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59

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 ...


34

The terms strong and weak don't actually refer to processing, or optimization power, or any interpretation leading to "strong AI" being stronger than "weak AI". It holds conveniently in practice, but the terms come from elsewhere. In 1980, John Searle coined the following statements: AI hypothesis, strong form: an AI system can think and have a mind (in the ...


25

If I'm not mistaken you're looking for Roko's Basilisk, in which an otherwise benevolent future AI system tortures simulations of those who did not work to bring the system into existence


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 ...


21

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 ...


16

The rhetorical point of the Turing Test is that it places the 'test' for 'humanity' in observable outcomes, instead of in internal components. If you would behave the same in interacting with an AI as you would with a person, how could you know the difference between them? But that doesn't mean it's reliable, because intelligence has many different ...


16

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 ...


14

This is one of the most important issues in the philosophy of artificial intelligence. The most famous philosophical argument that attempts to address this issue is the Chinese Room argument published by the philosopher John Searle in 1980. The argument is quite simple. Suppose that you are inside a room and you need to communicate (in a written form) ...


13

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 ...


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

The problem of the Turing Test is that it tests the machines ability to resemble humans. Not necessarily every form of AI has to resemble humans. This makes the Turing Test less reliable. However, it is still useful since it is an actual test. It is also noteworthy that there is a prize for passing or coming closest to passing the Turing Test, the Loebner ...


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

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 ...


10

Some AI researchers do think RL is a path to AGI, and your intuition about how an agent would need to be proactive in selecting actions to learn about is exactly the area these researchers are now focused on. Much of the work in this area is focused on the idea of curiosity, and since 2014 this idea has gained a lot of traction in the research community. ...


10

I believe the term you are looking for is "(technological) singularity". https://en.wikipedia.org/wiki/Technological_singularity


8

In contrast to the philosophical definitions, which rely on terms like "mind" and "think," there are also definitions that hinge on observables. That is, a Strong AI is an AI that understands itself well enough to self-improve. Even if it is philosophically not equivalent to a human, or unable to perform all cognitive tasks that a human can, this AI can ...


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

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

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 classical Turing Test certainly does have limitations. Because I don't see it mentioned here yet, I'll suggest you read about The Chinese Room, which is one of the most commonly cited reasons why the Turing Test indeed falls short of ascertaining true 'consciousness'. However, I'd also note that Turing himself, in the original paper that proposed the ...


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

What 'infinite' means here could possibly be debated at some length, but that notwithstanding, here are two conflicting answers: 'Yes': Simulate all possible universes. Stop when you get to one containing a flavor of intelligence that passes whatever test you have in mind. Steven Wolfram has suggested something broadly along these lines. Problem: the state ...


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

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 ...


7

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 ...


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 ...


7

There are at least three free courses. MIT 6.S099: Artificial General Intelligence, organized by Lex Fridman, is a series of lessons and talks primarily given by a diverse set of guest appearances, such as Josh Tenenbaum (researcher and professor in computational cognitive science), Nate Derbinsky (who gives a lesson on cognitive architectures, Soar, etc.),...


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