Logic & Formal Reasoning


Hacker-Proof Coding

Communications of the ACM

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.


The Science of Brute Force

Communications of the ACM

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.


In Memoriam Alain Colmerauer: 1941-2017

#artificialintelligence

Alain Colmerauer, a French computer scientist and a father of the logic programming language Prolog, passed away on May 15 at the age of 76. He earned a degree in computer science from the Institut polytechnique de Grenoble (Grenoble Institute of Technology) in 1963, and a doctorate in the discipline in 1967 from the École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble, which is part of the Institut. He was promoted in 1979 to Professeur 1ère classe (Full Professor), and in 1988 to Professeur classe exceptionnelle (University Professor). From 1993 to 1995, he was Head of the Laboratoire d'Informatique de Marseille (LIM), a joint laboratory of the Centre National de la Recherche Scientifique, the University de Provence and the University de la Mediterranee.


Goedel's Incompleteness Theorem and the Emergence of AI

#artificialintelligence

Some leading scientists like Sir Roger Penrose even argue that Goedel showed with his Incompleteness Theorem that today's computers can never reach human level intelligence or consciousness, that humans will always be smarter than current computers or any computer algorithm can ever be and that computers will never in the true sense of the word "understand" anything like higher level mathematics, especially not mathematics that deals with trans-finite sets and numbers. Many famous mathematicians (and physicists) created fascinating new theories and discovered deep and far reaching mathematical results. He proved this by using his famous "diagonal" construction (see pic below) that showed that any supposedly complete enumerated list of irrational or real numbers R will always miss some irrational numbers, thereby proving that a complete enumeration of the real numbers by the natural numbers is impossible. Cantor has actually shown that there are even an infinite number of ever bigger infinities by showing that the set of all subsets of any given infinite set is always substantially bigger (cannot be put into a 1-1 relation) than the set itself.


Innovative mathematical model that simulates How the Mind Works

#artificialintelligence

This model allows quantifying --in precise way-- any fact, matter, phenomenon or thing, which has some relevance for the human being. In short, it is possible to quantify -- with mathematical precision-- any problem raised. In practical terms, the model offers an efficient universal methodology --which represents a drastic reduction in time and money-- for the application and development of all those tools or systems oriented to facilitate decision making (Systems of Support to Decisions or DSS, Games Theory, Decision Theory, Complex System, Expert Systems, etc.).The scope of this model is determined by its universal condition, charact…



Computer Scientists Close In on Perfect, Hack-Proof Code

WIRED

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.


Neural Abstract Machines & Program Induction workshop @ NIPS 2016

#artificialintelligence

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.


Artificial intelligence plus common sense

#artificialintelligence

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.


Artificial intelligence plus common sense

#artificialintelligence

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.