Episode Summary: There's been lot of hype around AI and ML in business over the past five years. Even among investors exist a lot of misconceptions about using ML in a business context, and how to get up to speed on and learn machine learning as it applies to utility in industry. Recently, I talked with Benjamin Levy of BootstrapLabs in San Francisco, whom I met through an investment banking friend in Boston. BootstrapLabs invests in Bay area companies, and Levy also travels around the world speaking about investing in AI companies and raising funds for new ventures. In this episode, Levy gives his perspective on what investors and executives get wrong about ML and and AI, and discusses how they can get up to speed and leverage the applications for these technologies and related expertise to really make a difference (i.e.

After decades on every sci-fi fan's wish list, personal robots are on the cusp of entering our homes. Now it's time to put them to work. Everyone knows Pepper, the child-sized humanoid robot launched back in 2014 who was created to welcome visitors to SoftBank Mobile stores in Japan. Now Pepper has scored a few jobs in the US, from giving directions in a shopping mall in San Francisco to pouring beer at Oakland International Airport's Pyramid Taproom. The diminutive Pepper is not alone, not even at airports.

Artificial Intelligence is a key topic at this year's World Economic Forum Annual Meeting. Artificial intelligence and increasing automation is going to decimate middle class jobs, worsening inequality and risking significant political upheaval, Stephen Hawking has warned. In a column in The Guardian, the world-famous physicist wrote that"the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining." He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years and decades. The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines.

"I was determined to do it precisely because I was told it was impossible." So says Yasuo Kuniyoshi, professor at the University of Tokyo's Graduate School of Information Science and Technology, in a quiet tone. However, the sharp glint in his eye betrays his grand ambition of developing a truly clever artificial intelligence to benefit humankind. Existing AI (Top) Self-driving cars most likely will one day be able to take people to their destination by recognizing simple instructions like, "Take me to the University of Tokyo." Because existing AI does not think the same way humans do, it cannot adapt to situations that are not in its playbook. Some current forms of artificial intelligence (AI), such as speech recognition and automated driving, are just as competent as humans--if not better--at carrying out their given tasks (figure 1).

BEIJING – Growing optimism about the next iPhone has propelled Apple Inc. to record highs. Halfway around the globe, a lesser-known Taiwanese company is riding that same wave of euphoria. Hon Hai Precision Industry Co., the main assembler of the U.S. company's smartphones, has gained 29 percent in the past year, with its shares touching a decade-high on optimism about Apple's 10th anniversary iPhone. That's helped the biggest company in Foxconn Technology Group defy a flat-lining mobile market on expectations Apple will use the anniversary to introduce its most advanced and popular device yet. The billionaire founder's installed robots throughout a juggernaut that spans China to Southeast Asia to shore up its manufacturing prowess, while investing in emergent fields from virtual reality to artificial intelligence.

Only Homo sapiens, of all the descendants of Homo erectus, survived on earth whereas other species such as homo soloensis, homo denisova, Homo neanderthalensis, Homo floresiensis faded away more than 40,000 years ago. What advantages did Homo sapiens possess that helped them to flourish while other species are extinct? Apparently a cognitive revolution (according to Prof. Yuval Harari in his famous book Sapiens) triggered by some kind of genetic mutation provided Homo Species with more cerebral power and thus they acquired an ability not possessed by any other species – ability to imagine things that did not exist. This ability helped them to invent things including powerful communicative languages, religion, tools and more. Does current cognitive revolution ushered under various nomenclatures such as artificial intelligence, cognitive computing etc provide more powers, this time to machines and bring in unprecedented progress to human life? Are public and private institutions geared towards initiating, leading and supporting this change?

Back in the 1990s, when IBM was close to becoming irrelevant as technology moved from mainframes to personal computing, Louis Vincent Gerstner, the then CEO of the over 100-year old company, embarked on a transformational journey. Two decades later, current chief Ginni Rometty is on a similar journey. With new technologies like mobility, cloud and artificial intelligence changing processes and systems, Rometty is in the middle of a transforming IBM to a solutions company. At the centre of her strategy is cognitive computing, which she believes will impact every sphere of our lives. BusinessLine was part of a select media round table where Rometty explained her strategy and how India fits into her scheme of things.

It was one of those amazing "we're living in the future" moments. In an October 2013 press release, IBM declared that MD Anderson, the cancer center that is part of the University of Texas, "is using the IBM Watson cognitive computing system for its mission to eradicate cancer." Well, now that future is past. The partnership between IBM and one of the world's top cancer research institutions is falling apart. The project is on hold, MD Anderson confirms, and has been since late last year.

Qualitative reasoning formalisms are an active research topic in artificial intelligence. In this survey we present a model-theoretic perspective on qualitative constraint reasoning and explain some of the basic concepts and results in an accessible way. In particular, we discuss the significance of omega-categoricity for qualitative reasoning, of primitive positive interpretations for complexity analysis, and of Datalog as a unifying language for describing local consistency algorithms.

Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as word2vec, was shown to provide state-of-the-art results on various linguistics tasks. In this paper, we show that item-based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe a method we name item2vec for item-based CF that produces embedding for items in a latent space. The method is capable of inferring item-item relations even when user information is not available. We present experimental results that demonstrate the effectiveness of the item2vec method and show it is competitive with SVD.