13

Pretty much any "kid in a garage" so to speak. Nobody knows how to build AI yet and a major breakthrough could come from anywhere. That could knock Google off their perch easily enough.


7

Don't forget the many Chinese companies heavily investing in AI. Not only the biggest 3 (Tencent, Alibaba, Baidu) but many others (JD.com, Sina, ...) and also startups (SenseTime, iCarbonX) are very active, in all AI sectors. They have a couple of advantages for the future: a huge and growing domestic market availability of large amounts of data ...


6

According to Open AI's Greg Brockman, the Gym website never had a big impact and so was never maintained. This is the reason he gives for shutting down the website. A read only export of the site was archived at https://gym.openai.com/read-only.html and if you attempt to access the old website through the url https://gym.openai.com you will be redirected ...


4

This is pure speculation on my part, but I also think Amazon only, because Bezos is an engineer/founder, cares about marketshare and utility over profits, almost certainly understands the implications of recent validations of strong-narrow AI, and will invest sufficiently to give Google a run for their money. (This partly has to do with how successful Bezos ...


4

It is possible remove noise from signal if what is considered noise is distinguishable from what is considered signal. This is true of documents, images, audio, or any other type of information. If dirty means wrong content, then probably not. If dirty means mispellings, then probably yes. If dirty means tilted by a few degrees and containing edge artifacts ...


3

This is also the topic of Image Processing (which has analytical solutions instead of learning) mostly through predesigned filters. The filter depends on the type of noise, (salt & pepper, Gaussian, etc.) i.e., for salt & pepper choosing the median in a window. There are a lot of denoising research in literature. There are also more recent learning ...


3

For a foundation, there is nothing better than Cybernetics by Norbert Wiener. It is surprising how advanced this MIT professor was, prior to Turing's thought experiment on a general purpose computing machine or the embodiment of the von Neumann architecture upon which most contemporary computers are based. In key ways his analysis of time series and ...


3

In my own opinion the biggest threats would be Nvidia because of their tight grip on the GPU market; it will be relatively easier for them to catch up with innovative ideas. Baidu is on the horizon though their research at this point seems to be domain specific, NLP to be precise.


3

I will assume that you mean the Strong AI or AGI race? Interesting question, given Google's investment in the technology it seems hard to imagine them giving up the throne at this point. However, there definitely are some competitors. Some that come to mind are Amazon, OpenAI, Baidu. These companies all have significant resources and have teams dedicated to ...


3

If we talk about Narrow AI, big companies have an evident advantage, as you say. Moreover, because they have the capability of buy small challenging companies. If we talk about General AI, the chances are equal, the muse will appear when they want to the person they want.


3

If you want to put your knowledge into practice, I recommend participating in AI challenges. There are different platforms hosting such challenges available. One established website for game challenges is The AI Games. You have actual challenges, can compete with other AI authors and learn from them through discussions on the message board. Don't expect to ...


3

Oligopoly vs Monopoly The terms in the question, multipolar and monolithic, appear to be referring to the micro-economic concepts of oligopoly and monopoly respectively. Although these concepts are not AI specific, they certainly apply to such development in the way the question suggests. Leading AI R&D is occurring in a relatively small number of ...


3

I recommend you focus on quality over quantity. Publishing a paper will boost your reputation and make you more recognised within your academic field (AI), however this is only if the paper provides useful insights into an important issue. Your paper is more likely to be accepted if it is well written and easy to understand, stimulates new important ...


2

You can cluster all your features in one matrix X, in which each line would be one element of the data set you want to construct, and each column would be a different feature of this element. You construct then a Y vector containing the different target classes, where the i-th element will be the target class of the i-th X element. For the following I ...


2

One important consideration here: in the last decade or two the machine learning and artificial intelligence fields, which contains the majority of reinforcement learning work, researchers have considered conferences to be the more impactful publishing venues than journals. The particular venue a researcher chooses depends on the data and/or application ...


2

A characteristic visual element for neural network might be the node diagram, which shows (schematically) the nodes in the layers of a neural network and the connections between the nodes, both within and between the layers, that get weighted as the network is trained. If you do a Google search on "neural network node diagram", you will see what I mean. ...


2

Researchers may follow specific mathematical frameworks, techniques to come-up with amazing works just like in any field, but I believe in Darwinian natural selection as a base theory for human's discoveries as well as for the Evolutionary Neural Net Architectures. "Principle by which each slight variation [of a trait], if useful, is preserved".


2

Although there is a strong element of "try and see" that has driven successful architectures, the drivers for what to try are often inspired by underlying theory or knowledge from other disciplines. Specifically for basic CNN, which led to AlexNet and many of the best image processing, the concept of using local receptive fields in layers was inspired by ...


2

Poel's paper on Translating Values into Design Requirements articulates a framework for mapping abstract values and norms into concrete design constraints that an engineer could work with. The example used in the paper is mapping beliefs about animal welfare to design constraints on chicken coups. The newer paper by Tubella et al. on Governance by Glass-...


2

Not all of the mistrust aimed at AI systems is unjustified, particularly when it comes to neural networks and other such systems that rely on large training data sets. There are a number of high profile cases, facial recognition being one that has often (understandably) received a lot of flak, where improperly configured training data has resulted in skewed ...


1

It depends a little on what you mean by "the same rounds, just with no public cards dealt." If you mean that each player will just be dealt 2 cards, and no public cards exist, then really we're playing a sort of "high card" game. The best hand is just a pair of aces, CFR will solve this quickly, because the number of possible game states is extremely small ...


1

They are found using the infinite monkeys approach: The infinite monkey theorem states that a monkey hitting keys at random on a typewriter keyboard for an infinite amount of time will almost surely choose a neural network architecture that appears to work well on the given data set and parameters. You assign thousand grad students around the world with ...


1

Who stands a chance against Google in the AI race? The Google TPU is a GPU sized chip developed for deep learning, it is a matrix processor specialized for neural network work loads. "The latest-generation Cloud TPU v3 Pods (more than 1,000 individual TPU chips) are liquid-cooled for maximum performance, and each one delivers more than 100 petaFLOPs ...


1

Google may appear to have a strong advantage over its competition in Narrow AI due to its acquisition of other AI companies such as Boston dynamics. Google also has extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions which is useful as training ...


1

I'm surprised no one has mentioned IBM. They first build DeepBlue and beat the highest ranked chess grandmaster. Then they crushed the Jeopardy champs. Now they are merchandising the variants of Watson.


1

Artificial Intelligence is an current emerging technological trend. Almost all organizations are adopting AI technology today as a result there is a multi fold increase in the demand for Artificial Intelligence professionals. So,If you want to make a career in AI.This would be the best time. Here are the list of some best AI certifications: Artificial ...


1

Artificial neural network to solve Crossword puzzles, this model might help you to get a start on evolving your own. https://github.com/fh295/DefGen2 A brute force of word combinations would solve the matrix, but might not get the context right and might be the simplest model if you are not in for a neural network.


1

The easiest way is by using System. Speech. Recognition; here is described how to use it Just change this code: colors.Add(new string[] { "red", "green", "blue" }); And add results of a database query, for example, with Entity Framework and LINQ instead of new string [] {"red", "green", "blue"}. Something like this: WordsToRecognize.Add( context.MyTable ...


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