It's also a question of making models behave in line with organizational strategic objectives AND ethics. Following this logic, an essential part of effective model management really becomes outcomes assessment, not just in terms of accuracy, but are we ethically in line and producing ethical outcomes. We have a realistic chance to teach desired behaviors in our predictive models. In this age of analytics sandboxes and playpens, what kind of structures can we envision?

Artificial intelligence enables Staples to automate ordering and customer service through its Easy Button. Staples Inc. is testing technology enabling business customers to order products by voice via its Easy Button. The office supplies retailer is applying machine learning technology to the button, allowing customers to press it to order or reorder a product by voice, or to ask common order-related questions, such as when an order will be delivered or the status of a return. The move is part of a big push by Staples Inc., No. 21 in the Internet Retailer 2016 B2B E-Commerce 300, to use machine learning to automate ordering and customer service, says Ryan Bartley, director of mobile for Staples. Machine learning refers to computer programs that teach themselves to grow and change when exposed to new data, without being programmed by an individual.

MIT Media Lab director Joi Ito recently published a thoughtful essay titled "Society-in-the-Loop Artificial Intelligence," and has kindly credited me with coining the term. Now that it is out there, I wanted to elaborate a little on what I mean by "society in the loop," and to highlight the gap that it bridges between the humanities and computing. What I call "society in the loop" is a scaled up version of an old idea that puts the "human in the loop" (HITL) of automated systems. In HITL systems, a human operator is a crucial component of a control system, handling challenging tasks of supervision, exception control, optimization and maintenance. Recently, a number of articles have been written about the importance of applying HITL thinking to Artificial Intelligence (AI) and machine learning systems (e.g. HITL AI has been going on for a while.

It is dangerous to attribute too much intelligence to these systems. When I enrolled in Computer Science in 1995, Data Science didn't exist yet, but a lot of the algorithms we are still using already did. And this is not just because of the return of the neural networks, but also because probably not that much has fundamentally changed since back then. At least it feels to me this way. Which is funny considering that starting this year or so AI seems to finally have gone mainstream.

Apple and Intel acquired machine learning startups. Palantir purchased a data visualization startup. And AWS rolled out an analytics service for streaming real time data. We've got all this and more in our Big Data Roundup for the week ending August 14, 2016. The company acquired Turi, an artificial intelligence (AI) and machine learning startup, in a deal reportedly worth 200 million.

With the Intel acquisition of artificial intelligence startup Nervana Machine learning is into mainstream focus for pushing the boundaries of technology. No doubt the deal was timed to get some of the luster from NVIDIA and their stellar earnings. Nervana follows last years buy of Altera as Intel actively expands from reliance on its core CPU base. We will continue to invest in leading edge technologies that complement and enhance Intel s AI portfolio. This fits well with Altera's field programmable gate arrays (FPGAs) and programmable logic devices (PLDs).

Technology in and around the LHC can sometimes be a useful exemplar for how technologies may evolve in the more mundane world of IoT devices, clouds and intelligent systems. I wrote recently on how LHC teams manage Big Data; here I want to look at how they use machine learning to study and reduce that data. The reason high-energy physics needs this kind of help is to manage the signal-to-noise problem. Of O(1012) events/hour only 300 produce Higgs bosons. Real-time pre-filtering significantly reduces this torrent of data to O(106) events/hour but that s still a very high noise level for a 300 event signal.

IBM Watson is one of the best real-world examples of cognitive computing. Cognitive computing is rapidly transforming big data analytics initiatives that began with reports and dashboards generated from nonstandard data into something more substantial. In fact, the cognitive computing market is expected to generate revenue of 13.7 billion by 2020, registering a CAGR (compound annual growth rate) of 33.1% during the forecast period of 2015 - 2020. Cognitive computing is a branch of artificial intelligence. It combines principles of science and engineering to produce "intelligent" machines that learn from the data they ingest in ways that hope to emulate the learning and thought processes of the human mind.

At times we will proceed very quickly through the syllabus. You should only worry a little bit. We will return and review theoretical concepts later. When tutorials and practical exercises are given, you should focus on how you make things work in practice. This will give you practical experience upon which you can found your theoretical understanding.

Google Now is an intelligent personal assistant developed by Google. Google Now is available within the Google Search mobile application for Android and iOS, as well as the Google Chrome web browser on personal computers. Google Now uses a natural language user interface to answer questions, make recommendations, and perform actions by delegating requests to a set of web services. Along with answering user-initiated queries, Google Now proactively delivers to users information that it predicts (based on their search habits) they may want. It was first included in Android 4.1 ("Jelly Bean"), which launched on July 9, 2012, and was first supported on the Galaxy Nexus smartphone.