Experimental tests have shown that the new system, which is based on the artificial intelligence algorithm known as "reservoir computing," not only performs better at solving difficult computing tasks than experimental reservoir computers that do not use the new algorithm, but it can also tackle tasks that are so challenging that they are considered beyond the reach of traditional reservoir computing. The results highlight the potential advantages of self-learning hardware for performing complex tasks, and also support the possibility that self-learning systems--with their potential for high energy-efficiency and ultrafast speeds--may provide an extension to the anticipated end of Moore's law. The researchers, Michiel Hermans, Piotr Antonik, Marc Haelterman, and Serge Massar at the Université Libre de Bruxelles in Brussels, Belgium, have published a paper on the self-learning hardware in a recent issue of Physical Review Letters. "On the one hand, over the past decade there has been remarkable progress in artificial intelligence, such as spectacular advances in image recognition, and a computer beating the human Go world champion for the first time, and this progress is largely based on the use of error backpropagation," Antonik told Phys.org. "On the other hand, there is growing interest, both in academia and industry (for example, by IBM and Hewlett Packard) in analog, brain-inspired computing as a possible route to circumvent the end of Moore's law.
The idea that university CS programs are taking bright young minds and fashioning them into algorithm and data structure whiz-kids defies the observations of almost any incoming CS student or their instructor. Many CS freshmen enter college already having a passion for computers and likely a privileged amount of access to technology and mentorship. Like myself, they were given computers as children by parents who were themselves close to technology. They have computer usage skills (how to configure your machine, how to fix basic computer problems) and have parents (or tutors) who introduced them to programming. For those without that background, freshman CS can prove very challenging.
Associate Professor Julian Togelius works at the intersection of artificial intelligence (AI) and games--a largely unexplored juncture that he has shown can be the site of visionary and mind-expanding research. Could games provide a better AI test bed than robots, which--despite the way they excite public imagination--can be slow, unwieldy and expensive? According to him, the answer is resoundingly yes. "I'm teaching computers to be more creative than humans," he says. Togelius, a member of the NYU Tandon School of Engineering's Department of Computer Science and Engineering, is at the forefront of the study of procedural content generation (PCG)--the process of creating game content (such as levels, maps, rules, and environments) by employing algorithms, rather than direct user input.
SAN FRANCISCO -- New research from Google shows that black students are less likely to have computer science classes in school and are less likely to use computers at home even though they are 1.5 times more interested in studying computer science than their white peers. The findings are part a report released Tuesday by Google in partnership with Gallup that puts the spotlight on the racial and gender gap in K-12 computer science education. Google says its aim with the research, which surveyed thousands of students, parents, teachers, principals and superintendents, is to increase the numbers of women, blacks and Latinos in computer science. Computer science classes are popping up in K-12 schools around the country. The growing effort is coming from many quarters -- the National Science Foundation, the College Board, Freada Kapor's SMASH Academy, Black Girls Code, Girls Who Code, Code.org and major tech companies such as Google -- all searching for the best way to put computers and computer know-how in the hands of kids from all racial, ethnic and socioeconomic backgrounds.