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The BS-Industrial Complex of Phony A.I.

539 points by scottlocklin 7y ago

xiaolingxiao 7y ago
I can attest. while doing research in a T1 university, all the professors were mildly disgusted by the hype pushed out by startups, and even Google's own internal marketing department. Nonetheless they too are minting the same nonsense in the "introduction" part of academic research, it's a clear case of everyone is playing the game, so "I have to play or be left behind."
- ImaCake 7y ago
  I used to work on fundamental molecular microbiology. We looked at what happened when DNA replication went wrong in E. coli. What I used to do when writing or speaking about it was to start with cancer or antibiotic resistance as if anyone in my field gave a crap about either of those topics. Sure, we do care about those things in the broad sense, but we didn't consider ourselves to be on the front line of solving either of those problems.
  - deleted 7y ago
    [deleted]
antonvs 7y ago
Paywall.
- 3xblah 7y ago
  Not when you have Javascript turned off.
- gumby 7y ago
  Just delete your medium cookies.
kranner 7y ago
https://outline.com/FP487e https://outline.com/FP487e
gok 7y ago
"I was able to bullshit about A.I., so the whole field is bullshit."
moneytide1 7y ago
These types of AI promotion seem to be a sort of cop-out that suggest we all look forward to a hands-off future where computers will be able to do everything for us. Then human minds will be allocated away from thoughtful interaction with their environment and into an all-hands-on-deck scenario where neural net operations are given top priority so they can churn out some answers.
- tachyonbeam 7y ago
  My main short-term fear is that increased automation will lead to an increasingly isolated society. I can already get almost everything delivered through amazon, order takeout through an app without speaking to anyone. Watch movies on Netflix without needing to go to a video store. What's the world going to be like when drone deliveries become a thing, and I don't even have to speak to a delivery driver? How will it affect kids if they do all their schooling online? I think that, even before AGI happens, AI assistants will become placeholder friends for a lot of people. You'll be able to have a conversation with Siri or Alexa. Eventually, people might have pseudo relationships with robot boyfriend/girlfriends. Imagine having a friend who is anything you want them to be, does everything you want, and most importantly, never challenges you or tells you anything you don't want to hear. People will get used to that, and it will become difficult for them to have real human relationships. In other words, technology is enabling everyone to function without directly interacting with others. People might choose not to interact with other humans out of convenience, insecurity, fear. Japan already has a population of "herbivores", people who choose not to get into relationships, and the rest of the world could become like that too. I hope we find a way to reverse this trend. Short documentary on hikikomori in Japan: https://www.youtube.com/watch?v=wE1UIK85E3E https://www.youtube.com/watch?v=wE1UIK85E3E
  - moneytide1 7y ago
    Your short-term fear is seeming to be validated over time as your listed "person-less" capabilities are constantly being implemented. I myself have lived brief bouts of the hikikomori lifestyle, although I still provide for myself as opposed to being taken care of by family as many are in the video you linked. But over the past few months, I've been prioritizing face-to-face conversations, especially with new people. This is the timeless, natural "neural net": When we interact in this way, we isolate certain topics that are more relevant to our modern condition because both parties involved understand there is a time constraint. There is a sort of natural algorithmic process going on in both our minds because we are calculating the ideal things to say with this "new person". If we are speaking in proximity to other people, our conversation can take on a whole new shape because there is a "public" element and perhaps we are trying to dialogue for an audience that could eventually participate if they choose to. All of these mechanisms that keep people in check with each other are completely lost when out-sourced to programmed automation and looped control. Perhaps it is not all bad though, because ordering through services like eBay/Amazon quickly and efficiently could ultimately save resources/emissions as we are having items routed to us that would historically take up space in a brick/mortar location. But the concern we are sharing here is that community is compromised when everyone has the option to anonymously do everything.
mindgam3 7y ago
> The technologists know it’s bullshit. Fed up with the fog that marketers have created, they’ve simply ditched A.I. and moved on to a new term called “artificial general intelligence.” Not to detract from an otherwise excellent BS takedown, but unfortunately the author fails to mention that there’s a non-zero possibility that AGI itself is merely taking the bullshit to the next level. It continues to astound me how some technologists actually believe AGI is not just inevitable but around the corner. When to my naive perspective (as a machine learning rank amateur but with several decades experience as a professional human being) all I see is machines that can do some form of pattern recognition, but nothing resembling the common sense that the words “general intelligence” seemed to indicate at one point. Minor quibbles about truth and meaning of words aside, I have to support any article that skewers the soft underbelly of the phony AI ecosystem as effectively as this one does.
- povertyworld 7y ago
  While it's true the AI hype is getting a bit overboard, couldn't one make the argument that humans, and to a lesser extent other animals, are just a collection responses to pattern recognition like when I see a thing that looks like this I should eat it, but when I see a thing that sounds like that I should run away? After all, what is common sense but "believe it when I see it" type thinking which is most certainly pattern intuition? The overhype part is that there are armies of lowly paid Mechanical Turk tier workers tagging all these data sets, so that these pattern recognition algorithms have something to train on.
  - Thorentis 7y ago
    Sure, one could make that argument. But suggesting that we are close to creating a general intelligence on-par with humans is entirely unfounded. Even if we assume that the only thing differentiating humans from other forms of intelligence (including artificial neural networks) is more pattern to learn, then that still requires data sets millions if not billions the size of what we currently have. Plus, we know very little about the types of patterns we would need to train an AI on in order for them to reach the AGI level. I think it's safe to say that "artificial general intelligence" is just another iteration of the BS Industrial Complex.
waynecochran 7y ago
Getting ready for the next AI winter.... this is a cyclic phenomena.
- raverbashing 7y ago
  Hopefully we don't take decades again for a simple but important change like changing tanh to relu activations.
  - dijksterhuis 7y ago
    my bet is on capsule networks, Hinton is usually on point with his stuff
- tim333 7y ago
  Or the singularity. Then it isn't.
  - waynecochran 7y ago
    Not yet ... not anywhere close to human neural cortex.... still several orders of magnitude to go...
colechristensen 7y ago
Progress of civilization could be summarized in the slow march of BS elimination parallel with the creation of creative new forms of BS (people don't actually learn anything, they just form the same crazy opinions about something new) Strikeout "of Phony AI." The BS-Industrial Complex is huge and the rise of the Internet has made it worse by empowering the less-informed to share ideas. That is somewhat the price you pay for progress. The hopeful idealistic information superhighway myth of the 90s turned into something else.
- ethbro 7y ago
  I look at BS as an inevitable symptom of the Singularity. As we approach the capacity of human reason, fewer people are able to keep up with the world, and are therefore more susceptible to it.
  - colechristensen 7y ago
    I don't know, look back two thousand years and you see plenty of it. More like it's a symptom of humanity. Animals are stupid machines, humans aren't nearly as far away from them as we'd think ourselves.
solidasparagus 7y ago
The author seems confused about what artificial general intelligence is. People have not meaningfully moved towards AGI - it's still a distant pipe dream. The closest we've gotten is probably a Dota bot that's pretty good as long as you give the bot a huge advantage. Which is an incredible piece of technology, but about as close to AGI as an ant is to a human.
- Causality1 7y ago
  Not even an ant. If AGI is a human then what we have is the equivalent of synthetic RNA molecules.
  - klmr 7y ago
    What? Ants don’t have general intelligence, and even ant colonies’ decision making (= simple swarm intelligence) is readily replicable in a programmed system, and has been, for a while. I don’t think a gradual scale is very helpful because I don’t think that the progression from current-generation AI to AGI is going to be gradual (it will require at least one paradigm shift). That said, if you want to compare AI progress to actual animals then our current-gen AI way beyond ants. Note that, while we haven’t fully mapped the neurons/connectome of ants yet, this is unnecessary to emulate their decision-making power. And we have mapped (and can simulate) the full connectome of simpler animals (e.g. C. elegans, P. dumerilii) so we’re definitely a long way beyond single molecules.
    - computerex 7y ago
      If you are referring to the open worm project, then the conclusions you have drawn are exactly the opposite of what I have drawn. As I understand it open worm is a hodge podge of statistical and numerical methods to try and replicate the sensorimotor behavior of c elegans. Open worm is neither complete, accurate nor elegant, despite knowing c elegans connectome and having mapped the some 900 cells in the worms body.
      - klmr 7y ago
m0zg 7y ago
There is a lot of froth, as in any hot field. However, unlike before, there are many cases where AI actually works now. Some perceptual tasks work better than a human, in fact. We can quibble about the naming and whatnot, but that's not something you can say about the last AI winter. It's sort of like dotcom bust of 00, sure things imploded back then, but there's no sign whatsoever e-commerce will implode at any time in the future because unlike before it actually works this time.
- ethbro 7y ago
  > Some perceptual tasks work better than a human, in fact. [...] that's not something you can say about the last AI winter Eh. I'd say that's somewhat apples to oranges. A) There were some useful and successful expert systems. B) Things seemed to be going swimmingly, until they hit a fundamental wall. C) We're working with a few orders of magnitude greater compute than they had access to.
  - m0zg 7y ago
    Sure, but we did figure out how to make things more robust and generalizable, at least for perceptual tasks so far. Knowledge representation and probabilistic reasoning are still non-existent, though. Moreover, nobody is even working on any of that, for fear of being compared to Doug Lenat.
    - snaky 7y ago
      So robust and generalizable that adding a pixel here and a pixel there, too small to be even recognized by a human, confuse image recognition system to the point it confuse panda with gibbon?
      - Quetelet 7y ago
        These adversarial examples are generated in a very artificial way that will not be present in natural images (and if you’re thinking of security issues, the attacker needs access to your model...) They’re still an interesting topic to explore but hardly evidence that neural nets don’t generalize.
mattigames 7y ago
Everything is bullshit until is not, humans were talking about transportation without using animal forces for decades before it became a reality, and a lot of people were highly skeptical of such thing being even possible until it actually happened in 1804 (first steam train), same thing happens with Artificial Intelligence, and we are in such uncharted territory that someone could say AGI is just 10 years away and someone else say 100 years away and both get the same amount of credibility, meaning near none cause we don't even know what is that we don't know to achieve AGI.
- tim333 7y ago
  Unless you believe the Kurzweil argument that we will figure what is needed from reverse engineering the human brain in which case you guestimate a timeline.
- dboreham 7y ago
  Your example isn't quite as it seems : "trains" (cars running on rails) were used in mining for hundreds of years prior. The steam engine was first documented in 1698. What happened in 1804 was someone figured out the manufacturing processes to make a steam engine light enough and powerful enough to usefully pull a train of cars over some reasonable distance.
dreamcompiler 7y ago
This happened right before the first AI Winter in the late 80s: AI (in the form of expert systems) solved a number of hard problems and was hyped as being able to solve every problem. Reality set in when we figured out: 1. It didn't scale and 2. Getting 80% of the problem solved was easy, but getting that last 20% was very, very hard. Maybe several orders of magnitude harder than the first 80%. Nowadays we don't seem to have problem 1 quite so much, but problem 2 is still there in a big way. Witness self-driving cars, where driving on an interstate highway in broad daylight is easy, but driving through a snow-covered construction zone at night is impossible. Or just dealing with a bicyclist on the road without killing them. We're not going to have AGI any time soon.
- computerex 7y ago
  Right. There has been a lot of progress within the last decade in narrow AI, but AGI remains as elusive as ever.
- misterman0 7y ago
  >> We're not going to have AGI any time soon. In your mind, how many inventions are we from GAI? 1? 1000? Also, given we have our very brightest working in the problem space (MS, Google, FB, OpenAI ...) how long would each remaining invention take? It seems plausible to me that we are only one invention from GAI, one novel combination of two or more existing solutions and that this may come from anyone and at any time.
  - dorkwood 7y ago
    Applying this method of thinking, is there anything that we aren't one invention away from?
    - misterman0 7y ago
      I'm just trying to stay positive. If you talk optimistically about AI (or big data for that matter) you quickly get downvoted these days. I'm curious as to why that is. I thought this was 'marica. Isn't this 'marica? Edit: my apologies, that was condescending. I should have finished of with a wink of an eye.
lkrubner 7y ago
I've been collecting examples of where the ads that I see are based on extremely simple algorithms of the type that could have easily been supported 30 years ago, and yet I keep reading articles that suggest that the advertising industry is deploying sophisticated tools to target ads to me. I wrote about this recently: ------------------------- Despite much talk about Machine Learning and AI improving advertising results, what I’m seeing is getting worse and worse. Despite billions invested, the ads shown to me are much less relevant than that ads that I saw on the Web 10 years ago. I hired 3 developers from Fullstack Academy. They were all great, so I went and checked out the website, curious about the curriculum. And now, every website I go to, I see an advertisement for Fullstack Academy. (See screenshot.) I’ve been writing software for 20 years. I’ve written semi-famous essays about software development. I am not going back to school. I do not need to go to a dev bootcamp. So why show me ads, as if I’m thinking of going to school? For the last several years I’ve been seeing articles about the surveillance economy. In theory, advertisers know more about me than ever before. In theory, they know about my entire life. And yet, the ads I see are less targeted than what I used to see online 10 years ago. http://www.smashcompany.com/business/when-will-machine-learning-and-ai-improve-advertisings-ability-to-target-people http://www.smashcompany.com/business/when-will-machine-learn...
- dijksterhuis 7y ago
  I willing to take a punt and guess that the models they’re using are using short term/isolated data, not 10 years worth of your entire browsing history.
  - plaidfuji 7y ago
    Haha, yeah. When n=1, both human and computer will say “keep doing what you’re doing!”
- foldingmoney 7y ago
  Apparently you didn't get the targeted ad telling you that advertisers' dark patterns have become so sophisticated that free will is literally fiction now. /s, obviously
- 7y ago
galaxyLogic 7y ago
What's the most difficult thing AI should be be able to solve but can not as of yet? I would say it is writing a program which writes an AI program. Why? Because it is so difficult for us to define what exactly an AI program should be able to do. This shows that we have an issue with not being able to ask the right question. If we could answer exactly what the AI should be able to do then it would be much easier to create such a program and also create a program that writes such a program. We could say that an AI program should pass the Turing Test and many have written programs that more or less pass it. But so now, write a program that writes several different programs that all pass the Turing Test one better than the previous one. I don't really have an idea how I would start writing such a program that writes a program that passes the Turing Test better than previous AI programs. That makes me guess we are still far off from General AI. But I of course may be wrong, just because I don't know how to do something does not mean others would not.
- jbay808 7y ago
  By this argument, humans also aren't general intelligences, because we haven't been able to write one yet either.
- foldingmoney 7y ago
  >We could say that an AI program should pass the Turing Test and many have written programs that more or less pass it. But so now, write a program that writes several different programs that all pass the Turing Test one better than the previous one. That's essentially what machine learning is, though.
- IRLIamOffline 7y ago
  I agree that defining a goal along with metrics would be very helpful to make meaningful progress towards AGI. However, defining this test is extremely hard, to a point where I'm not sure if we could define a test like this. So far it seems, that just by defining a test we can come up with a narrow AI that optimizes for this test.
- chrshawkes 7y ago
  We know what we want it to do, we want it to have some basic ability to think for itself. That is something we just simply can't do. Back propagation is far from a spanking for acting out of line. AI has no ability to understand it's acting like a fool or how to deal with uncertainty with emotions which cause us to act without regards to consequences and in many cases reality. It lacks understanding of what future consequences its trying to prevent such as our daily decisions to get up and go to work each morning. The AI has no understanding of it's future and the consequences of not going to work until it's fired 40,000 times for not showing up or it's children are taken from him/her/it. I'm glad people are finally waking up to the fact that AI is not ML and AI is all hype at the moment. Google used algorithms quite effectively to adapt and learn, but they have no greater understanding of what we want, just what we and others have wanted in the past.
dijksterhuis 7y ago
I despise the term Artificial Intelligence. This is all PROBABILISTIC MODELLING. Nothing to do with AI/AGI/whatever. The computers aren’t thinking or learning. It’s just modelling fancy probability statistics. E.g. classical neural networks are basically a load of linear regression equations with an activation function stuck on the end of each of them. No magic. Just lots of linear regression. This stuff only works when: 1) you are trying to solve a specific problem that is suited to probabilistic models 2) you have a data set that is sufficiently large, varied and specific 3) the model is developed, trained, tested, implemented and updated in a rigorous and sensible manner
- Quetelet 7y ago
  Actually most modern neural networks are not probabilistic, they are deterministic function approximators. Also your point 3) isn’t quite correct either, often a “standard” architecture and training procedure (e.g. ResNet50 with Adam) will work on a new task with sufficient training data and minimal modification of the model.
  - dijksterhuis 7y ago
    The only nnets I mentioned were “classical” as a purposefully over simplified example. Yeah, they can model any function, but historically they were used for probabilistic density functions (if I remember correctly). Most of what the article talked about can be done with much simpler models, which is what I get peeved about. Also, yes, you can transfer learn with resnet. But if I throw my bank statements at it, it’ll do bugger all. Similarly, if I throw new images at resnet in a silly way, it won’t transfer properly.
    - Quetelet 7y ago
      You might be confusing the historical use of the sigmoid activation function with probabilistic modeling, neural networks in the 80s were used similarly to how they are today, albeit at a much smaller scale due to hardware limitations at the time. The development of neural networks is a major contribution of the machine learning community, so even if you’d like to split hairs about whether the “computer is learning” (“learning” has a a precise technical definition by the way), NNs are not “just statistics.”
ackbar03 7y ago
Of all the hypes going around (blockchain mostly lol) I think ai is going to have the most substance to it though. I would say the breadth of problems being solved are much wider and there is still a lot of research which hasn't really found its way to actual implementation yet
stareatgoats 7y ago
> The BS-Industrial Complex of Brilliant! This is really a thing, and the computer industry is (and has always been?) rife with it.
- harry8 7y ago
  IBM Global Services. Oracle. Accenture. Any company with 100+ employees who does consulting involving the design, implementation and maintenance of computer systems for any government bureaucracy. Is there anyone around here who thinks this industry sector is something else than industrial grade BS and if every single one of those companies disappeared overnight that we would not be in a better place as a civilization very, very quickly as we were forced to pick up the pieces. Industrial quantities of BS are the norm, right? Most of us do startups to do something more than to schmooze, threaten and ultimately bilk customers paying with other peoples money. We kind of want to do tech.
  - adev_ 7y ago
    > Most of us do startups to do something more than to schmooze, threaten and ultimately bilk customers paying with other peoples money. Do you know Theranos ? That's the definition itself of bullshit and it was a "startup". https://en.wikipedia.org/wiki/Theranos https://en.wikipedia.org/wiki/Theranos Bullshit comes from 5000+ employees to companies with 5 dudes. Scale does not change anything. Business culture, profit as only value and the culture of fake it until you do it are the source of the problem. And against that their is not magic solution, excepted trust a lot less the ones that speak and trust a lot more the one that do. In the good old Nerd world, we named that Show me the code
atoav 7y ago
At the Biennale in Venice (one of the most important art shows there is) I saw a work which looked like this: There was a metal frame holding two glas plates with ventian sediment inbetween (sand, soil, mud). In the center there was another metal frame which formed a hole. There also were to PCB boards with ATMEGA micro controllers. In the text the artist claimed she controlled the biome of the soil with an AI using various sensors and pumps. This was clearly a fake, as you could see nothing like that on the PCB. Accidentally (?) she managed to create the best representation of AI I have seen in art: all that counts is that you call it AI even if it is a simple algorithm. AI is the phrase behind which magic hides and people love magic. Everything that has the aura of “humans don’t fully understand how it works in detail” will be used by charlartans, snake oil salesmen and conmen. If even artists slap “AI” onto their works to sell it, you know we are past the peak now.
- solidasparagus 7y ago
  We might be headed for the trough of disillusionment, but in terms of technology and industry applications, we are still in the early phase of modern AI (big data + ML).
  - VBprogrammer 7y ago
    I'm doing a bit of hiring at the moment. It's hard to find a single CV which doesn't have some kind of machine learning slant. As much as I think there are plenty of advances in ML left to take, I doubt every graduate will step out into a good application for existing machine learning tools.
    - misterman0 7y ago
      Just like not all astrophysicists reach Nobel prize level achievements not all machine-learning graduates will innovate in their field. But to help you out, in order to find people who are likely to innovate, find out if that is their goal. If it is not then you know not to hire them. But if that's their goal, maybe start some kind of "program" where you give these people a chance. 6 months. A year maybe. If the are close to something or if you're pleased with them even though they aren't really innovating, then give them a "tenure" so to speak. An evaluation process, it's very common here in Sweden. I mean how much money can you really loose in six months? You can kick that shithole straight out day one if that's your perogative. It's a good deal for both parties.
nl 7y ago
AGI will arrive as soon as someone can arrive at a reasonable definition of intelligence. Try it. Everything I've seen is already achievable by computers.
- AstralStorm 7y ago
  Solving novel problems. Show me. By novel I mean multiple categories. A system that can serve as archive, mathematician, calculator, can move a robot, drive a car and additionally make coffee from scratch. Oh and talks (speaks and understands and acts upon orders) in 3 human languages at decent levels plus can roughly explain what it's doing. Oh and can learn more unrelated skills. Hey, people do it all the time.
  - nl 7y ago
    A system that can serve as archive, mathematician, calculator, can move a robot, drive a car and additionally make coffee from scratch. Oh and talks (speaks and understands and acts upon orders) in 3 human languages at decent levels plus can roughly explain what it's doing. Oh and can learn more unrelated skills. I'm a bit unclear if this is supposed to be a definition of intelligence. Stephen Hawking would fail this test, but no one would argue he isn't intelligent.
yonkshi 7y ago
AGI is a gradient, not an arbitrary threshold. We are not capable of recreating human level intelligence yet, but our modern algorithms had become magnitudes better at generalization and sample efficiency. And this trend is not showing any signs of slowing down. Take PPO for example (powers the OpenAI 5 dota agent), the same algorithms can be used for robotic arms as it does with video games. Two completely different domains of tasks now generalizable under one algorithm. That to me is a solid step towards more general AI.
- taurath 7y ago
  It’s a gradient but according to the marketers it’s basically going to overtake humanity any week now.
  - SomeOldThrow 7y ago
    For the problem you’re trying to tackle, this startup has already solved it and will show you insights previously impossible!
  - yonkshi 7y ago
    I agree. I think a big part of this problem is that smaller companies usually cannot afford AI research. I would even go as far as to say there are more AI companies than capable AI researchers, and this causes a large number of faux-AI companies poisoning the AI branding.
  - misterman0 7y ago
    "AI any week now" What marketers proclaim that? Are they saying that or are they saying there is _utility_ in AI, now? Because me thinks, there is real utility, now, but it's going to take years until it overtakes us. Years!
    - tim333 7y ago
      I'm not sure anyone said any week now but Musk probably came closest https://www.entrepreneur.com/article/323278 https://www.entrepreneur.com/article/323278
derka0 7y ago
The hype is BS but narrow AI in the context of automation is here. Job are so specialised nowadays (driving, cashiers, fulfilment, paralegal, diagnostician ...) that a narrow AI (i.e. a glorified automation algorithm) that can do just 10% better at a cheaper cost will take down the job. The confusion is real (AI, AGI, terminator...) but pattern recognition softwares powered with big data has already proven business value and are here to stay.
AstralStorm 7y ago
Next: NoAI, like NoSQL. All natural real intelligence, fully organic and explainable. Just add caffeine. ;)
- j88439h84 7y ago
  Brilliant.
mrbanks 7y ago
i.e. Babylon Health
wolfi1 7y ago
if there is no natural intelligence around, you need an artificial one
ecmascript 7y ago
I think honestly think westworld (yes the tv-series) has the best explanation of why general intelligence is a hard problem to solve. They mention consciousness but I think the same apply to intelligence in general. Humans in my mind aren't different from say a program you write except that we have a lot more input and possible outputs depending on a much larger variant of external variables. If we could build machines that have eyesight just as we do, muscles just as we do etc I'm sure we could reverse-engineer the human being. https://www.youtube.com/watch?v=S94ETUiMZwQ https://www.youtube.com/watch?v=S94ETUiMZwQ
- toxik 7y ago
  I find this analysis reductionist. You're basically saying "brains aren't hard to reproduce once you have biological sensors and actuators." Why not? They're _extremely_ delicate, intricate organs. Claim 2 is also a difficult one: of course you can easily claim consciousness doesn't exist, but it is impossible to argue by logic. You need a metaphysical philosophical framework, and then it's already left the realm of empirically observable truths.
  - dspillett 7y ago
    I'm not sure the claim is that consciousness doesn't exist. More that it is an emergent property of complex systems rather than something that is (or can be) deliberately programmed.
    - ecmascript 7y ago
      Precisely. It's the complex system that gives us an illusion of consciousness. At least, that is what I naively believe in since there is a lack of evidence for anything else.
      - goatlover 7y ago
        So you think experience is itself an illusion? When you kick a rock and feel pain, you're not really experiencing pain? Is the rock also an illusion?
Iv 7y ago
"Deep Learning projects are typically written in Python. AI projects are typically PowerPoints."
macawfish 7y ago
Ben Goertzel.
Barrin92 7y ago
In my opinion the term intelligence itself is misplaced for machine learning tasks. Every problem that is solved with ML and "big data" appears to me to be a perception problem (which wouldn't be surprising because the mechanism is inspired by human vision, not cognition, which it lends itself to naturally). As a specific example, a few months ago or so openai released their text generation tool and branded it as "too dangerous too release", claiming it could , with the help of AI, generate believable texts. But what it generated was simply natural sounding gibberish. There were plenty of sentences in the text along the lines of "before the first human walked the earth, humans did.."" What, for me at least, lies at the core of intelligence is understanding semantics. An intelligent system can recognise the sentence above as flawed because it could extract meaning. Everything coming out of the field of ML seems to me just like sophisticated statistics. In many ways symbolic AI to me still seems more valuable, profit aside.
- kaolti 7y ago
  I agree. Extracting meaning is NOT a math problem, meaning comes from the human context and context is infinite. Hence different humans extract different meanings. I think AI and ML are great for processing large amounts of data and looking for patterns. Patterns on their own don't mean anything though, it's always up to us to interpret them.
- Wiretrip 7y ago
  In the text generation tool outlined above (and indeed many of the convnet-based visual networks), the hidden layers are there precisely to extract 'meaning'. The lower layers (closer to the source input) deal with syntax and feed upwards to hidden layers that extract semantic features, which in turn feed upwards to more layers, each with a bigger overview of the semantic features and thus ultimately the context. That's the idea anyway.
  - foldr 7y ago
    >the hidden layers are there precisely to extract 'meaning That is just wishful thinking, no? I mean, there is no particular reason to think that the hidden layers will actually do this with any high degree of success.
Wiretrip 7y ago
For a real emperor's new clothes moment, look at SpinVox! https://en.wikipedia.org/wiki/SpinVox https://en.wikipedia.org/wiki/SpinVox
benreesman 7y ago
If everyone sophisticated enough to be on this site would just use the term “applied computational statistics” (even just in their own thoughts) instead of “deep learning” or AI, the world would be a better place. Gradient descent finds some fun minimia (my current venture is heavily based on that idea) but to assign more agency to Adam or RMSProp than they merit is just an exercise in feeding the trolls.
- mbeex 7y ago
  Couldn’t agree more. All these delusional discussions: Is it intelligence? Is it true intelligence? How far is it to become true...? Skynet rising? To be fair: The last question is certainly adequate regarding the application of unverified/-able algorithms in a life-changing incarnation as a virtually unsupervised decision making quality. This is horrible. But it is another question (and better answered skipping the pseudo-philosphical part)
- YeGoblynQueenne 7y ago
  Could you please explain in what sense deep learning is "applied computational statistics"? What about classical planning, SAT solvers, automated theorem proving, game-playing agents and classical search? Could you please explain how one or more of those are "applied computational statistics"? Further- I don't understand the comment about "agency". Could you clarify? Why is "agency" required for a technique or an algorithm to be considered an AI technique?
  - plaidfuji 7y ago
    I don’t know anything about the underlying algorithms for the examples you rattled off, but deep learning trains a graph of neuron weights such that they are statistically optimized to minimize error in computed output labels for some domain of input data. Very much “applied computational statistics”.
    - YeGoblynQueenne 7y ago
      The examples I gave are classic AI algorithms that are very easy to look up on wikipedia. They do not compute any statistics. I'm not sure what you mean about "neuron weights that are statistically optimised". Modern-era, deep neural nets train their weights with backpropagation, which is basically an application of the chain rule, from calculus. They do not use statistics for that. For example, calculating the mean of a set of values or calculating the pearson correlation coefficient of two variables are computations typical in statistics. Could you please clarify what you mean by (applied) "computational statistics", so that I don't have to double-guess you? Edit: Do you really not know what a SAT solver is? Not to be rude but if that is the case, from where do you draw your confidence about the correct terminology to use for AI?
mikorym 7y ago
So can I call it "second year linear algebra" now instead of "AI"?
bjoernbu 7y ago
Imho it has gone further. In a way, all the things described as not actually AI now "are" AI, because the term AI has been used in that way so many times. I don't think we'll ever use a better (more accurate) term for the ML- and data-driven value current systems create. Instead "true" AI will get a new fancy name to build the next hype around in several year.
East-Link 7y ago
Rudimentry machine learning algorithms are indeed AI, by common usage. Try typing into Google Images something like "ai machine learning deep learning venn diagram" and you'll see that by common usage, machine learning is a strict subset of AI.
throwaway287391 7y ago
> In this way, Dynamic Yield is part of a generation of companies whose core technology, while extremely useful, is powered by artificial intelligence that is roughly as good as a 24-year-old analyst at Goldman Sachs with a big dataset and a few lines of Adderall. For the last few years, startups have shamelessly re-branded rudimentary machine-learning algorithms as the dawn of the singularity, aided by investors and analysts who have a vested interest in building up the hype. Welcome to the artificial intelligence bullshit-industrial complex. As an AI researcher, I think a lot of people are a little too sensitive to the term "AI" and make a lot of big assumptions upon hearing it. It's a very general term that doesn't really imply any particular degree of complexity or sophistication. Labeling simple machine learning algorithms and heuristics as "AI" isn't at all unique to this era of hype that began in the last ~5 years -- rather that's how the term has been used in academia for many decades. If you took a college class called "AI" or looked up some of the most popular textbooks on AI [1], you'd find that a lot of it is dedicated to search algorithms (breadth-first, depth-first, A*), linear classifiers, and feature engineering. If you think "artificial intelligence" is a bad name for these things, fine -- but don't blame the recent wave of hype, this is what the term AI means and has pretty much always meant. So go ahead and call your startup's linear regression "AI", and if the VCs leap to fund you under the impression that it means you'll be behind the singularity, that's on them. AI != deep learning. AI != AGI. [1] e.g., "Artificial Intelligence: A Modern Approach" by Russell and Norvig
chewz 7y ago
> The Turk, also known as the Mechanical Turk or Automaton Chess Player (German: Schachtürke, "chess Turk"; Hungarian: A Török), was a fake chess-playing machine constructed in the late 18th century. From 1770 until its destruction by fire in 1854 it was exhibited by various owners as an automaton, though it was eventually revealed to be an elaborate hoax.[1] https://en.wikipedia.org/wiki/The_Turk https://en.wikipedia.org/wiki/The_Turk
YeGoblynQueenne 7y ago
>> Deep learning algorithms have proven to be better than humans at spotting lung cancer, a development that if applied at scale could save more than 30,000 patients per year. It's not easy to scale deep learning because deep neural nets have a very strong tendency to overfit to their training dataset and are very bad at generalising outside their training dataset. In a medical context this means that, while a particular deep learning image classifier might be very good at recognising cancer in images of patients' scans collected from a specific hospital, the same classifier will be much worse in the same task on images from a different hospital (or even from a different department in the same hospital). To overcome this limitation, the only thing anyone knows that works to some extent is to train deep neural nets with a lot of data. If you can't avoid overfitting, at least you can try to overfit to a big enough sample that most common kinds of instances in your domain of interest will be included in it. So basically to scale a diagnostic system based on deep neural net image classification to the nation level one would have to train a deep learning image classifier with the data from all hospitals in that nation. This is not an easy task, to say the least. It's not undoable, but it's not as simple as having someone at Hospital X download a pretrained model in Tensorflow and train its last few layers on some CT scans.
- bodono 7y ago
  This statement is false, as recently demonstrated by DeepMind on retinal scans. Not only did they generalize outside of the training dataset but they were able to use the features learned by the model on an entirely different type of scanning device. https://www.nature.com/articles/s41591-018-0107-6.epdf?author_access_token=PAbvHEuv_YYmrPVbG5HqKdRgN0jAjWel9jnR3ZoTv0P43NEH20hFuvBoJk6cvICihn8kmL6tmejFlnuPlbT_0KmJgK6N07SPh_ZLy0Nxb0-LAGIDBaH1fjJTkD9ahUEQpRlEudtlG9E1v3ca9xNQcQ%3D%3D https://www.nature.com/articles/s41591-018-0107-6.epdf?autho... "Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device."
  - YeGoblynQueenne 7y ago
    In the paper you link to, the researchers trained an image classifier on data collected from 32 sites of the Moorfiel NHS trust. The trained model was tested on, presumably held-out, data from the same dataset. This is an example of scaling a model beyond a dataset collected from a single site. It is not contrary to what I say in my comment. The researchers further tested their model on data obtained from a different device than it was originally trained on. This data was collected from the same hospital sites. The original model performed poorly on this new data and was re-trained to improve its performance. This does not demonstrate an ability to generalise to unseen data- only an ability to adjust a model to new data, by re-training.
nottorp 7y ago
Is Medium for pay now? They told me to sign up to get "one more free story".
- Veedrac 7y ago
  Medium lets writers opt-in to a paywall. It is not the default, but does come with some perks for authors.
holografix 7y ago
Repeat with me: Machine Learning != AI
a_imho 7y ago
It is pretty much spot on, but I'm not convinced anyone should really care. When was software not hype driven?
pron 7y ago
In 1949, some years after the invention of neural networks, Norbert Weiner, one of the leading minds of the time, was convinced that AI (AGI as you may call it) or a full understanding of the brain is no more than five years away. Alan Turing thought Weiner was delusional, and that it may take as much as fifty years. Seventy years later, we are nowhere near insect-level intelligence. I don't see any fundamental barrier preventing us from achieving AI, but if someone from the future came to me and said that AI will be achieved in 2130, I would find that quite reasonable. If they said it will be achieved in 2030 or 2230, I would find those equally reasonable. Our current scientific understanding is that we have no idea how far we are from AI, we don't know what the challenges are, and we don't even know what intelligence is. We certainly have no idea whether the approach we are now taking (statistical clustering, AKA deep learning) is a path that leads to AI or not. In the sixties, the leading minds of that time were also working hard on the problem and did not find it any further away from us as we do today. That some people are optimistic is irrelevant. The fact is that we just have no idea.
- cr0sh 7y ago
  > Seventy years later, we are nowhere near insect-level intelligence. That's arguable: For instance, we have the entire connectome of c. elegans mapped out; we can easily simulate it, and it seems to act the same as the actual nematode. So, in one sense, we are at that level. However, we still have no clue how such a simple system actually works to produce the level of "intelligence" it has. So in that sense, we're not at that level at all. > We certainly have no idea whether the approach we are now taking (statistical clustering, AKA deep learning) is a path that leads to AI or not. One clue we do have: We may not be on the right path with that method; it's something the "grandfather" (or whatever) of AI (Hinton) has mentioned, and which I have stated before about... That is, the fact that we currently have no understanding of the mechanism by which biological neural networks implement anything like "backpropagation". From what we currently understand, as I currently understand it, we have yet to find such a mechanism that would allow for it. It's also one of the leading reasons why our current artificial neural networks consume so much power, as compared to biological systems...
  - 7y ago
plaidfuji 7y ago
Sure, “AI” as it is used today implies “software that codifies decision-making using data”. No, it’s not the T3000. But as the author acknowledges: > Dynamic Yield can pay for itself many times over by helping McDonald’s better understand its customers Ok, so it’s not hype - it is delivering real value. “AI” is just a marketing term to help C-suite suits and Silicon Valley sales reps get on the same page about what’s being sold with as few words as possible. What’s being sold is software that helps make optimal decision using data. AI isn’t a rigorously defined academic term, so people will use it how they want. It’s only hype when real value isn’t delivered.
- epr 7y ago
  > What’s being sold is software that helps make optimal decision using data Doesn't this apply to virtually all software?
  - plaidfuji 7y ago
    In an extremely reductionist sense, maybe. Do I use Microsoft Word to automate decision making? No. Does Facebook help me make important life choices? Heh. How about this: Amazon.com is not AI, but their recommendation engine is.
    - deleted 7y ago
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arbuge 7y ago
Consider this article: http://fortune.com/longform/single-family-home-ai-algorithms/ http://fortune.com/longform/single-family-home-ai-algorithms... If you read it, you'll find that their methods to value homes and renovations are based on algorithms written to value mortgages in the 80s, 90s, and early 00s. I'm going to bet that there's not much of what the average HNer would think constitutes AI going on in there.
dr_dshiv 7y ago
We should be focused on designing "smart systems" that optimize measurable outcomes Who cares how complex the algorithm is! What matters is that it works better. Is there a measurable outcome that matters? Can the system optimize that outcome over time, through a coordination of human processes and technology design? That is what organizations need. Not hyperparameters.
orpep90nxkfo 7y ago
This reminds me of the article the other day about the internet being an SEO wasteland Basically our business networks run the same way (not a shock at all): sycophants spam aristocratic investors with half assed bullshit solutions to juice the odds of hooking one
JustSomeNobody 7y ago
This is no different than anything else. You hype what you're working on so people get interested and throw money at you. AR/VR glasses, AI, self driving cars, it's all the same. You generate interest, make lots of money and who cares if it ever gets to market.
bernardv 7y ago
I totally agree with the gist of this article. This hype is being propagated by a lot of folks who are willingly clueless, as for example, in the data science crowd. This band-wagon is crowded and isn’t stopping any time soon. It irks me to no end to comme across tutorial-style articles proclaiming to teach an AI algorithm, also known as ‘linear regression’. What bugs me the most though, are the countless ‘influencers’ on LinkedIn which spew rubbish about machine learning, AI and all the wonderful things that are just around the corner. Lastly, it doesn’t help when countless articles/books are written on the subject of AI dangers, AI ethics and are ‘robots coming for us?’. These add fuel to the fire of hype. In the end, this behavior will only guarantee the eventual blowing-up of the bubble, when promises are not delivered.
cirgue 7y ago
There is a massive positive, though, for the 'geeks building the future": AI is where everyone else is looking. If you know where you should be looking, you have a decisive advantage over the rest of the market.
- bombingwinger 7y ago
  Doesn’t your last sentence go for literally everything?
  - cirgue 7y ago
    Of course it does, but we can say with confidence that attention and capital are misallocated toward a specific, identifiable set of activities. That's rare.
  - chrshawkes 7y ago
    Everything but the inevitable AI winter to come. :)
module0000 7y ago
TLDR; machine learning == "AI", just as much as colocated servers == "cloud"
DonHopkins 7y ago
In 1996 I made this AIML (Artificial Intelligence Marketing Language) parody by taking an actual VRML article from some shameless trade rag, and globally replacing "Virtual Reality" with "Artificial Intelligence". (from "ArtificialPostModernIntelligenceInterActivity", V2 #4 April 1996, p. 20) https://www.donhopkins.com/home/catalog/text/SupportForAIML.html https://www.donhopkins.com/home/catalog/text/SupportForAIML.... Another closely related technology is BSML: Bull Shit Markup Language. (Note: most of the features described in the BLINK tag extension were eventually implemented by FLASH!) https://www.donhopkins.com/home/catalog/text/bsml.html https://www.donhopkins.com/home/catalog/text/bsml.html At one point years later, somebody actually emailed me, asking me to take it down, because they were developing a "real AIML [TM]" product, and found my parody of their unique original idea to be beneath their dignity, distracting, and confusing to their potential customers using google to search for their prestigious "AIML" product.
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diehunde 7y ago
The problem is when you work at a company that tells you, "we are different, we are not BS like the other A.I. companies"
tabtab 7y ago
Just AI? IT is filled with BS and fads. Dilbert is a documentary, not just a comic strip.
nsajko 7y ago
It seems the author has deleted the post. Maybe Dynamic Yield asked him to take it down? Anyway, currently it is accessible through https://outline.com/FP487e https://outline.com/FP487e
soobrosa 7y ago
Been deleted, cached is at https://webcache.googleusercontent.com/search?q=cache:RV8OOzgmjJsJ:https://gen.medium.com/the-bs-industrial-complex-of-phony-a-i-44bf1c0c60f8+&cd=1&hl=en&ct=clnk&gl=de&client=safari https://webcache.googleusercontent.com/search?q=cache:RV8OOz...
luc4sdreyer 7y ago
Seems like the post has been taken down.

539 points by scottlocklin 7y ago

I can attest. while doing research in a T1 university, all the professors were mildly disgusted by the hype pushed out by startups, and even Google's own internal marketing department. Nonetheless they too are minting the same nonsense in the "introduction" part of academic research, it's a clear case of everyone is playing the game, so "I have to play or be left behind."

ImaCake 7y ago
I used to work on fundamental molecular microbiology. We looked at what happened when DNA replication went wrong in E. coli. What I used to do when writing or speaking about it was to start with cancer or antibiotic resistance as if anyone in my field gave a crap about either of those topics. Sure, we do care about those things in the broad sense, but we didn't consider ourselves to be on the front line of solving either of those problems.
- deleted 7y ago
  [deleted]

antonvs 7y ago

Paywall.

3xblah 7y ago
Not when you have Javascript turned off.
gumby 7y ago
Just delete your medium cookies.

kranner 7y ago

https://outline.com/FP487e https://outline.com/FP487e

gok 7y ago

"I was able to bullshit about A.I., so the whole field is bullshit."

moneytide1 7y ago

These types of AI promotion seem to be a sort of cop-out that suggest we all look forward to a hands-off future where computers will be able to do everything for us. Then human minds will be allocated away from thoughtful interaction with their environment and into an all-hands-on-deck scenario where neural net operations are given top priority so they can churn out some answers.

tachyonbeam 7y ago
My main short-term fear is that increased automation will lead to an increasingly isolated society. I can already get almost everything delivered through amazon, order takeout through an app without speaking to anyone. Watch movies on Netflix without needing to go to a video store. What's the world going to be like when drone deliveries become a thing, and I don't even have to speak to a delivery driver? How will it affect kids if they do all their schooling online? I think that, even before AGI happens, AI assistants will become placeholder friends for a lot of people. You'll be able to have a conversation with Siri or Alexa. Eventually, people might have pseudo relationships with robot boyfriend/girlfriends. Imagine having a friend who is anything you want them to be, does everything you want, and most importantly, never challenges you or tells you anything you don't want to hear. People will get used to that, and it will become difficult for them to have real human relationships. In other words, technology is enabling everyone to function without directly interacting with others. People might choose not to interact with other humans out of convenience, insecurity, fear. Japan already has a population of "herbivores", people who choose not to get into relationships, and the rest of the world could become like that too. I hope we find a way to reverse this trend. Short documentary on hikikomori in Japan: https://www.youtube.com/watch?v=wE1UIK85E3E https://www.youtube.com/watch?v=wE1UIK85E3E
- moneytide1 7y ago
  Your short-term fear is seeming to be validated over time as your listed "person-less" capabilities are constantly being implemented. I myself have lived brief bouts of the hikikomori lifestyle, although I still provide for myself as opposed to being taken care of by family as many are in the video you linked. But over the past few months, I've been prioritizing face-to-face conversations, especially with new people. This is the timeless, natural "neural net": When we interact in this way, we isolate certain topics that are more relevant to our modern condition because both parties involved understand there is a time constraint. There is a sort of natural algorithmic process going on in both our minds because we are calculating the ideal things to say with this "new person". If we are speaking in proximity to other people, our conversation can take on a whole new shape because there is a "public" element and perhaps we are trying to dialogue for an audience that could eventually participate if they choose to. All of these mechanisms that keep people in check with each other are completely lost when out-sourced to programmed automation and looped control. Perhaps it is not all bad though, because ordering through services like eBay/Amazon quickly and efficiently could ultimately save resources/emissions as we are having items routed to us that would historically take up space in a brick/mortar location. But the concern we are sharing here is that community is compromised when everyone has the option to anonymously do everything.

mindgam3 7y ago

> The technologists know it’s bullshit. Fed up with the fog that marketers have created, they’ve simply ditched A.I. and moved on to a new term called “artificial general intelligence.” Not to detract from an otherwise excellent BS takedown, but unfortunately the author fails to mention that there’s a non-zero possibility that AGI itself is merely taking the bullshit to the next level. It continues to astound me how some technologists actually believe AGI is not just inevitable but around the corner. When to my naive perspective (as a machine learning rank amateur but with several decades experience as a professional human being) all I see is machines that can do some form of pattern recognition, but nothing resembling the common sense that the words “general intelligence” seemed to indicate at one point. Minor quibbles about truth and meaning of words aside, I have to support any article that skewers the soft underbelly of the phony AI ecosystem as effectively as this one does.

povertyworld 7y ago
While it's true the AI hype is getting a bit overboard, couldn't one make the argument that humans, and to a lesser extent other animals, are just a collection responses to pattern recognition like when I see a thing that looks like this I should eat it, but when I see a thing that sounds like that I should run away? After all, what is common sense but "believe it when I see it" type thinking which is most certainly pattern intuition? The overhype part is that there are armies of lowly paid Mechanical Turk tier workers tagging all these data sets, so that these pattern recognition algorithms have something to train on.
- Thorentis 7y ago
  Sure, one could make that argument. But suggesting that we are close to creating a general intelligence on-par with humans is entirely unfounded. Even if we assume that the only thing differentiating humans from other forms of intelligence (including artificial neural networks) is more pattern to learn, then that still requires data sets millions if not billions the size of what we currently have. Plus, we know very little about the types of patterns we would need to train an AI on in order for them to reach the AGI level. I think it's safe to say that "artificial general intelligence" is just another iteration of the BS Industrial Complex.

waynecochran 7y ago

Getting ready for the next AI winter.... this is a cyclic phenomena.

raverbashing 7y ago
Hopefully we don't take decades again for a simple but important change like changing tanh to relu activations.
- dijksterhuis 7y ago
  my bet is on capsule networks, Hinton is usually on point with his stuff
tim333 7y ago
Or the singularity. Then it isn't.
- waynecochran 7y ago
  Not yet ... not anywhere close to human neural cortex.... still several orders of magnitude to go...

colechristensen 7y ago

Progress of civilization could be summarized in the slow march of BS elimination parallel with the creation of creative new forms of BS (people don't actually learn anything, they just form the same crazy opinions about something new) Strikeout "of Phony AI." The BS-Industrial Complex is huge and the rise of the Internet has made it worse by empowering the less-informed to share ideas. That is somewhat the price you pay for progress. The hopeful idealistic information superhighway myth of the 90s turned into something else.

ethbro 7y ago
I look at BS as an inevitable symptom of the Singularity. As we approach the capacity of human reason, fewer people are able to keep up with the world, and are therefore more susceptible to it.
- colechristensen 7y ago
  I don't know, look back two thousand years and you see plenty of it. More like it's a symptom of humanity. Animals are stupid machines, humans aren't nearly as far away from them as we'd think ourselves.

solidasparagus 7y ago

The author seems confused about what artificial general intelligence is. People have not meaningfully moved towards AGI - it's still a distant pipe dream. The closest we've gotten is probably a Dota bot that's pretty good as long as you give the bot a huge advantage. Which is an incredible piece of technology, but about as close to AGI as an ant is to a human.

Causality1 7y ago
Not even an ant. If AGI is a human then what we have is the equivalent of synthetic RNA molecules.
- klmr 7y ago
  What? Ants don’t have general intelligence, and even ant colonies’ decision making (= simple swarm intelligence) is readily replicable in a programmed system, and has been, for a while. I don’t think a gradual scale is very helpful because I don’t think that the progression from current-generation AI to AGI is going to be gradual (it will require at least one paradigm shift). That said, if you want to compare AI progress to actual animals then our current-gen AI way beyond ants. Note that, while we haven’t fully mapped the neurons/connectome of ants yet, this is unnecessary to emulate their decision-making power. And we have mapped (and can simulate) the full connectome of simpler animals (e.g. C. elegans, P. dumerilii) so we’re definitely a long way beyond single molecules.
  - computerex 7y ago
    If you are referring to the open worm project, then the conclusions you have drawn are exactly the opposite of what I have drawn. As I understand it open worm is a hodge podge of statistical and numerical methods to try and replicate the sensorimotor behavior of c elegans. Open worm is neither complete, accurate nor elegant, despite knowing c elegans connectome and having mapped the some 900 cells in the worms body.
    - klmr 7y ago

m0zg 7y ago

There is a lot of froth, as in any hot field. However, unlike before, there are many cases where AI actually works now. Some perceptual tasks work better than a human, in fact. We can quibble about the naming and whatnot, but that's not something you can say about the last AI winter. It's sort of like dotcom bust of 00, sure things imploded back then, but there's no sign whatsoever e-commerce will implode at any time in the future because unlike before it actually works this time.

ethbro 7y ago
> Some perceptual tasks work better than a human, in fact. [...] that's not something you can say about the last AI winter Eh. I'd say that's somewhat apples to oranges. A) There were some useful and successful expert systems. B) Things seemed to be going swimmingly, until they hit a fundamental wall. C) We're working with a few orders of magnitude greater compute than they had access to.
- m0zg 7y ago
  Sure, but we did figure out how to make things more robust and generalizable, at least for perceptual tasks so far. Knowledge representation and probabilistic reasoning are still non-existent, though. Moreover, nobody is even working on any of that, for fear of being compared to Doug Lenat.
  - snaky 7y ago
    So robust and generalizable that adding a pixel here and a pixel there, too small to be even recognized by a human, confuse image recognition system to the point it confuse panda with gibbon?
    - Quetelet 7y ago
      These adversarial examples are generated in a very artificial way that will not be present in natural images (and if you’re thinking of security issues, the attacker needs access to your model...) They’re still an interesting topic to explore but hardly evidence that neural nets don’t generalize.

mattigames 7y ago

Everything is bullshit until is not, humans were talking about transportation without using animal forces for decades before it became a reality, and a lot of people were highly skeptical of such thing being even possible until it actually happened in 1804 (first steam train), same thing happens with Artificial Intelligence, and we are in such uncharted territory that someone could say AGI is just 10 years away and someone else say 100 years away and both get the same amount of credibility, meaning near none cause we don't even know what is that we don't know to achieve AGI.

tim333 7y ago
Unless you believe the Kurzweil argument that we will figure what is needed from reverse engineering the human brain in which case you guestimate a timeline.
dboreham 7y ago
Your example isn't quite as it seems : "trains" (cars running on rails) were used in mining for hundreds of years prior. The steam engine was first documented in 1698. What happened in 1804 was someone figured out the manufacturing processes to make a steam engine light enough and powerful enough to usefully pull a train of cars over some reasonable distance.

dreamcompiler 7y ago

This happened right before the first AI Winter in the late 80s: AI (in the form of expert systems) solved a number of hard problems and was hyped as being able to solve every problem. Reality set in when we figured out: 1. It didn't scale and 2. Getting 80% of the problem solved was easy, but getting that last 20% was very, very hard. Maybe several orders of magnitude harder than the first 80%. Nowadays we don't seem to have problem 1 quite so much, but problem 2 is still there in a big way. Witness self-driving cars, where driving on an interstate highway in broad daylight is easy, but driving through a snow-covered construction zone at night is impossible. Or just dealing with a bicyclist on the road without killing them. We're not going to have AGI any time soon.

computerex 7y ago
Right. There has been a lot of progress within the last decade in narrow AI, but AGI remains as elusive as ever.
misterman0 7y ago
>> We're not going to have AGI any time soon. In your mind, how many inventions are we from GAI? 1? 1000? Also, given we have our very brightest working in the problem space (MS, Google, FB, OpenAI ...) how long would each remaining invention take? It seems plausible to me that we are only one invention from GAI, one novel combination of two or more existing solutions and that this may come from anyone and at any time.
- dorkwood 7y ago
  Applying this method of thinking, is there anything that we aren't one invention away from?
  - misterman0 7y ago
    I'm just trying to stay positive. If you talk optimistically about AI (or big data for that matter) you quickly get downvoted these days. I'm curious as to why that is. I thought this was 'marica. Isn't this 'marica? Edit: my apologies, that was condescending. I should have finished of with a wink of an eye.

lkrubner 7y ago

I've been collecting examples of where the ads that I see are based on extremely simple algorithms of the type that could have easily been supported 30 years ago, and yet I keep reading articles that suggest that the advertising industry is deploying sophisticated tools to target ads to me. I wrote about this recently: ------------------------- Despite much talk about Machine Learning and AI improving advertising results, what I’m seeing is getting worse and worse. Despite billions invested, the ads shown to me are much less relevant than that ads that I saw on the Web 10 years ago. I hired 3 developers from Fullstack Academy. They were all great, so I went and checked out the website, curious about the curriculum. And now, every website I go to, I see an advertisement for Fullstack Academy. (See screenshot.) I’ve been writing software for 20 years. I’ve written semi-famous essays about software development. I am not going back to school. I do not need to go to a dev bootcamp. So why show me ads, as if I’m thinking of going to school? For the last several years I’ve been seeing articles about the surveillance economy. In theory, advertisers know more about me than ever before. In theory, they know about my entire life. And yet, the ads I see are less targeted than what I used to see online 10 years ago. http://www.smashcompany.com/business/when-will-machine-learning-and-ai-improve-advertisings-ability-to-target-people http://www.smashcompany.com/business/when-will-machine-learn...

dijksterhuis 7y ago
I willing to take a punt and guess that the models they’re using are using short term/isolated data, not 10 years worth of your entire browsing history.
- plaidfuji 7y ago
  Haha, yeah. When n=1, both human and computer will say “keep doing what you’re doing!”
foldingmoney 7y ago
Apparently you didn't get the targeted ad telling you that advertisers' dark patterns have become so sophisticated that free will is literally fiction now. /s, obviously
7y ago

galaxyLogic 7y ago

What's the most difficult thing AI should be be able to solve but can not as of yet? I would say it is writing a program which writes an AI program. Why? Because it is so difficult for us to define what exactly an AI program should be able to do. This shows that we have an issue with not being able to ask the right question. If we could answer exactly what the AI should be able to do then it would be much easier to create such a program and also create a program that writes such a program. We could say that an AI program should pass the Turing Test and many have written programs that more or less pass it. But so now, write a program that writes several different programs that all pass the Turing Test one better than the previous one. I don't really have an idea how I would start writing such a program that writes a program that passes the Turing Test better than previous AI programs. That makes me guess we are still far off from General AI. But I of course may be wrong, just because I don't know how to do something does not mean others would not.

jbay808 7y ago
By this argument, humans also aren't general intelligences, because we haven't been able to write one yet either.
foldingmoney 7y ago
>We could say that an AI program should pass the Turing Test and many have written programs that more or less pass it. But so now, write a program that writes several different programs that all pass the Turing Test one better than the previous one. That's essentially what machine learning is, though.
IRLIamOffline 7y ago
I agree that defining a goal along with metrics would be very helpful to make meaningful progress towards AGI. However, defining this test is extremely hard, to a point where I'm not sure if we could define a test like this. So far it seems, that just by defining a test we can come up with a narrow AI that optimizes for this test.
chrshawkes 7y ago
We know what we want it to do, we want it to have some basic ability to think for itself. That is something we just simply can't do. Back propagation is far from a spanking for acting out of line. AI has no ability to understand it's acting like a fool or how to deal with uncertainty with emotions which cause us to act without regards to consequences and in many cases reality. It lacks understanding of what future consequences its trying to prevent such as our daily decisions to get up and go to work each morning. The AI has no understanding of it's future and the consequences of not going to work until it's fired 40,000 times for not showing up or it's children are taken from him/her/it. I'm glad people are finally waking up to the fact that AI is not ML and AI is all hype at the moment. Google used algorithms quite effectively to adapt and learn, but they have no greater understanding of what we want, just what we and others have wanted in the past.

dijksterhuis 7y ago

I despise the term Artificial Intelligence. This is all PROBABILISTIC MODELLING. Nothing to do with AI/AGI/whatever. The computers aren’t thinking or learning. It’s just modelling fancy probability statistics. E.g. classical neural networks are basically a load of linear regression equations with an activation function stuck on the end of each of them. No magic. Just lots of linear regression. This stuff only works when: 1) you are trying to solve a specific problem that is suited to probabilistic models 2) you have a data set that is sufficiently large, varied and specific 3) the model is developed, trained, tested, implemented and updated in a rigorous and sensible manner

Quetelet 7y ago
Actually most modern neural networks are not probabilistic, they are deterministic function approximators. Also your point 3) isn’t quite correct either, often a “standard” architecture and training procedure (e.g. ResNet50 with Adam) will work on a new task with sufficient training data and minimal modification of the model.
- dijksterhuis 7y ago
  The only nnets I mentioned were “classical” as a purposefully over simplified example. Yeah, they can model any function, but historically they were used for probabilistic density functions (if I remember correctly). Most of what the article talked about can be done with much simpler models, which is what I get peeved about. Also, yes, you can transfer learn with resnet. But if I throw my bank statements at it, it’ll do bugger all. Similarly, if I throw new images at resnet in a silly way, it won’t transfer properly.
  - Quetelet 7y ago
    You might be confusing the historical use of the sigmoid activation function with probabilistic modeling, neural networks in the 80s were used similarly to how they are today, albeit at a much smaller scale due to hardware limitations at the time. The development of neural networks is a major contribution of the machine learning community, so even if you’d like to split hairs about whether the “computer is learning” (“learning” has a a precise technical definition by the way), NNs are not “just statistics.”

ackbar03 7y ago

Of all the hypes going around (blockchain mostly lol) I think ai is going to have the most substance to it though. I would say the breadth of problems being solved are much wider and there is still a lot of research which hasn't really found its way to actual implementation yet

stareatgoats 7y ago

> The BS-Industrial Complex of Brilliant! This is really a thing, and the computer industry is (and has always been?) rife with it.

harry8 7y ago
IBM Global Services. Oracle. Accenture. Any company with 100+ employees who does consulting involving the design, implementation and maintenance of computer systems for any government bureaucracy. Is there anyone around here who thinks this industry sector is something else than industrial grade BS and if every single one of those companies disappeared overnight that we would not be in a better place as a civilization very, very quickly as we were forced to pick up the pieces. Industrial quantities of BS are the norm, right? Most of us do startups to do something more than to schmooze, threaten and ultimately bilk customers paying with other peoples money. We kind of want to do tech.
- adev_ 7y ago
  > Most of us do startups to do something more than to schmooze, threaten and ultimately bilk customers paying with other peoples money. Do you know Theranos ? That's the definition itself of bullshit and it was a "startup". https://en.wikipedia.org/wiki/Theranos https://en.wikipedia.org/wiki/Theranos Bullshit comes from 5000+ employees to companies with 5 dudes. Scale does not change anything. Business culture, profit as only value and the culture of fake it until you do it are the source of the problem. And against that their is not magic solution, excepted trust a lot less the ones that speak and trust a lot more the one that do. In the good old Nerd world, we named that Show me the code

atoav 7y ago

At the Biennale in Venice (one of the most important art shows there is) I saw a work which looked like this: There was a metal frame holding two glas plates with ventian sediment inbetween (sand, soil, mud). In the center there was another metal frame which formed a hole. There also were to PCB boards with ATMEGA micro controllers. In the text the artist claimed she controlled the biome of the soil with an AI using various sensors and pumps. This was clearly a fake, as you could see nothing like that on the PCB. Accidentally (?) she managed to create the best representation of AI I have seen in art: all that counts is that you call it AI even if it is a simple algorithm. AI is the phrase behind which magic hides and people love magic. Everything that has the aura of “humans don’t fully understand how it works in detail” will be used by charlartans, snake oil salesmen and conmen. If even artists slap “AI” onto their works to sell it, you know we are past the peak now.

solidasparagus 7y ago
We might be headed for the trough of disillusionment, but in terms of technology and industry applications, we are still in the early phase of modern AI (big data + ML).
- VBprogrammer 7y ago
  I'm doing a bit of hiring at the moment. It's hard to find a single CV which doesn't have some kind of machine learning slant. As much as I think there are plenty of advances in ML left to take, I doubt every graduate will step out into a good application for existing machine learning tools.
  - misterman0 7y ago
    Just like not all astrophysicists reach Nobel prize level achievements not all machine-learning graduates will innovate in their field. But to help you out, in order to find people who are likely to innovate, find out if that is their goal. If it is not then you know not to hire them. But if that's their goal, maybe start some kind of "program" where you give these people a chance. 6 months. A year maybe. If the are close to something or if you're pleased with them even though they aren't really innovating, then give them a "tenure" so to speak. An evaluation process, it's very common here in Sweden. I mean how much money can you really loose in six months? You can kick that shithole straight out day one if that's your perogative. It's a good deal for both parties.

nl 7y ago

AGI will arrive as soon as someone can arrive at a reasonable definition of intelligence. Try it. Everything I've seen is already achievable by computers.

AstralStorm 7y ago
Solving novel problems. Show me. By novel I mean multiple categories. A system that can serve as archive, mathematician, calculator, can move a robot, drive a car and additionally make coffee from scratch. Oh and talks (speaks and understands and acts upon orders) in 3 human languages at decent levels plus can roughly explain what it's doing. Oh and can learn more unrelated skills. Hey, people do it all the time.
- nl 7y ago
  A system that can serve as archive, mathematician, calculator, can move a robot, drive a car and additionally make coffee from scratch. Oh and talks (speaks and understands and acts upon orders) in 3 human languages at decent levels plus can roughly explain what it's doing. Oh and can learn more unrelated skills. I'm a bit unclear if this is supposed to be a definition of intelligence. Stephen Hawking would fail this test, but no one would argue he isn't intelligent.

yonkshi 7y ago

AGI is a gradient, not an arbitrary threshold. We are not capable of recreating human level intelligence yet, but our modern algorithms had become magnitudes better at generalization and sample efficiency. And this trend is not showing any signs of slowing down. Take PPO for example (powers the OpenAI 5 dota agent), the same algorithms can be used for robotic arms as it does with video games. Two completely different domains of tasks now generalizable under one algorithm. That to me is a solid step towards more general AI.

taurath 7y ago
It’s a gradient but according to the marketers it’s basically going to overtake humanity any week now.
- SomeOldThrow 7y ago
  For the problem you’re trying to tackle, this startup has already solved it and will show you insights previously impossible!
- yonkshi 7y ago
  I agree. I think a big part of this problem is that smaller companies usually cannot afford AI research. I would even go as far as to say there are more AI companies than capable AI researchers, and this causes a large number of faux-AI companies poisoning the AI branding.
- misterman0 7y ago
  "AI any week now" What marketers proclaim that? Are they saying that or are they saying there is _utility_ in AI, now? Because me thinks, there is real utility, now, but it's going to take years until it overtakes us. Years!
  - tim333 7y ago
    I'm not sure anyone said any week now but Musk probably came closest https://www.entrepreneur.com/article/323278 https://www.entrepreneur.com/article/323278

derka0 7y ago

The hype is BS but narrow AI in the context of automation is here. Job are so specialised nowadays (driving, cashiers, fulfilment, paralegal, diagnostician ...) that a narrow AI (i.e. a glorified automation algorithm) that can do just 10% better at a cheaper cost will take down the job. The confusion is real (AI, AGI, terminator...) but pattern recognition softwares powered with big data has already proven business value and are here to stay.

AstralStorm 7y ago

Next: NoAI, like NoSQL. All natural real intelligence, fully organic and explainable. Just add caffeine. ;)

j88439h84 7y ago
Brilliant.

mrbanks 7y ago

i.e. Babylon Health

wolfi1 7y ago

if there is no natural intelligence around, you need an artificial one

ecmascript 7y ago

I think honestly think westworld (yes the tv-series) has the best explanation of why general intelligence is a hard problem to solve. They mention consciousness but I think the same apply to intelligence in general. Humans in my mind aren't different from say a program you write except that we have a lot more input and possible outputs depending on a much larger variant of external variables. If we could build machines that have eyesight just as we do, muscles just as we do etc I'm sure we could reverse-engineer the human being. https://www.youtube.com/watch?v=S94ETUiMZwQ https://www.youtube.com/watch?v=S94ETUiMZwQ

toxik 7y ago
I find this analysis reductionist. You're basically saying "brains aren't hard to reproduce once you have biological sensors and actuators." Why not? They're _extremely_ delicate, intricate organs. Claim 2 is also a difficult one: of course you can easily claim consciousness doesn't exist, but it is impossible to argue by logic. You need a metaphysical philosophical framework, and then it's already left the realm of empirically observable truths.
- dspillett 7y ago
  I'm not sure the claim is that consciousness doesn't exist. More that it is an emergent property of complex systems rather than something that is (or can be) deliberately programmed.
  - ecmascript 7y ago
    Precisely. It's the complex system that gives us an illusion of consciousness. At least, that is what I naively believe in since there is a lack of evidence for anything else.
    - goatlover 7y ago
      So you think experience is itself an illusion? When you kick a rock and feel pain, you're not really experiencing pain? Is the rock also an illusion?

Iv 7y ago

"Deep Learning projects are typically written in Python. AI projects are typically PowerPoints."

macawfish 7y ago

Ben Goertzel.

Barrin92 7y ago

In my opinion the term intelligence itself is misplaced for machine learning tasks. Every problem that is solved with ML and "big data" appears to me to be a perception problem (which wouldn't be surprising because the mechanism is inspired by human vision, not cognition, which it lends itself to naturally). As a specific example, a few months ago or so openai released their text generation tool and branded it as "too dangerous too release", claiming it could , with the help of AI, generate believable texts. But what it generated was simply natural sounding gibberish. There were plenty of sentences in the text along the lines of "before the first human walked the earth, humans did.."" What, for me at least, lies at the core of intelligence is understanding semantics. An intelligent system can recognise the sentence above as flawed because it could extract meaning. Everything coming out of the field of ML seems to me just like sophisticated statistics. In many ways symbolic AI to me still seems more valuable, profit aside.

kaolti 7y ago
I agree. Extracting meaning is NOT a math problem, meaning comes from the human context and context is infinite. Hence different humans extract different meanings. I think AI and ML are great for processing large amounts of data and looking for patterns. Patterns on their own don't mean anything though, it's always up to us to interpret them.
Wiretrip 7y ago
In the text generation tool outlined above (and indeed many of the convnet-based visual networks), the hidden layers are there precisely to extract 'meaning'. The lower layers (closer to the source input) deal with syntax and feed upwards to hidden layers that extract semantic features, which in turn feed upwards to more layers, each with a bigger overview of the semantic features and thus ultimately the context. That's the idea anyway.
- foldr 7y ago
  >the hidden layers are there precisely to extract 'meaning That is just wishful thinking, no? I mean, there is no particular reason to think that the hidden layers will actually do this with any high degree of success.

Wiretrip 7y ago

For a real emperor's new clothes moment, look at SpinVox! https://en.wikipedia.org/wiki/SpinVox https://en.wikipedia.org/wiki/SpinVox

benreesman 7y ago

If everyone sophisticated enough to be on this site would just use the term “applied computational statistics” (even just in their own thoughts) instead of “deep learning” or AI, the world would be a better place. Gradient descent finds some fun minimia (my current venture is heavily based on that idea) but to assign more agency to Adam or RMSProp than they merit is just an exercise in feeding the trolls.

mbeex 7y ago
Couldn’t agree more. All these delusional discussions: Is it intelligence? Is it true intelligence? How far is it to become true...? Skynet rising? To be fair: The last question is certainly adequate regarding the application of unverified/-able algorithms in a life-changing incarnation as a virtually unsupervised decision making quality. This is horrible. But it is another question (and better answered skipping the pseudo-philosphical part)
YeGoblynQueenne 7y ago
Could you please explain in what sense deep learning is "applied computational statistics"? What about classical planning, SAT solvers, automated theorem proving, game-playing agents and classical search? Could you please explain how one or more of those are "applied computational statistics"? Further- I don't understand the comment about "agency". Could you clarify? Why is "agency" required for a technique or an algorithm to be considered an AI technique?
- plaidfuji 7y ago
  I don’t know anything about the underlying algorithms for the examples you rattled off, but deep learning trains a graph of neuron weights such that they are statistically optimized to minimize error in computed output labels for some domain of input data. Very much “applied computational statistics”.
  - YeGoblynQueenne 7y ago
    The examples I gave are classic AI algorithms that are very easy to look up on wikipedia. They do not compute any statistics. I'm not sure what you mean about "neuron weights that are statistically optimised". Modern-era, deep neural nets train their weights with backpropagation, which is basically an application of the chain rule, from calculus. They do not use statistics for that. For example, calculating the mean of a set of values or calculating the pearson correlation coefficient of two variables are computations typical in statistics. Could you please clarify what you mean by (applied) "computational statistics", so that I don't have to double-guess you? Edit: Do you really not know what a SAT solver is? Not to be rude but if that is the case, from where do you draw your confidence about the correct terminology to use for AI?

mikorym 7y ago

So can I call it "second year linear algebra" now instead of "AI"?

bjoernbu 7y ago

Imho it has gone further. In a way, all the things described as not actually AI now "are" AI, because the term AI has been used in that way so many times. I don't think we'll ever use a better (more accurate) term for the ML- and data-driven value current systems create. Instead "true" AI will get a new fancy name to build the next hype around in several year.

East-Link 7y ago

Rudimentry machine learning algorithms are indeed AI, by common usage. Try typing into Google Images something like "ai machine learning deep learning venn diagram" and you'll see that by common usage, machine learning is a strict subset of AI.

throwaway287391 7y ago

> In this way, Dynamic Yield is part of a generation of companies whose core technology, while extremely useful, is powered by artificial intelligence that is roughly as good as a 24-year-old analyst at Goldman Sachs with a big dataset and a few lines of Adderall. For the last few years, startups have shamelessly re-branded rudimentary machine-learning algorithms as the dawn of the singularity, aided by investors and analysts who have a vested interest in building up the hype. Welcome to the artificial intelligence bullshit-industrial complex. As an AI researcher, I think a lot of people are a little too sensitive to the term "AI" and make a lot of big assumptions upon hearing it. It's a very general term that doesn't really imply any particular degree of complexity or sophistication. Labeling simple machine learning algorithms and heuristics as "AI" isn't at all unique to this era of hype that began in the last ~5 years -- rather that's how the term has been used in academia for many decades. If you took a college class called "AI" or looked up some of the most popular textbooks on AI [1], you'd find that a lot of it is dedicated to search algorithms (breadth-first, depth-first, A*), linear classifiers, and feature engineering. If you think "artificial intelligence" is a bad name for these things, fine -- but don't blame the recent wave of hype, this is what the term AI means and has pretty much always meant. So go ahead and call your startup's linear regression "AI", and if the VCs leap to fund you under the impression that it means you'll be behind the singularity, that's on them. AI != deep learning. AI != AGI. [1] e.g., "Artificial Intelligence: A Modern Approach" by Russell and Norvig

chewz 7y ago

> The Turk, also known as the Mechanical Turk or Automaton Chess Player (German: Schachtürke, "chess Turk"; Hungarian: A Török), was a fake chess-playing machine constructed in the late 18th century. From 1770 until its destruction by fire in 1854 it was exhibited by various owners as an automaton, though it was eventually revealed to be an elaborate hoax.[1] https://en.wikipedia.org/wiki/The_Turk https://en.wikipedia.org/wiki/The_Turk

YeGoblynQueenne 7y ago

>> Deep learning algorithms have proven to be better than humans at spotting lung cancer, a development that if applied at scale could save more than 30,000 patients per year. It's not easy to scale deep learning because deep neural nets have a very strong tendency to overfit to their training dataset and are very bad at generalising outside their training dataset. In a medical context this means that, while a particular deep learning image classifier might be very good at recognising cancer in images of patients' scans collected from a specific hospital, the same classifier will be much worse in the same task on images from a different hospital (or even from a different department in the same hospital). To overcome this limitation, the only thing anyone knows that works to some extent is to train deep neural nets with a lot of data. If you can't avoid overfitting, at least you can try to overfit to a big enough sample that most common kinds of instances in your domain of interest will be included in it. So basically to scale a diagnostic system based on deep neural net image classification to the nation level one would have to train a deep learning image classifier with the data from all hospitals in that nation. This is not an easy task, to say the least. It's not undoable, but it's not as simple as having someone at Hospital X download a pretrained model in Tensorflow and train its last few layers on some CT scans.

bodono 7y ago
This statement is false, as recently demonstrated by DeepMind on retinal scans. Not only did they generalize outside of the training dataset but they were able to use the features learned by the model on an entirely different type of scanning device. https://www.nature.com/articles/s41591-018-0107-6.epdf?author_access_token=PAbvHEuv_YYmrPVbG5HqKdRgN0jAjWel9jnR3ZoTv0P43NEH20hFuvBoJk6cvICihn8kmL6tmejFlnuPlbT_0KmJgK6N07SPh_ZLy0Nxb0-LAGIDBaH1fjJTkD9ahUEQpRlEudtlG9E1v3ca9xNQcQ%3D%3D https://www.nature.com/articles/s41591-018-0107-6.epdf?autho... "Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device."
- YeGoblynQueenne 7y ago
  In the paper you link to, the researchers trained an image classifier on data collected from 32 sites of the Moorfiel NHS trust. The trained model was tested on, presumably held-out, data from the same dataset. This is an example of scaling a model beyond a dataset collected from a single site. It is not contrary to what I say in my comment. The researchers further tested their model on data obtained from a different device than it was originally trained on. This data was collected from the same hospital sites. The original model performed poorly on this new data and was re-trained to improve its performance. This does not demonstrate an ability to generalise to unseen data- only an ability to adjust a model to new data, by re-training.

nottorp 7y ago

Is Medium for pay now? They told me to sign up to get "one more free story".

Veedrac 7y ago
Medium lets writers opt-in to a paywall. It is not the default, but does come with some perks for authors.

holografix 7y ago

Repeat with me: Machine Learning != AI

a_imho 7y ago

It is pretty much spot on, but I'm not convinced anyone should really care. When was software not hype driven?

pron 7y ago

In 1949, some years after the invention of neural networks, Norbert Weiner, one of the leading minds of the time, was convinced that AI (AGI as you may call it) or a full understanding of the brain is no more than five years away. Alan Turing thought Weiner was delusional, and that it may take as much as fifty years. Seventy years later, we are nowhere near insect-level intelligence. I don't see any fundamental barrier preventing us from achieving AI, but if someone from the future came to me and said that AI will be achieved in 2130, I would find that quite reasonable. If they said it will be achieved in 2030 or 2230, I would find those equally reasonable. Our current scientific understanding is that we have no idea how far we are from AI, we don't know what the challenges are, and we don't even know what intelligence is. We certainly have no idea whether the approach we are now taking (statistical clustering, AKA deep learning) is a path that leads to AI or not. In the sixties, the leading minds of that time were also working hard on the problem and did not find it any further away from us as we do today. That some people are optimistic is irrelevant. The fact is that we just have no idea.

cr0sh 7y ago
> Seventy years later, we are nowhere near insect-level intelligence. That's arguable: For instance, we have the entire connectome of c. elegans mapped out; we can easily simulate it, and it seems to act the same as the actual nematode. So, in one sense, we are at that level. However, we still have no clue how such a simple system actually works to produce the level of "intelligence" it has. So in that sense, we're not at that level at all. > We certainly have no idea whether the approach we are now taking (statistical clustering, AKA deep learning) is a path that leads to AI or not. One clue we do have: We may not be on the right path with that method; it's something the "grandfather" (or whatever) of AI (Hinton) has mentioned, and which I have stated before about... That is, the fact that we currently have no understanding of the mechanism by which biological neural networks implement anything like "backpropagation". From what we currently understand, as I currently understand it, we have yet to find such a mechanism that would allow for it. It's also one of the leading reasons why our current artificial neural networks consume so much power, as compared to biological systems...
- 7y ago

plaidfuji 7y ago

Sure, “AI” as it is used today implies “software that codifies decision-making using data”. No, it’s not the T3000. But as the author acknowledges: > Dynamic Yield can pay for itself many times over by helping McDonald’s better understand its customers Ok, so it’s not hype - it is delivering real value. “AI” is just a marketing term to help C-suite suits and Silicon Valley sales reps get on the same page about what’s being sold with as few words as possible. What’s being sold is software that helps make optimal decision using data. AI isn’t a rigorously defined academic term, so people will use it how they want. It’s only hype when real value isn’t delivered.

epr 7y ago
> What’s being sold is software that helps make optimal decision using data Doesn't this apply to virtually all software?
- plaidfuji 7y ago
  In an extremely reductionist sense, maybe. Do I use Microsoft Word to automate decision making? No. Does Facebook help me make important life choices? Heh. How about this: Amazon.com is not AI, but their recommendation engine is.
  - deleted 7y ago
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arbuge 7y ago

Consider this article: http://fortune.com/longform/single-family-home-ai-algorithms/ http://fortune.com/longform/single-family-home-ai-algorithms... If you read it, you'll find that their methods to value homes and renovations are based on algorithms written to value mortgages in the 80s, 90s, and early 00s. I'm going to bet that there's not much of what the average HNer would think constitutes AI going on in there.

dr_dshiv 7y ago

We should be focused on designing "smart systems" that optimize measurable outcomes Who cares how complex the algorithm is! What matters is that it works better. Is there a measurable outcome that matters? Can the system optimize that outcome over time, through a coordination of human processes and technology design? That is what organizations need. Not hyperparameters.

orpep90nxkfo 7y ago

This reminds me of the article the other day about the internet being an SEO wasteland Basically our business networks run the same way (not a shock at all): sycophants spam aristocratic investors with half assed bullshit solutions to juice the odds of hooking one

JustSomeNobody 7y ago

This is no different than anything else. You hype what you're working on so people get interested and throw money at you. AR/VR glasses, AI, self driving cars, it's all the same. You generate interest, make lots of money and who cares if it ever gets to market.

bernardv 7y ago

I totally agree with the gist of this article. This hype is being propagated by a lot of folks who are willingly clueless, as for example, in the data science crowd. This band-wagon is crowded and isn’t stopping any time soon. It irks me to no end to comme across tutorial-style articles proclaiming to teach an AI algorithm, also known as ‘linear regression’. What bugs me the most though, are the countless ‘influencers’ on LinkedIn which spew rubbish about machine learning, AI and all the wonderful things that are just around the corner. Lastly, it doesn’t help when countless articles/books are written on the subject of AI dangers, AI ethics and are ‘robots coming for us?’. These add fuel to the fire of hype. In the end, this behavior will only guarantee the eventual blowing-up of the bubble, when promises are not delivered.

cirgue 7y ago

There is a massive positive, though, for the 'geeks building the future": AI is where everyone else is looking. If you know where you should be looking, you have a decisive advantage over the rest of the market.

bombingwinger 7y ago
Doesn’t your last sentence go for literally everything?
- cirgue 7y ago
  Of course it does, but we can say with confidence that attention and capital are misallocated toward a specific, identifiable set of activities. That's rare.
- chrshawkes 7y ago
  Everything but the inevitable AI winter to come. :)

module0000 7y ago

TLDR; machine learning == "AI", just as much as colocated servers == "cloud"

DonHopkins 7y ago

In 1996 I made this AIML (Artificial Intelligence Marketing Language) parody by taking an actual VRML article from some shameless trade rag, and globally replacing "Virtual Reality" with "Artificial Intelligence". (from "ArtificialPostModernIntelligenceInterActivity", V2 #4 April 1996, p. 20) https://www.donhopkins.com/home/catalog/text/SupportForAIML.html https://www.donhopkins.com/home/catalog/text/SupportForAIML.... Another closely related technology is BSML: Bull Shit Markup Language. (Note: most of the features described in the BLINK tag extension were eventually implemented by FLASH!) https://www.donhopkins.com/home/catalog/text/bsml.html https://www.donhopkins.com/home/catalog/text/bsml.html At one point years later, somebody actually emailed me, asking me to take it down, because they were developing a "real AIML [TM]" product, and found my parody of their unique original idea to be beneath their dignity, distracting, and confusing to their potential customers using google to search for their prestigious "AIML" product.

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diehunde 7y ago

The problem is when you work at a company that tells you, "we are different, we are not BS like the other A.I. companies"

tabtab 7y ago

Just AI? IT is filled with BS and fads. Dilbert is a documentary, not just a comic strip.

nsajko 7y ago

It seems the author has deleted the post. Maybe Dynamic Yield asked him to take it down? Anyway, currently it is accessible through https://outline.com/FP487e https://outline.com/FP487e

soobrosa 7y ago

Been deleted, cached is at https://webcache.googleusercontent.com/search?q=cache:RV8OOzgmjJsJ:https://gen.medium.com/the-bs-industrial-complex-of-phony-a-i-44bf1c0c60f8+&cd=1&hl=en&ct=clnk&gl=de&client=safari https://webcache.googleusercontent.com/search?q=cache:RV8OOz...

luc4sdreyer 7y ago

Seems like the post has been taken down.