First it was checkers (draughts to you and me), then chess, then Jeopardy!, then Go and now poker. One after another, these games, all of which require significant amounts of intelligence and expertise if they are to be played well, have fallen to the technology we call artificial intelligence (AI). And as each of these milestones is passed, speculation about the prospect of "superintelligence" (the attainment by machines of human-level capabilities) reaches a new high before the media caravan moves on to its next obsession du jour. Never mind that most leaders in the field regard the prospect of being supplanted by super-machines as exceedingly distant (one has famously observed that he is more concerned about the dangers of overpopulation on Mars): the solipsism of human nature means that even the most distant or implausible threat to our uniqueness as a species bothers us. The public obsession with the existential risks of artificial superintelligence is, however, useful to the tech industry because it distracts attention from the type of AI that is now part of its core business. This is "weak AI" and is a combination of big data and machine-learning – algorithms that ingest huge volumes of data and extract patterns and actionable predictions from them.

You might not expect to find a player named Libratus around a poker table in a high-stakes game of no-limit Texas Hold'em. Yet it was Libratus – an artificial intelligence (AI) – that emerged triumphant from a gruelling 20-day tournament that culminated late last Monday in a dramatic victory over four of the world's top players. The victory – which saw Libratus pocket $1.7m in fake chips at the expense of the quartet of serious pros – stunned the generally unshockable world of poker. But more than that, it reopened the increasingly urgent debate about the potential – and possible dangers – of AI, or intelligent machines. If machines are clever enough to beat humans at a game that requires intuition, bluffing skills, intelligence as well as a capacity to retain data – then what else is possible?

This is a guest post written by Scott Middleton, founder and CEO of stratejos as well as part-time sausage maker. Will you still be doing your job in 5-10 years or will a robot do it for you? This is a question knowledge workers have started asking themselves as AI is becoming more capable and widely adopted. Atlassian co-founder and CEO, Mike Cannon-Brookes, has said AI will play a major role in the future of team productivity. Bank of America Merrill Lynch predicts that AI will have a $9 trillion impact on knowledge work over the coming decade.

A major change is coming, over unknown timescales but across every segment of society, and the people playing a part in that transition have a huge responsibility and opportunity to shape it for the best. What will trigger this change? Recently, some of the top minds in AI and related fields got together to discuss how we can ensure AI remains beneficial throughout this transition, and the result was the Asilomar AI Principles document. The intent of these 23 principles is to offer a framework to help artificial intelligence benefit as many people as possible. But, as AI expert Toby Walsh said of the Principles, "Of course, it's just a start. The Principles represent the beginning of a conversation, and now that the conversation is underway, we need to follow up with broad discussion about each individual principle. The Principles will mean different things to different people, and in order to benefit as much of society as possible, we need to think about each principle individually. As part of this effort, I interviewed many of the AI researchers who signed the Principles document to learn their take on why they signed and what issues still confront us. Today, we start with the Value Alignment principle. Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation. Stuart Russell, who helped pioneer the idea of value alignment, likes to compare this to the King Midas story. When King Midas asked for everything he touched to turn to gold, he really just wanted to be rich. He didn't actually want his food and loved ones to turn to gold. We face a similar situation with artificial intelligence: how do we ensure that an AI will do what we really want, while not harming humans in a misguided attempt to do what its designer requested? "Robots aren't going to try to revolt against humanity," explains Anca Dragan, an assistant professor and colleague of Russell's at UC Berkeley, "they'll just try to optimize whatever we tell them to do.

These and many other insights are from the McKinsey Global Institute's study The Age of Analytics: Competing In A Data-Driven World published in collaboration with McKinsey Analytics this month. You can get a copy of the Executive Summary here (28 pp., free, no opt-in, PDF) and the full report (136 pp., free, no opt-in, PDF) here. Five years ago the McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity (156 pp., free no opt-in, PDF), and in the years since McKinsey sees data science adoption and value accelerate, specifically in the areas of machine learning and deep learning. The study underscores how critical integration is for gaining greater value from data and analytics. Posted in Featured Posts, Technology / Software Tagged analytics, enosix, enosix integration, enosiX Salesforce integration, enosix SAP integration, Louis Columbus' blog, machine learning, McKinsey 2016 Study, McKinsey Analytics, McKinsey Global Institute', McKinsey's 2016 Analytics Study

In this special guest feature, Cecilia Pizzurro, Senior Director, Strategic Data Projects at LOGICnow, discusses the convergence of data/machine learning and cybersecurity, and the idea that these two are playing off of each other in a more meaningful way than ever before. Cecilia leads a team of data scientists and software engineers in Cambridge (US) and Newcastle (UK). These teams use machine learning and big data analytics to find business value in the vast amount of customer data gathered from LOGICnow's products. She was also the co-founder and CTO of the The Dolomite Group, a South American mining consortium, pioneering machine learning and big data analyses to improve mining efficiency and reduce environmental impact in Peru. This company is currently finalizing its acquisition by a Chilean mining company.

For the past few years we've seen big data and machine learning take more of a foothold in companies, with many believing the era of artificial intelligence (AI) is here. What is clear is that with advances being made on an almost daily basis now, businesses need to prepare for a vastly different future. But, in order to take advantage, some companies may need to make a dramatic adjustment in how they work. At the moment, for many companies, AI and big data are viewed in a way that limits the potential they have to offer. They are often seen as something that can help cut operational costs, rather than as a fundamental approach for generating increases in productivity, output and improved certainty over corporate direction.

Many of the discussions on automation surround how it will impact manual labor. However, while it's easy to see how robotics can take over precise and repetitive manufacturing tasks, the rise of artificial intelligence (AI) is allowing for more cognition-based tasks to be taken over by computers as well. Chui stated, "In about 60 percent of occupations, over 30 percent of the things that people do could be automated -- either using robots or artificial intelligence, machine learning, deep learning, all of these technologies that we're hearing more and more about."

Facebook's artificial intelligence (AI) team has built a visual search system that can recognize content that appears in photos and return relevant search results. Called Lumos, Facebook originally created the platform so that its visually impaired users could understand the content of photos. But Facebook recognized that everyone could benefit from this type of visual search system. Facebook's image search system can detect and segment objects, scenes, animals, places, and clothes that appear in images or videos – and understand them. For instance, let's say you search for "black shirt photo."

Robots are taking increasingly bigger roles in life and business – moving well beyond manufacturing and into transportation, education, medicine and care for the elderly. But ethics and law haven't caught up. Dr. Kate Darling, a pioneer in the fields, is helping quicken the pace. A leading expert in robot ethics, she is a researcher at the Massachusetts Institute of Technology (MIT) Media Lab where she investigates social robotics and conducts experimental studies on human-robot interaction. Darling explores the emotional connection between people and life-like inventions, seeking to influence technology design and policy direction.