A few years ago, I asked CIOs about data science and it turned into a yawner of a discussion. However, in the last few years as chief data officers have made their mark at more and more enterprises, CIOs have needed to build their data chops. Given this, it was time to assess where CIOs are today. To do this, I ran a #CIOChat on AI and Data Science. From this discussion, it was clear CIOs are spending more time considering the "I" part of their titles.

Tom Davenport has literally written the book on analytics. Actually, several of them, to be precise. Over the course of his career, Tom has established himself as the authority on analytics and how their role in the modern organization has evolved in recent years. Tom is a distinguished professor at Babson College, a research fellow at the MIT Initiative on the Digital Economy, and a senior advisor at Deloitte Analytics. The discussion was timely as Tom had just written an article about a financial services company that had trained its employees on human-centered design so that they could ensure any use of AI would be customer-driven and valuable. "If you survey organizations and ask them, 'Does your company have a data-driven culture?' they almost always say no. Surveys even show a kind of negative movement over recent years in that regard. And it's because nobody really addresses that issue. They only address the technology side." Eventually, I think some fraction of [AI and analytics solutions] get used and are moderately effective, but there is not nearly enough focus on this. A lot of analytics people think their job is to create models, and whether anybody uses it or not is not their responsibility…We don't have enough people who make it their jobs to do that sort of thing. I think we need this new specialist, like a data ethnographer, who could sort of understand much more how people interact with data and applications, and how many ways they get screwed up.--Tom I don't know how you inculcate it or teach it in schools, but I think we all need curiosity about how technology can make us work more effectively. It clearly takes some investment, and time, and effort to do it.-- TD Wealth's goal was to get [its employees] to experientially understand what data, analytics, technology, and AI are all about, and then to think a lot about how it related to their customers.

TANYA OTT: I'm Tanya Ott and this is the Press Room, where we talk about the issues that are or should be important to your business. In October, the Massachusetts Institute of Technology announced it's going to spend $1 billion dollars--that's with a big capital B--to create a new college focused on Artificial Intelligence.1 That is a huge investment. MIT says it's already raised two-thirds of the money and plans to start classes next fall. In announcing the school, MIT's president said he wants to "educate the bilinguals of the future."

Tom Davenport, a professor at Babson College near Boston, a Fellow of the MIT Initiative on the Digital Economy, a co-founder of the International Institute for Analytics, and a senior advisor to Deloitte's analytics practice, shares his views on AI and analytics in an interview with TCS. He is co-authored the 2016 book'Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.' Davenport, the man responsible for making big data and analytics a topic of boardroom discussions, explains his views on the connection between analytics and artificial intelligence, automation and augmentation, opportunities arising from cognitive technologies, and how companies should address AI's impact on jobs. Davenport argues that the largest and most sophisticated branch of AI today is machine learning. While asserting that AI is primarily based on big data and analytics, Davenport believes any company that would skip analytics and go straight to AI is less likely to be successful. He explains that every industry has major opportunities from cognitive technologies and AI.

This article was written by Richard Downes. Richard is a Specialist Recruiter / Headhunter in the areas of Analytics, Data Science and Artificial Intelligence / Machine Learning and NLP (Natural Language Processing). His work within Analytics covers Predictive Analytics, Consumer Insight / Shopper Insight, and Loyalty right the way through to Credit and Risk. Four years ago an article was written for the Harvard Business Review by both Tom Davenport and DJ Patil entitled "Data Scientist: The Sexiest Job of the 21st century."The With this evolution, I now see the waters have become even harder to navigate, with companies now looking to hire people proficient in an even greater number of disciplines.

Among main themes were AI and Deep Learning - both real progress and hype, Machine Learning, Security, Quantum Computing, AlphaGo Zero, and more. In 2017 we saw Big Data give way to AI at center stage of the technology hype cycle. This excessive media and practitioner attention on AI included positive news (increasingly powerful machine learning algorithms and AI applications in numerous industries, including automotive, medical imaging, security, customer service, entertainment, financial services) and negative news (threats of machines taking our jobs and taking over our world). We also witnessed a growth in value-producing innovations around data, including greater use of APIs, as-a-Service offerings, data science platforms, deep learning, and cloud machine learning services from the major providers. Specialized applications of data, machine learning, and AI included machine intelligence, prescriptive analytics, journey sciences, behavior analytics, and IoT.

Artificial intelligence is set this year to go mainstream as organizations implement AI into an increasing number of decision-support system and design ever-smarter machines. For an idea on just how hot, artificial intelligence professionals are showing up on a number of the top technology job reports for most in-demand job roles. One study suggested AI professionals could soon overtake the data scientist for the number one slot. One expert that sees the trend first hand is Tom Davenport, President's Distinguished Professor of Information Technology and Management at Babson College, and cofounder of the International Institute for analytics. Davenport says early adopters of artificial intelligence are now ready for phase two – putting it to work for customers and getting real value from it.

This article is by Featured Blogger Tom Davenport from his LinkedIn page. It's been 10 years since Jeanne Harris and I published our book, Competing on Analytics, and we've just finished updating it for early-fall (2017) re-publication. We realized during this process that there have been a lot of changes in the world of analytics, although some things have remained the same. The timeless issues of analytical leadership, change management, and culture haven't evolved much in 10 years, and in many cases those remain the toughest problems to address. But analytical data, technology, and the people who use them have changed a lot.

This article is by Featured Blogger Tom Davenport from his LinkedIn page. There is a growing crisis in the world of analytics and cognitive technologies, and as of yet there is no obvious solution. The crisis was created by a spate of good news in the field of cognitive technology algorithms: they're working! Specifically, a relatively new and complex type of algorithms--deep learning neural networks (DLNN)--have been able to learn from lots of labeled data and accomplish a variety of tasks. They can master difficult games (Go, for example), recognize images, translate speech, and perform many more tasks as well as or better than the best humans.

At the inaugural O'Reilly AI conference, 66 artificial intelligence practitioners and researchers from 39 organizations presented the current state-of-AI: From chatbots and deep learning to self-driving cars and emotion recognition to automating jobs and obstacles to AI progress to saving lives and new business opportunities. There is no better place to imbibe the most up-to-date tech zeitgeist than at an O'Reilly Media event as has been proven again and again ever since the company put together the first Web-related meeting (WWW Wizards Workshop in July 1993). The conference was organized by Ben Lorica and Roger Chen, with Peter Norvig and Tim O'Reilly acting as honorary program chairs. Here's a summary of what I heard there, embellished with a few references to recent AI news and commentary: In contrast to traditional software, explained Peter Norvig, Director of Research at Google, "what is produced [by machine learning] is not code but more or less a black box--you can peak in a little bit, we have some idea of what's going on, but not a complete idea." Tim O'Reilly recently wrote in "The great question of the 21st century: Whose black box do you trust?": Because many of the algorithms that shape our society are black boxes… because they are, in the world of deep learning, inscrutable even to their creators – [the] question of trust is key.