The engineer at the heart of the upcoming Waymo vs Uber trial is facing dramatic new allegations of commercial wrongdoing, this time from a former nanny. Erika Wong, who says she cared for Anthony Levandowski's two children from December 2016 to June 2017, filed a lawsuit in California this month accusing him of breaking a long list of employment laws. The complaint alleges the failure to pay wages, labor and health code violations, and the intentional infliction of emotional distress, among other things. Yet in this unusual 81-page complaint, Wong also claims knowledge of a large swath of Levandowski's personal and business dealings. She does so in great detail, including dozens of overheard names, the license-plate numbers of cars she observed at a Levandowski property, and an extensive list of the BDSM gear she claims he kept in his bedroom.

Recently, Stanford Researchers Michal Kosinski and Yilun Wang trained a machine powered by artificial intelligence (AI) to detect sexual orientation of people to an accuracy of 81%, simply by scanning photos of faces. Kosinski and Wang only created the algorithm to highlight the potential and potential dangers of AI; however, in a world where the persecution of homosexuals is still widespread, the backlash against their creation was fierce. Our JPSP paper warning that sexual orientation can be predicted from faces is now available at https://t.co/d1AAc6t67O It's "junk science" that "threatens the safety and privacy of LGBTQ and non-LGBTQ people alike," said gay advocacy groups like Glaad and the Human Rights Campaign. They have "invented the algorithmic equivalent of a 13-year-old bully," wrote Greggor Mattson, the director of the Gender, Sexuality and Feminist Studies Program at Oberlin College.

A veteran filed a lawsuit against a Connecticut hospital claiming a scalpel was left in his body for four years after his surgery, reports said Monday. Faxon Law Group filed the lawsuit against the Department of Veterans Affairs on behalf of 61-year-old United States Army veteran Glenford Turner, who had his cancerous prostate removed in robot-assisted laparoscopic surgery at the VA Connecticut Healthcare System, West Haven Campus in 2013. Turner's lawyer, Joel Faxon told the Boston Globe that the surgery took five hours instead of the one hour it should have taken. There was no standard measure of X-ray performed afterward to ensure that no surgical materials had been left behind. Turner returned to the VA hospital on March 29, 2017 for an MRI after complaining of dizziness and long-term abdominal pain.

They are: Ryan Alshak, the CEO of Ping, Alex Hewitt, the director of operations for vTestify, Tunji Williams, the co-founder and CEO of dealWIP, as well as David Schnurman, the CEO of Lawline and author of the upcoming book, Break through Your Walls: Bring Your Entrepreneurial Sledgehammer. This Q&A has been condensed. Ari Kaplan: Tell us about your company. Ryan Alshak: Ping is using artificial intelligence to solve the biggest pain point of every law firm lawyer, which is manually tracking billable hours. We're leveraging our in-house data science team to understand what we can learn from that time and billing information.

AI Robot, Sophia, became the first robot citizen. But was there more to the story and, although impressive and featuring genuine AI, do the animatronic features of Sophia suggest the goal here is the appearance of humanity – rather than an extension of it? This caused widespread uproar amongst human rights groups and on social media, and it was picked up by many of the big media outlets, but it turns out that the whole affair was mainly a PR stunt. The eye-grabbing headlines were a well strategised ploy to promote a tech summit in Saudi Arabia, but some experts say this sort of approach to robot rights is actively damaging, both to public understanding of technology and to civil society itself. So what about Sophia's amazing conversational abilities that led to the robot having an argument with Elon Musk?

The commoditisation of corporate legal services has been going on for some time. Since 2007, in-house legal teams have been squeezed for resources. Law firms are under increased cost pressures, made worse by increased competition from within and outside the legal sector. Because of the inelastic nature of legal spend on high-value bespoke projects, many legal activities regarded as low value-add, such as bulk contract analysis, are passed on to low-cost providers. Data in the US has demonstrated price erosion in the legal industry, with declining revenue per lawyer that may only be offset by cost reduction and an increase in non-equity partners.

We are living in interesting times. Multiple technologies, improving exponentially, are converging. I have been chronicling this convergence for several years in my newsletter, Exponential View. As Bill Gates said, "Most people overestimate what they can do in one year and underestimate what they can do in ten years." Likewise, most annual predictions overestimate what can occur in a year, and underestimate the power of the trend over time.

"What do judges know that we cannot teach a computer?" There is a substantial public sentiment that distrusts legal rules and state structures and looks to technology for solutions. After all, many trust their smartphones more than they trust their government. But what may seem as a fairly modern libertarian opinion, voiced in pitch decks and technology conferences, and buoyed by the success of the information economy, has much deeper roots. Such ambitions of a technology centric society were voiced more than forty years ago by John McCarthy, an influential computer scientist and professor at Stanford who coined the term, "artificial intelligence", and nurtured it into a formal field of research.

Imagine a world in which the human resources function as we know it vanishes and is replaced by automation, outsourcing, and self-organizing teams. Or a world in which top talent is fought over so fiercely that the most adept tech workers hire agents to negotiate and manage their careers. It may sound like science fiction. But the world of work is changing so fast that either scenario could become reality. Megatrends such as digitization, the rise of automation, and shifting demographics are disrupting the way we work, and the way companies relate to workers.

A general Data Science workflow in machine learning consists of the following steps: gather data, clean and prepare data, train models and choose the best model based on validation and test errors or other performance criteria. Usually we – particularly we Data Scientists or Statisticians who live for numbers, like small errors and high accuracy – tend to stop at this point. Let's say we found a model that predicted 99% of our test cases correctly. In and of itself, that is a very good performance and we tend to happily present this model to colleagues, team leaders, decision makers or whoever else might be interested in our great model. We assume that our model is trustworthy, because we have seen it perform well, but we don't know why it performed well.