Senior Analyst, AI & Quantum Computing Paul Smiith-Goodson dives deeper as for the past two and a half years, Dr. Puri and a team from IBM Research and the MIT-IBM Watson AI Lab have worked on a massive code-intensive AI for Code project.
Backed by investors such as technologist Tej Kohli, Rewired invests across five verticals including machine learning, robotics, bionics, sensors, mapping and localization.
Big Blue hopes to create the ImageNet of training resources for AI-powered programming tools Share
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Think IBM has assembled a massive silo of source code for teaching machine-learning programs about programming.
Dubbed Project CodeNet, the set contains, we re told, 14 million code samples totaling 500 million lines in more than 55 programming languages, from Java, C, and Go to COBOL, Pascal, and FORTRAN. Truth be told, more than three-quarters of it all is in C++ and Python.
This source code wasn t taken from production nor in-development applications: it was collected from entries submitted to two programming contests organized in Japan: Aizu and AtCoder. In these contests, competitors are challenged to write the necessary code to turn a given set of inputs into a set of desired outputs. About half of the samples work as expected, and the rest are labeled as either wrong solutions, non-building, or buggy.
This new robotics challenge could bring us closer to human-level AI
Pushing the boundaries of embodied AI Ben Dickson is the founder of TechTalks. He writes regularly about business, technology and politics. Follow him on Twitter and Facebook
(show all) Ben Dickson is the founder of TechTalks. He writes regularly about business, technology and politics. Follow him on Twitter and Facebook
Since the early decades of artificial intelligence, humanoid robots have been a staple of sci-fi books, movies, and cartoons. Yet, after decades of research and development in AI, we still have nothing that comes close to the Jetsonsâ Rosey the Robot.
Credits: Image: Lillie Paquette/MIT School of Engineering
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Not so long ago, watching a movie on a smartphone seemed impossible. Vivienne Sze was a graduate student at MIT at the time, in the mid 2000s, and she was drawn to the challenge of compressing video to keep image quality high without draining the phone’s battery. The solution she hit upon called for co-designing energy-efficient circuits with energy-efficient algorithms.
Sze would go on to be part of the team that won an Engineering Emmy Award for developing the video compression standards still in use today. Now an associate professor in MIT’s Department of Electrical Engineering and Computer Science, Sze has set her sights on a new milestone: bringing artificial intelligence applications to smartphones and tiny robots.