14

State-of-the-art is a tough bar, because it's not clear how it should be measured. An alternative criteria, which is akin to state-of-the-art, is to ask when you might prefer to try an SVM. SVMs have several advantages: Through the kernel trick, the runtime of an SVM does not increase significantly if you want to learn patterns over many non-linear ...


10

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 ...


7

Deep Learning and Neural Networks are getting most of the focus because of recent advances in the field and most experts believe it to be the future of solving machine learning problems. But make no mistake, classical models still produce exceptional results and in certain problems, they can produce better results than deep learning. Linear Regression is ...


5

Nim was actually one of the first games ever played by an electronic machine. It was called the Nimatron and was displayed at the 1940 New York World's Fair. It is also well known that neural networks can model the Xor-function, if they have enough layers. Despite that, Marvin Minsky is supposed to have killed neural networks in the sixties, by asserting ...


4

There is actually a book called Artificial General Intelligence by Ben Goertzel and Cassio Pennachin. It's a bit out of date (from 2008), and published as a Springer-Verlag monograph (which tends to have fairly low editorial standards). This one is also an anthology, with each chapter written by a different author. It's probably not suitable as an ...


4

In addition to the answers already posted, I think IBM's Watson deserves a mention. It did something pretty impressive with its Jeopardy win, possibly as impressive as AlphaGo. Sadly, since then, there don't seem to have been a lot of really public demos of Watson, as IBM is positioning the technology as a tool for companies and other organizations, and ...


4

Normally when you write a program, you are acting like a boss that micromanages the job, telling the workers how to accomplish a task, perhaps without even letting them know what the purpose is. What you are hoping to be is a boss that gives the workers a goal and allows them to determine how to accomplish it. In many ways, that is one of the aims of AI. We ...


3

As far as I know, no "true" (i.e. as intellectual and physically capable as a human) artificial general intelligent system (AGI) has been implemented or is practically useful (this is confirmed by Ben Goertzel, who is one of the leading researchers in AGI [1, 2]). The closest to a practical AGI might be Sophia (or similar robots), which may look ...


3

The state of AGI research is pursuing the few problems that we have been able to break off from the gigantic research problem. These are terms which can be more thoroughly looked into. A few of the main focuses are: One Shot learning - You know how a person can sometimes learn to do something by something by seeing literally 1 example of it? Well current ...


2

The question of whether nets can be trained to take over more and more of what was entirely within the domain of production systems was asked (to the dismay of those who worked on first order predicate calculus inference in the LISP community) back in the early 1990s. Artificial Networks Performing Logical Inference At Stanford University's Department of ...


2

AlphaGo is the most sophisticated Artificial Intelligence program created by humans. It is a computer program that is developed by Google DeepMind to play the board game "Go". The game is different than other games, as The number of potential legal board positions is greater than the number of atoms in the universe. It has way more legal board ...


1

I would suggest using a neural network with back propagation. From what I know, they can be applied to many different circumstances and work well. For your more simpler and repetitive tasks like moving an object, you can just use simpler regression methods.


1

An interesting model I encountered in a course is Facebook Prophet. Prophet takes into account trends, seasonality, and holidays for its predictions. As you can probably guess, this is a model that fits Facebook's needs very well. I'll give a brief introduction then provide a link where you can read more. Prophet fits a couple of functions of time ...


1

The blog post Unsupervised Cross-lingual Representation Learning (2019), the related paper and slides by Sebastian Ruder (a researcher currently at DeepMind) summarize what you are looking for. In fact, the authors write We will introduce researchers to state-of-the-art methods for constructing resource-light cross-lingual word representations and discuss ...


1

The paper Artificial General Intelligence: Concept, State of the Art, and Future Prospects (2014), by Ben Goertzel (one of the people that are really still very interested in AGI), surveys the field of artificial general intelligence (AGI), its progress, approaches, mathematical formalisms, engineering, and biology-inspired perspectives, and metrics for ...


1

It is already combined. Adaptive entropy techniques are already used in most of the best compression encoders. This is true for file encoders, video encoders, and audio encoders. We use it in the solar lab to optimize sample rates in data acquisition. In fact, pattern recognition and compression are very tightly coupled if you consider autoencoders and ...


Only top voted, non community-wiki answers of a minimum length are eligible