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55

I think this is a fairly common misconception about AI and computers, especially among laypeople. There are several things to unpack here. Let's suppose that there's something special about infinity (or about continuous concepts) that makes them especially difficult for AI. For this to be true, it must both be the case that humans can understand these ...


22

We are absolutely nowhere near, nor do we have any idea how to bridge the gap between what we can currently do and what is depicted in these films. The current trend for DL approaches (coupled with the emergence of data science as a mainstream discipline) has led to a lot of popular interest in AI. However, researchers and practitioners would do well to ...


20

The technological singularity is a theoretical point in time at which a self-improving artificial general intelligence becomes able to understand and manipulate concepts outside of the human brain's range, that is, the moment when it can understand things humans, by biological design, can't. The fuzziness about the singularity comes from the fact that, from ...


18

I think your premise is flawed. You seem to assume that to "understand"(*) infinities requires infinite processing capacity, and imply that humans have just that, since you present them as the opposite to limited, finite computers. But humans also have finite processing capacity. We are beings built of a finite number of elementary particles, forming a ...


17

No, with a but. We can have creative yet ethical problem-solving if the system has a complete system of ethics, but otherwise creativity will be unsafe by default. One can classify AI decision-making approaches into two types: interpolative thinkers, and extrapolative thinkers. Interpolative thinkers learn to classify and mimic whatever they're learning ...


15

Common sense knowledge is the collection of premises that everyone, in a certain context (hence common sense knowledge might be a function of the context), takes for granted. There would exist a lot of miscommunication between a human and an AI if the AI did not possess common sense knowledge. Therefore, common sense knowledge is fundamental to human-AI ...


12

TL;DR: The subtleties of infinity are made apparent in the notion of unboundedness. Unboundedness is finitely definable. "Infinite things" are really things with unbounded natures. Infinity is best understood not as a thing but as a concept. Humans theoretically possess unbounded abilities not infinite abilities (eg to count to any arbitrary number as ...


11

Good question! AlphaZero, though a major milestone, is most definitely not an AGI :) AlphaGo, though strong at the game of Go, is narrowly strong ("strong-narrow AI"), defined as strength in a single problem or type of problem (such as Go and other non-chance, perfect information games.) AGI, at minimum, must be about as strong as humans in all problems ...


10

A good answer to this question depends on what you want to use the labels for. When I think about "optimization," I think about a solution space and a cost function; that is, there are many possible answers that could be returned and we can know what the cost is of any particular answer. In this view, the answer is "yes"--pattern recognition is a case ...


10

Informally, AI-complete problems are the most difficult problems for an AI. The concept is not mathematically defined yet, as e.g. NP-complete problems. However, intuitively, these are the problems that require a human-level or general intelligence to be solved. Real natural language understanding is believed to be an AI-complete problem (this is also ...


10

We need this kind of common sense knowledge if we want to get computers to understand human language. It's easy for a computer program to analyse the grammatical structure of the example you give, but in order to understand its meaning we need to know the possible contexts, which is what you refer to as "common sense" here. This was emphasised a lot in ...


9

In Haskell, you can type: print [1..] and it will print out the infinite sequence of numbers, starting with: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,...


8

"Current artificial intelligence research" is a pretty broad field. From where I sit, in a mostly CS realm, people are focused on narrow intelligence that can do economically relevant work on narrow tasks. (That is, predicting when components will fail, predicting which ads a user will click on, and so on.) For those sorts of tools, the generality of a ...


8

I believe humans can be said to understand infinity since at least Georg Cantor because we can recognize different types of infinites (chiefly countable vs. uncountable) via the concept of cardinality. Specifically, a set is countably infinite if it can be mapped to the natural numbers, which is to say there is a 1-to-1 correspondence between the elements ...


7

The authors do actually give an English definition in terms of the well-known agent formulation of AI: We intend this usage to be intuitive: death means that one sees no more percepts, and takes no more actions. It would seem that this becomes possible for a reinforcement learning agent such as AIXI in a formulation that uses semi-measures of ...


7

Everyone dealing with neural networks misses an important point when comparing systems with human like intelligence. A human takes many months to do anything intelligible, let alone being able to solve problems where adult humans can barely manage. That and the size of human brain is enormous compared to our neural networks. Direction might be right, but the ...


7

(There's a summary at the bottom for those who are too lazy or pressed for time to read the whole thing.) Unfortunately to answer this question I will mainly be deconstructing the various premises. As I mentioned before, humans understand infinity because they are capable, at least, counting infinite integers, in principle. I disagree with the premise ...


6

Steve Omohudro wrote a paper called Basic AI Drives that steps through why we would expect an AI with narrow goals to find some basic, general concepts as instrumentally useful for their narrow goals. For example, an AI designed to maximize stock market returns but whose design is silent on the importance of continuing to survive would realize that its ...


6

This is something of an orthogonal answer, but I think Brooks didn't go about his idea the right way. That is, subsumption architecture is one in which the 'autopilot' is replaced by a more sophisticated system when necessary. (All pieces receive the raw sensory inputs, and output actions, some of which turn off or on other systems.) But a better approach ...


6

Deep Learning is mostly successful in supervised learning, whereas the brain builds categories mostly in an unsupervised way. We don't yet know how to do that. (Take a look at google brain: 16,000 cores and all this thing can do is recognise cats and human faces with pretty abysmal accuracy.) Deep Learning uses highly unstructured activations, i.e. the high ...


6

Good morning! You're using an extremely general term ("AI") for an extremely specific idea ("something human made that is almost identical to the human mind"). Thus, your question is not what you think it is. AI, according to John McCarthy (who Wikipedia claims coined the term and is the equivalent of a rockstar in the AI field), is the engineering of ...


6

I don't think I can give you a true answer to the actual question as posed, as I don't have a strict definition of "general intelligence". Nor do I have a solid definition of "critical" in context. But... if we lean on our naive / intuitive understanding of what intelligence is and what it means to be critical, you might translate this as "would a general ...


6

We have not been able to create a truly intelligent AI yet, according to your definition. So we have no real life proof-of-concept that shows that it actually works. But based on the current research, there is no known property of the human brain that cannot be modeled in software/hardware. We do not understand the human brain enough yet - and most likely ...


6

OpenCog is an open source AGI project. But it is is also incredibly complex and IMHO not a good idea (I have not fully read his theories). You can learn the essential ideas behind OpenCog from the co-founder Ben Goertzel site as well. Or, you can participate in the philosophical discussion regarding AGI. For strictly AGI, decision theory, logic, and math ...


6

Maybe. AI has a long history of encountering mathematical impossibilities and then working around them already. While the individuals who solved these problems don't get as much press as Newton, Einstein, or Hawking, a case could be made that their contributions to human knowledge are on a similar scale. Unfortunately, their results don't relate to physical ...


6

I don't think there is a single standard word or phrase that covers just this concept. Perhaps recursive self-improvement matches the idea concisely - but that is not specific AI jargon. Very little is understood about what strength this effect can have or what the limits are. Will 10 generations of self-improvement lead to a machine that is 10% better, 10 ...


5

The cleanest result we have on this issue is the "no free lunch" theorem. Basically, in order to make a system perform better at a specific task, you have to degrade its performance on other tasks, and so there is a flexibility-efficiency tradeoff. But to the broader question, or whether or not your thinking is correct, I think it pays to look more closely ...


5

As Matthew Graves explained in another answer No free lunch theorem confirms the flexibility - efficiency trade-off. However, this theorem is describing a situation where you have a set of completely independent tasks. This often doesn't hold, as many different problems are equivalent in their core or at least have some overlap. Then you can do something ...


5

I can offer two (at first sight, conflicting) perspectives on this: Firstly: If the letter string 'abc' becomes 'abd' what would "doing the same thing" to 'ijk' look like? This is just one example of a problem (so-called 'letter-string analogy problems') that is not easily framed as an optimization problem - there is a range of answers that appear ...


5

The concept of 'survival instinct' probably falls in the category of what Marvin Minsky would call a 'suitcase word', i.e. it packages together a number of related phenomena into what at first appears to be a singular notion. So it's quite possible that we can construct mechanisms that have the appearance of some kind of 'hard-coded' survival instinct, ...


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