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The related ACM article describes a few specific technical contributions, which led the ACM to award them. Geoffrey Hinton Backpropagation: In a 1986 paper, "Learning Internal Representations by Error Propagation", co-authored with David Rumelhart and Ronald Williams, Hinton demonstrated that the backpropagation algorithm allowed neural nets to ...


5

They would probably have followed the same sequence we do: be amazed at the capabilities, ask how it is done, wonder whether this is really intelligence and (or) point out our narrow the performance was, require more next time to be impressed again.


3

There are a lot of examples of animals that have been trained by humans (to perform some specific task). For example, dogs, tigers or chimpanzees. Nonetheless, none of them have exhibited a general intelligence comparable to that of humans. Why is that? It is believed that the intelligence of mammals is (at least partially) determined by the size of the ...


3

The technique used by AlphaGo is "Monte Carlo Tree Search", combined with a very well trained neural network. The network's job is to estimate the quality of different board states and moves. This estimation is deterministic. If you show AlphaGo the same board on two different occasions, it thinks it is exactly as good (or bad) on both occasions. Monte ...


3

My sense is that they would, based on a high-level take of Babbage and Lovelace's view of the potential capability of the "analytic engine". If Babbage's Tic-Tac-Toe machine had been built, I am sure that would have been regarded as machine intelligence. Nimatron (Edward Condon) may have been the first game AI, and the capability seems similar to what ...


3

John R. Pierce led the Bell Labs research team that created the first transistor and gave it its name. He was later the Chief Engineer at the Jet Propulsion Laboratory at CalTech. His relationship to artificial intelligence research was mainly in regard to language translation. He wrote the following.1 The computer has opened up to linguists a host of ...


3

This question has been studied academically for decades, and is really an extension of the work on Philosophy of Mind that was done in the two or three centuries before that. A good resource is Mind Design II, though it's getting a little bit old now. The modern schools of thought are: Cognitivism. This is in decline, but was extremely popular in the 70's ...


2

I tend to take a reductionist view and see economic incentives as the overwhelming driver of AI research, without regard to consequences. I see the entire field as based on optimization, which includes automation of repetitive tasks. It's about reducing cost/downside and increasing return-on-investment in any area to which intelligence can be applied, ...


2

The Mac Pro uses AMD GPUs. These don't support CUDA, but instead support the OpenCL framework. TensorFlow, the most popular deep learning library, uses CUDA to run on GPUs, although OpenCL support is in the works. That said, one of the main approaches to OpenCL support, SYCL, isn't planning to support OSX: We have no plans to support OSX in near future. ...


2

Defining what it means to understand something is a complex philosophical question, with answers that can split the AI community into different camps. Clearly an algorithm that associates the ASCII characters of word like "if" with a set of numbers based on statistics of where it appears in a corpus of reference texts is missing the essence of subjective ...


2

Nice question! I think there are a couple of issues at work here. Is the historical weakness of GOFAI in relation to non-trivial combinatorial games partly a function of the structure of the games studied, where game states and token values cannot be precisely quantified? I think the short answer is yes. The real issue is in the last part: ...


1

This is a very vague question as well as a really vague statement. Deciding on your career based on "one person who did x said y..." shouldn't be a sole guide to anything in life. Even gathering a multitude of opinions would be useless if your sample is biased on either opinion, good or bad. Personally if I had the rep I would flag the question as it is also ...


1

Artificial intelligence is reshaping almost all industries. Artificial Intelligence has become the current talk of the town. We are not realizing a fact that we have already become dependent on artificial intelligence, from making online orders to self-driving cars, AI has already entered our lives. Companies are depending on AI for their future projects ...


1

Maybe, but it depends to a very large degree on the choice of definition. One of the biggest challenges for AI researchers, neuroscientists, philosophers, and psychologists, has been that the layperson's understanding of intelligence does not appear to correspond to a well-defined concept. This point was most famously exploited by John R. Searle in his ...


1

Of course you can (read through to the end). You just need to teach it how it is taught to a baby. But first, you need to create the baby's brain. So you need to build a brain that learns from videos, poll videos, and not just understands but practices and understands other people's reactions. Sorry, but that's not enough. You would have the same work as ...


1

The determination of likelihood of advancements in any science or technology over a decade are dependent upon several technical features of culture. Not all are technical because they are elements in hardware and software design. Some are. Market forces Perpetuation of traditions in education Feasibility Availability of resources Cost of research Cost of ...


1

Computational Learning Theory gives us an interesting framework to understand what statistical learning is doing. The gist of it is, we can model the process of statistical learning as one of formal deduction. The learning itself does not require a random element. This shouldn't be too surprising. Consider a classic decision tree learner like C4.5 or ID3: ...


1

Yes, a baby can be considered an AI. Will this be our future? I don't know. That is exactly what some people are looking for, to create an AI that can live. We have several AIs that each time more surprises us. But none of them question their own existence, none of them wants to know or require attention from their god (developer) because of their ...


1

Machine learning algorithms lay the foundation for deep learning architectures so they pretty much play a role in which ever deep learning application one can make. The improvement of these machine learning algorithms coupled with deep learning techniques have lead to the establishment of "strong-narrow AI"


1

It sounds like you are trying to do some kind of semi-supervised learning. In semi-supervised learning, some data points are labelled (you know which class they belong to), and others are not. There are classification algorithms designed specifically for this kind of problem, like a transductive-SVM. I personally have not found these techniques to be more ...


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