Sarah Jeong, a technology journalist hired by the New York Times and vilified online for tweets comparing "dumbass f****** white people" to dogs and saying they would "all go extinct soon", has been targeted for harassment by dishonest trolls, her former employer has claimed. Editors at The Verge, an online tech magazine, denounced what they called "disingenuous" criticism of Ms Jeong by "people acting in bad faith". The senior writer had been the victim of a Gamergate-style campaign designed to "divide and conquer by forcing newsrooms to disavow their colleagues", they suggested. Ms Jeong, 30, posted a string of offensive and apparently racist messages including "#CancelWhitePeople" and "white men are bulls***" up to five years ago. After being uncovered they quickly spread and were picked up by conservative media including the Daily Caller and Gateway Pundit websites.

One of our biggest learnings is that AI is best trained by diverse teams that help identify the right questions for AI algorithms to solve. For example, several teams used multi-terabytes of operational data in wealth management to train algorithms to drive higher trading income. The obvious approach was to focus on day traders, who are mostly single, 30-35 year old white males. One of the teams – with a set of diverse members beyond the usual data engineers and neural net experts – addressed that objective and also identified an even larger opportunity targeting single 50-55 year old women, which uncovered a high investible assets segment that previously had gone untapped. Diverse teams think of questions others may not even know to ask.

Bayesian network modelling is a well adapted approach to study messy and highly correlated datasets which are very common in, e.g., systems epidemiology. A popular approach to learn a Bayesian network from an observational datasets is to identify the maximum a posteriori network in a search-and-score approach. Many scores have been proposed both Bayesian or frequentist based. In an applied perspective, a suitable approach would allow multiple distributions for the data and is robust enough to run autonomously. A promising framework to compute scores are generalized linear models. Indeed, there exists fast algorithms for estimation and many tailored solutions to common epidemiological issues. The purpose of this paper is to present an R package abn that has an implementation of multiple frequentist scores and some realistic simulations that show its usability and performance. It includes features to deal efficiently with data separation and adjustment which are very common in systems epidemiology.

The Great Elephant Census, conducted in 2014 and 2015, counted more than 350,000* elephants across 18 African countries. Human observers in small planes flew some 294,000 kilometers during more than 1,500 hours to systematically count the animals. Could a future census be managed locally, using unmanned aerial vehicles (UAVs, a.k.a. Although surveying the large animals in their individual reserves is a smaller job than the Great Elephant Census, such surveys cost managers substantial time and money. A Swiss research team recently tested a new approach to wildlife surveys.

Of all corporate functions, sales by its very nature is surely the most people-focused. While it may no longer involve quite as much face-to-face interaction as it once did, selling has remained emphatically a job for people rather than machines. However, artificial intelligence (AI) and machine-learning are already starting to make major inroads into the sales process, adding an extra dimension to everything from marketing automation to customer relationship management. According to Salesforce Research, high-performing teams are at least twice as likely to be using intelligent sales technologies such as artificial intelligence, sentiment analysis, next-step analysis and deep-learning. So, what further changes in the sales environment can we expect to see over the coming years?

The first industrial revolution witnessed the emergence of mechanical production and the second was fuelled by electrically powered mass production. The third industrial revolution was driven by the internet and automated production. All three revolutions focused on scalable efficiency (doing things right) and moved towards scalable adaptability (doing the right thing). The fourth industrial revolution will be different. It will bring significant changes to the way we live, interact and do business.

With the continuous evolution of technology and new developments arising from the need to integrate technology to efficiently deliver an excellent digital consumer experience, more women are taking charge by being part of this evolution through their involvement in local communities tailored to effectively share resources and current trends in data science and Machine Learning. The WiMLDS community which comprises of data scientist and machine learners aims at increasing representation of women data scientist into the tech space, the luck of therefore presented an opportunity to build up this local community where the majority are self taught and hence are able to keep up with the ever highly advancing technology. "We are all largely self taught so we found each other while looking for data science and machine learning communities to aid our learning journeys. There was no such community in existence and the opportunity presented itself to start a local chapter of Women in Machine Learning and Data Science. So we jumped at it and now it has been almost 2 years," says Kathleen Siminyu Head of data science at Africa's Talking.

Artificial intelligence startup Racetrack.ai said it has secured $5million from a group of high net-worth individuals in a funding round that has valued it at $21 million. "With the fresh capital, we are now looking forward to announce our next overseas operation base in the USA, Mauritius and Singapore," Subrat Parida, founder of Bengaluru-based Racetrack.ai, "We further plan to raise our Series A by the end of this year." The 50-member startup, founded in 2015, offers two flagship products --a chatbot for sales and support, and a business accelerator that helps in better business planning and execution. "This move will help us delve into newer business verticals like retail, banking and healthcare. This boost in our physical presence will open new doors for us to partner with intercontinental brands and decision-makers while helping us to create global footprint in the AI industry," Parida said in a statement.

A recent Chatham House paper on the impact of artificial intelligence on international relations raises the prospect of a "power gap" fuelled by the concentration of AI investment and expertise in the US and China. Looking beyond the strategic rivalry between those technological superpowers, states across the world will seek to deploy emerging technologies, especially AI, because these systems are cheaper and more accessible than the traditional military, financial and economic tools of statecraft. The impacts of emerging technologies are uneven and unpredictable, depending on the specific uses. China and the Western countries writing the programs stand to benefit most from systems that are attuned to anticipate their languages, goals and needs. Consumers in India and other developing countries already complain that their AI assistants are frustrating to use and less helpful than the English-speaking versions.

The first dedicated masters degree program for machine intelligence in Africa is launching in September with backing from tech leaders Google and Facebook. The African Institute for Mathematical Sciences (AIMS), which created the program, says the African Masters of Machine Intelligence (AMMI) is crucial so African countries don't get left behind as advancements in machine intelligence rapidly develop. "The lack of MI researchers from Africa means that many opportunities to use MI to create a better and more stable world are being missed," said Moustapha Cissé, founder of the program. He noted Africa is on the lower end of a "technology gap" in the field. This is why the program is called the "African Masters" in machine intelligence, as a branding strategy but also because the challenges they are choosing to focus on in the program will be challenges and insights relevant to Africa.