Rob May is the CEO and Co-Founder of Talla, a platform for intelligent information delivery in Slack and Hipchat. Previously, Rob was the CEO and Co-Founder of Backupify, (acquired by Datto in 2014). Before that, he held engineering, business development, and management positions at various startups. Rob has a B.S. in Electrical Engineering and a MBA from the University of Kentucky. He is also a well known angel investor, a venture partner at Pillar, and is the creator and writer of the widely-read and highly-regarded AI newsletter, Technically Sentient. Rob May will be speaking at the Gigaom AI Now in San Francisco, February 15-16th. In anticipation of that, I caught up with him to ask a few questions.

Robot" Rami Malek has been nominated as best male actor in a drama series for his performance as Elliot Alderson in the hit USA Network series at the 23rd SAG Awards. The actor took to Twitter to share his delight over the nomination. "Gotta thank my peers for the love and respect they've showed me with this @SAGawards nomination," Malek said. Fans will know if Malek won the award when the ceremony airs on Jan. 29, 2017. Aside from a possible win from Malek, fans can also expect to see the actor in a project that has been waiting to be made for years now. The actor was tapped to replace Sasha Baron Cohen in the long-gestating Queen biopic titled "Bohemian Rhapsody," according to Variety. During an appearance on "Jimmy Kimmel Live," Malek opened up for the first time about playing the legendary musician Freddie Mercury. Who doesn't know that music? Of course I have to prepare," Malek said about the status of the film.

Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. Some, with no familiarity with data science, will want to create a legal framework about how data can be analyzed, how the algorithms should behave, and to force public disclosure of algorithm secrets. I believe that they will fail, though Obamacare is an example where predictive algorithms were required to ignore metrics such as gender or age, to compute premiums, resulting in more expensive premiums for everyone. The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance.

How should we be thinking about machine learning and AI? originally appeared on Quora - the knowledge sharing network where compelling questions are answered by people with unique insights. The big issue that I am interested in is: what does it mean for us to live with machines that are not sentient but appear as if they are? Let's start by quickly defining sentience and intelligence (at least for this discussion). We'll say that intelligence is the ability solve complex problems, handle varied input and achieve goals. An abacus is not intelligent.

Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. Some, with no familiarity with data science, will want to create a legal framework about how data can be analyzed, how the algorithms should behave, and to force public disclosure of algorithm secrets. I believe that they will fail, though Obamacare is an example where predictive algorithms were required to ignore metrics such as gender or age, to compute premiums, resulting in more expensive premiums for everyone. The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance.

The big issue that I am interested in is: what does it mean for us to live with machines that are not sentient but appear as if they are? Let's start by quickly defining sentience and intelligence (at least for this discussion). We'll say that intelligence is the ability solve complex problems, handle varied input and achieve goals. An abacus is not intelligent. Sentience is the ability to feel, to have first person experience.

Robots have already been put to work across a number of industries. They are manufacturing cars, taking care of the elderly, doing housework, homework, and even entering literary awards. It is not surprising then that new robots have been developed to conduct job interviews. One such robot, Matlda, has been programmed to conduct 25-minute interviews in which she works through a roster of up to 76 questions. She records and analyses the interviewee's responses, monitors facial expressions and compares them to other successful employees within the hiring company.

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid).

Here I offer a few off-the-beaten-path interesting problems that you won't find in textbooks, data science camps, or in college classes. These problems range from applied maths, to statistics and computer science, and are aimed at getting the novice interested in a few core subjects that most data scientists master. The problems are described in simple English and don't require math / stats / probability knowledge beyond high school level. My goal is to attract people interested in data science, but who are somewhat concerned by the depth and volume of (in my opinion) unnecessary mathematics included in many curricula. I believe that successful data science can be engineered and deployed by scientists coming from other disciplines, who do not necessarily have a deep analytical background yet are familiar with data.

In the summer of 1967, Secretary of Defense Robert McNamara commissioned a group of thirty-six scholars to write a secret history of the Vietnam War. The project took a year and a half, ran to seven thousand pages, and filled forty-seven volumes. Only a handful of copies were made, and most were kept under lock and key in and around the Beltway. One set, however, ended up at the RAND Corporation, in Santa Monica, where it was read, from start to finish, by a young analyst there named Daniel Ellsberg. Ellsberg was dismayed by what he learned. For a generation, the U.S. government had been lying to the American people about the Vietnam War. He put the first of the volumes in his briefcase, praying that the security guards at RAND would not stop him, and made his way to a small advertising agency in West Hollywood, where a friend told him there was a Xerox machine he could use. "It was a big one, advanced for its time, but very slow by today's standards," Ellsberg writes in his 2002 autobiography, "Secrets: A Memoir of Vietnam and the Pentagon Papers": It could do only one page at a time, and it took several seconds to do each page. I tried pressing the book down on the glass to do two pages at a time, but the middle section was faint and uneven. Fortunately the books were bound with metal tapes through holes so they could be taken apart. . . . The machine didn't collate, and the bar had to come back and travel just as slowly for each copy.