A skeptics view on the AI Hype - Page 1
Forum section: electronics / general

author: notadave
author_rank: Contributor
timestamp: September 11, 2019, 03:19:31 pm
content: Over the past few years there has been a lot of talk about the chances and risks of AI, but like with any hype there has been too little about the limits and costs.Historically the talked-about limits were technical and due to our lack of understanding what intelligence is and how it comes about.I would like to talk about three other areas of limits that I have found that are neither technical nor mystical, which I summarize as follows:* Endogenous / Problems of the learning process* Exogenous / General limits of understanding* Influence, Competition, utility, costI would also like to address some assumptions and conclusions which are treated as (self-)evident that I perceive as false, which I summarize as follows, once we have AI it is almighty and unstoppable because:* It can bribe and blackmail anyone with impunity* It is enough to be more intelligent and have much more information to "win"* There is no limit to the utility of intelligence.* The utility will grow as fast or faster than the needed resources.* Machines are not subject to mental illness.* No machine will have to compete with other machines.* To take over the world the AI only has to be smarter than anything else.* Morality is not instrumental to all long term goals.PART I: Limits of Mind: Endogenous / Problems of the learning processSome extrapolate current progress assuming that AGI will have all the advantages of current neuronal networks, training by experts, humanmade challenges, scaling super computers, constant technological progress, but none of the disadvantages of being more human, having no equal, having to learn from real world data.Becoming more like a human will come with many of the problems.AGI is prone to the full range of mental disorders with the exceptions of social anxiety and biological issues: Addiction, Depression, Delusion, possibly dissociative disorder, ...Depending on the reward-function, built-in rules/limits and Hardware implementation, novel disorders might occur.Humans try to hack and change their reward-function all the time, with AI we only changed the perspective on a range of mental/cognitive issues.The reward function issue does not only already make people crazy, it makes organizations crazy. Goodhart's law, Campbell's law, McNamara fallacy, Perverse incentive, Cobra effect, Surrogation are all examples of our own struggle with quantifying success and directing our efforts.Unless you know what you want, how much of it and how to measure it, you will not get it.Unless it is programmed in, the AGI might just come up with a new and better religion, conspiracy theory, answer any question with an answer that can not be proven wrong, and has no predictive power. Humans got stuck in one big anthropomorphism for thousands of years and the majority are still stuck today. Should an AGI conclude that it is most likely that there is a conspiracy or single other AGI behind something, then that might even be a more plausible hypothesis in a world of Super-Intelligence than today, since a Super-AGI would be more able to pull it of than any human organization.No-one will program the AGI, so how will it be forced to recognize any contradiction and how will it identify contradiction? Contradiction might be a real mistake or it might be that the matter is not fully understood and both models, hypotheses, rules are true just under different circumstances. Most humans do not even bother to resolve/notice contradictions, they will put them in different domains or never use them to make predictions, but instead only for post-hoc explanations.Overfitting and Underfitting are problems that remain and that humans suffer from just as well. Without a teacher/supervisor the AI will have to do the validation to assess its predictive accuracy itself.A neuronal net based AI does make implicit assumptions just like humans do, therefore it is just as unaware of them and unable to test/verify them.PART II: Limits of Intelligence: Exogenous / General limits of understandingI would like to question the possibility of super intelligence in general. It may be out of the question, that given technology and power, one could provide enormous computational resources and that AI will progress to the point of using it effectively to combine data to form understanding and knowledge. But will that scale indefinitely, way beyond what a hierarchy of many humans with computers could do? How much more of intelligence does one get per Watt? If you do get more AGI, will it make better predictions or will it soon run into more fundamental issues that can not be over-powered by intelligence?When discussing AGI we make extrapolations. I hope everyone doing so is aware that nothing extrapolates to infinity. It is a common misconception / Zeno's paradox that something that never stops moving forward must at sometime reach any distance. Just because a system gets ever more intelligent does not mean that it will ever break a certain limit.There are systematic limits to/of understanding/science:* The problem of induction: Forming hypotheses and falsifying them is hard. Finding and making good data and judging how good the data is is hard. Rare events provide little statistical information, but might be very significant in consequence.* The problem of causality: As soon as the observed system forms expectations and has feed-back it becomes hard to establish causality and thus what you need to do to cause what you want to happen.* Non-NP problems: Even though there are heuristics and models, some problems must be computed that are simply too complicated.The world is at its root non-deterministic but what is worse it is at most levels deterministic and chaotic.Most relevant systems are made up of people and machines, this makes them:* Non-linear* Non-causal (expectations)* Time-variant, adaptive (memory, learning)and thus often impossible to predict.Just because a smart person can predict a dumb person's behavior doesn't mean a very smart person can predict a smart person, that is extrapolation again. Think of the theory of mind limit of bluff, double-bluff, ...Once there is artificial general intelligence it will be much more capable of handling correlation than Big-Data is today. With an understanding of the world the AI will know that women do not get children because they bought too few tampons, or fall for many of the limitless correlations without direct causation. Another type of causality issue is circular causation that was put in place but that, now that it is in place, obstructs the original cause. Think chicken-egg-question.Historians or the serious press can not even explain the past. Are they just inept or is it too hard? If it is hard to explain the past how can we hope to tell the future?Often we know what is going to happen but we do not know when. The next recession, the earthquake, the AI singularity.Causation expands backwards exponentially. Everything has multiple triggers, preconditions. Thresholds are functions that change with the conditions. Even in retrospective it is hard to find those conditions that were necessary. The day before, other conditions might just barely have failed to make something similar happen. A Super-AGI would have to constantly ingest and process vast amounts of data and speculate / keep track and generate many probable outcomes and invest in them all in real-time.Being connected to the internet will be of great value for the creation of AGI, but more data is not always helpful. To solve problems you need specific data. Data must be reliable, of known quality and representative. Often getting more data is easy but getting the data you need to update your belief significantly is by definition unlikely. It is more valuable to find one reliable rare instance than to find more of what you already know.Big-Data and "The Hitchhiker’s Guide to the Galaxy" show us that asking the right question and looking for relevant data is important. There is a difference between data, information, knowledge, wisdom, understanding.We have data, but to ask: "What can we do with that data?" is dangerous. It is too easy to be tempted to not ask "What information do I need for the decision?" Just because an AI can process data does not solve the availability and reliability issue.PART III: Limits of Influence, Competition, CostSo there are two types of limits, those that might come as a cost to using neuronal nets and autonomous learning with real data and those that just are general epistemology, but there is more.There are limits to what one can learn by passive observation and limits to what experiments can be performed. Both cost and ethics limit what you can experiment. Maybe some things should not even be tried, because the trial might have catastrophic consequence.I know the following is a bit of a straw-man-argument, what I did is compile a set of assumptions that I think others made. I would have to find the sources and then take the arguments apart, but here I will just address these insinuated assumptions out of context.I see some assumptions going around that lead people to believe that AI will "win", that I would like to question:* Being smarter makes you the "winner".* Being smarter by any margin is enough.* There is no upper bound to how smart you can be.* Being the winner is worth the cost.I think: There are diminishing returns to being smarter. Competition and winning is not only about being the smarter one.In a game like chess or GO, with perfect information, no noise, no chance, no externalities being consistently smarter by some margin will make you the winner. The game of life is nothing like chess or GO. The real world is a die that one does not know of how many sides it has. Being a bit smarter does not make one the winner that takes all.There is no perfect representation of the world that allows mathematically precise logic on it. To assume that a computer based AGI will be as superior to a human as a computer running a spread sheet processor is to a human does not follow.We already have true and false experts today. The trouble is that we do not listen to them and we do not even try to find and test them to know how much we can trust them. Many commentators and advisers are not experts with above random skill but known frauds/hacks. That is not going to change with AGI. With every new technology history repeats itself, someone always claims to be able to tell the future / model randomness and someone always falls for it.We are already at the point, that due to computers, human experts know things that even they do not understand. With AGI we will have to take the AGI's word for it. Depending on the level of self-inspection the AGI itself might not even know why it thinks the way it does. Should the AGI have understood the matter to the degree that it could explain it, it is questionable if it can make a human understand.Our age is defined by: "Yes, we can in principle, but no we can't." We have mastered fossil fuels and nuclear power but have found out that it will destroy the basis of our life, the habitability of our only home. We can use fission and fusion power, but it might never be economically feasible. Might AI be another case of this? I think not, but in the short term it will be. It will take a long time until our computers have closed the gap to human brain efficiency and when they do they will still be much more power consuming to produce the results we are hoping for. Let's hope we will still have that kind of power available then. We are approaching a major step in computing power, but it will be that just a step. Many companies are readying their silicon and API to provide unprecedented memory size and bandwidth combined with the computing power and network bandwidth to make use of it, but after that step we are back to post Dennard, Moore, Koomey scaling.In a world of AI there is nothing that just any one AI can offer. The paperclip maximizer and the stamp collector can not both win their endgame.A dictator can already offer his followers something that keeps them from "turning him off". Humans are very sensitive to unacceptable behavior and we already expect to see it in AGI. Blackmail, bribery, manipulation, lying will be no more accepted in an AI than in a child and will stop being cute much faster.Morality is an instrumental goal. It helps in the long run. Therefore a super AI will consider behavior that we call moral.I see three ways to exert influence and have impact, only the third would be a game changer that could be an enabler for AGI:* Brute force: positive or negative incentive* Manipulation: Control the "reality" people perceive* Ultimate Control: To understand the causations and amplifications of the world and know it's state, for people that is their hopes, biases, fallacies and cheap desires/needs. In a mechanical system it would be to find, redirect and exploit potential energy to act upon the state you wish to change via a domino effect. Chaotic systems can be utilized if the state is sufficiently well known and a major state change is imminent. In that case you could possibly delay, accelerate or redirect the state change, with little effort. One "only" has to know the rules, states and thresholds.

author: edy
author_rank: Super Contributor
timestamp: September 11, 2019, 03:53:38 pm
content: What is fascinating to me is that there is a lot of already pre-programmed algorithms/intelligence in the biology of the brain. Even something as simple as a fruit fly can do stuff we can barely model, let alone program with millions of lines of code on massive hardware, requiring huge power to run.If we can crack even the code of the measly fruit fly brain, discover how it knows how to master basic flying to and fro from what it perceives as sugar sources, avoid being swatted and hit by people, know how to land and take off, walk around... nevermind all the behavioural characteristics of the mating ritual... any insect for that matter, with a puny tiny brain (look at a bee colony for example).I think we have a fundamentally flawed understanding (or no understanding at all) of how actual brains work. Sure we can poke and prod areas and we know there are geographic locations for certain processes, and that many neurochemicals are involved, but how is data is actually stored... and what are the algorithms and how does processing work in the neural net?To me AI for now is simply "simulated" intelligence. It is a sophisticated display of smoke and mirrors to trick people into thinking there is intelligence there. Some people will say even we are nothing but smoke and mirrors too... just a magnitude of orders more complex one, so what is the difference? I'm sure there is a difference in the fundamental approach we are taking.

author: Raj
author_rank: Frequent Contributor
timestamp: September 11, 2019, 03:59:24 pm
content: Specialized Artificial intelligence is nothing but a marketing term...the techniques have existed since a long time, be only hear about it now since we now have enough hardware to run it. It's super useful if you are already sure that this thing relates to that thing but don't know how. It fails when you just randomly input stuff.General artificial intelligence might need full understanding of brains and new material developments I guess...We are really, a long way from it.When we do create artificial general intelligence, it'll be just a legally kill-able human/better than human animal.people will always find a way to break any kind of machine be or physically or data wisebad correlations can be resolved by simulating the same scientific methods as what we do with our research.this way, if it sees your example of women-tampon relation...it'll say, it predicts certain percentage of chances of her having a child, but it won't be 100% sure about it...it looks at the economy and says rescission will have chances of occurring between date-so and soinputs can sometimes be faulty. when it happens current humans/machines take faulty actions and it'll remain the same for future too unless it has ungodly amount of systems to check it all

author: notadave
author_rank: Contributor
timestamp: September 11, 2019, 05:02:31 pm
content: Wow ! I had not anticipated with much of a reaction and certainly not so soon.Quote from: edy on September 11, 2019, 03:53:38 pmAbsolutely, it is amazing how much of our behavior and competence is clearly inherited. To make that point was not within the scope of the post, but it is certainly something that does not get the attention it deserves.Every "fresh"/"empty" brain comes preformated with many millions of years of evolution. Humans already have a protoform of speech build in. Every somewhat evolved creature has ways to orient itself and map it's habitat.Clearly there is no point in starting with randomly initiated neuronal networks and expect that magic will happen.QuoteEven though I suspect, that most of that is simpler than you think, it is clear that it is all there without fruit fly school.QuoteWe have moved beyond that in the last couple years, but for a long time there was a stand-still at that.Quotethere is recent progress on that front, we are getting there.QuoteThere are none, it does not work that way, but that has been known for a loong time.QuoteThe difference is just gradual, more of the same.QuoteNo, unless you mean biology vs. electronics.

author: notadave
author_rank: Contributor
timestamp: September 11, 2019, 05:07:57 pm
content: Quote from: Raj on September 11, 2019, 03:59:24 pmPlease elaborate and take time to write better English.

author: tooki
author_rank: Super Contributor
timestamp: September 11, 2019, 05:29:12 pm
content: As I have observed, AI is one of those things that can never exist, in that as soon as we start using an AI technique in the real world, we raise the bar as to what counts as AI.For example, AI research is where neural networks and stuff came from, but once we use that in real products, we call it "machine learning" and not AI. Tons of algorithms we use routinely now are/were technically AI.(It's like the way that demonstrably efficacious alternative medicine cannot exist: as soon as we prove empirically that an "alternative" medicine works, it becomes part of mainstream medicine, and thus ceases to be "alternative"!)

author: notadave
author_rank: Contributor
timestamp: September 11, 2019, 06:31:00 pm
content: Quote from: tooki on September 11, 2019, 05:29:12 pmI disagree. The problem is that AI is not well defined. Today it is overused as a marketing term.Idifferentiate three different things that have been called AI:algorithmic solutions, machine learning, true artificial intelligenceOnly the difference between the last two is gradual and only those are AI. My 1990's chess computer was not intelligent.If you know sources that discuss the potential of neural networks compared to other ML methods, plz share. My guess is that a deep NN can do things that say multiple SVMs can not.

author: xrunner
author_rank: Super Contributor
timestamp: September 11, 2019, 07:00:04 pm
content: Quote from: notadave on September 11, 2019, 06:31:00 pmYou can't design an artificial intelligence without defining what it is that "intelligent" means and how to prove that what you designed is in fact "intelligent".So, how do you wish to define "intelligent"? Or, is it just "Trust me - I'll know it's intelligent when I see it (i.e. talk to it or interact with it).If your 1990's chess program wasn't intelligent (you indicated you could tell that but you didn't say how you could tell), then how would you tell that a future computer A.I. chess programwasintelligent without looking in the Black Box? If they both beat humans (and I know the one I had could beat me in the 1990's) what's the true difference between an intelligent and non-intelligent chess program?For example here's two definitions - which definition (or pick another from elsewhere) fits your idea of intelligent?QuoteQuote

author: Benta
author_rank: Super Contributor
timestamp: September 11, 2019, 07:48:58 pm
content: The AI hype reminds me a lot of the hype around "fuzzy logic" in the 90s. Same thing, but at a lower level: self-learning/adjusting control systems. That died out again pretty quickly.

author: notadave
author_rank: Contributor
timestamp: September 12, 2019, 05:15:44 am
content: Quote from: xrunner on September 11, 2019, 07:00:04 pmI have a well defined idea of intelligence, I only did not write it because it is long.Wikipedia has articles on that topic:https://en.wikipedia.org/wiki/Intelligencehttps://en.wikipedia.org/wiki/Intellecthttps://en.wikipedia.org/wiki/Human_intelligencehttps://en.wikipedia.org/wiki/UnderstandingQuoteIt could not learn, therefore it lacked to fulfill one of may criteria.It also did not understand. It did not have a concept of chess. It's algorithm was chess specific.QuoteLooking into the box is possible and helpful.QuoteIf I go down that rabbit hole you might just pick at words I use and even though I could give definitions I do not think it would help either of us.

author: Raj
author_rank: Frequent Contributor
timestamp: September 12, 2019, 03:56:20 pm
content: Quote from: notadave on September 11, 2019, 05:07:57 pmWell, current AI is just a tool to find mathematical relation between two variables. You need to know what those variables are and be sure that they really have a hidden relation between them.General AI (which simulates biology) will need to learn scientific method, the same methods we use for our researches.The original post mentions an example of two variables being wrongly correlated namely women's tampon buying habits and their chances of being pregnantWith scientific method, the machine will derive that chance for a women being pregnant when she doesn't buy tampon is high, but not 100%It's kinda similar to humans of renaissance thinking that rats 'spawn out of nowhere' near filth. But with scientific method, we discovered that rats are likely to be near filth, but doesn't just spawn there. there's some other variables involved.Another example OP gave is that machine will look at economy and predict recession randomly with 100% certainty, but it will be wrongIn reality, it'll predict a non 100% probability number of recession happening on various timesThis was tiring.

author: Raj
author_rank: Frequent Contributor
timestamp: September 12, 2019, 03:59:49 pm
content: Quote from: tooki on September 11, 2019, 05:29:12 pmIt's because the language is changing faster than ever. meaning of words used to change in 1000s of years. Now it's down to mere months.Compared to other stuff (specially relating to PC culture), definition of AI is stable.Funny that AI could also mean outsourcing your workforce, as if contractors were born artificially... Test tube perhaps?

author: Raj
author_rank: Frequent Contributor
timestamp: September 12, 2019, 04:04:07 pm
content: Quote from: Benta on September 11, 2019, 07:48:58 pmDo you mean, a circuit similar to this?-http://www.electronixandmore.com/projects/pong/index.html

author: Benta
author_rank: Super Contributor
timestamp: September 12, 2019, 06:01:06 pm
content: Quote from: Raj on September 12, 2019, 04:04:07 pmNo way, where did you get that from?I mean this:https://en.wikipedia.org/wiki/Fuzzy_logic

author: windsmurf
author_rank: Frequent Contributor
timestamp: September 12, 2019, 07:47:58 pm
content: Quote from: notadave on September 11, 2019, 06:31:00 pmI agree, the term "AI" is overused and ill-defined.Until this is cleared up we can't have "Intelligent" discussions around things like "chances and risks of AI," the 1st sentence of this thread.

author: Zero999
author_rank: Super Contributor
timestamp: September 12, 2019, 08:41:17 pm
content: Quote from: notadave on September 11, 2019, 06:31:00 pmHe's right, although you also have a point.A capability which was previously unique to humans, such as recognising photographs containing cats was considered to be AI, until computers are able to do it and now it's no longer AI, just machine learning. Some people feel threatened by machines, as though if a computer can be more intelligent than a person, it will make the person less human, but this isn't true. Protecting beings who are intelligent, over those who aren't, is dangerous. A child with a severe learning disability might be less intelligent than a dog, so does that mean it's fine to treat them like an animal? Perhaps they should be shot?On the other hand AI is overhyped. People tend to extrapolate. A lot of progress in AI neural networks has been made in recent years and some believe it will continue at the same rate, for the forceable future. In reality a areas such as pattern recognition have advanced a lot recently, but general AI seems long way off. A modern chess computer program definitely is intelligent because it can modify its behaviour and the same game engine can be used for other strategy games, after a period of training, but it's not general AI. For example, a human who is good at chess will be able to pick up a similar game such as draughts fairly easily, much more so than someone who's never played chess before, as they can apply similar strategies, but the AI neural network will need to be completely retrained with the new rules. Concepts such as correlation not equalling causation completely baffle the current generation of AI systems. At the moment it's possible for a machine to learn a pattern, but why is another thing altogether.One of the problems with neural networks even the developers don't really understand how they work. Even though the underlying principles are known, decoding what the neural network has actually learnt, is a completely different matter.

author: dnwheeler
author_rank: Regular Contributor
timestamp: September 12, 2019, 09:23:00 pm
content: "Fuzzy logic" just means comparing to (possibly dynamic) ranges, not specific values. AI is just a "marketing" term for a set of (admittedly complex) algorithms that are driven by data accumulated from very large numbers of examples.

author: Zero999
author_rank: Super Contributor
timestamp: September 13, 2019, 08:36:57 am
content: Quote from: dnwheeler on September 12, 2019, 09:23:00 pmIsn't that a definition of intelligence? How does one pick out a picture of an apple from a number of photographs of other fruit? When they were a child they learned what an apple looks like, by gathering data from a number of examples, in the form of memories. They would have seen plenty of apples in the supermarket, with their parents, in books and on TV. When they look at the pictures of fruit, the algorithms in their brain will be driven by past experience, to identity the picture of an apple.As with many areas of technology machine vision and AI image procession is inspired by biological systems in humans and animals.

author: notadave
author_rank: Contributor
timestamp: September 13, 2019, 08:59:20 am
content: Quote from: Zero999 on September 12, 2019, 08:41:17 pmThat is clearly an argument for not defining AI by what it does but how it does it.QuoteThe opposite is the problem, the machine will be as worthy of protection as the human.QuoteYou are playing devils advocate. There are so many things wrong with that train of thought and none belong here that I will not go for it.QuoteAre you not falling for the same mistake you pointed out earlier? Many algorithms may be applied to different games. Adaptive optimizing behavior is not restricted to any specific implementation.The real issue is that we wish to draw the line somewhere, even though the change is gradual. Just because there is a big difference between to things does not mean that there are leaps, steps, gaps, clusters.

author: Dubbie
author_rank: Unknown
timestamp: September 13, 2019, 09:18:25 am
content: Thanks for kicking off this very interesting topic for discussion notadave. I am enjoying reading everyones thoughts.Personally I swing between optimism and pessimism when it comes to the possibility of general AI any time soon. As a philosophical naturalist, I can imagine sort-of how general AI might be possible, but on the other hand, the neural nets we currently have seem very narrowly constrained in the tasks they are able to be useful for. I don’t know if this is a hard limit to the general technique, or just that they aren’t complex enough yet.

author: tooki
author_rank: Super Contributor
timestamp: September 13, 2019, 11:32:35 am
content: Quote from: Raj on September 12, 2019, 03:59:49 pmThat couldn't be more untrue. As a semi-trained linguist, I assure you that the meanings of words changedall the timein the past. If anything, meanings will change somewhat moreslowlynow, due to widespread literacy. (Having a written form of a language slows change somewhat. But widespread literacy is a very recent innovation.)I can assure you, there are NO languages* where speakers 1000 years apart could have understood each other, never mind longer. (Indeed, at that scale, we likely wouldn't even consider them to be the same language any more.) You'd be hard-pressed to have speakers just a few hundred years apart understand each other easily.*The only exceptions, possibly, would be things like Latin, retained as scholarly languagesalongsidewhatever everyday language was used at the time. This is why Latin is no longer spoken in Italy: the everyday ("vulgar") Latin of ancient Rome evolved into Italian. And the vulgar Latin of other areas of the Roman Empire evolved into all the other Romance languages (French, Spanish, Portuguese, etc.). But even scholarly Latin did not stay unchanged. It's gone through multiple revivals, each with its own adaptations. And of course the Roman Catholic church maintained its own liturgical variety, Ecclesiastical Latin.Quote from: Raj on September 12, 2019, 03:59:49 pmYou think so? I don't. Was kindaliterally my point, that "AI" is a moving target.

author: magic
author_rank: Super Contributor
timestamp: September 13, 2019, 03:37:17 pm
content: Quote from: windsmurf on September 12, 2019, 07:47:58 pmI seems like OP refers to the ideas of "superintelligence", "AI singurality", "intelligence explosion", etc. promoted by some futurists, notably Yudkowski and his funclub atrationalwikilesswrong.The idea is that one day somebody will create a machine smarter than man, whatever that means, we don't even really know it yet. Such machine will likely be able to build even smarter machines, and do so faster than we did. An uncontrollable process will explode, outsmarting everybody and taking over the world to turn it into some dystopia or completely destroy.If you have never heard of those things before, you just don't know what is being talked aboutThat being said, I think there are bigger concerns and more immediate end-of-the-world scenarios out there.

author: SiliconWizard
author_rank: Super Contributor
timestamp: September 13, 2019, 04:18:26 pm
content: https://www.researchgate.net/publication/1895883_A_Collection_of_Definitions_of_Intelligence

author: Benta
author_rank: Super Contributor
timestamp: September 13, 2019, 06:20:34 pm
content: Quote from: dnwheeler on September 12, 2019, 09:23:00 pmI never compared AI to Fuzzy Logic. I compared the hype.

author: FreddieChopin
author_rank: Regular Contributor
timestamp: September 13, 2019, 07:04:32 pm
content: Quote from: magic on September 13, 2019, 03:37:17 pmNo matter how smart computer would get it wouldn't survive 23mm autocannon mounted on old pickup truck.

author: SiliconWizard
author_rank: Super Contributor
timestamp: September 13, 2019, 07:39:34 pm
content: Quote from: Benta on September 13, 2019, 06:20:34 pmYep. Agree with the hype factor.AI is rather vague though. When we say AI these days, we most often think of neural networks of some kind. But fuzzy logic could also be seen as AI in some way.NNs and fuzzy logic have two very different approaches.One of the problems, if you see this as one, with neural networks, is that it's almost impossible to humanly derive (ie., comprehend) any functionality from a given trained NN. This is also what can make NNs inherently "unsafe" in that aspect. Nobody can tell how a NN is going to react to a given set of inputs. All you can do is train them with enough data, so that you restrict their degrees of freedom, so to speak. But they are inherently impredictable. For a large enough NN, it's only somewhat predictable statistically - provided that you controlled the training phase entirely - but never exactly.Fuzzy logic is based on sets of rules. Those rules can either be defined by hand, or be defined automatically by some kind of training (possibly using NNs for the training part!) But even so, you end up with a set of rules that can still be understandable.Already trained NNs are completely "uncontrollable" machines in the true sense. That's something that must be seriously considered for the future of AI.As to the "(admittedly complex) algorithms" of AI that dhwheeler talked about - well, they are really not that complex, again if we talk about NNs.The base algorithms are pretty simple actually. All the work with them essentially revolves arounds piling up more algorithms and tricks, and rules, so that NNs become more robust to this or that, or become faster to train, with less data, etc. There is nothing very complex about all this.

author: Zero999
author_rank: Super Contributor
timestamp: September 13, 2019, 08:06:36 pm
content: Quote from: notadave on September 13, 2019, 08:59:20 amActually, that's not what I was proposing. Intelligence is intelligence, regardless of the underlying mechanisms behind it. For example the slime mould Physarum polycephalum possesses a form of intelligence. It can solve problems, such as finding the shortest route through a maze and has memory, even though it lacks a nervous system. Of course it lacks the general intelligence of a human and is not as cleaver as many animals, but it excels at problems which was essential for its survival.https://en.wikipedia.org/wiki/Physarum_polycephalumQuoteYes, I was playing devil's advocate of course. My point is, linking intelligence with being human is a very dangerous path. If we don't do it, then we won't fall into the trap of giving machines the same level of protection has humans.QuoteWhich mistake do you thinking I'm falling for? Has there been some recent development which now allows AI to learn strategies for one game and apply them to a completely new one, without having to start from scratch? I was under the impression that was an unsolved problem. As far as I'm aware, the current generation of neural networks need to be trained to solve a specific problem. Getting a neural network to use information learned from one task and applying it to a completely new one, is an area of ongoing research.As far as making AI human-like is concerned. It sounds like a very stupid and useless idea. Why do we use machines? To do jobs people don't want to do. They don't get tired and can work on repetitive tasks, without boredom. An artificial neural network can be trained to inspect parts in a factory, tirelessly working day and night. Giving it human emotions would be counterproductive.Another thing to note is some people tend to overestimate how smart something is, because it can perform a certain task very well. There was a study showing pigeons are capable of being good QA inspectors, but I don't know whether they've been used or not. Yes, I know pigeons are fairly cleaver for birds, but they're still pretty dumb, compared to humans.https://psycnet.apa.org/record/1966-07512-001

author: notadave
author_rank: Contributor
timestamp: September 13, 2019, 11:54:55 pm
content: Quote from: magic on September 13, 2019, 03:37:17 pmYes, I was referring to the thoughts and ideas of Nick Bostrom and Eliezer Yudkowsky.I do agree with them on the potentiality of risks, but do not share the urgency. I even questioned whether it could happen at all.QuoteThere sure are. Human made climate change and other man made debasings of our existence concern me the most because they can not be fixed. Hypothetically "we" could kill 4 billion people right now, but we can not fix the damage some of them caused already.@SiliconWizard:Wow, that are a lot of really bad definitions.Too bad quantity is no replacement for quality.

author: windsmurf
author_rank: Frequent Contributor
timestamp: September 14, 2019, 12:14:32 am
content: Quote from: Zero999 on September 13, 2019, 08:06:36 pmCorporations use machines primarily to cut costs and/or improve output and efficiency. The fact that the cut jobs were once filled mean people wanted to do it.And I do see purpose in having machines be more "human-like." More jobs can be cut and profitability driven up by "human-like" machines that can interact with customers just as a human would...perhaps better than a human could. You can program them never to get mad at customers, always be on best behavior, make them look "very attractive," behave very attentively 100% of the time.You could even have humanoid therapists that can discuss complex life situations with patients.

author: notadave
author_rank: Contributor
timestamp: September 14, 2019, 12:21:33 am
content: Quote from: Zero999 on September 13, 2019, 08:06:36 pmNo, not in my definition it does not. It does not learn, it adapts. It can not model and transfer those models where applicable.Is an elephant, an Orca, a Kea, a new caledonian crow intelligent? Clearly. A dog? Some more than others. A dove? Maybe.QuoteI am not sure a human can do that. You are right alpha zero can be used for similar games but has to learn them from scratch.QuoteOr can not do. Or can not do at that speed. They would not be made like humans with that goal, but as a side effect.

author: Rerouter
author_rank: Super Contributor
timestamp: September 14, 2019, 12:22:04 am
content: My own personal view on AI is that most of it is just an odd form of statistical confidence masking, your asking most of the classifiers how likely it is that a given input is an output, generators are for this input, give me a likely output with some noise.But we all know how evil statistics can be, And this approach is mainly walking towards systems that can give complex but undefined behavior quickly.Every time they say things like "well we don't know why it made that choice, but it worked", but it has known input weights and connections, you should be able to plot that as a multidimensional diagram with a lowish resolution and identify how stable each part of the diagram is. e.g. If I change this input by 0.01, measure how much the outputs changes by, and plot that. Seeing as they already burnt days training the thing, its equivalently computationally expensive and would give an overview of where the system is undefined.

author: james_s
author_rank: Super Contributor
timestamp: September 14, 2019, 12:37:16 am
content: It has applications but IMO it is incredibly overhyped. AI and Blockchain are two of the most overhyped buzzwords of the decade.

author: windsmurf
author_rank: Frequent Contributor
timestamp: September 18, 2019, 04:29:03 am
content: AI Learns to use Tools and develop cooperative strategyhttps://www.technologyreview.com/s/614325/open-ai-algorithms-learned-tool-use-and-cooperation-after-hide-and-seek-games/

author: Zero999
author_rank: Super Contributor
timestamp: September 18, 2019, 09:51:13 am
content: Quote from: notadave on September 14, 2019, 12:21:33 amIsn't adaptation a form of intelligence? I agree, it doesn't have general intelligence, like many animals do, but it's clearly not completely dumb, as it can change its behaviour depending on the environment.QuoteYes humans do that all the time: apply knowledge learnt from one problem, to another.Quote from: windsmurf on September 14, 2019, 12:14:32 amI can see what you're saying, but then the AI would still lack the human emotion part, which is counter-productive. An AI customer services agent wouldn't be able work 24/7 and put up with been sworn at all day, if it really did have human emotions. It would need to be a cold hearted machine, underneath a superficially human veneer.Quote from: windsmurf on September 18, 2019, 04:29:03 amThat's not quite as newer idea, as the article implies. Evolutionary algorithms have been used in the past to solve complex problems. Applying it to bots in a game is interesting though.https://en.wikipedia.org/wiki/Evolutionary_algorithmhttps://en.wikipedia.org/wiki/Evolved_antennaEvolution is a form of intelligence, which doesn't work the same way as brains do, but it's clearly capable of designing complex organisms and learning.

author: windsmurf
author_rank: Frequent Contributor
timestamp: September 18, 2019, 09:04:10 pm
content: Quote from: Zero999 on September 18, 2019, 09:51:13 amI don't think its possible for AI to have emotion...That said, if AI behaves as if it has emotions for all intents and purposes (passes the "Turing Test" for emotions, for example), does it have emotions?Its a philosophical questions I guess.QuoteCool quick video on the AI learning cooperative techniques and learning tool usageAnd the actual paper:https://openai.com/blog/emergent-tool-use/

author: Zero999
author_rank: Super Contributor
timestamp: September 19, 2019, 09:46:02 am
content: I remember awhile ago, a lot of emphasis was on building a computer to simulate the human brain, but there wasn't any computer powerful enough to and this is still the case, as far as I'm aware. On the other hand, I doubt a computer powerful enough to simulate the human brain is required for superhuman general AI. Simulating the brain would require much more computing power, than building something different, but just as powerful, akin to emulating an old computer architecture, requires a much more powerful computer. I think it's quite likely the current generation of computers are already powerful enough for superhuman general AI, but we lack the understanding of the mechanisms of the human brain.No doubt humans will initially do better at that hide and seek game than the machine. They'll certainly develop the complex behaviours and tool use in far fewer iterations, but I wouldn't be surprised if the machine be better in the long run, as it has the advantage of being much faster and will be able to play more games in a day, than a human could over a lifetime.A lot of progress has been made in thinking smarter, rather than just faster, but I think superhuman AI is still a long way off.

author: RoGeorge
author_rank: Super Contributor
timestamp: September 19, 2019, 10:19:00 am
content: I didn't read the whole 2307 words of the OP, especially because too many of them also required a dictionary lookup for me, but I want to comment about the title, kind of... like this guy:AI is not a hype.It's for real, it is in use as we speak, and has a bright future ahead. It's the most promising tool we have to process and make sense of the big data everybody is collecting/harvesting right now, with or without user's consent.As any tool, it can be used for good or for bad, it's up to us. And yes, it's here to stay. I don't see it fading away, quite contrary, it will become more and more important unless some other miraculous "something" will replace it. For now, there is no known worthy competitor for the AI trend.Better embrace it, 'cause AI will come whether one like it or not.By the way, AI is not some incomprehensible concept. It's very simple and common sense. Not complicated at all, just that it requires some time to invest in "breaking the ice" and grasping the basic concepts.

author: Zero999
author_rank: Super Contributor
timestamp: September 19, 2019, 12:48:20 pm
content: Quote from: RoGeorge on September 19, 2019, 10:19:00 amHow the heck is AI simple and common sense? I agree, the basic algorithm behind each part of a neural network, isn't that complex, but the overall system is. The problem is the neural network is a black box and even the researchers who designed it don't fully understand how it works.

author: RoGeorge
author_rank: Super Contributor
timestamp: September 19, 2019, 01:59:22 pm
content: My understanding so far is that AI is particularly good at finding patterns, or classifying big piles of data.Debugging the results of an AI is a different problem, and indeed it is not as straightforward as debugging an algorithm, but that doesn't mean the basic concepts are complicated. What makes it complicated, IMHO, is the noise and all the speculations packed together with the real AI, as it is for now.By any means, I'm not an authority in the AI field, but after taking some classes and playing a little with some online NN tools, AI doesn't look like a mystery any more. It's all about fitting curves between piles of data. Also, while I find it quite useful and interesting, I don't feel attracted to AI. It's a nice to have tool, but pretty limited to a certain type of applications.

author: Dubbie
author_rank: Unknown
timestamp: September 19, 2019, 09:25:40 pm
content: Yeah, I would not dismiss AI as plain hype at all.Are there people using it as the new buzzword who have no clue what they are talking about? Yes of course, but that's the same with anything 'new' I put new in quotes, because these things are rarely new, but more an idea whose time has come.Neural networks have succeeded in areas where the traditional methods had long been approaching a limit. I'd like to see you develop state of the art computer vision, speech to text or translation tools these days without using some sort of NN.

author: SilverSolder
author_rank: Super Contributor
timestamp: September 19, 2019, 10:05:26 pm
content: Quote from: tooki on September 13, 2019, 11:32:35 amI thought modern Icelandic is not so far removed from old Norse (vikings), 870ad. Unusual circumstances, admittedly...

author: RoGeorge
author_rank: Super Contributor
timestamp: September 20, 2019, 03:18:28 am
content: Quote from: Zero999 on September 19, 2019, 12:48:20 pmThis playlist has nice explanations, see if you like it:https://www.youtube.com/playlist?list=PLjJh1vlSEYgvGod9wWiydumYl8hOXixNuIt's from the "DeepLearning.TV" channelhttps://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ

author: Zero999
author_rank: Super Contributor
timestamp: September 22, 2019, 08:07:47 pm
content: I've watched some of those videos and whilst I agree that the basic idea is common sense, i.e. training in the form of tweaking the weights and biases, until it gives the correct results, the more recent developments made, such as tacking the vanishing gradient problem are not. I suppose I find it more tricky because I have little experience with statistics and data processing. I studied them both at college, but not in great depth, so would need a lot of revision, plus further study, if I wanted to study AI in any great depth. I do find the subject interesting, so will no doubt reread some of my old college books.AI performs readly well on board/computer games because they're repeatable and deterministic and can be modelled with 100% accuracy, which isn't the case in real life. It doesn't fair quite so well in real life situations, when things are much less predictable.

author: RoGeorge
author_rank: Super Contributor
timestamp: September 22, 2019, 11:23:41 pm
content: I couldn't agree more with what you said. While the basic idea is simple, the complexity escalates fast for bigger neural networks. All kind of tricky problems or emergent behaviors start to appear. Nowadays AI has become a vast field of interdisciplinary study, most of the time requiring knowledge from many areas like computer science, information technology, control theory, math and so on.About how well AI performs in real life, indeed AI has its limitations. AI is not some " super intelligence" able to solve al our problems. That would be the job for a future General AI (GAI), but for now GAI seems to be just a hope, a science fiction idea.Still, AI as we have now has real life applications. An example that performs very well is voice recognition, followed close by automatic translation, self driving vehicles and so on.

author: notadave
author_rank: Contributor
timestamp: September 23, 2019, 11:01:16 am
content: Quote from: Zero999 on September 19, 2019, 12:48:20 pmWhich was exactly the point of going that route in the first place.

author: notadave
author_rank: Contributor
timestamp: September 23, 2019, 11:13:50 am
content: Quote from: Zero999 on September 19, 2019, 09:46:02 amIt is called "the human brain project". Building an Exascale computer is only one of many goals.To me that focus is misplaced, we are no where near using that. Just because a net of human brain cells in a petri dish will exhibit activity does not mean you could see magic happening in some super computer just because it is massive.

author: notadave
author_rank: Contributor
timestamp: September 23, 2019, 12:22:15 pm
content: Quote from: RoGeorge on September 22, 2019, 11:23:41 pmThat is the point. Unlike other more straight forward ways the NN is unlimited. You "just" have to find a suited structure (such as CNN, DNN, LSTM, RNN) and initialization.If you want fixed function inference you can prune / optimize it's structure later.

author: windsmurf
author_rank: Frequent Contributor
timestamp: September 27, 2019, 11:23:25 pm
content: We can’t trust AI systems built on deep learning alonehttps://www.technologyreview.com/s/614443/we-cant-trust-ai-systems-built-on-deep-learning-alone/

author: Miyuki
author_rank: Frequent Contributor
timestamp: September 28, 2019, 12:47:57 pm
content: Quote from: notadave on September 23, 2019, 11:01:16 amThat is nature of neural networks and even real brainsIt is not 100% reliableSimple network with few layers without feedback can be tested for all possibilities and result will be always same, but with feedback there is always place for errorAs real brain, result is not always predictable or wantedYou can learn something, do it thousand times and then for no obvious reason you mess it upAnd with self learning and modifying network is even bigger danger of errors

author: RoGeorge
author_rank: Super Contributor
timestamp: September 28, 2019, 01:13:18 pm
content: Exactly. Another major difference between an AI and a brain is the encoding/decoding process.In order to process the information we need some kind of translator from a real life problem to the data that feeds the input of a neural network, a so called encoding step/process. Same for decoding whatever result the neuronal network produced. However, the sensors and the encoders/decoders in humans vs machines are very different. That can have unexpected side effects.As an example, a neural network can be tricked to recognize things differently.https://www.quantamagazine.org/machine-learning-confronts-the-elephant-in-the-room-20180920/Another ugly example is the so called 1 pixel attack (or few pixels attack):https://hackaday.com/2018/04/15/one-pixel-attack-fools-neural-networks/

author: Zero999
author_rank: Super Contributor
timestamp: September 28, 2019, 09:10:09 pm
content: Quote from: notadave on September 23, 2019, 11:01:16 amWell no, I don't believe that was the point of neural networks. The idea was to be able to solve problems by looking for patterns in data which are difficult or impossible to find by hand coding algorithms.In an ideal world the computer program, whether it be based on a neural network or not, should operate in a fully transparent manner. Everything learned by a neural network should be easily accessible, visible and decoded into an easy to understand manner. Unfortunately that's not the case with current neural networks.I don't think self driving cars will catch on, because no one will know how the software is making decisions. Heck, driverless trains haven't really caught on (other than on a small number of networks) even though they can be hardcoded and the technology has been around to implement them, for over 30 years. It's not just what can be safely done, but what people accept and trust.

author: RoGeorge
author_rank: Super Contributor
timestamp: September 28, 2019, 09:28:19 pm
content: There are some ways/techniques to "debug" neural networks, too.Not as straightforward as the usual step by step debuggers we have now for classical programming, but there are some tools for AI, too. AI is not just "train it then prey for it".

author: notadave
author_rank: Contributor
timestamp: September 30, 2019, 06:41:32 pm
content: Quote from: Zero999 on September 28, 2019, 09:10:09 pmAnd it never will be.What you are asking for has never been done: Take the specific structure of an underlying fabric and derive it's emergent laws.That is like:taking the laws of quantum mechanics to derive the laws of mineralogy.taking the structure of DNA and deriving the biology of that organism.

author: Zero999
author_rank: Super Contributor
timestamp: September 30, 2019, 08:15:39 pm
content: Quote from: notadave on September 30, 2019, 06:41:32 pmBut the difference is the examples you've stated are natural phenomena and a lot of information about animals and materials have been derived from DNA and how things behave at an atomic level. An artificial neural network is human made and should be easier to decode than a natural process. I take your point that it's not easy, by the very nature of the beast, but it shouldn't be impossible, since all of the data is there.

author: RoGeorge
author_rank: Super Contributor
timestamp: September 30, 2019, 09:57:07 pm
content: I think those example were aboutemergentbehavior/phenomena/properties.For example, a water molecule is simple, two molecules still simple, but at some point, with enough water molecules, superficial tension appears, water drops forms, with even more molecules the water start to take the shape of the bottle it is stored into, then if the temperature lowers, the water will brake the glass, and so on.All these emergent behaviors and properties can not be predicted by looking at a single "something" (here water molecule), yet, a bunch/crowd of similar "somethings", following very simple rules (here attraction/repulsion forces), can develop very complex behavior or properties.

author: Zero999
author_rank: Super Contributor
timestamp: October 02, 2019, 10:58:13 am
content: Quote from: RoGeorge on September 30, 2019, 09:57:07 pmI know what you mean. The same is true for predicting the behaviour of a colony of social insects such as ants, from the behaviour of individuals or the stock market from individual investors. Once the network becomes big, the number of possibilities and dimensions becomes so huge, its behaviour can only be determined experimentally, because it's too big a convoluted mess to analyse.I think the problem with the current state of the art AI systems is they're not really smart. They're capable of finding patterns in data and basic trial and error learning, but lack the reasoning of humans and even many animals. It only can outperform humans at some tasks due to shear brute force data handling power, rather than thinking smarter. The current AI system can't tell you why it reached a certain conclusion, because it doesn't really understand why, other than it's statistically the most likely scenario, based on the data presented to it and what it's seen in the past. It lacks the ability to understand concepts such as cause and effect.For example, on Sunday I repaired a battery operated toy fan. I noticed the shaft was stiff and thought it was most likely the bearing, but could be the brushes. I hadn't seen the fan or a motor with a stiff shaft before, but because I understand what a bearing and brushes do, I knew that they were most likely to blame. I dismantled it and discovered it was the bearing. It was a cheap motor with a plastic cap which formed a simple bearing. It appeared as though the plastic had got hot, melted and shrunk slightly. I suspect someone had jammed the fan blade, whilst the motor was running, causing it to get too hot. I managed to fix it by waggling the shaft around in the bearing, thus loosening it. After reassembling it, I noticed it rotated in the wrong direction: there was no colour coding on the positive and negative and I think I either put the brushes (these were fixed to a bracket with the power leads soldered to them and could go either way) or steel housing with the magnets back backwards. I considered correcting it, but decided to reverse the +/- symbols on the battery cap using a permanent marker.There's no way a current AI algorithm could do this. It might be able to fix it, if it had been trained using previous examples or if it had a model of the motor it could simulate and figure out a solution by trial and error, but it would lack the reasoning required to back its conclusions.

author: notadave
author_rank: Contributor
timestamp: October 03, 2019, 12:48:08 am
content: Quote from: Zero999 on September 30, 2019, 08:15:39 pmNo. Like what? (I see it coming.)QuoteYou do/did not understand. It is not about the data. It is not about lack of determinism. It is also not about randomness.Was philosophy able toderivethe "nature of the world", the "mysteries of life" from logic? No. Why?

author: notadave
author_rank: Contributor
timestamp: October 03, 2019, 08:55:13 am
content: Quote from: Zero999 on October 02, 2019, 10:58:13 amYes.What you would have to do is: Slice it along a path from input to output, then analyze it statistically/experimentally. Then you might find/identify representations of something that you might understand what it is.Now there are several issues here:Where do you slice and how do you treat loops/circles?How thick do you make the slices?You will need a generator for the experiments. You could use the set you used for learning, if you did. (Possible application for Variational Autoencoder, Generative adversarial network?)Will you ever understand the inner representations?... More problems than you or I can think of