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We have also been watching these machine learning models for 6 months: - increase the volatility in virtually every financial market they touched - be exploit
by cjhanks 6y ago
We have also been watching these machine learning models for 6 months:
- increase the volatility in virtually every financial market they touched
- be exploited by adversarial learning networks to amplify funded propaganda as news
- use poorly contrived sentiment analysis to generate incomprehensibly meaningless news headlines
These non-linear "function approximators" have absolutely unpredictable and insane non-linear behavior where learned information was non-existent or sparse.
God help us all if one of these artificial intelligence devices is driving the road and sees a red stop sign that is a square, rather than a hexagon.
- Veedrac 6y agohttps://youtu.be/hx7BXih7zx8?t=513 https://youtu.be/hx7BXih7zx8?t=513
- kordlessagain 6y ago> complicated when you get to the long tail of it Well, there's the problem right there.
- lokedhs 6y agoWatching this presentation did not make me any more confident about going in a self-driving car. Based on the way it was presented, I got the feeling that they are just essentially manually identifying cases and addressing them as they see them. Is that solution really helping to make the system more robust when encountering an unexpected situation?
- deleted 6y ago[deleted]
- faitswulff 6y agoBut it has also generated prodigious amounts of erotic fiction so that balances out some of those points, right?
- neal_jones 6y agoI honestly don’t know if this is a joke or if there is a bunch of erotic fiction I’ve been missing
- pas 6y agoDeepfakes (faceswap but for porn). Decensored hentai. And of course the question came up again: how ethical are generated pictures depicting illegal content?
- faitswulff 6y agoI was referring to AI Dungeon, actually, and yes it was a joke. But 100% true.
- eanzenberg 6y agoThis is so strange. If you use facebook, google, netflix, apple, microsoft, amazon or a whole host of other services you are interfacing with AI all the time. To think there’s no value there is asinine. Comes up a lot on HN. Seems like people set in their ways who don’t want to progress forward.
- enchiridion 6y agoIsn't copy and pasting within a thread generally discouraged?
- fock 6y agooh yeah, magnificient AI at Google search. Picked up my ebook-reader again, wanted to know about the state of linux there. so do a search: "<model of ebookreader> linux ssh" (since a good shell is the point, where you can start developing). Turns out, the first 3 pages want to sell me the same thing I already own, with one outlier selling nutritional supplements. Oh well done AI!
- bronco21016 6y agoIt’s doing exactly as it’s trained. Nudge the useds to buy more trinkets. Now just imagine how good it could be if it was being trained to actually give good search results instead of selling.
- YeGoblynQueenne 6y agoUnfortunately, Google Search (and Amazon and MS search and all three companies' assistants) doesn't work that well when it comes to selling you what you want to buy either: https://github.com/elsamuko/Shirt-without-Stripes https://github.com/elsamuko/Shirt-without-Stripes But it sure can identify stripes.
- juped 6y agoidk, the thing Google has been doing lately where they suddenly render a block of ads under your mouse as you're clicking on a result seems like the kind of thing an AI would do to increase ad clicks
- irrational 6y agoSo Skynet will be insane?
- cjhanks 6y agoMost definitely insane and probably well dressed.
- Kaibeezy 6y agoWhere are stop signs hexagons? Am I being a pedantic numpty or am I illustrating a point about the many ways errors creep in, regardless of the natural- or artificial-ness of the intelligence?
- dragosmocrii 6y agoNorth America. Where are stop signs not hexagonal?
- dragosmocrii 6y agoOh, I checked, they're octagonal xD
- FriendlyNormie 6y agoThanks for your brilliant contributions to the discussion so far, dipshit.
- somberi 6y agohttps://www.youtube.com/watch?v=C99SZRbw50E https://www.youtube.com/watch?v=C99SZRbw50E
- parineum 6y agoWhen obscured by overgrowth.
- joe_the_user 6y agoYou're being a pedantic. Human beings are tremendously better at driving than machines despite sometimes saying hexagonal rather than octagonal. Humans and current AIs both make mistakes but humans manage a kind of robustness, ability to deal gracefully with unexpected situations, that current AIs don't seem to be progressing towards.
- lopmotr 6y agoWhat happened to sensor fusion? There's no reason self-driving AI has to be as unreliable as toy or research AI. People made these same FUD arguments about computer in cars decades ago. Home computers were unreliable so cars will surely crash if their brakes or throttles are controlled by computers too.
- abetusk 6y agoIntelligence is the amalgamation of many smaller problems working together and building on top of each other. * Facial recognition/detection * Facial synthesis (deepfakes) * Speech synthesis, including mimickry * Speech recognition * Natural language processing * Gait/walking algorithms * Motion planning * etc. Complexity arises from simple units working together in parallel. We're working on the smaller, specialized problems that will, in the next generation, be put together to build more complex and complete systems. I'm no fan of the 'black box' nature of neural networks but it's clear they're getting results. As they become more accessible to the lay person, we'll see a profusion of use cases that are both anticipated and surprising. I'm always flabbergasted by the doom prediction. The path we're on seems apparent.
- cjhanks 6y agoI agree with the notion that artificial intelligence is a graph of smaller problems, as is human perception. The problem is a question of informational density. Biological systems are computationally very dense. Far more dense than the 4nm transistor fabrication available today, and with a far larger volume of size. Consequentially, the computational capability of most AI systems is far lower than its biological equivalent. And as you find in most information finite discretization problems - the lower density information system will alias against the higher information system. So, that means you will have a hierarchy/pipeline of computational stages - each aliasing reality. Eventually, you will find that your parameterization of each perceptual stage has a strange property. The size of each subsequent layer is important... but the relative computational space of each subsequent stage is even more important. Because mismatched stages results in nothing but numerical interference and noise. And I think that is where we are today. The IQ of a krill shrimp.
- natmaka 6y agoIsn't "the amalgamation of many smaller problems working together and building on top of each other" a fair description of the theorical Unix system? Aren't your criteria for "intelligence" human-centric, implying that there is no other form of "intelligence"? Aren't your criteria of the "black box" type, given that AFAIK no human can really completely explain how he recognizes faces/does NLP/walks/...?
- EE84M3i 6y agoSerious (and likely ignorant) question - what does linearity have to do with anything here? linear over what and why does non-linearity make something 'unpredictable'?
- tylerhou 6y agoI assume they are using non-linear to mean non-continuous, which implies that there can be large, hard-to-understand changes in behavior when the input is changed only a small amount.
- kccqzy 6y agoPolynomials with large degrees are continuous. It's just that they can still change by a large amount (i.e. having a large derivative) when the input is changed by a small amount. I invite you to construct the Lagrange polynomial (i.e. interpolating polynomial) for points on a nice, simple curve with some noise. They will, by definition, pass through every point given, and yet it will likely behave very badly outside the range of the given points.
- tylerhou 6y agoThere is nothing wrong with using a non-linear model, though; x^2 or x^3 regressions make sense on many datasets. Non-continuous is also not the perfect terminology, but I argue that it is more precise than non-linear: the chief idea being that the model "changes unpredictably."
- kccqzy 6y agoSure you can argue things however you want, if you also decide to ignore hundreds of years of mathematical terminology.
- cjhanks 6y agoIf you have ever opened up Excel or a similar program. One of the more useful options is to generate a regression line-fit on your data points. One option is to specify a polynomial function, you can specify how many coefficients you want. One of the measurements is the mean-squared-error between the line-fit and the points. You can add as many polynomial coefficients as you want, and you will be able to decrease the mean squared error. But the more polynomial's you choose, two things will be true: 1. The line-fit will be far more likely to go through the points. 2. At points in the line where there was no data, the line will less approximate the underlying physical reality. That same mathematical property is what is relevant here. There is nothing inherently evil about non-linearity, when the non-linear math model properly maps to the physical reality. But when you over fit a line, many of the functional solutions may be completely wrong.
- giardini 6y agoYou're right: a human driver will stop even at a hexagonal stop sign, even though most are octagonal. Much safer behavior!8-))
- yellowstuff 6y agoHow have machine learning algorithms negatively affected financial markets in the last 6 months? Markets have been volatile because information about the real world has been volatile. I don't think markets in an earlier era would have handled a global pandemic any more robustly than they did in 2020.
- ipiz0618 6y ago"AI" is a very vague term. What you described aren't entirely "machine learning", but a combination of existing linguistic techniques and machine (deep) learning. People confuse what AI can do, and what is AI all the time. It also doesn't help when there are so many inexperienced data scientist making promises that they can't achieve. In your example, I'd argue that a human is not necessarily a better driver than a machine. An attentive and careful driver is certainly better than a machine right now, but there are many who drive carelessly. While a person is unlikely to mistake a square stop sign as something else, there are so many drivers that would simply ignore the sign, and traffic lights in general. They'd also drive dangerously because of road rage, and inattentiveness. And the majority of traffic accidents are caused by these drivers. A machine is unlikely to do these. That said, until we figure out how to run all these deep learning models without a crazily expensive and power-consuming GPU, it is unlikely AI would be used as general purpose programs.
- dreamcompiler 6y agoWhether humans or AI are "better" drivers is completely beside the point. The point is that we can characterize human drivers. We know where they succeed and where they fail, both in a statistical sense and in an individual sense based on their age, attention, vision, chemical impairment, etc. But we cannot characterize ML networks. We take it on faith that they work and then we find (because somebody dies) that they run right into an overturned truck or a pedestrian or under a truck crossing the road. Until we can characterize the behavior of these systems, they must not be put in control of life-critical processes like driving.
- ipiz0618 6y agoI also agree on this. I think in terms of liability humans who one can sue when they make a mistake is more valuable than a machine. That's why in life critical applications companies who are capable of taking the risk are scarce, because when accidents happen, the company has to take responsibility. It cannot be resolved by just firing employees.
- 6y ago
- phreeza 6y agoI'd argue that the main driver of volatility over the last few months was the Coronavirus, and not AI...
- pgwhalen 6y agoI’m curious if you have a source that ML has increased financial markets volatility.