FILE - In this Wednesday, Sept. 29, 2015 file photo, Sahara, a rare red-haired female Hamadryas Baboon holds 3 weeks old dark-furred baby in the Ramat Gan Safari Park near Tel Aviv, Israel. A new study in France shows that baboons can make human-like vowel sounds, and its authors say the discovery could help scientists better understand the evolution of human speech. The study was published in the journal Plos One on Wednesday Jan. 11, 2017 by a team of scientists. FILE - In this Wednesday, Sept. 29, 2015 file photo, Sahara, a rare red-haired female Hamadryas Baboon holds 3 weeks old dark-furred baby in the Ramat Gan Safari Park near Tel Aviv, Israel. A new study in France shows that baboons can make human-like vowel sounds, and its authors say the discovery could help scientists better understand the evolution of human speech.

This article was originally published on The Conversation. How would you feel about getting therapy from a robot? Emotionally intelligent machines may not be as far away as it seems. Over the last few decades, artificial intelligence (AI) has got increasingly good at reading emotional reactions in humans. But reading is not the same as understanding. If AI cannot experience emotions themselves, can they ever truly understand us?

David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend. This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.

Current take: Statistics, not machine learning, is the real deal, but unfortunately suffers from bad marketing. On the other hand, to the extent that bad marketing includes misguided undergraduate curriculums, there's plenty of room to improve for everyone. I had two thoughts reading this. Machine learners invent annoying new terms, sound cooler, and have all the fun. They have way less funding and influence than it seems they might deserve.

CB Insights unveiled the AI 100--a list of 100 of the most promising private companies applying artificial intelligence algorithms across industries, from healthcare to auto to fintech--at the Innovation Summit today. The companies were selected from a pool of nearly 500 applicants and nominees based on several criteria, including data submitted by the companies, responses to interview questions, technology focus, investor profile, team profile, mosaic scores, and funding history. "From financial services to healthcare to transport, incumbent companies in every industry are seeing that AI will reshape their industries. And as so often happens, transformational innovation comes from emerging companies. In the case of AI, a lot of the groundbreaking work is being done by the AI 100. The companies in the AI 100 are accelerating research, improving efficiency, and making many game-changing advancements that will be felt for decades to come," CB Insights CEO Anand Sanwal said in a press release.

Top Machine Learning & Data Mining Books - in this post, we have scraped various signals (e.g. We have combined all signals to compute a score for each book and rank the top Machine Learning and Data Mining books. The readers will love the list because it is data-driven & objective. This book is very well rated on Amazon website and is written by three professors from USC, Stanford and University of Washington. The book's authors: Gareth James, Daniela Witten, Trevor Hastie, & Rob Tibshirani all have backgrounds in statistics.

What don't you know that you need to know, and how do you know you don't know? Imagine putting that into a search engine, expecting a coherent answer. The answers we seek are at the core of discovery and learning, motivated by necessity and pleasure, by job displacement, or leadership uncertainty in a complex and fast changing world. The process of learning requires a detailed knowledge of ourselves and of our world, whether a human or a machine is tasked to help. We can fill gaps in our skills and knowledge through web searches, discussion with experts and like-minded people, reading books, working through an education curriculum, learning online with video tutorials, and absorbing a vast amount of content flowing through online news channels and aggregators.

What's ahead as the field matures? Since the concept took hold, big data has made big waves. The field of analytics has developed rapidly since the McKinsey Global Institute (MGI) released its landmark 2011 report, Big data: The next frontier for innovation, competition, and productivity. But much value remains on the table as organizations wrestle with issues of strategy and implementation. In this episode of the McKinsey Podcast, MGI partner Michael Chui and McKinsey senior partner Nicolaus Henke speak with McKinsey Publishing's Simon London about the changing landscape for data and analytics, opportunities in industries from retail to healthcare, and implications for workers. Simon London: Welcome to this edition of the McKinsey Podcast. Today we're going to be talking about data analytics and how organizations can use the unprecedented volume of data at their disposal to transform industries, create new business models, and, frankly, make better decisions across everything they do. Joining me here in London to discuss the issues is Nicolaus Henke, the global leader of McKinsey Analytics and chairman of QuantumBlack, an acquisition McKinsey made in 2015. And joining us from San Francisco is Michael Chui, a partner with the McKinsey Global Institute. Nico and Michael are among the coauthors of The age of analytics: Competing in a data-driven world, which is a new McKinsey Global Institute research report. Nicolaus Henke: Thank you very much. Simon London: Before we get into detail on the latest research, I think it might be helpful to take a step back and clarify what we mean in terms of the age of analytics. Cynics would say, "Come on.

Beating expert poker players differs from past AI successes against human competitors in games such as Jeopardy and Go. Researchers behind a poker-playing AI system called DeepStack say it's the first algorithm to have ever beaten poker pros in heads-up no-limit Texas hold'em. The claim, if verified, would mark a major milestone in the development of artificial-intelligence systems. Beating expert poker players differs from past AI successes against human competitors in games such as Jeopardy and Go because each player's hand provides only an incomplete picture about the state of play and requires a program to navigate tactics, such as bluffing, based on asymmetrical information. DeepStack is the work of a collaboration between researchers at the University of Alberta and two Czech universities, who say in a new non-peer reviewed paper that it's the "first computer program to beat professional poker players in heads-up no-limit Texas hold'em".

In a landmark achievement for artificial intelligence, a poker bot developed by researchers in Canada and the Czech Republic has defeated several professional players in one-on-one games of no-limit Texas hold'em poker. Perhaps most interestingly, the academics behind the work say their program overcame its human opponents by using an approximation approach that they compare to "gut feeling." "If correct, this is indeed a significant advance in game-playing AI," says Michael Wellman, a professor at the University of Michigan who specializes in game theory and AI. "First, it achieves a major milestone (beating poker professionals) in a game of prominent interest. Second, it brings together several novel ideas, which together support an exciting approach for imperfect-information games."