While it may be some time before we commute to work in flying cars or seek a transfer to our company's lunar outpost, another concept once thought outside the realm of modern reality is now increasingly ordinary in the contemporary workplace: working side-by-side with robots and machines capable of artificial intelligence. This article provides an overview of some of the ways in which these once-futuristic technologies are being integrated in today's work environment, and offers best practice suggestions for human resources professionals and in-house counsel adapting to these developments. We have reached the point of "minimum viability" when it comes to artificial intelligence (AI) – we can now count on the reliable use of AI products to perform meaningful work. Long past are the days when AI was little more than a novelty (remember asking iPhone's Siri whether it was raining outside?). The technology to integrate AI into necessary functions is now available, the data needed to power AI has been accumulated, and investors are pouring money into AI systems to make them a worthwhile part of everyday life. Having reached minimum viability, we now stand on the cusp of revolution.

Artificial intelligence (AI) is just beginning to come into its own in terms of its use by lawyers and within the legal industry. Within the next few years, we will find ourselves on the cusp of a revolution in the practice of law led by the adoption of artificial intelligence -- in particular, by in-house lawyers. Much like email changed the way we do business every day, AI will become ubiquitous -- an indispensable assistant to practically every lawyer. Those who do not adopt and embrace the change will get left behind. Those who do will ultimately find themselves freed up to do the two things there always seems to be too little time for: thinking and advising.

The Ford Foundation's Michael Brennan discusses the many studies showing how algorithms can magnify bias -- like the prevalence of police background check ads shown against searches for black names. What's worse is the way that machine learning magnifies these problems. If an employer only hires young applicants, a machine learning algorithm will learn to screen out all older applicants without anyone having to tell it to do so. Worst of all is that the use of algorithms to accomplish this discrimination provides a veneer of objective respectability to racism, sexism and other forms of discrimination. I recently attended a meeting about some preliminary research on "predictive policing," which uses these machine learning algorithms to allocate police resources to likely crime hotspots.

When Hollywood inevitably makes a biopic about Travis Kalanick, the embattled CEO of Uber--the most valuable private company in the world--screenwriters will have a hard time toning down reality to make it sound halfway believable. In the past few months alone, details have emerged about stolen trade secrets from Google regarding self-driving cars, sneaky tracking techniques for evading authorities, and, most alarmingly, allegations of ignored sexual harassment complaints and the most noxious office environment west of Wall Street. There were even reports that a top executive obtained the medical report of a woman who was raped by an Uber driver--and that CEO Travis Kalanick viewed the document. It all culminated in the company tapping former US attorney general Eric Holder to conduct an independent investigation into Uber's policies and culture. On Tuesday morning, during a highly anticipated all-hands meeting at Uber's headquarters in San Francisco, the board of directors shared the results of Holder's investigation: a 13-page document filled with recommendations for how to fix Uber's culture, including ceding some of Kalanick's power to a chief operating officer, who has yet to be hired, one of more than a dozen executive roles that are now empty.

Many modern data sets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective/well suited for characterizing a surface in two or three dimensions but may suffer from difficulties when representing higher dimensional surfaces. Motivated by high throughput toxicity testing where observed dose-response curves are cross sections of a surface defined by a chemical's structural properties, a model is developed to characterize this surface to predict untested chemicals' dose-responses. This manuscript proposes a novel approach that models the multidimensional surface as a sum of learned basis functions formed as the tensor product of lower dimensional functions, which are themselves representable by a basis expansion learned from the data. The model is described, a Gibbs sampling algorithm proposed, and is investigated in a simulation study as well as data taken from the US EPA's ToxCast high throughput toxicity testing platform.

Speaking at the Meltdown Festival in London, the controversial computer programmer said that sources at Silicon Valley – which is regarded as the tech capital of the world – say they are close to creating an ultra-powerful AI. He adds people will shortly begin uploading their brains to machines, essentially giving them immortality. The 45-year old told festival goers via a video link from the Ecuadorian embassy: "I know from our sources deep inside the Silicon Valley institution[s] that they genuinely believe that they are going to produce AI that's so powerful, relatively soon, that people will have their brains digitised, uploaded to these AIs and live forever in simulation, therefore have eternal life."

Confidence interval is abbreviated as CI. In this new article (part of our series on robust techniques for automated data science) we describe an implementation both in Excel and Perl, and discuss our popular model-free confidence interval technique introduced in our original Analyticbridge article, as part of our (open source) intellectual property sharing. This is part of our series on data science techniques suitable for automation, usable by non-experts. The next one to be detailed (with source code) will be our Hidden Decision Trees. Figure 1 is based on simulated data that does not follow a normal distribution: see section 2 and Figure 2 in this article. Classical CI's are just based on 2 parameters: mean and variance.

Genevera I. Allen (left) is professor in the Departments of Statistics, and the Electrical and Computer Engineering, at Rice University. Corinne Cath (right) is a doctoral student at the Alan Turing Institute, the national institute for data science in UK. Below are extracts of recent interviews that are most relevant to our audience. Links to full interviews are provided. Genevera, what do you think of the shift from "Statistics" to "Statistical Learning and Data Science" in the statistics community (The "Data vs Math" Question?)

Artificial intelligence (AI) is developing to the point where it could be considered negligent not to use it to automate legal processes, the premier international conference on the topic heard yesterday. A pre-conference workshop on AI and legal practice heard that technologies are already being deployed. Despite the hype, there is real substance here,' Dan Rubins, co-founder of Silicon Valley-based Legal Robot said. The workshop was opened by David Halliwell, director of knowledge and innovation at interrnational firm Pinsent Masons, whose innovation team includes data scientists, knowledge and process engineers and machine learning experts. 'Lawyers have expectations that AI is magic fairy dust you can sprinkle onto work.

The American transportation industry has been calling for national rules governing self-driving cars, and it looks like it might get its wish. Senators Bill Nelson, Gary Peters and John Thune have unveiled the principles they'll use to craft legislation that greenlights autonomous vehicles. Safety will be the top priority, they say, but they also want make sure the law is "tech neutral," clears up the roles of federal and state governments and improves cars' online security. And importantly, they want to "reduce existing roadblocks" in the law -- after all, many laws assume that someone needs to take the wheel. Don't get your hopes up for legislation in the immediate future.