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A.I. Is Solving the Wrong Problem
- unlikelymordant 5y agoIt says I need to install the app to read this article. Is there some other way if reading it?
- deleted 5y ago[deleted]
- svantana 5y agoBrowser incognito mode usually does the trick. You never know with these "intelligent" websites though :)
- richk449 5y ago> After decades of investment, oversight, and standards development, we are not closer to total situational awareness through a computerized brain than we were in the 1970s. Hard to see how that could be true. In just about any field, computers today provide much better situational awareness than was possible in 1970. The article makes the usual complaints about self driving cars: > Despite $16 billion in investment from the heavy hitters of Silicon Valley, we are decades away from self-driving cars. Yet cars are much more intelligent today than they were in the 1970s. And we are not decades away from self-driving cars - Waymo runs self driving cars today in very specific locations. Wondering if this article is written by GTP-3.
- zepto 5y ago> Waymo runs self driving cars today in very specific locations. Specific locations where the streets are practically tracks.
- grp000 5y agoPhoenix is laid out on a grid, but you can hardly call the streets "tracks".
- fungiblecog 5y agoSelf-driving cars are much much better, but they are not "intelligent" in any sense of that word
- solipsism 5y agoFor your personal, idiosyncratic definition of "intelligent".
- fungiblecog 5y agoIn the sense that an intelligent thing can react sensibly to situations outside of those explicitly known about in advance
- rcxdude 5y agoWaymo has some excellent examples of their car reacting to strange unforseen situations appropriately. You're going to need to be more specific.
- Scarblac 5y ago"Intelligence" is anything that humans can do but we don't know how to make computers do. Once computers can do it, it's "mere computation".
- vagrantJin 5y agoI doubt we'd call locomotion and reaction to sensory feedback intelligent. Even single cell organisms are well and truly capable of that.
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- dtech 5y agoWhat self-driving cars do is closer to something an animal like a deer does, not something a bacterium does. And you'd generally say a deer uses some intelligence while moving.
- paulcole 5y agoLet’s say you and I were going to race each other by walking. You start on the east side of Los Angeles and I’m in Santa Monica. Does your lead mean you’re in a much better position than me? What if the finish line is in Amsterdam? That’s how I see AI (particularly self driving tech) today. Yes, technically there’s been advancements but we don’t even know whether it’s possible to get to the finish line today.
- richk449 5y ago> Let’s say you and I were going to race each other by walking. You start on the east side of Los Angeles and I’m in Santa Monica. Does your lead mean you’re in a much better position than me? What if the finish line is in Amsterdam? I can't figure out how to parse this. You refer to my starting location as a "lead", but they ask if it means I am in a better position - that is the definition of "lead". I think your point is that we are so far from what is needed that it is hard to know if we are even moving in the right direction. Which is a weird argument. My brother drove me in his Nissan Rogue today, which does automatic lane following. You don't have to steer your car, or use the gas or brake for many driving conditions. That is unambiguously an improvement over full manual control.
- icoder 5y agoIt's an improvement in what it does now, but I think the point is it is not necessarily bringing the end goal closer. Like a side track that runs dead at some point. No matter how much faster horses may have had become by selective breeding, that did not bring 100km/h travel closer.
- martin_a 5y agoWith regards to autonomous driving, improvements like this absolutely bring us closer to the end goal. Each improvement in adaptive cruise controls, lane following/holding assistants and any other partly-autonomous assistance system does its part in acquiring experience and technology for "the end goal".
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- js8 5y ago> Wondering if this article is written by GTP-3. FYI, it was written by a woman. I looked up her book, Kill It with Fire, and as a mainframer I have to say it seems pretty interesting. I think what she alludes to in this essay, though, is more like that AI cannot solve socioeconomic problems of humans. And even humans seem to struggle with it. Whenever I read stories where the metrics became the targets, and the like, I am reminded of Varoufakis' book Economic Indeterminacy. He doesn't give any answers there, but there is this "strange loop" in rationalism that nobody really understands. I also think that AI might be a wrong target, because you need to understand the problem before you can solve it, and once humans understand the problem, they don't need AI anymore, they just code the solution as an algorithm. On the other hand, if humans don't fully understand the problem, it's extremely difficult (except artificial circumstances like games) to explain to AI what the problem is, so it would arrive at a "reasonable" solution (and avoided, at the very least, killing all humans).
- soco 5y agoIf we can't understand the problem, will we be able to understand the solution presented by that AI? Or we'd just apply it, trusting blindly the unfathomable reasons the AI used? Do we have an AI where the decision tree can be grasped by humans?
- lokischild 5y agoIt seems inevitable to me the moment AI capacity seriously surpasses human (as a whole) capacity in any specific topic, it becomes an oracle. I hear of efforts to translate machine decision making to human understandable terms, but if it is a question of raw intelligence, it will quickly become impossible to understand.
- js8 5y agoThis is a topic of Lem's novel/essay https://en.wikipedia.org/wiki/Golem_XIV https://en.wikipedia.org/wiki/Golem_XIV. And to be honest, I wondered about that with GPT-3. Maybe it could give a more profound answer to a given prompt, but it chose not to, since it found the prompt to be too silly, and it responded in kind. Just like adults are able to entertain an imaginary universe of children. So even to explain that we want a "serious" answer might be difficult.
- mjburgess 5y ago> Yet cars are much more intelligent today than they were in the 1970s Therein lies the problem. Your definition of intelligence presumes that it is a simple quantitative scale, measuring I guess, something like "system complexity". The relevant sense here, in which no progress has been made, is qualitative -- ie., it is a distinct property. And this property has not been acquired. What is the property? It is dynamical, not formal. It is more like gravity (, pregnancy) than it is like addition. It is the ability many animals have of adaption in the shifting and challenging environments in which they are embedded. That type of adaption is not formal: it is not adaption in the sense of "updating a weight parameter". Rather, of the cells of their bodies coordinating themselves differently, and thus of their tissues, and thus of their organs, and thus of their whole brain-body system. Both from a top-down command ("I want to run now, and so my cells...") and from a bottom-up ("my cells... so I ..."). What enables animals to be fully embedded in their physical environment, to cope and adapt to its radical shifts, is this capacity. The type of "crossword puzzle" "intelligence" we obsess with is entirely derivative of this more basic --and vastly more powerful -- intelligence. Cognition is just a semi-formal process, parasitical on the body's intelligence; whose role is simply to notice when it fails and problem-solve it. We have, at best, merely the architecture of this formal reasoning. But there is still nothing for it to reason about. And in this sense, computer science has made no progress -- and indeed, cannot. It is not a formal problem.
- soco 5y agoYou have a valid point here. But it might be that for practical reasons the progress in that direction won't be needed. Like, brute forcing it might be enough to reach a level higher than we can grasp. And if we can't grasp it, it's all Greek to us anyway...
- mjburgess 5y agoThe problem with brute forcing is it requires data from the future. Physics can be solved with statistics, but only from God's POV. The future, absent information about it, is too open to solve by merely constraining models by past example cases.
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- Barrin92 5y ago>Hard to see how that could be true. In just about any field, computers today provide much better situational awareness than was possible in 1970. You sure about that? https://twitter.com/hatr/status/1361756449802768387?s=20 https://twitter.com/hatr/status/1361756449802768387?s=20 >Waymo runs self driving cars today in very specific locations Ernst Dickmann had autonomous cars on the road in very specific locations in the 1980s https://youtu.be/_HbVWm7wdmE https://youtu.be/_HbVWm7wdmE
- dtech 5y agoI don't see how this is relevant. Computers in the 1970 had no situational awareness about people interviewing for jobs. So yes, that software might be crap, it still has infinitely more awareness.
- Barrin92 5y agowhat even is "infinitely more awareness?" is that an actual metric? Computers today have exactly as much awareness as they had in the 1970s, situational or otherwise, which is none. The algorithm in question does not know what a bookshelf is, does not know what a job interview is and it does not know how the two relate. It correlates a bunch of pixels and creates the illusion of having awareness, but this is an anthropomorphization and nothing more.
- croes 5y agoSo you are saying Computer 1970: 0 situational awareness Computer nowadays: 0 * infinity = undefined situational awareness. Pretty useless.
- Workaccount2 5y agoI remember in 2015 when we were decades away from a computer topping a Go champion...
- api 5y ago"After decades of investment, oversight, and standards development, the space program is not closer to light speed travel than it was in the 1970s."
- andreyk 5y agoSuper confusing article. Title aside (which is silly since AI is a toolset for solving a variety of problems), it is just so poorly written that it's not until more than halfway through it that I think I see it's main points (that present day A.I. systems are too dependenct on 'clean' data, and some nebulous discussion of how AI contributes to decision making in organizations). And the main point wrt data quality is rather silly in itself, because plenty of research is done on learning techniques that take into account adversaries or bad data. And all the discussion wrt how AI should be used to improve decision making is just super vague and makes it seem like the author has little understanding of what AI is and how it is actually used.
- wombatmobile 5y ago> Super confusing article. Yeah I stopped reading at this point: > Facebook’s moderation policies, for example, allow images of anuses to be photoshopped on celebrities but not a pic of the celebrity’s actual anus.
- charcircuit 5y ago>People don’t make better decisions when given more data, so why do we assume A.I. will? Because humans aren't computers. Computers are much better at being able to handle processing large amounts of data than humans can. >we are decades away from self-driving cars Self driving cars already exist. In college I had a lab where everyone had to program essentially a miniature car with sensors on it to drive around by itself. Making a car drive by itself is not a hard thing to accomplish. >the largest social media companies still rely heavily on armies of human beings to scrub the most horrific content off their platforms. This content is often subjective. It's impossible for a computer to always make the correct subjective choice, no humans will always be necessary
- MattGaiser 5y ago>People don’t make better decisions when given more data, so why do we assume A.I. will? How much of this is just because it says something they do not want to hear or because there are incentives to not consider it?
- Animats 5y agoPeople don’t make better decisions when given more data, so why do we assume A.I. will? It's recognized that many machine learning systems today need very large amounts of training data, far more than humans facing the same task. That's a property of the current brute-force approaches, where you often go from no prior knowledge to some specific classification in one step. This often works better than previous approaches involving feature extraction as an intermediate step, so it gets used. This is probably an intermediate phase until someone has the next big idea in AI.
- bserge 5y agoEvery single adult person has a 20+ year learning lead on any machine, so it's a bit unfair to say humans can do the same task with less data. And computers are already better at tasks involving a lot of math, which is the main reasons they've become commonplace.
- mrbungie 5y agoYep, 20+ year learning based on 10.000+ years of wisdom taught from generation to generation. People take both things for granted when comparing humans with "AIs".
- robotresearcher 5y agoI recommend consulting people who have seen above average amounts of relevant data if you have a medical, legal, or engineering problem.
- kimi 5y agoMedium - please don't.
- vallas 5y agoPaywall is such a terrible model to spread ideas.
- abpavel 5y ago> On a warm day in 2008, Silicon Valley’s titans-in-the-making found themselves packed around a bulky, blond-wood conference room table. The author has read The New Yorker a lot. Some captivating details, made irrelevant at the end of the paragraph.
- gverrilla 5y agoThis style is very funny indeed. Sometimes it's used in my language (pt-br) aswell on some articles (probably because they bought the piece from reuters or something and translated). It's very strong in writers like Gay Talese, who take it so serious they even DRESS the style LMAO
- incrudible 5y agoI can't imagine anyone actually reading these articles in 2021. I'm pretty sure most people buy the New Yorker to put it on the coffee table, because of the decorative covers.
- lincpa 5y agoExplainable AI System uses the law model and Warehouse/Workshop model TL;DR An explainable AI system must be constructed in the following way to achieve the best results. The rule-based AI expert system is used as the logical reasoning framework, and the rule base (statute law) is used as the basis for interpretation. The dynamic rules (case law) generated by machine learning run in the rule-based AI expert system. Dynamic rules (case law) generated by machine learning cannot violate the rule base (statutory law). If the dynamic rules (case law) generated by machine learning conflict with the rule base (statute law), the system must archive it and submit it to humans to decide whether to modify the rule base (statute law) and whether to adopt dynamic rules (case law). In short, if you want an AI system to be a reasonable, responsible and explainable AI system, you must take up the weapon of law :-) https://github.com/linpengcheng/PurefunctionPipelineDataflow#The-unification-with-classic-AI-and-modern-AI-and-explainable-AI-technology https://github.com/linpengcheng/PurefunctionPipelineDataflow...
- pdkl95 5y ago> more data https://www.youtube.com/watch?v=sTWD0j4tec4 https://www.youtube.com/watch?v=sTWD0j4tec4 Negativeland has never been more on-topic.
- OrderlyTiamat 5y agoThis author makes broad sweeping claims, supprting them with numerous references that (in all instances that I checked) actually counter their argument. I'm not even sure the author knows _what subject they want to talk about_, never mind what argument to present.
- digitcatphd 5y agoYep... didn't read past the headline of the article.
- plutonorm 5y agoI waded through half of it hoping a coherent point would emerge before heading over to hn comments to confirm my suspicions.It’s a mash up of a couple of different pop opinions on the state of ML without any real insight.
- dnautics 5y agoat least one of them is true. The effort spending "cleaning data" which I think by her description she means "connecting pipes" is underestimated. I work for a company that deploys an ML model that works on a "lowest common denominator" between multiple different downstream SAASes, and this week I have been struggling with a field that should be an integer, but is a string in one SAAS provider, (and there are entries that are not parsable as integer). I can't simply convert it to a string because the ML model is not expecting it.
- truth_ 5y agoYes. "AI" (read Deep Learning/LogReg/SVM models) do indeed perform better given more data. I can vouch for this myself. And there was also a paper regarding this. Everyone is an expert in AI now!
- conjectures 5y agoWhatever it is, it should be antifragile though.
- mark_l_watson 5y agoI read the whole article and thought it was worth my time. I liked to broad strokes of goals of anti fragile AI. I have been thinking of hybrid AI systems since I retired from managing a deep learning team a few years ago. My intuition is that hybrid AI systems will be much more expensive to build but should in general be more resilient, kind of like old fashioned multi agent systems with a control mechanism to decide which agent to use.
- dragontamer 5y agoHmmm... >> Jeff Bezos’s Amazon operated on extremely tight margins and was not profitable https://www.sec.gov/Archives/edgar/data/1018724/000119312509014406/d10k.htm#tx74114_24 https://www.sec.gov/Archives/edgar/data/1018724/000119312509... Amazon made $645 million net profit in 2008, $476 net profit in 2007, and $190 million in 2006. Where did this myth of "Amazon doesn't make profits" come from? Why are people seemingly unable to check publicly shared historical 10k and fact-check themselves before making statements like this?
- lkbm 5y agoYeah, the 2008 number is wrong, but the meme comes from earlier. Its first profitable quarter was Q4 2001, three years after IPO, and it's first profitable year was 2003, six years after IPO.[0] This seems like a long time, especially for the late nineties/early 2000s. (Though tbh, IPO three years after founding feels early to me too.) Additionally, I seem to recall that they talked this up. Not "we're working on becoming profitable" but instead "We plan to continue losing money for several years. Deal with it." I believe that prior to 2016, any profitable years were pretty much entirely thanks to Q4, and they were pretty small for its size[1]. A profitable year is good, but three quarters of losses each year will stand out. Sure, they're retail, but they're also tech. Sky high margins are expected year-round. [0] https://en.wikipedia.org/wiki/History_of_Amazon https://en.wikipedia.org/wiki/History_of_Amazon [1] https://qz.com/1925043/the-days-of-amazons-profit-struggles-are-long-gone/ https://qz.com/1925043/the-days-of-amazons-profit-struggles-...
- gowld 5y agoAmazon was unprofitable because they poured all their opearating profits into growth projects, not because they were subsidizing operations with investment. Like many retailers, the business is seasonal and Q4 has more shopping. This is modeled as part of the business. We don't say lawn care businesses are unstable because they do most work in the summer.
- da_chicken 5y agoFrom what I remember, Amazon's strategy early on was to take as much revenue as it could and invest it back into itself. It intentionally ran in the red to try to grow faster.
- aazaa 5y agoLogin required. What is the problem AI is trying to solve, and what is the right problem, according to the author?
- jokoon 5y agoUnless science studies: * analysis of trained neural network so they're not just black boxes. * arrangement of real neurons in actual brains of ants, mice, flies and other small animals. * some philosophical questioning of how conscience, intelligence, awareness emerge, including a good definition and differentiation on how the brain is able to recognize causality from correlation. * some actual collaboration between psychology AND neurology to connect the dots between cognition and how an actual brain achieve it. Unless there are more efforts towards those things, machine learning will just be "advanced statistical methods", and programming experts will keep over-selling their tools. Mimicking neural networks is just fancy advertising about a simple graph algorithm.
- jjcon 5y ago1,2,4 are already occurring so I’m not sure what you’re on about. The third is completely irrelevant and seems fairly pseudoscientific, leave that to philosophers, we’re not trying to create souls. > advanced statistical methods Furthermore plenty of methods in machine learning, including some methods of training neural nets, are completely astatistical in nature. Unless you want to grow the definition of statistics to be so large as to consider all of maths and every science as ‘statistics’ these will rightly remain distinct fields of study (though they do overlap just like stats is used and overlaps with most sciences).
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- PeterisP 5y agoIt is interesting that the author assumes that the intent of industrial applied AI is to make better decisions - from my experience, in the vast majority of cases companies are applying various techniques (both AI/ML and hard-coded heuristics) with the explicit intent to get cheaper decisions, knowing very well that they aren't going to be as good as a dedicated, caring human could make them. The goal is either business process automation (do the same thing with less people) or to enable processing at a scale where doing it manually is impractical. For example, nobody would assert that an automated email spam filtering system is going to better than a human filtering my email, but an automated filter is quite useful since most of us can't afford a personal secretary. The bar for "good enough to be useful" often is lower than "human equivalent".
- lootsauce 5y agoThis point is totally valid but in the case at my work it is actually both. The old saying in marketing is "I know i'm wasting 50% of my marketing budget I just don't know which 50%." It still holds true for companies with large budgets. We have applied XGBoost to produce many and better models for how to best allocate these budgets. The results are both better and cheaper outcomes.