Tonight, we hear from Sebastian Thrun. He founded Google X, the semi-secret research facility that began development of Google's self-driving car. SEBASTIAN THRUN, Founder, Google X: Artificial intelligence is to the human brain what the steam engine has been to the human muscle. Before the agricultural revolution, most of us were farmers, and our distinguishing capabilities were our physical strength and agility. And then we invented machines that make us stronger and, all of a sudden, one farmer can make food for 150 people.

Natural-language-facilitated human-robot cooperation (NLC), in which natural language (NL) is used to share knowledge between a human and a robot for conducting intuitive human-robot cooperation (HRC), is continuously developing in the recent decade. Currently, NLC is used in several robotic domains such as manufacturing, daily assistance and health caregiving. It is necessary to summarize current NLC-based robotic systems and discuss the future developing trends, providing helpful information for future NLC research. In this review, we first analyzed the driving forces behind the NLC research. Regarding to a robot s cognition level during the cooperation, the NLC implementations then were categorized into four types {NL-based control, NL-based robot training, NL-based task execution, NL-based social companion} for comparison and discussion. Last based on our perspective and comprehensive paper review, the future research trends were discussed.

We consider revenue maximization in online auctions and pricing. A seller sells an identical item in each period to a new buyer, or a new set of buyers. For the online posted pricing problem, we show regret bounds that scale with the best fixed price, rather than the range of the values. We also show regret bounds that are almost scale free, and match the offline sample complexity, when comparing to a benchmark that requires a lower bound on the market share. These results are obtained by generalizing the classical learning from experts and multi-armed bandit problems to their multi-scale versions. In this version, the reward of each action is in a different range, and the regret w.r.t. a given action scales with its own range, rather than the maximum range.

SaaS solutions firm Gong last week released the findings of a study on sales phone conversations, including an analysis of gender speaking patterns. Women delivered slightly longer sales monologues, averaging about 9-12 seconds longer than those their male counterparts gave, but men generally spoke faster, the study found. Men tended to speak at a faster pace, averaging about 2.88 words per second compared to the average 2.79 seconds for women, the study found. Men also paused longer -- averaging about 1.5 seconds compared to women's 1.3 seconds. Women had a higher degree of success in progressing deals, with a 54 percent probability of reaching the next milestone, while men registered a 49 percent probability, the data indicated.

You'd be forgiven if, settling into the fall 2003 "Literature of the 16th Century" course at University of California, Berkeley, you found the unassuming 70-year-old man standing at the front of the lecture hall a bit eccentric. For one thing, the class syllabus, which was printed on the back of a rumpled flyer promoting bicycle safety, seemed to be preparing you for the fact that some readings may feel toilsome. "Don't worry," it read on the two weeks to be spent with a notoriously long allegorical poem; it's "only drudgery if you're reading it for school." Phew! you thought, then, Wait a second... You might have wondered what you had gotten yourself into. Then again, if you had enrolled in Stephen Booth's class, chances are that you already knew. By this time, Booth had been teaching Shakespeare to Berkeley undergraduates for decades and had earned the adulation of thousands of students.

Organizations like Insight Data science founded by Jake Klamka is specifically designed for helping PhD's transition into industry. At the other end of the spectrum, aspiring data scientists, who have enough domain expertise and are keen to pursue this art can take umbrage from the example of Clare Corthell who has embarked on a self crafted journey to embrace the art of data science purely on online learning MOOCs. In Fact she has herself come out with a curriculum for data science with the Open Source Data Science Masters--OSDSM- program. These courses can help you to bridge the gap in your learning and practicing the craft. The OSDSM is a collection of open source resources that will help you to acquire skills necessary to be a competent entry level data scientist. You can access the curriculum here . You have to be adept at learning and upgrading on the job and on the fly. Kunal Punera the Co founder / CTO at Bento labs talks about this aspect when he says.. I spent two years at RelateIQ. I worked on building the data mining system from scratch -- and by the time I left I had built most of the data products deployed in RelateIQ.

Okay, first, let's be clear on one thing. As an article in McKinsey Quarterly put it, "Machine learning is based on a number of earlier building blocks, starting with classical statistics." But this relationship can lead to confusion. Where do statistical methods end and AI like Machine Learning (ML) begin? "Statistics is just about the numbers, and quantifying the data.

When Liz Wessel was a sophomore at the University of Pennsylvania, she received an unexpected email that would help shape her career, even if she didn't know it at the time. The message didn't come from a professor or advisor, though. It came from beverage giant Anheuser-Busch. The company wanted Wessel to be a campus ambassador, a role that involved promoting its mechanical engineering openings to fellow students. "I thought it was crazy that Anheuser-Busch needed a sophomore to help them with hiring mechanical engineering students for their full time jobs," she says.

A computing educator has to balance teaching efficiently and motivating the student. Efficient teaching means teaching abstractly, emphasizing practice, and preferring direct instruction over having students "figure it out." Motivating the student means giving the students authentic situations, real-world complexity, and reasons to practice. I recently wrote an essay describing this tension (http://bit.ly/2nFRuGZ). Herbert Simon (one of the three authors of the Science article first answering the question "What is Computer Science?"; http://bit.ly/2nFIzpf)

When you played through Super Mario Bros. or Doom for the very first time, chances are you didn't try to speedrun the entire game but instead started exploring -- this despite not really knowing what to expect around the next corner. It's that same sense of curiosity, the desire to screw around in a digital landscape just to see what happens, that a team of researchers at UC Berkeley have imparted into their computer algorithm. And it could drastically advance the field of artificial intelligence. Google's AlphaGo AI, the one that just repeatedly dominated the world's top Go players, uses what's called a Monte Carlo tree search function to decide its next move. Each "branch", or decision, in that tree has a weighted value that's determined from previous experiences and the relative rewards associated with them.