If you think Tickle Me Elmo is freaky already, wait until those talking monsters can beat you at Jeopardy!. Sesame Workshop, the educational nonprofit behind Sesame Street and its iconic characters, this week announced a partnership with IBM Watson to develop edtech for pre-school children. Sesame Workshop is no newcomer to tech. The organization previously set up a venture fund in partnership with Collaborative Fund. And they currently offer mobile games, story apps, e-books and digital Family Toolkits to help parents and caregivers navigate challenging topics like autism, incarceration, and more.
On February 16th, Antonio García Morte, CTO (Chief Technology Officer) at the CADC participated in the "Artificial Intelligence Days at ETSIT" to give a workshop to the students of the Technical School of Telecommunications Engineers of the Polytechnic University from Madrid. The workshop was attended by 106 engineering students of different specialties such as; Telecommunications, Computing and Biomedical, interested in all the advances that are happening in the field of artificial intelligence.During the session will be treated in a very interactive way all the advances that are happening in the field of artificial intelligence. Antonio García Morte, conducted a very interactive workshop with students discovering the fundamental theoretical aspects, such as search algorithms, deep learning, or genetic algorithms, to reach the most important practical advances of our time, such as the self-driven car. The teaching objective of the session was to provide to the student a clear mental map of the different areas that make up the artificial intelligence, together with the theoretical foundations and practical applications of each one of them.
Intelligent machines won't be ruling the world anytime soon – but what happens when they turn you down for a loan, crash your car or discriminate against you because of your race or gender? On one level, the answer is simple: "It depends," says Bryant Walker Smith, a law professor at the University of South Carolina who specializes in the issues raised by autonomous vehicles. But that opens the door to a far more complex legal debate. "It seems to me that'My Robot Did It' is not an excuse," says Oren Etzioni, CEO of the Seattle-based Allen Institute for Artificial Intelligence, or AI2. The rapidly rising challenges that face America's legal system and policymakers were the focus of today's first-ever White House public workshop on artificial intelligence, presented at the University of Washington School of Law.
On weekdays, Daisuke Kuramoto, 36, is just another computer engineer who develops education materials for an e-learning content provider. But once a month, he becomes Qramo, organizer of a computer programming workshop for children. "If you say I am'teaching' programming, that's incorrect," said Kuramoto, who heads the Tokyo-based volunteer group Otomo. "At the workshop, I'm just a participant who loves to play around with programming." Kuramoto started the workshop in 2008 and launched Otomo the following year, recruiting professional programmers, computer science students, parents and others with a knack for the activity.
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 , probabilistic programming , inductive logic programming , reinforcement learning , and recently in deep learning. However, despite the common goal, there seems to be little communication and collaboration between the different fields focused on this problem. Recently, there have been many success stories in the deep learning community related to learning neural networks capable of using trainable memory abstractions. This has led to the development of neural networks with differentiable data structures such as Neural Turing Machines , Memory Networks , Neural Stacks [7, 8], and Hierarchical Attentive Memory , among others. Simultaneously, neural program induction models like Neural Program-Interpreters  and the Neural Programmer  have created much excitement in the field, promising induction of algorithmic behavior, and enabling inclusion of programming languages in the processes of execution and induction, while remaining trainable end-to-end. Trainable program induction models have the potential to make a substantial impact on many problems involving long-term memory, reasoning, and procedural execution, such as question answering, dialog, and robotics. 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. Through this workshop we look to identify common challenges, exchange ideas and lessons learned from the different fields, as well as establish a (set of) standard evaluation benchmark(s) for approaches that learn with abstraction and/or reason with induced programs.