O'Neil is the author of the blog mathbabe.org. She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism, Tow Center and was employed as Data Science Consultant at Johnson Research Labs. O'Neil earned a mathematics Ph.D. from Harvard University. Topics discussed include her book "Weapons of Math Destruction," predictive policing models, the teacher value added model, approaches to auditing algorithms and whether government regulation of the field is needed. You can keep up-to-date with the podcasts via our RSS feed, and they are available via SoundCloud and iTunes.

SAS says its new visual data mining and machine learning software, available later this month, will "feed this need for smarter analytics". The company was speaking about its new solution at its Analytics Experience conference in Las Vegas attended by more than 10,000, on-site and online, to discuss business issues. "SAS Data Mining and Machine Learning is built on the company's solid expertise and reputation of delivering scalable and adaptable analytics that solve real business problems and yield measurable business value," said Jonathan Wexler, SAS analytics product manager. "This software helps provide positive outcomes to increase profitability, better understand customer behaviour and decrease the cost of doing business." Wexler said advanced analytics offer insight to businesses, but "machine learning and deep learning algorithms take it deeper, insights that were previously out of reach".

Calls for Proposals with a blind reviewing process are more common to academic conferences than industry ones, but there are several important web conferences such as RailsConf that use the same kind of process (we actually use their open source system -- see our previous article). The idea is that proposals are reviewed by members of the program committee who do not know who the authors are, nor which organizations they're from (hence "blind reviewing"). This year we had 2 program chairs who succeeded to Francisco Martin (our '14 and '15 program chair) and who were in charge of turning reviews into decisions of acceptance/rejection: Keiran Thompson and Florian Douetteau. Those decisions are based on average ratings and on "Strong Accepts" given by reviewers. I invited Florian and Keiran to be program co-chairs after consulting with the PAPIs advisory board, and this was before Dataiku became a sponsor of the '16 edition -- it goes without saying that invitations to the committee are unconditional to sponsorship of any kind.

Wondering what we these numbers are? These stats depict the active users, of the above mentioned social media platforms, globally. This basically means that almost everyone is active on some or the other social media platform at a given point of time. Social Media is giving voice to the general mass. People discuss issues, debate, opine and review.

We're thrilled to have Chaney Ojinnaka, CEO of VendorMach and VP of Enterprise, Ivan Kotorov in as guest speakers for Byte Academy students and a limited number of members. VendorMach is a network driven platform that uses AI (predictive analytics) to produce the VM Trust score. The score is then used to offer invoice receivables financing and insurance products to SMBs while enabling enterprises to better manage their supply chain. Founded in London, VendorMach recently opened its US office in New York. Topics covered over the evening will include: VendorMach and founders' overview, building a startup, AI and machine learning, data science in fintech, supply chain finance, FinTech environment London v. US (pre/post Brexit), pros & cons of an accelerator and more.

On the morning of March 9th at the Four Seasons Hotel in Seoul, South Korea, the world changed forever. A computer program called AlphaGo used the relatively new social science of cognitive computing to strategize and beat one of the top masters in the world at the ancient Chinese board game Go. It was hailed as one of the most defining moments in the development of artificial intelligence so far. At NETSCOUT, we watched the development of the AlphaGo program, originally created by British company DeepMind before being acquired by Google in 2014, with great interest. Cognitive computing is something we do every day at NETSCOUT to ensure that our solutions deliver the digital strategies of our clients with maximum efficiency. We like to sometimes compare ourselves to conductors helping to keep the trains of digital networks running on time, and while that is an accurate description of our service assurance platform, it doesn't really dive deeply into the intelligence that our products provide.

To tackle my aunt's puzzle, the expert systems approach would need a human to squint at the first three rows and spot the following pattern: The human could then instruct the computer to follow the pattern x * (y 1) z. Even when machines teach themselves, the preferred patterns are chosen by humans: Should facial recognition software infer explicit if/then rules, or should it treat each feature as an incremental piece of evidence for/against each possible person? And so they designed deep neural networks, a machine learning technique most notable for its ability to infer higher-level features from more basic information. These questions have constrained efforts to apply neural networks to new problems; a network that's great at facial recognition is totally inept at automatic translation.

A few months ago, my aunt sent her colleagues an email with the subject, "Math Problem! She thought her solution was obvious. Her colleagues, though, were sure their solution was correct--and the two didn't match. Was the problem with one of their answers, or with the puzzle itself? My aunt and her colleagues had stumbled across a fundamental problem in machine learning, the study of computers that learn. Almost all of the learning we expect our computers to do--and much of the learning we ourselves do --is about reducing information to underlying patterns, which can then be used to infer the unknown.

It's not just gigantic search engines that are looking to acquire tech and talent around emerging areas like speech recognition. Even design and craft marketplaces can use a little machine learning and artificial intelligence to make their wheels turn a little better. Today, the popular handmade-goods site Etsy announced it has acquired a startup called Blackbird Technologies, which developed algorithms for natural language processing, image recognition and analytics -- similar to those used by Amazon and Google for product and other searches -- and then "democratized" them to be used by any company of any size. At Etsy, the tech will be used to improve its own search features. Financial terms of the deal have not been disclosed, but we're asking and will update if we learn more.

As artificial intelligence (AI) continues to move from the summer of hype to the fall tech conference news cycle, mass confusion has begun on what AI can be used for. From fears of SKYNET, to hopes for the computer in StarTrek and Jarvis in Iron Man, the value will come from defining the proper outcomes. AI is more than just a fad. With a market size of 100B by 2025, Constellation sees the AI subsets of machine learning, deep learning, natural language processing, and cognitive computing taking the market by storm (see Figure 1). The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity.