesses that doesn't match reality, a new AI winter may commence. I think this is not only inevitable, but necessary! This time around it has been a lot more useful, due in no small part to the advance in hardware since 1970s. Unfortunately this has caused many important people to believe far too much of the hype and not see it's current limitations. As a result they have started integrating it into important part of our societies - i find this alarming - not for the reason most people find it alarming i.e "because it's too smart", but because it's far far too dumb in combination with people assuming it's very smart. I think a lot of this problem stems from inappropriately anthropomorphising ML with terms like "AI" when we are no where near the stage that we need to have the philosophical debate about where something is sentient or "intelligent". The ML we are doing with NNs is still at the "tiny-chunk-of-very-specifically-engineered-piece-of-brain" stage. It's important people understand this before we start integrating what are essentially basic statistical mechanisms into our societies. For those in pursuit of better ML and things like real AI aka AGI, I also think having the hype blow away will do more good in the form of clarity and lack of noise than it will harm in lack of funding.

perl4ever 7y ago

When I saw this item, I thought not of misinformation produced about AI, but misinformation produced by AI. Some of the Google searches I have done recently turn up mostly automatically written articles that plug in numbers and facts but make no sense at all, because there is no real understanding. (Yes, this is probably not "real" AI, but it's the sort of thing people are and will be trying to apply AI to) It seems to me that there is no way to get to a serene, well-functioning society that utilizes intelligent machines, because in the process of developing software that can digest and write intelligently about topics humans are interested in, we will inevitably come up with less intelligent software first, that produces plausible misinformation far cheaper than humans. And economics will drive out, is already driving out, real research and journalism. Human society would be drowned by automatically generated misinformation by the time a machine is truly intelligent, whether that is in a couple years or a decade or longer. I'm increasingly thinking that the people who are worried about artificial intelligence ending the world as we know it have slightly missed the mark - superhuman intelligence is a singularity that we are approaching, but we are going to be ripped apart by the tides beforehand and maybe that will prevent the endpoint from ever happening.

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blazespin 7y ago

An AI winter will not happen. We are past the point of no return. The vast majority of research is net positive research. The only risk is misappropriation of investment, but that has already been smoothed over quickly. I’d get the complaint if AI was not doing anything useful as it wasn’t in ‘74, but it simply is. For example, plate recognition, drones and facial recognition. These are brutally efficient technologies. To imagine any government under investing in these is a total misunderstanding of the value that AI brings to military, security, and economic dominance. It’s a race between nations and the only danger is the computers taking over (not in a general AI way, but in a way that everything is run by algorithms no human can understand)

mbruce 7y ago
This is primarily deep learning which is not general purpose AI
- laichzeit0 7y ago
  We’re talking about AI here. And deep learning is AI. If you go to university and research or study AI that’s what you’d currently be learning. AGI does not currently exist and is a word used mostly by laymen and the media who don’t understand the current state of AI.
not_a_moth 7y ago
You sure? Without a doubt, there's an investment bubble for AI companies in industry: a huge amount of companies have added "AI" to their proposition, where the AI is only marginally useful if not useful at all, for the purpose of marketing and inflating valuation. On research side, it's now clear the current AI paradigm will not produce the kind of massive, society-shifting promises that were made to investors, which is the focus of the article. Yes deep learning is here to stay for narrow tasks, but the investment contraction for the rest could certainly feel like a Winter.
- marcosdumay 7y ago
  > it's now clear the current AI paradigm will not produce the kind of massive, society-shifting promises that were made to investors It's correct in that it won't produce the society-shifting promises. But there is a huge amount of money on non-society-shifting innovation. Besides, I really doubt the media predictions are what actually was told to investors.

KKKKkkkk1 7y ago

The term AI winter is IMHO misrepresenting the true state of affairs. Since day one, the field's modus operandi was one of overpromising and underdelivering. That has not changed. We see how Alphabet plows billions of dollars into DeepMind [1] and all they get in return is a series of game-playing bots. If unproductive activities are defunded, this creates an opportunity for productive ones to thrive. "Winter" is not a suitable word to describe this. [1] https://www.wired.com/story/deepminds-losses-future-artificial-intelligence/ https://www.wired.com/story/deepminds-losses-future-artifici...

andreyk 7y ago
This is a ridiculously reductive take on DeepMind... they produce a TON of great research, and have helped advance the state of RL considerably (speaking as an AI researcher here, having read many of their papers). Google should be commended for investing in largely basic AI research, even if once in a while they make a PR splash out of it, IMO.
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scottlegrand2 7y ago

The funny thing for me is that if The Economist had just hacked the input into GPT-2 so that it was an ongoing conversation they would have found that it's OK at holding a conversation, better than I expected when I did so. The conversation that I posted at: https://medium.com/@scottlegrand/my-interview-with-the-worlds-most-dangerous-ai-tm-473ebdde04b2 https://medium.com/@scottlegrand/my-interview-with-the-world... is performed in one shot. And I think it shows both the abilities and the limitations of GPT-2 and similar models. I am 100% role playing with the language model and prompting it to go in the directions it goes, but it surprised me several times, and eventually it all fell apart because I didn't perform any transfer learning on the model I just used the raw GPT-2 XL model to measure whether further work would be worth the effort and I would conclude yes it would be. The first thing I need to do is dramatically increase the length of its input context. It's pretty good at running with an ensuing script because I suspect much of its training data was formatted that way. But since I ran out of context symbols, it eventually suffers from several incidents of amnesia and eventually effective multiple personality disorder. It also contradicts itself several times but then no more than the typical thought leader or politician does IMO. What The Economist did was effectively erase the thing's memory between questions. So they were starting fresh with each question. And I think that's why they had to do the best of 5.

andreyk 7y ago

It's kind of funny just how much complaining about AI hype and bad coverage there is (on Twitter, and on this very subreddit), it feels like there is more of that now than actually bad coverage. This article suggests some actions to take at the very bottom, which amount to including some discussion of the limitations of the work in the paper -- not a bad idea, but then again the misleading coverage usually does not stem from claims in the papers themselves in the first place. Shameless but relevant plug warning, I run this effort Skynet Today with the aim to let AI researchers/experts present various advancements/topics to a general audience without hype and with context. Everyone on the team is volunteering their time for free and we can always use more people to help us tackle various subjects, so if you care about this issue feel free to take a look here: https://www.skynettoday.com/contribute https://www.skynettoday.com/contribute

g82918 7y ago
> It's kind of funny just how much complaining about AI hype and bad coverage there is (on Twitter, and on this very subreddit) I think your copy and paste script broke here. Please put some effort into promoting yourself.
YeGoblynQueenne 7y ago
Skynet Today is very good. I wish you had more original articles because the ones you have, I enjoy a lot (your editorials and briefs). I see you are calling for contributions, I hope you get some good ones. Writing good articles- there's no other way to combat bad coverage. Keep it up please and all the best.

euske 7y ago

The more I read articles like this (or pretty much ANY article recently), the more I'm convinced that what we really need is: transparency. I don't mean just AI, but transparency on everything: Transparency from researchers, news outlets, governments, retailers, and YouTubers. It's a great shame that a lot of IT has been used for bringing more confusion and obfuscation to the people, whereas it could be used to provide a more coherent/honest view of the world. We should demand more transparency on every matter.

Veedrac 7y ago

Sigh. > The Economist [...] said that GPT-2’s answers were “unedited”, when in reality each answer that was published was selected from five options > [Erik Bryjngjolffson] tweeted that the interview was “impressive” and that “the answers are more coherent than those of many humans.” In fact the apparent coherence of the interview stemmed from (a) the enormous corpus of human writing that the system drew from and (b) the filtering for coherence that was done by the human journalist. If your success rate is ≥20%, the coherence is coming from the model, not the selection process. This is just basic statistics. > OpenAI created a pair of neural networks that allowed a robot to learn to manipulate a custom-built Rubik's cube Jeez, I've already corrected you here... well, why not have to do it again? > publicized it with a somewhat misleading video and blog that led many to think that the system had learned the cognitive aspects of cube-solving The side not stated: OpenAI said explicitly in the blog that they used an unlearned algorithm for this, and sent a correction to a publisher that got this wrong. > the cube was instrumented with Bluetooth sensors During training, but they ended up with a fully vision-based system. > even in the best case only 20% of fully-scrambled cubes were solved No, 60% of fully scrambled cubes were solved. 20% of maximally difficult scrambles were solved. > one report claimed that “A neural net solves the three-body problem 100 million times faster” [...] but the network did no solving in the classical sense, it did approximation All solvers for this problem are approximators, and vice-versa. The article you complain about states the accuracy (“error of just 10^(-5)”) in the body of text. > and it approximated only a highly simplified two degree-of-freedom problem As reported: “Breen and co first simplify the problem by limiting it to those involving three equal-mass particles in a plane, each with zero velocity to start with.” > MIT AI lab famously assigned Gerald Sussman the problem of solving vision in a summer [https://dspace.mit.edu/handle/1721.1/6125 https://dspace.mit.edu/handle/1721.1/6125] I... sigh “The original document outlined a plan to do some kind of basic foreground/background segmentation, followed by a subgoal of analysing scenes with simple non-overlapping objects, with distinct uniform colour and texture and homogeneous backgrounds. A further subgoal was to extend the system to more complex objects. So it would seem that Computer Vision was never a summer project for a single student, nor did it aim to make a complete working vision system.” http://www.lyndonhill.com/opinion-cvlegends.html http://www.lyndonhill.com/opinion-cvlegends.html > Geoff Hinton [said] that the company (again The Guardian’s paraphrase), “is on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation.” Four years later, we are still a long way from machines that can hold natural conversations absent human intervention ‘Four years later’ to natural conversation is not a reasonable point of criticism when the only timeline given was ‘within a decade’ for a specified subset of the problem. > [In 2016 Hinton said] “We should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists.” [...] but thus far no actual radiologists have been replaced So Hinton actually said “People should stop training radiologists now. It’s just completely obvious that within five years, deep learning is going to do better than radiologists, because it's going to be able to get a lot more experience. It might be 10 years, but we've got plenty of radiologists already.” 2019 is not 2026. “thus far no actual radiologists have been replaced” is thus not a counterargument. > Andrew Ng, another well-known figure in deep learning, wrote that “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.” [...] Ng’s claim thus far has proven incorrect. I agree. This quote captures the wrong nuance of the issue. Well, finally finding one point by Gary Marcus that isn't misleading, I think I'm going to call this a day.