We propose scalable methods to execute counting queries in machine learning applications. To achieve memory and computational efficiency, we abstract counting queries and their context such that the counts can be aggregated as a stream. We demonstrate performance and scalability of the resulting approach on random queries, and through extensive experimentation using Bayesian networks learning and association rule mining. Our methods significantly outperform commonly used ADtrees and hash tables, and are practical alternatives for processing large-scale data.

Referred to as an approach for either plan or goal recognition, the original method proposed by Ramirez and Geffner introduced a domain-based approach that did not need a library containing specific plan instances. This introduced a more generalizable means of representing tasks to be recognized, but was also very slow due to its need to run simulations via multiple executions of an off-the-shelf classical planner. Several variations have since been proposed for quicker recognition, but each one uses a drastically different approach that must sacrifice other qualities useful for processing the recognition results in more complex systems. We present work in progress that takes advantage of the shared state space between planner executions to perform multiple goal heuristic search. This single execution of a planner will potentially speed up the recognition process using the original method, which also maintains the sacrificed properties and improves some of the assumptions made by Ramirez and Geffner.

According to the science magazine Nature, chemists are heralding a new artificial intelligence platform as a significant milestone. The platform has the potential to accelerate the process of drug discovery, and it should be able to make organic chemistry more efficient. The new platform is designed to help chemists to plan the syntheses of small organic molecules. Traditionally, chemists use the process of retrosynthesis, which is an established problem-solving technique whereby target molecules are recursively transformed into increasingly simpler precursors. The goal of retrosynthetic analysis is structural simplification.

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.

"Steps Toward Robust Intelligence" in

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.