It's great to have you here to talk about data science at GitHub. But before we get there, I want to find out a bit about you, and I want to talk about how you got into data science, what you do at GitHub, but I'd like to take a slightly tangential approach to finding about you first by just asking you what you're thinking about at the moment with respect to data science, or what keeps you up at night, or what really is exciting you? Omoju: The thing I've been thinking about a lot is the term artificial intelligence and the fact that it is such a misnomer because the work that we do is not necessarily artificial intelligence. Most of us in industry don't work on A.I. We work on massive mathematical problems that are basically variants of some kind of linear algebra. And that's what we do. So I've been thinking a lot about that, and then using the right kind of terms, like maybe we're doing things like augmenting human intelligence, or been building like data intensive platforms and ...

Western economies have gone through three industrial revolutions and are now on the cusp of a fourth; industry 4.0. As with previous revolutions, this latest has been inspired by a technological leap. In the late 18th Century, the birth of mechanisation saw the First Industrial Revolution. The development of mass production and automation characterised the Second and Third, but what is driving the Fourth and what does it mean for business and manufacturers? Industry 4.0 has been accelerated by the growth of, the Internet of Things (IoT), artificial intelligence (AI) and connected machinery which has given birth to smart factories.

FPF and Immuta released the first-ever framework for practitioners to manage risk in artificial intelligence and machine learning models. The joint whitepaper, Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models, provides business executives, data scientists, and compliance professionals with a strategic guide for governing the legal, privacy, and ethical risks associated with this technology. Lately a lot of thought, work and advocacy has been going into looking at personal data as a fungible commodity: one that can be made scarce and bought, sold, traded and so on. Good though this might be, it also steers attention away from a far more important issue it would be best to solve first: personal agency. The U.S. has generally approached privacy rules on a sector-by-sector basis, meaning the health care industry has different privacy standards than the financial industry.

It's been an exciting, but complex year in the data world. Just as last year, the data tech ecosystem has continued to "fire on all cylinders". If nothing else, data is probably even more front and center in 2018, in both business and personal conversations. Some of the reasons, however, have changed. On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Since the inception of artificial intelligence in the 1950s, we've been trying to find ways to measure progress in the field of AI. For many, the golden criteria for AI the Turing Test, an evaluation of whether a computer can exhibit human behavior. But the Turing Test only defines whether AI can fool humans, not compete with them, and it's very hard to say how deep the Test goes. A much better arena to test the extent of AI's intelligence, many scientists believe, are games, domains where contestants can measure and compare their success and clearly determine which one performs better.

Deal will add pricing optimization and forecasting replenishment to supply chain software suite, JDA says. Supply chain technology firm JDA Software Group Inc. today said it has acquired the German artificial intelligence provider Blue Yonder GmbH, in a move it said would allow its Luminate platform of software products to generate more automated decisions and forecasts. Scottsdale, Ariz.-based JDA launched its Luminate platform in May, saying the platform would extend existing JDA applications by using artificial intelligence (AI) and advanced analytics to improve users' ability to predict consumer demand and deliver faster fulfillment. Adding additional AI and machine learning capabilities from the new acquisition will allow JDA users to generate autonomous, profitable business decisions and use their supply chains as a competitive advantage, particularly in the areas of pricing optimization and forecasting replenishment, JDA said. JDA did not disclose the terms of the deal, saying the acquisition had not yet closed.

Housed in a tall plastic cylinder, Amazon's Alexa is far from physically resembling a woman. Yet when asked about its gender, the system curiously responds it is "female in character." A closer look at recent developments in artificial intelligence shows Alexa is the rule rather than the exception. From Apple's Siri to Hanson Robotics' humanoid robot Sophia, it seems that the future is female indeed -- but not in the way we intended. Artificial intelligence and robotics may intend to free us from many human limitations, but it seems that gender stereotypes are not one of them.

A world's first QUT-led study has used artificial intelligence to analyse regional personality characteristics estimated solely from language patterns in 1.5 billion Twitter posts and uncover hotspots and cold spots of entrepreneurial personality and activity across the US. QUT's Associate Professor Martin Obschonka from the Australian Centre for Entrepreneurship Research teamed up with researchers from the London School of Economics and Political Science, the University of Pennsylvania and the University of Mannheim. Their paper, Big Data, artificial intelligence and the geography of entrepreneurship in the United States has just been published online via the Centre for Economic Policy Research (London, UK) and the Center for Open Science (Charlottesville, USA). Professor Obschonka said the study proved a Twitter-based personality estimate is as successful in predicting local differences in actual entrepreneurial activity (e.g., local start-up rates) as regional personality data collected by means of millions of standard personality tests. "What we have discovered here is that social media – how language is used in Twitter - is a reliable marker of economic vitality in a region," Professor Obschonka said.

AI and machine learning are proving to be the only methods in which organizations will meet customer demands at scale and as the race to win customers' heart becomes tighter than ever, carriers are having to compete with tech giants for AI's vital ingredients: talent and data. Which is why the Insurance Analytics Canada Summit (Sep 25-26, Toronto) is bringing together the industry's heavyweights to discuss how AI and advanced analytics can be utilized to deliver unparalleled performance, business growth, and truly actionable insights. Over 300 senior executives will delve into new strategies for embedding analytics insights that will supercharge performance, accelerate underwriting, fast-track claims, and ultimately deliver greater returns in a highly competitive insurance market. To find our more, visit the website: http://bit.ly/2z4auJJ To save $200 on your ticket, simply enter the discount code 4954GBFR200 when registering.

TechCrunch has reported that Facebook is acquiring London-based startup, Bloomsbury AI, as part of its efforts to fight against fake news on the world's largest social network. Bloomsbury's product is an NLP engine that helps machines answer questions on information derived from documents. TechCrunch's sources report that Facebook plans to use the firm's team and technology in policing the platform, and combating the scourge of bullshit fake news stories that have proliferated since the 2016 US general election. This is easily the biggest UK AI acquisition this year, and one of the most interesting since Google sucked up the machine learning powerhouse DeepMind in 2014. The deal is believed to be valued between $23 and $30 million, which is a far cry from DeepMind's $500 million asking price.