# Tag Info

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Does this addition of curosity changes clarifai into a true AI? As per my answer to this question, we don't know what the ingredients for a 'true AI' are. Via the Turing Test and its variants, the best we can do is "know one when we see one". Curiosity certainly appears necessary for intelligence, though it doesn't seem sufficient - a lemming-like creature ...

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when the AI has difficulty in classifying a image or its objects it should ask a human for help just like a curious child It's called active learning, it's already used quite often.

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First, a note on the question itself. Humans have been endowed with personalities by nature, and it is not clear (to me at least) if this is a feature or a bug. In my opinion, this is a statement that constrains the question, since it assumes that the personality is given. To me, it feels a bit like playing god: Artificial (given) Intelligence would ...

3

'Personality' is something of a 'suitcase word' (Minsky) for quite a large collection of (presumably reasonably consistent) observable traits. It seems clear that there is a certain collective advantage in having a consistent personality - specifically that it affords observers some learning gradient in an otherwise uncertain environment. This is of ...

3

Yes, this was an active area of research in a number of different AI fields. Probably the most directly related work is Bongard, Zykov & Lipson's self-repairing robots from the early 2000's. There's some more recent work from Mark Yim that you can see here too. There are lots of different ways to do this, but Bongard et al's approach was probably the ...

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It's a well known concept that's already used What we call "curiosity" in humans and animals is in effect the chosen level of the "exploit vs explore" tradeoff for any active system. For example, the field of reinforcement learning is one approach that studies implementations of what essentially is the equivalent of curiosity; and we have research on how ...

2

It looks like you have some common misconceptions about AI and neural networks. First, AI programs generally do not try to imitate the human behaviour of a human brain. Instead, they try to imitate some higher-level behaviour. For example, they might imitate the reasoning process that you go through when you make a plan. In this context, the building-blocks ...

2

Object detection can conceivably imitate what the human visual system does. Research along these lines began in the 1980s in multiple laboratories and was termed Computer Vision. How do we see ... and detect things and their location so quick[?] Is the reason that we have huge gigantic network in our brain and we are trained since birth to till now and ...

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The Title Question Is there any paper, article or book that analyzes the feasibility of achieving AGI through brain-simulation? Yes. There are various analyses that have been published. We have some early work like Some Philosophical Problems From the Standpoint of Artificial Intelligence, John McCarthy and Patrick J. Hayes, Stanford University, 1969. ...

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The model you describe is a kind of a leaky integrate-and-fire (LIF) neuron (see p. 7). It is leaky because the membrane potential decreases steadily in the absence of input. In contrast, in the simple integrate-and-fire (IF) model the membrane potential is retained indefinitely until the neuron spikes, at which point it is reset to 0. However, LIF neurons ...

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Measuring and classifying human abilities related to intelligence have been done through a number of metrics. Grades — When a student has very high grades, other students, teachers, and siblings tend to think and say that they are smart, even if they spend much time studying. College readiness test results — This is actually the most tuned ...

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Regression for models more complex than $y = a x + b$ is a convergence strategy. Surface fitting algorithms, such as Levenberg–Marquardt, are often successful at achieving regression using a damped version of least squares as an optimization criterion. The marriage of regression and the multilayer perceptron, an early model artificial network, led to the use ...

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Good question. It is related to the genetic algorithm concept, automated bug detection, and continuous integration. Early Genetically Inspired Algorithms Some of the Cambridge LISP code in the 1990s worked deliberately toward self-improvement, which is not the same as self-repair, but the two are conceptual siblings. Some of those early LISP algorithms ...

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The question and the example are a few contradictory. The example is about a physical brain damage. Computer systems with the ability to self-repair exists from 1970's. They can repair a damaged disk (RAID), replace a CPU by an idle one (active/passive), mark faulty memory blocks, redirect network traffic from broken links to available ones, ... nowadays ...

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I don't think it is important to build a specialized circuitry for face recognition. Our face recognition is hardwired by evolution. I think it is due to advantages like kin selection and kin altruism. You need to know who your brother is to help him, because he carries 1/4th your dna. So it is irrelevant in the case of building robots. Knowing a name (...

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Specialised neural circuitry to recognise faces is common in all our closest animal relatives. This means that it is likely an evolutionary adaptation that is many millions of years old. Babies can pay attention to faces basically from the moment they are born. There are certain types of brain damage that make it impossible to recognise faces. Being ...

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Curiosity by itself does not improve intelligence. It increases the chances of better understanding a given subject, given that curiosity is coupled with actions in that direction. For example: I am curious about how to make pancakes and decide to find a recipe but stop at the first instance of an answer with steps to follow. Curiosity needs to be coupled ...

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