Conventional wisdom today says that robots will put millions of factory workers out of jobs. In this Hello World segment, journalist Ashlee Vance visits the Toronto headquarters of startup Kindred AI to learn about the cutting-edge technology behind our robot replacements from Kindred AI's refreshingly human co-founders, Suzanne Gilbert and George Babu. Join journalist and best-selling author Ashlee Vance on a quest to find the freshest, weirdest tech creations and the beautiful freaks behind them. Bloomberg is the First Word in business news, delivering breaking news & analysis, up-to-the-minute market data, features, profiles and more: http://www.bloomberg.com

Artificial Intelligence and Machine Learning are among the most trending technologies these days. Artificial Intelligence teaches computers to behave like a human, to think, and to give a response like a human, and to perform the actions like humans perform. As the name suggests, Machine Learning means the Machine is Learning. This is the technique through which we teach the machines about things. It is a branch of Artificial Intelligence and I would say it is the foundation of Artificial Intelligence.

With the rapid evolution of technology, new tools for creativity and development are constantly emerging. Musicians today are beginning to use machine learning, where computers "learn" over time by being fed large amounts of data, to create music in new and innovative ways. The computers process this data and identify patterns, allowing them to act on future data. After identifying these patterns, computers can classify new information, make predictions, or even generate novel, creative content. In the world of music, the possible applications of this technology are endless.

Though deep neural networks have great success in natural language processing, they are limited at more knowledge intensive AI tasks, such as open-domain Question Answering (QA). Existing end-to-end deep QA models need to process the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size. This is prohibitive for practical tasks such as QA from Wikipedia, a novel, or the Web. We propose to solve this scalability issue by using symbolic meaning representations, which can be indexed and retrieved efficiently with complexity that is independent of the text size. We apply our approach, called the N-Gram Machine (NGM), to three representative tasks. First as proof-of-concept, we demonstrate that NGM successfully solves the bAbI tasks of synthetic text. Second, we show that NGM scales to large corpus by experimenting on "life-long bAbI", a special version of bAbI that contains millions of sentences. Lastly on the WikiMovies dataset, we use NGM to induce latent structure (i.e. schema) and answer questions from natural language Wikipedia text, with only QA pairs as weak supervision.

Using the latest artificial intelligence software, the devices tracked students' behavior and read their facial expressions, grouping each face into one of …

Artificial Intelligence (AI) has the potential to have transformative impact on national security. It is also seen that AI is essentially a dual use technology.

Ray Kurzweil's impact on my life in general but especially on what I have been doing for the past 3 or 4 years is hard to exaggerate. It is a simple fact that, if I haven't read his seminal book The Singularity is Near, I would be neither blogging nor podcasting about exponential technologies, not to mention going to Singularity University. And so it was with great excitement and some trepidation that I went to interview Dr. Kurzweil in his office in Boston. Part of my trepidation came from some technical concerns: I wish I could buy a better camera. I wish I could hire a team of audio and video professionals so that I can focus on the interview itself.