The radiotherapy system team uses powerful verification methods ranging from automated theorem proving tools to manual proofs written by hand and checked by a proof assistant (a program that checks the correctness of proofs in expressive logic). To do this, DeepSpec is building tools for verifying that programs conform to deep specifications--granular, precise descriptions of how software behaves based on formal logic and mathematics--and that software components such as OS kernels provably conform to their deep specifications. Another DeepSpec member, Yale University computer science professor Zhong Shao, along with a team of researchers there, wrote an operating system called CertiKOS which uses formal verification to ensure the code behaves exactly as is intended. DeepSpec is building tools for verifying programs, and software components such as OS kernels, conform to deep specifications.

This has changed in the last two decades, due to the progress in Satisfiability (SAT) solving, which by adding brute reason renders brute force into a powerful approach to deal with many problems easily and automatically. This combination of enormous computational power with "magical brute force" can now solve very hard combinatorial problems, as well as proving safety of systems such as railways. To solve the Boolean Pythagorean Triples Problem, it suffices to show the existence of a subset of the natural numbers, such that any partition of that subset into two parts has one part containing a Pythagorean triple. This performance boost resulted in the SAT revolution:3 encode problems arising from many interesting applications as SAT formulas, solve these formulas, and decode the solutions to obtain answers for the original problems.

The aspiration to create formally verified software has existed nearly as long as the field of computer science. Now those same small coding errors open massive security vulnerabilities on networked machines that allow anyone with the know-how free rein inside a computer system. To see how this works, imagine writing a computer program for a robot car that drives you to the grocery store. Between the lines it takes to write both the specification and the extra annotations needed to help the programming software reason about the code, a program that includes its formal verification information can be five times as long as a traditional program that was written to achieve the same end.

Machine intelligence capable of learning complex procedural behavior, inducing (latent) programs, and reasoning with these programs is a key to solving artificial intelligence. The problems of learning procedural behavior and program induction have been studied from different perspectives in many computer science fields such as program synthesis [1], probabilistic programming [2], inductive logic programming [3], reinforcement learning [4], and recently in deep learning. Recently, there have been many success stories in the deep learning community related to learning neural networks capable of using trainable memory abstractions. The aim of the NAMPI workshop is to bring together researchers and practitioners from both academia and industry, in the areas of deep learning, program synthesis, probabilistic programming, inductive programming and reinforcement learning, to exchange ideas on the future of program induction with a special focus on neural network models and abstract machines.

In the future, a new generation of autonomous robots is set to complete tasks autonomously, even if something unforeseeable happens. With the support of the Austrian Science Fund FWF, information technology experts in Graz are working to advance the development of artificial intelligence and equip robots with common sense. In a recently completed project sponsored by the Austrian Science Fund FWF, Steinbauer and his team set out to provide a robot with something akin to common sense. Testing autonomous systems requires enormous computing power, because they involve a very high level of computational complexity.

In the future, a new generation of autonomous robots is set to complete tasks autonomously, even if something unforeseeable happens. With the support of the Austrian Science Fund FWF, information technology experts in Graz are working to advance the development of artificial intelligence and equip robots with common sense. In a recently completed project sponsored by the Austrian Science Fund FWF, Steinbauer and his team set out to provide a robot with something akin to common sense. Testing autonomous systems requires enormous computing power, because they involve a very high level of computational complexity.

One hundred and fifty years of mathematics will be proved wrong if a new computer program stops running. The program is a simulated Turing machine, a mathematical model of computation created by codebreaker Alan Turing. Now Scott Aaronson and Adam Yedidia of the Massachusetts Institute of Technology have created three Turing machines with behaviour that is entwined in deep questions of mathematics. Now, Yedidia and Aaronson have created a Turing machine with 7918 states that has this property.

The app is classroom notes on the subject for Information technology (IT), Computer Science engineering, discrete mathematics & Mathematics students. The purpose of the App is faster learning of the subject and quick revisions of the topics. The Topics are created in manner to quickly absorb the subject. It covers 138 topics of Automata in detail.

So, take a look at the best free machine learning ebooks listed below and make your pick as to which one you would want to read first and then go along with other ones. This book on inductive logic programming (ILP) that a research field at the intersection of machine learning and logic programming aims at a formal framework besides providing the practical algorithms for inductively learning relational descriptions in the form of logic programs. This book makes readers learn a principled, practical, probabilistic approach to learning in kernel machines in quite a different and easy way. Do share your reviews once you have gone through these free machine learning ebooks.

This work posits a way to integrate first order logic rules with neural networks structures. Student networks directly uses the labelled data and learns model distribution P then given the logic rules, teacher networks adapts distribution Q as keeping it close to P but in the constraints of the given logic rules. This function distills the information adapted by the given rules into student network. My take away is, it is perfectly possible to use expert knowledge with the wild deep networks.