The recent successes of AI have captured the wildest imagination of both the scientific communities and the general public. Robotics and AI amplify human potentials, increase productivity and are moving from simple reasoning towards human-like cognitive abilities. Current AI technologies are used in a set area of applications, ranging from healthcare, manufacturing, transport, energy, to financial services, banking, advertising, management consulting and government agencies. The global AI market is around 260 billion USD in 2016 and it is estimated to exceed 3 trillion by 2024. To understand the impact of AI, it is important to draw lessons from it's past successes and failures and this white paper provides a comprehensive explanation of the evolution of AI, its current status and future directions.

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We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment. An interactive version of this paper is available at https://worldmodels.github.io

It works with pattern matching mechanism,which attempt to describe objects, events or processes in terms of their qualitative logics and computational relationships. To respond a situation flexibly. To make sense out of ambiguous or contradictory messages. Next Normal Intelligence To recognize relative importance of different elements of situations . Expressing emotions depends upon the situation.

This paper continues the research that considers a new cognitive model based strongly on the human brain. In particular, it considers the neural binding structure of an earlier paper. It also describes some new methods in the areas of image processing and behaviour simulation. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with 'full linking'. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared to. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.

Whether it's beating us at games like the board game Go or stealing our jobs, the killer combination of artificial intelligence and robots are owning us puny humans left and right. The latest example of a high-tech achievement that will make you feel on the verge of extinction? A robot that's capable of completing a Rubik's Cube puzzle in just 0.38 seconds flat -- which includes image capture and computation time, along with physically moving the cube. Not only is that significantly faster than the human world record of 4.59 seconds, but it's also a big improvement on the official robot world record of 0.637 seconds, as set in late 2016. The 0.38-second achievement isn't yet an official record, but if it manages to achieve the same results under record-testing conditions it certainly will be.

A pair of hardware hackers have beat the world record for solving a Rubik's cube robotically, completing the task in almost half the time. The Guinness World Record was set just over a year ago by a Hungarian architect and his'Sub1 Reloaded' machine when it solved a Rubik's cube in 0.637 seconds. That record, however, has now been demolished. Software developer Jared Di Carlo and MIT Biometrics Lab Master's student Ben Katz devised a contraption that can solve a Rubik's cube in a stunning 0.38 seconds. Software developer Jared Di Carlo and MIT Biometrics Lab Master's student Ben Katz built a'Rubik's Contraption' that's capable of solving the complicated puzzle in a mere 0.38 seconds The researchers discovered that they could easily beat the world record by using a different kind of motor on their'Rubik's Contraption.' 'We noticed that all of the fast Rubik's Cube solvers were using stepper motors, and thought that we could do better if we used better motors,' Di Carlo wrote in a blog post.

For robots to solve real world tasks, they often require the ability to reason about both symbolic and geometric knowledge. We present a framework, called KABouM, for integrating knowledge-level task planning and motion planning in a bounding geometry. By representing symbolic information at the knowledge level, we can model incomplete information, sensing actions and information gain; by representing all geometric entities-- objects, robots and swept volumes of motions--by sets of convex polyhedra, we can efficiently plan manipulation actions and raise reasoning about geometric predicates, such as collisions, to the symbolic level. At the geometric level, we take advantage of our bounded convex decomposition and swept volume computation with quadratic convergence, and fast collision detection of convex bodies. We evaluate our approach on a wide set of problems using real robots, including tasks with multiple manipulators, sensing and branched plans, and mobile manipulation.

The evolution of artificial intelligence (AI) grew with the complexity of the languages available for development. In 1959, Arthur Samuel developed a self-learning checkers program at IBM on an IBM 701 computer using the native instructions of the machine (quite a feat given search trees and alpha-beta pruning). But today, AI is developed using various languages, from Lisp to Python to R. This article explores the languages that evolved for AI and machine learning. The programming languages that are used to build AI and machine learning applications vary. Each application has its own constraints and requirements, and some languages are better than others in particular problem domains.

Here we are dealing with the oldest data set, created billions of years ago -- the set of integers -- and mostly the set consisting of two numbers: 0 and 1. All of us have learned how to write numbers even before attending primary school. Yet, it is attached to the most challenging unsolved mathematical problems of all times, such as the distribution of the digits of Pi in the decimal system. The table below reflects this contrast, being a blend of rudimentary and deep results. It is a reference for statisticians, number theorists, data scientists, and computer scientists, with a focus on probabilistic results.