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61

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 ...


36

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


24

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 ...


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 ...


18

Commonsense 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, commonsense knowledge is fundamental to human-AI ...


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 ...


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

If by true AI, you mean 'like human beings', the answer is - no-one knows what the appropriate computational mechanisms (neural or otherwise) are or indeed whether we are capable of constructing them. What Artificial Neural Nets (ANNs) do is essentially 'nonlinear regression' - perhaps this is not a sufficiently strong model to express humanlike behaviour. ...


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 ...


13

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. So, ...


12

The halting problem is an example of a general phenomenon known as Undecidability, which shows that there are problems no Turing machine can solve in finite time. Let's consider the generalization that it is undecidable whether a Turing Machine satisfies some non-trivial property P, called Rice's theorem. First note that the halting problem applies only if ...


12

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 ...


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 ...


12

Neural networks, deep learning and other supervised learning algorithms do not "take actions" by themselves, they lack agency. However, it is relatively easy to give a machine agency, as far as taking actions is concerned. That is achieved by connecting inputs to some meaningful data source in the environment (such as a camera, or the internet), and ...


11

In my opinion, this would be Phaeaco, which was developed by Harry Foundalis at Douglas Hofstadter's CRCC research group. It takes noisy photographic images of Bongard problems as input and (using a variant of Hofstadter's 'Fluid Concepts' architecture) successfully deduces the required rule in many cases. Hofstadter has described the related success of ...


11

With AI technology at its current stage (or at least reasonably close to this stage), the jobs you proposed may very well be openings created by AI automation. However, sufficiently advanced AI technology--- the kind that can function as general purpose labor replacement--- will make even these jobs obsolete. This is because such an AI would be able to ...


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

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

Does the halting problem imply any limits on human cognition? Yes, absolutely--that there are pieces of code a human could look at and not be sure whether or not it will halt in finite time. (Certainly there are pieces of code that a human can look at and say "yes" or "no" definitely, but we're talking about the ones that are actually quite difficult to ...


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

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


9

There are at least two questions in your question: What are some of the methods used to program the successful go playing program? and Are those methods considered to be artificial intelligence? The first question is deep and technical, the second broad and philosophical. The methods have been described in: Mastering the Game of Go with Deep Neural ...


9

It depends on the definition of (artificial) intelligence. The position that Searle originally tried to refute with the Chinese room experiment was the so-called position of strong AI: An appropriately programmed computer would have a mind in the exact same sense as humans have minds. Alan Turing tried to give a definition of artificial intelligence with the ...


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,...


9

As far as I know, no AGI system has yet been created, so that's why there aren't yet many courses on AGI. However, there are a few courses that attempt to address AGI as the main topic but from different perspectives. Below, I will mention the ones that I found and partially followed, and give some info about them. MIT 6.S099: Artificial General Intelligence ...


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

There are two broad types of responses to philosophical queries like this. The first is to make analogies and refer to intuition; one could, for example, actually calculate the necessary size for such a Chinese room, and suggest that it exists outside the realm of intuition and thus any analogies using it are suspect. The second is to try to define the ...


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