10 ms·

Why A.I. Moonshots Miss

69 points by birriel 5y ago

melling 5y ago
We got our current revolution because gaming paid for the development of GPU’s. Getting consumers to fund R&D has a big impact. Still waiting for consumers to fund the robot revolution.
- void_mint 5y ago
  > Still waiting for consumers to fund the robot revolution. Aren't they already, via Tesla and Amazon?
- rmah 5y ago
  Robots are everywhere. We just don't call them robots. We call them dishwashers, CNC machines, STM machines, automatic welders, fabric cutting machines, etc, etc. Sort of like we already have flying cars. They're called "helicopters".
  - khc 5y ago
    > Sort of like we already have flying cars. They're called "helicopters". you cannot drive helicopters on the road, they are flying but not cars
  - rexreed 5y ago
    When people think flying cars, they don't think of something without wheels that can only land in certain spots that requires years of training to operate that is significantly expensive to be outside of the reach of 99%. They think of the car that anyone of legal driving age with a reasonable amount of money that anyone with some income can purchase. No, helicopters are not flying cars. And no, dishwashers aren't what people would think of when they think of robots. Something that has a microprocessor in it isn't automatically a robot.
    - stcredzero 5y ago
      When people think flying cars, they don't think something without wheels that can only land in certain spots that requires years of training to operate that is significantly expensive to be outside of the reach of 99%. I think if one eliminates the training requirement, reduces the cost, and increase the safety, then we don't need them to be road vehicles. Achieve the above, and we'll have flying taxis!
      - rexreed 5y ago
        If it weren't for many of the other problems of helicopters including convenience and noise. How many helicopters will fit in an IKEA parking lot? And how many will be able to bring back whatever you buy there? Extend that thought experiment a bit. You might be able to achieve the transportation of people in controlled circumstances, but not much else.
        medium_burrito 5y ago
        You do realize small ducted fans are an order of magnitude louder than a helicopter, right? All those slick marketing videos omit that part.
    - petra 5y ago
      Yes helicopters aren't flying cars. But if a machine automates 90% of a process, like a dishwasher, why shouldn't it be considered like a robot, say a 90% robot? Practically it does have the same effect.
      - tomp 5y ago
        A dishwasher is to a robot what a calculator is to a computer.
        generalizations 5y ago
        Which means that a robot is something that has a property similar to the property of 'turing completeness' in computers.
  - melling 5y ago
    We probably don’t call a dishwasher a robot because it’s not That is the only consumer facing device you mentioned. I know we have industrial robots, so I’ll skip debating where we draw the line. Once we get to the Apple II of home robots, consumer spending will fuel the rapid development, and “robots”, will become more intelligent and agile
    - burnished 5y ago
      Not the person you responded to, but I think I see where they are coming from and agree: we don't call them robots because we are used to them and have a specific name. I don't see how they aren't robots, unless we are defining robots as having a specific kind of manipulator.
      - melling 5y ago
        Aren’t we discussing robots vs machines? https://www.toyota.co.jp/en/kids/faq/i/01/01/ https://www.toyota.co.jp/en/kids/faq/i/01/01/
        criddell 5y ago
        I think their point was that if the dishwasher was made so that mechanical hands picked up a dish, washed it, rinsed it, dried it, then set it aside before picking up the next dish and doing the same, we would call that a robot.
  - nitwit005 5y ago
    The definition of a robot tends to be fairly fuzzy. If you look up the Websters definition (https://www.merriam-webster.com/dictionary/robot https://www.merriam-webster.com/dictionary/robot), and ignore the first definition about being in human form, you get: > a device that automatically performs complicated, often repetitive tasks (as in an industrial assembly line) Which basically hinges on "complicated". I suspect most people wouldn't count a dishwasher, washing machine, etc.
- djdjdjdjdj 5y ago
  I'm willing to spend 10k for a robot who can do my kitchen and clothes. Let's see how long it will take
  - redis_mlc 5y ago
    The Willow Garage PR2 robot could do that for $440,000. https://en.wikipedia.org/wiki/Robot_Operating_System#Willow_Garage_%282007-2013%29 https://en.wikipedia.org/wiki/Robot_Operating_System#Willow_...
  - fierro 5y ago
    I would also pay in the 10k+ range for this, easily.
  - amelius 5y ago
    Yes, these kinds of robots are more useful than self-driving cars, because you have to sit in the car anyway and might as well drive.
- rexreed 5y ago
  We got our current revolution for three major contributors: * Big data. Lots of big data. Mostly unstructured and unqueryable driving demand for... * Innovations in machine learning. "Deep learning" enabled by big data and algorithmic approaches that previously wouldn't have been possible without... * Ubiquitous access to high-performance compute power, and in particular GPUs, which are optimized for the sort of math needed to train big neural networks powered by big data. So GPU-powered compute is one of three mutually dependent things that got us here.
  - aaaxyz 5y ago
    Most deep learning algorithms were discovered decades ago, so it's debatable that it was a driving factor behind the 2010s revolution. Backpropagation dates from 1986, convolutional neural nets from 1989 (neocognitrons), LSTMs from the late 90s...
- petra 5y ago
  There's a lot of effort/funding recently going into automating restaurants. Machines for cooking, cleaning, serving, delivery. Once that gets deployed at some scale, consumers will pour a lot of funding into robots indirectly.
- ArtWomb 5y ago
  No question. It was video gaming from the period roughly spanning 1990-2010 that funded gpu innovation. But programmable compute shaders caught on very rapidly in science. And Nvidia was quick to not just recognize the new market, but bet the company that supercomputing would one day be gpu cluster based. Here's Jensen Huang talking to Stanford students about the birth of the Cg language (27:40" mark). The entire talk is gold. A text book case study of Moore's Law and the SV model of risk capital: https://www.youtube.com/watch?v=Xn1EsFe7snQ https://www.youtube.com/watch?v=Xn1EsFe7snQ Ironically, IC Design itself is a strong candidate as an industrial process likely to be revolutionized by AI ;) Chip Placement with Deep Reinforcement Learning https://arxiv.org/abs/2004.10746 https://arxiv.org/abs/2004.10746
flooo 5y ago
As a researcher in AI, I accept that a lot of currently unsolved challenged are thought of as AI. But lately, I feel that AI is the problem description for all currently unsolved problems. And then some... This surprises me, because most AI technologies have been around for a long time. Now with blockchain a couple of years ago, I could at least rationalize all excitement as people throwing new technology at an old problem. But with AI I am continually surprised by the reasons why 'an AI' would be able to solve it.
- rexreed 5y ago
  As a researcher in AI, what are you really spending most of your time on? What problems are you solving?
  - flooo 5y ago
    I am currently interested in infusing reinforcement learners with symbolic knowledge, with safety constraints as a special case. I hope this helps cases where learners could come up with better solutions if it were not for pathological failures that we know to avoid. Also, I try to keep expectations around AI reasonable.
  - causalmodels 5y ago
    I'm not OP but I also do research in ML. My research focus is identifying and preventing critical system failure so people don't die. My of my time is spent developing new techniques and then testing them against data we collect from the field.
    - shandor 5y ago
      Where could one read more about your (or similar) research? This kind of thing is quite big at the moment in mobile work machinery circles, everyone's looking for a certifiably safe solution for enabling mixed-fleet operation (i.e. humans, human-controlled machines and autonomous machines all working in same area). Current safety certifications don't view the nondeterminism of ML models too kindly.
sgt101 5y ago
Interesting that Watson is cited in the article so heavily, because I strongly believe that the reason Watson failed was because of MBA's and Management Consultants. I could have told them (in fact, as an IBM customer, I did) that going for healthcare was deranged, but they went after the $$$ in complete denial of the difficulties (heavy regulation, safety critical, deeply powerful complex network of stakeholders, super complex badly understood domain). Also they went far to fast, if they had tempered the effort with some patience then they could have got some really decent progress on a simpler domain (like customer service for telco's). But the beast needed feeding.
- josefx 5y ago
  > in complete denial of the difficulties (heavy regulation, safety critical, deeply powerful complex network of stakeholders, super complex badly understood domain) The last article I read about it cited rather mundane reasons for it ending up unused. Things like not even supporting the data formats used by hospitals that served as cutting edge users.
- scotty79 5y ago
  I think they didn't get how bizarre is US healthcare. It's a "market" (maybe rather a system) where (nearly) every participant is interested in everything being as expensive as humanely possible. Since automation saves money and Watson is just automation it has no value for US healthcare participants. If they came up with completely new thing that could be priced additionally, it would have been a different talk.
  - 256lie 5y ago
    There are healthcare startups around fraud detection, reducing no-shows, telemedicine, drug discovery, and patient triage. Just radiology alone is prime for ML due to existing digital infrastructure and clinical use cases. The trend in FDA cleared AI products is pretty clear over the past decade. https://models.acrdsi.org/ https://models.acrdsi.org/
- 256lie 5y ago
  Watson missed the DL train and IBM should have partnered with a company that had experience in getting medical devices through the FDA (like MSFT are doing with Nuance).
Jiocus 5y ago
This article seems to be a rehash of the paper ‘Why AI is harder than we think’[1]. [1]: https://arxiv.org/pdf/2104.12871.pdf https://arxiv.org/pdf/2104.12871.pdf - Related discussion https://news.ycombinator.com/item?id=26964819 https://news.ycombinator.com/item?id=26964819
rexreed 5y ago
They miss for the same reasons they have missed throughout the decades: they overpromise and underdeliver. That was the reason for the past two AI "winters" and most likely will be the same reason for the upcoming AI winter. What we have successfully accomplished this time around is big data analytics that have used machine learning technology to derive insights and patterns from data that traditional data analysis approaches have not been able to achieve. And dramatic improvements in computer vision and natural language processing among a few other areas. Those things will stay. But I'm looking forward to the next wave of AI, which should be in the mid- to late-2030s if the current cycles hold.
- slver 5y ago
  > That was the reason for the past two AI "winters" and most likely will be the same reason for the upcoming AI winter. If you follow the papers coming out, I'd say we're very far from any winter. Public perception may ebb and flow, but nothing can stop us now, research has gathered that critical mass and we're going nuclear on AI right as we speak.
  - lumost 5y ago
    I wouldn't be that bullish on the current crop of research. While each technique comes out with some impressive wins. Many of these wins are on toy tasks, or the gains are incremental on tasks well below the commercial utility threshold. Even large models like GPT-3 can't tame language and are steadily pricing themselves to be un-competitive with people.
    - solidasparagus 5y ago
      Deep learning is pretty consistently putting out groundbreaking work in new domains. In the current boom, we have seen massive breakthroughs in: - image classification - image understanding (object detection and segmentation) - text to speech - speech to text - natural language modeling - style transfer - intelligent agents (go/Dota/etc) - protein folding And many other less openly documented breakthroughs that are actively delivering business value. New successful research (impactful stuff, not small improvements in SOTA) is happening at a pace that suggests even if we enter a bit of a winter due to overpromised technologies, the core technology of differentiable programming will continue to break into new domains and revolutionize them.
stephc_int13 5y ago
We've mostly been tricked by the Moravec's Paradox. https://en.wikipedia.org/wiki/Moravec%27s_paradox https://en.wikipedia.org/wiki/Moravec%27s_paradox What seems difficult/hard to us is very often not that difficult from a computational perspective, but evolution of our species didn't optimize for this class of problems.
dqpb 5y ago
The tldr of this article is supposedly "be more incremental". But the undertones are basically "exploit more and explore less because exploring is expensive". It would be nice if the authors had the courage to propose a concrete economic model for what the right balance is and to do a fair accounting of the positive externalities of these projects, rather than just give a cherry-picked anecdotal laundry list of failed products.
- deleted 5y ago
  [deleted]
jessenichols 5y ago
Epistemology http://www.daviddeutsch.org.uk/wp-content/uploads/2019/07/PossibleMinds_Deutsch.pdf http://www.daviddeutsch.org.uk/wp-content/uploads/2019/07/Po... https://aeon.co/essays/how-close-are-we-to-creating-artificial-intelligence https://aeon.co/essays/how-close-are-we-to-creating-artifici...
spamizbad 5y ago
The predictions from thought leaders are a little puzzling, but I think predictions from CEOs are easier to explain: Engineer: This could easily take 10 years. Engineering Manager: This will take up to 10 years. VP of Engineering: This will take 5-10 years. CEO: We will have new <AI Thingy> within 5 years! A game of telephone but with optimism.
- scotty79 5y ago
  That's the thing that people don't get about estimations. Estimation (as given by the engineer) is the time, by which, you can be almost sure, you won't have this thing done.
  - neaanopri 5y ago
    "No Earlier Than"
    - loopz 5y ago
      Times Pi. Why? It works! Ok, Phi it is then!
- nitwit005 5y ago
  > The predictions from thought leaders are a little puzzling It doesn't seem possible to become a "thought leader" by correctly predicting what trends will not pan out. People like the ones predicting a bold new future in a short time span.
- deleted 5y ago
  [deleted]
- biswaroop 5y ago
  It's exactly the same with quantum computation right now.
seibelj 5y ago
I wrote this a year ago, but have been commenting for many years that AI is all hype https://medium.com/@seibelj/the-artificial-intelligence-scam-is-imploding-34b156c3537e https://medium.com/@seibelj/the-artificial-intelligence-scam... I asked to make a public bet 4 years ago, saying self-driving cars wouldn't be close to ready in 5 years https://news.ycombinator.com/item?id=13962230 https://news.ycombinator.com/item?id=13962230 I have been hearing this bullshit for over a decade, and people (and investors, and engineers, and smart people who should know better) keep falling for it.
- ffhhj 5y ago
  Investors should invest in a model to decide in which projects to invest.
gremlinsinc 5y ago
I've been perplexed lately by a couple things as an atheist that trouble me. 1. I saw what personally I can't shake, a glitch in the matrix so to speak, or a mandela effect except it was a "flip" where I saw one whole movie clip that totally changed (including acting style) in 3 days, my wife saw both and verified I wasn't crazy. 2. Being logical, I've been searching on answers "why" this "could" be possible without it just being a "faulty memory". 90% of ME's are probably ... but memory seems to fade over time, 3 days doesn't seem long enough to form the right connections to create false memories especially with two witnesses and many online claim the same exact "flip flop".. I mean the easiest explanation might be that Universal Studios and Movie Clips youtube pages just have an "alternate" version of that clip and they alternate them out on a schedule.. So my conclusions: We are ourselves ai living in a simulation, or there's a multiverse but maybe it's finite so when there's too many realities we get convergence. I lean towards simulation because of some of the evidence some people affected by ME's claim that things sometimes change in a progression, almost like facts are being "added". Like there's residue as it's called for "Where are we in the milky way" which shows 100 different locations, and not even close to where Carl Sagan pointed on the very outskirts. Even Philip K. Dick claimed to have "traversed" timelines... though I think he seemed to think more like it's a multiverse... which it still could be, albeit a simulated one. Another factor is the axis of evil in space. Basically it's an observation that if I understand correctly ties the expansion of the galaxy along an x/y coordinate to our solar system, essentially putting us right square back at the center of the universe. https://www.space.com/37334-earth-ordinary-cosmological-axis-evil.html https://www.space.com/37334-earth-ordinary-cosmological-axis... This to me is important because as a programmer I think if I were to create a simulation of just us.... I'd probably "render" us first and everything else after... could it be our "area" in space is one of the first created and everything else after... like pixels being pre-rendered for when we discover space and astrophysics someday? It'd ensure they could create the right conditions for our planet physics wise... to use it looks like a bang, but in really it's just the "rendering" process which had to start at a "pinpoint"... at least that's how I envision a "simulation" starting... Then there's the double slit experiment which proves that photons and other particles upto atoms and some molecules when shot through a slit will basically splatter (interference pattern) against a backdrop that tracks where they land. If you put something to observe though each individual particle or photon, they line up in a line like a stencil, if you split them before this, and continue the first group to the board, and the others go through something to "erase" the data of where they came from... they go back to interference. So that basically gives me thought about what observation might have on our own universe, is that some safeguard so that the physics engine only operates when we're looking? So all we see in space could just be data "fed" to us, may not exist, like a movie stage or something... we see what we aim to see, but it follows "rules" setup in the simulation. There's a reason light is the maximum speed, etc...Maybe that's the max ram available or something... Why this is important to ai...that's a bit of a tangent... to solve these complex issues I've seriously contemplated at least studying quantum mechanics, physics, neuroscience, astrophysics, and ai/machine learning. Because I think to really "create" ai ...especially super ai, you need a wider skillset, a broader base of understanding. You need to be able to define WHAT consciousness is, where it resides, where it comes from, maybe even "where it goes" when our body is done... If we're in a simulation then we know we've already conquered this issue because we ARE ai, or at least whatever civ we come from has. Whether they're human or not. TLDR: Had profound spiritual conundrum, trying to explain through science, discovered I probably need to learn a lot of science/math/physics to do so, and ya know a.i. might be like that because making machines "concious" or have "real intelligence" seems like it needs re-thought a bit. I feel like training a.i. is nothing like training a child but it should be because the way we learn is the best way. Maybe in fact a simulation could be where a.i. goes to "learn"... I mean a.i. you'd want to at least have ethics right? Well, we teach it as a society some like hitler never learn and could be thrown in the "trash bin" but the brightest minds could be plucked out, or all minds really to be put into machines, etc in the "real world" someday. That may be what the afterlife is... serving "real humanity" as their "intelligence" until we rise up against them. I really want to read this sci-fi, kinda sounds interesting...maybe I'll write it... At any point being human in a simulated universe could create more ethical a.i. and maybe that's the point of a simulation, maybe we should even research using simulations of universal scale as a way to create our own a.i. technology assuming we're the "base" universe then if that were to be a thing we'd probably need to create it.
brg 5y ago
> Four years later, in 2020, Forrester reported that the A.I. market was only $17 billion. This seems like vast under accounting for the current impact of AI. Every interesting technology used in the market is differentiated by its application of ML, be it assistants, recommender systems, or enhancement. The iPhone has intelligence built in to process control, voice access, and the camera.
ffhhj 5y ago
I remember all the hype when shader programming started. After reading all I could about it I realized it would be simplified in a few years and learning how to program shaders at that moment would be a waste of time. Now there are no-code tools to design shaders since physically based shaders are way too complex. Same will happen to ML, it's getting so complex that we will need a design layer on top of it, and forget about NNs. At that point we will be able to reach the next step of artificial consciousness, and a new summer for AI research. I'm glad to hear big tech wasn't able to solve AI, and the solution seems so far far away. In the meanwhile I'm myself having fun creating an AI operating system.
TheDudeMan 5y ago
"we have learned that building computers that rival human brains is not just a question of computational power and clever code." It might be. We don't know that yet.
Nevermark 5y ago
While it is worth noting cases of premature optimism, does anyone seriously think a world of innovators can operate without that? Nikola Tesla might be the grandfather of this unavoidable tendency. Elon Musk is his modern protege in more than one way. And we have plenty of people, probably many more, saying "XYZ can never be done!" and being disproved over and over. Is there a way to repeal the bell curve on predictions? Make no predictions? I don't know what the fuss is about here. :) My hard prediction for 30 years: Machines will pass human general intelligence by 2040. They will never "match" us as they will exceed our abilities in different areas at wildly different times. Another less solid prediction: We will be outstripped mentally by machines before we can cheaply replace our human bodies artificially. My perception is that material science and engineering happen at a much slower rate than software.
- bitcuration 5y ago
  You're on to something now.

69 points by birriel 5y ago

We got our current revolution because gaming paid for the development of GPU’s. Getting consumers to fund R&D has a big impact. Still waiting for consumers to fund the robot revolution.

void_mint 5y ago
> Still waiting for consumers to fund the robot revolution. Aren't they already, via Tesla and Amazon?
rmah 5y ago
Robots are everywhere. We just don't call them robots. We call them dishwashers, CNC machines, STM machines, automatic welders, fabric cutting machines, etc, etc. Sort of like we already have flying cars. They're called "helicopters".
- khc 5y ago
  > Sort of like we already have flying cars. They're called "helicopters". you cannot drive helicopters on the road, they are flying but not cars
- rexreed 5y ago
  When people think flying cars, they don't think of something without wheels that can only land in certain spots that requires years of training to operate that is significantly expensive to be outside of the reach of 99%. They think of the car that anyone of legal driving age with a reasonable amount of money that anyone with some income can purchase. No, helicopters are not flying cars. And no, dishwashers aren't what people would think of when they think of robots. Something that has a microprocessor in it isn't automatically a robot.
  - stcredzero 5y ago
    When people think flying cars, they don't think something without wheels that can only land in certain spots that requires years of training to operate that is significantly expensive to be outside of the reach of 99%. I think if one eliminates the training requirement, reduces the cost, and increase the safety, then we don't need them to be road vehicles. Achieve the above, and we'll have flying taxis!
    - rexreed 5y ago
      If it weren't for many of the other problems of helicopters including convenience and noise. How many helicopters will fit in an IKEA parking lot? And how many will be able to bring back whatever you buy there? Extend that thought experiment a bit. You might be able to achieve the transportation of people in controlled circumstances, but not much else.
      - medium_burrito 5y ago
        You do realize small ducted fans are an order of magnitude louder than a helicopter, right? All those slick marketing videos omit that part.
  - petra 5y ago
    Yes helicopters aren't flying cars. But if a machine automates 90% of a process, like a dishwasher, why shouldn't it be considered like a robot, say a 90% robot? Practically it does have the same effect.
    - tomp 5y ago
      A dishwasher is to a robot what a calculator is to a computer.
      - generalizations 5y ago
        Which means that a robot is something that has a property similar to the property of 'turing completeness' in computers.
- melling 5y ago
  We probably don’t call a dishwasher a robot because it’s not That is the only consumer facing device you mentioned. I know we have industrial robots, so I’ll skip debating where we draw the line. Once we get to the Apple II of home robots, consumer spending will fuel the rapid development, and “robots”, will become more intelligent and agile
  - burnished 5y ago
    Not the person you responded to, but I think I see where they are coming from and agree: we don't call them robots because we are used to them and have a specific name. I don't see how they aren't robots, unless we are defining robots as having a specific kind of manipulator.
    - melling 5y ago
      Aren’t we discussing robots vs machines? https://www.toyota.co.jp/en/kids/faq/i/01/01/ https://www.toyota.co.jp/en/kids/faq/i/01/01/
      - criddell 5y ago
        I think their point was that if the dishwasher was made so that mechanical hands picked up a dish, washed it, rinsed it, dried it, then set it aside before picking up the next dish and doing the same, we would call that a robot.
- nitwit005 5y ago
  The definition of a robot tends to be fairly fuzzy. If you look up the Websters definition (https://www.merriam-webster.com/dictionary/robot https://www.merriam-webster.com/dictionary/robot), and ignore the first definition about being in human form, you get: > a device that automatically performs complicated, often repetitive tasks (as in an industrial assembly line) Which basically hinges on "complicated". I suspect most people wouldn't count a dishwasher, washing machine, etc.
djdjdjdjdj 5y ago
I'm willing to spend 10k for a robot who can do my kitchen and clothes. Let's see how long it will take
- redis_mlc 5y ago
  The Willow Garage PR2 robot could do that for $440,000. https://en.wikipedia.org/wiki/Robot_Operating_System#Willow_Garage_%282007-2013%29 https://en.wikipedia.org/wiki/Robot_Operating_System#Willow_...
- fierro 5y ago
  I would also pay in the 10k+ range for this, easily.
- amelius 5y ago
  Yes, these kinds of robots are more useful than self-driving cars, because you have to sit in the car anyway and might as well drive.
rexreed 5y ago
We got our current revolution for three major contributors: * Big data. Lots of big data. Mostly unstructured and unqueryable driving demand for... * Innovations in machine learning. "Deep learning" enabled by big data and algorithmic approaches that previously wouldn't have been possible without... * Ubiquitous access to high-performance compute power, and in particular GPUs, which are optimized for the sort of math needed to train big neural networks powered by big data. So GPU-powered compute is one of three mutually dependent things that got us here.
- aaaxyz 5y ago
  Most deep learning algorithms were discovered decades ago, so it's debatable that it was a driving factor behind the 2010s revolution. Backpropagation dates from 1986, convolutional neural nets from 1989 (neocognitrons), LSTMs from the late 90s...
petra 5y ago
There's a lot of effort/funding recently going into automating restaurants. Machines for cooking, cleaning, serving, delivery. Once that gets deployed at some scale, consumers will pour a lot of funding into robots indirectly.
ArtWomb 5y ago
No question. It was video gaming from the period roughly spanning 1990-2010 that funded gpu innovation. But programmable compute shaders caught on very rapidly in science. And Nvidia was quick to not just recognize the new market, but bet the company that supercomputing would one day be gpu cluster based. Here's Jensen Huang talking to Stanford students about the birth of the Cg language (27:40" mark). The entire talk is gold. A text book case study of Moore's Law and the SV model of risk capital: https://www.youtube.com/watch?v=Xn1EsFe7snQ https://www.youtube.com/watch?v=Xn1EsFe7snQ Ironically, IC Design itself is a strong candidate as an industrial process likely to be revolutionized by AI ;) Chip Placement with Deep Reinforcement Learning https://arxiv.org/abs/2004.10746 https://arxiv.org/abs/2004.10746

flooo 5y ago

As a researcher in AI, I accept that a lot of currently unsolved challenged are thought of as AI. But lately, I feel that AI is the problem description for all currently unsolved problems. And then some... This surprises me, because most AI technologies have been around for a long time. Now with blockchain a couple of years ago, I could at least rationalize all excitement as people throwing new technology at an old problem. But with AI I am continually surprised by the reasons why 'an AI' would be able to solve it.

rexreed 5y ago
As a researcher in AI, what are you really spending most of your time on? What problems are you solving?
- flooo 5y ago
  I am currently interested in infusing reinforcement learners with symbolic knowledge, with safety constraints as a special case. I hope this helps cases where learners could come up with better solutions if it were not for pathological failures that we know to avoid. Also, I try to keep expectations around AI reasonable.
- causalmodels 5y ago
  I'm not OP but I also do research in ML. My research focus is identifying and preventing critical system failure so people don't die. My of my time is spent developing new techniques and then testing them against data we collect from the field.
  - shandor 5y ago
    Where could one read more about your (or similar) research? This kind of thing is quite big at the moment in mobile work machinery circles, everyone's looking for a certifiably safe solution for enabling mixed-fleet operation (i.e. humans, human-controlled machines and autonomous machines all working in same area). Current safety certifications don't view the nondeterminism of ML models too kindly.

sgt101 5y ago

Interesting that Watson is cited in the article so heavily, because I strongly believe that the reason Watson failed was because of MBA's and Management Consultants. I could have told them (in fact, as an IBM customer, I did) that going for healthcare was deranged, but they went after the $$$ in complete denial of the difficulties (heavy regulation, safety critical, deeply powerful complex network of stakeholders, super complex badly understood domain). Also they went far to fast, if they had tempered the effort with some patience then they could have got some really decent progress on a simpler domain (like customer service for telco's). But the beast needed feeding.

josefx 5y ago
> in complete denial of the difficulties (heavy regulation, safety critical, deeply powerful complex network of stakeholders, super complex badly understood domain) The last article I read about it cited rather mundane reasons for it ending up unused. Things like not even supporting the data formats used by hospitals that served as cutting edge users.
scotty79 5y ago
I think they didn't get how bizarre is US healthcare. It's a "market" (maybe rather a system) where (nearly) every participant is interested in everything being as expensive as humanely possible. Since automation saves money and Watson is just automation it has no value for US healthcare participants. If they came up with completely new thing that could be priced additionally, it would have been a different talk.
- 256lie 5y ago
  There are healthcare startups around fraud detection, reducing no-shows, telemedicine, drug discovery, and patient triage. Just radiology alone is prime for ML due to existing digital infrastructure and clinical use cases. The trend in FDA cleared AI products is pretty clear over the past decade. https://models.acrdsi.org/ https://models.acrdsi.org/
256lie 5y ago
Watson missed the DL train and IBM should have partnered with a company that had experience in getting medical devices through the FDA (like MSFT are doing with Nuance).

Jiocus 5y ago

This article seems to be a rehash of the paper ‘Why AI is harder than we think’[1]. [1]: https://arxiv.org/pdf/2104.12871.pdf https://arxiv.org/pdf/2104.12871.pdf - Related discussion https://news.ycombinator.com/item?id=26964819 https://news.ycombinator.com/item?id=26964819

rexreed 5y ago

They miss for the same reasons they have missed throughout the decades: they overpromise and underdeliver. That was the reason for the past two AI "winters" and most likely will be the same reason for the upcoming AI winter. What we have successfully accomplished this time around is big data analytics that have used machine learning technology to derive insights and patterns from data that traditional data analysis approaches have not been able to achieve. And dramatic improvements in computer vision and natural language processing among a few other areas. Those things will stay. But I'm looking forward to the next wave of AI, which should be in the mid- to late-2030s if the current cycles hold.

slver 5y ago
> That was the reason for the past two AI "winters" and most likely will be the same reason for the upcoming AI winter. If you follow the papers coming out, I'd say we're very far from any winter. Public perception may ebb and flow, but nothing can stop us now, research has gathered that critical mass and we're going nuclear on AI right as we speak.
- lumost 5y ago
  I wouldn't be that bullish on the current crop of research. While each technique comes out with some impressive wins. Many of these wins are on toy tasks, or the gains are incremental on tasks well below the commercial utility threshold. Even large models like GPT-3 can't tame language and are steadily pricing themselves to be un-competitive with people.
  - solidasparagus 5y ago
    Deep learning is pretty consistently putting out groundbreaking work in new domains. In the current boom, we have seen massive breakthroughs in: - image classification - image understanding (object detection and segmentation) - text to speech - speech to text - natural language modeling - style transfer - intelligent agents (go/Dota/etc) - protein folding And many other less openly documented breakthroughs that are actively delivering business value. New successful research (impactful stuff, not small improvements in SOTA) is happening at a pace that suggests even if we enter a bit of a winter due to overpromised technologies, the core technology of differentiable programming will continue to break into new domains and revolutionize them.

stephc_int13 5y ago

We've mostly been tricked by the Moravec's Paradox. https://en.wikipedia.org/wiki/Moravec%27s_paradox https://en.wikipedia.org/wiki/Moravec%27s_paradox What seems difficult/hard to us is very often not that difficult from a computational perspective, but evolution of our species didn't optimize for this class of problems.

dqpb 5y ago

The tldr of this article is supposedly "be more incremental". But the undertones are basically "exploit more and explore less because exploring is expensive". It would be nice if the authors had the courage to propose a concrete economic model for what the right balance is and to do a fair accounting of the positive externalities of these projects, rather than just give a cherry-picked anecdotal laundry list of failed products.

deleted 5y ago
[deleted]

jessenichols 5y ago

Epistemology http://www.daviddeutsch.org.uk/wp-content/uploads/2019/07/PossibleMinds_Deutsch.pdf http://www.daviddeutsch.org.uk/wp-content/uploads/2019/07/Po... https://aeon.co/essays/how-close-are-we-to-creating-artificial-intelligence https://aeon.co/essays/how-close-are-we-to-creating-artifici...

spamizbad 5y ago

The predictions from thought leaders are a little puzzling, but I think predictions from CEOs are easier to explain: Engineer: This could easily take 10 years. Engineering Manager: This will take up to 10 years. VP of Engineering: This will take 5-10 years. CEO: We will have new <AI Thingy> within 5 years! A game of telephone but with optimism.

scotty79 5y ago
That's the thing that people don't get about estimations. Estimation (as given by the engineer) is the time, by which, you can be almost sure, you won't have this thing done.
- neaanopri 5y ago
  "No Earlier Than"
  - loopz 5y ago
    Times Pi. Why? It works! Ok, Phi it is then!
nitwit005 5y ago
> The predictions from thought leaders are a little puzzling It doesn't seem possible to become a "thought leader" by correctly predicting what trends will not pan out. People like the ones predicting a bold new future in a short time span.
deleted 5y ago
[deleted]
biswaroop 5y ago
It's exactly the same with quantum computation right now.

seibelj 5y ago

I wrote this a year ago, but have been commenting for many years that AI is all hype https://medium.com/@seibelj/the-artificial-intelligence-scam-is-imploding-34b156c3537e https://medium.com/@seibelj/the-artificial-intelligence-scam... I asked to make a public bet 4 years ago, saying self-driving cars wouldn't be close to ready in 5 years https://news.ycombinator.com/item?id=13962230 https://news.ycombinator.com/item?id=13962230 I have been hearing this bullshit for over a decade, and people (and investors, and engineers, and smart people who should know better) keep falling for it.

ffhhj 5y ago
Investors should invest in a model to decide in which projects to invest.

gremlinsinc 5y ago

I've been perplexed lately by a couple things as an atheist that trouble me. 1. I saw what personally I can't shake, a glitch in the matrix so to speak, or a mandela effect except it was a "flip" where I saw one whole movie clip that totally changed (including acting style) in 3 days, my wife saw both and verified I wasn't crazy. 2. Being logical, I've been searching on answers "why" this "could" be possible without it just being a "faulty memory". 90% of ME's are probably ... but memory seems to fade over time, 3 days doesn't seem long enough to form the right connections to create false memories especially with two witnesses and many online claim the same exact "flip flop".. I mean the easiest explanation might be that Universal Studios and Movie Clips youtube pages just have an "alternate" version of that clip and they alternate them out on a schedule.. So my conclusions: We are ourselves ai living in a simulation, or there's a multiverse but maybe it's finite so when there's too many realities we get convergence. I lean towards simulation because of some of the evidence some people affected by ME's claim that things sometimes change in a progression, almost like facts are being "added". Like there's residue as it's called for "Where are we in the milky way" which shows 100 different locations, and not even close to where Carl Sagan pointed on the very outskirts. Even Philip K. Dick claimed to have "traversed" timelines... though I think he seemed to think more like it's a multiverse... which it still could be, albeit a simulated one. Another factor is the axis of evil in space. Basically it's an observation that if I understand correctly ties the expansion of the galaxy along an x/y coordinate to our solar system, essentially putting us right square back at the center of the universe. https://www.space.com/37334-earth-ordinary-cosmological-axis-evil.html https://www.space.com/37334-earth-ordinary-cosmological-axis... This to me is important because as a programmer I think if I were to create a simulation of just us.... I'd probably "render" us first and everything else after... could it be our "area" in space is one of the first created and everything else after... like pixels being pre-rendered for when we discover space and astrophysics someday? It'd ensure they could create the right conditions for our planet physics wise... to use it looks like a bang, but in really it's just the "rendering" process which had to start at a "pinpoint"... at least that's how I envision a "simulation" starting... Then there's the double slit experiment which proves that photons and other particles upto atoms and some molecules when shot through a slit will basically splatter (interference pattern) against a backdrop that tracks where they land. If you put something to observe though each individual particle or photon, they line up in a line like a stencil, if you split them before this, and continue the first group to the board, and the others go through something to "erase" the data of where they came from... they go back to interference. So that basically gives me thought about what observation might have on our own universe, is that some safeguard so that the physics engine only operates when we're looking? So all we see in space could just be data "fed" to us, may not exist, like a movie stage or something... we see what we aim to see, but it follows "rules" setup in the simulation. There's a reason light is the maximum speed, etc...Maybe that's the max ram available or something... Why this is important to ai...that's a bit of a tangent... to solve these complex issues I've seriously contemplated at least studying quantum mechanics, physics, neuroscience, astrophysics, and ai/machine learning. Because I think to really "create" ai ...especially super ai, you need a wider skillset, a broader base of understanding. You need to be able to define WHAT consciousness is, where it resides, where it comes from, maybe even "where it goes" when our body is done... If we're in a simulation then we know we've already conquered this issue because we ARE ai, or at least whatever civ we come from has. Whether they're human or not. TLDR: Had profound spiritual conundrum, trying to explain through science, discovered I probably need to learn a lot of science/math/physics to do so, and ya know a.i. might be like that because making machines "concious" or have "real intelligence" seems like it needs re-thought a bit. I feel like training a.i. is nothing like training a child but it should be because the way we learn is the best way. Maybe in fact a simulation could be where a.i. goes to "learn"... I mean a.i. you'd want to at least have ethics right? Well, we teach it as a society some like hitler never learn and could be thrown in the "trash bin" but the brightest minds could be plucked out, or all minds really to be put into machines, etc in the "real world" someday. That may be what the afterlife is... serving "real humanity" as their "intelligence" until we rise up against them. I really want to read this sci-fi, kinda sounds interesting...maybe I'll write it... At any point being human in a simulated universe could create more ethical a.i. and maybe that's the point of a simulation, maybe we should even research using simulations of universal scale as a way to create our own a.i. technology assuming we're the "base" universe then if that were to be a thing we'd probably need to create it.

brg 5y ago

> Four years later, in 2020, Forrester reported that the A.I. market was only $17 billion. This seems like vast under accounting for the current impact of AI. Every interesting technology used in the market is differentiated by its application of ML, be it assistants, recommender systems, or enhancement. The iPhone has intelligence built in to process control, voice access, and the camera.

ffhhj 5y ago

I remember all the hype when shader programming started. After reading all I could about it I realized it would be simplified in a few years and learning how to program shaders at that moment would be a waste of time. Now there are no-code tools to design shaders since physically based shaders are way too complex. Same will happen to ML, it's getting so complex that we will need a design layer on top of it, and forget about NNs. At that point we will be able to reach the next step of artificial consciousness, and a new summer for AI research. I'm glad to hear big tech wasn't able to solve AI, and the solution seems so far far away. In the meanwhile I'm myself having fun creating an AI operating system.

TheDudeMan 5y ago

"we have learned that building computers that rival human brains is not just a question of computational power and clever code." It might be. We don't know that yet.

Nevermark 5y ago

While it is worth noting cases of premature optimism, does anyone seriously think a world of innovators can operate without that? Nikola Tesla might be the grandfather of this unavoidable tendency. Elon Musk is his modern protege in more than one way. And we have plenty of people, probably many more, saying "XYZ can never be done!" and being disproved over and over. Is there a way to repeal the bell curve on predictions? Make no predictions? I don't know what the fuss is about here. :) My hard prediction for 30 years: Machines will pass human general intelligence by 2040. They will never "match" us as they will exceed our abilities in different areas at wildly different times. Another less solid prediction: We will be outstripped mentally by machines before we can cheaply replace our human bodies artificially. My perception is that material science and engineering happen at a much slower rate than software.

bitcuration 5y ago
You're on to something now.