How well do Robinhood's financials stack up against incumbent online brokerages? While we wait for the seven-year-old company's long-planned IPO, Alex Wilhelm examined Morgan Stanley's big $13 billion purchase of E-Trade for fresh data comparison points. Robinhood has 10 million accounts -- twice what E-Trade has -- but it also appears to make much less money per user and has far fewer assets under management, as he covered for Extra Crunch. So while its fee-free approach has destroyed a key revenue stream for competitors, it still has to grow its own "order-flow" business into its private-market valuation. One solution is to make the platform stickier via social features.

Navy chiefs are planning a fleet of "killer" robot ships which can think for themselves, we can reveal. They will have stealth technology, advanced radar, lasers and rail-guns capable of firing shells at 4,500mph. The vessels will use artificial intelligence – computerised brains – to work out tactics far more quickly than humans. Scientists say they will operate on their own or from a control room on shore – or act as mother ships to a fleet of smaller craft designed to overwhelm conventional ships. The Royal Navy says humans would set the limits of a battle and let the ships do the rest.

When Stephen Hawking warned of the dangers of Artificial Intelligence in 2015, his concerns were about the Superhuman AI that would pose an existential risk to humanity. But in recent years, much more imminent danger of AI has emerged that even a genius like Hawking could not have predicted. Deepfakes depict people in videos they never appeared in, saying things they never said and doing things they never really did. Some of the harmless ones have the actor Nicolas Cage's face superimposed on his Hollywood's peers while the more serious and dangerous ones target politicians like the US House Speaker Nancy Pelosi. Deeptrace, a cybersecurity startup based in Amsterdam found 14,698 deepfakes in June and July, an 84% increase since December of 2018 when the number of AI-manipulated videos was 7,964.

Using machine learning, a Stanford-led research team has slashed battery testing times – a key barrier to longer-lasting, faster-charging batteries for electric vehicles. Using a new machine learning method, a Stanford-led research team has slashed battery testing times – a key barrier to longer-lasting, faster-charging batteries for electric vehicles – by nearly fifteenfold. Battery performance can make or break the electric vehicle experience, from driving range to charging time to the lifetime of the car. Now, artificial intelligence has made dreams like recharging an EV in the time it takes to stop at a gas station a more likely reality, and could help improve other aspects of battery technology. For decades, advances in electric vehicle batteries have been limited by a major bottleneck: evaluation times.

The influence of human judgement is ubiquitous in datasets used across the analytics industry, yet humans are known to be sub-optimal decision makers prone to various biases. Analysing biased datasets then leads to biased outcomes of the analysis. Bias by protected characteristics (e.g. race) is of particular interest as it may not only make the output of analytical process sub-optimal, but also illegal. Countering the bias by constraining the analytical outcomes to be fair is problematic because A) fairness lacks a universally accepted definition, while at the same time some definitions are mutually exclusive, and B) the use of optimisation constraints ensuring fairness is incompatible with most analytical pipelines. Both problems are solved by methods which remove bias from the data and returning an altered dataset. This approach aims to not only remove the actual bias variable (e.g. race), but also alter all proxy variables (e.g. postcode) so the bias variable is not detectable from the rest of the data. The advantage of using this approach is that the definition of fairness as a lack of detectable bias in the data (as opposed to the output of analysis) is universal and therefore solves problem (A). Furthermore, as the data is altered to remove bias the problem (B) disappears because the analytical pipelines can remain unchanged. This approach has been adopted by several technical solutions. None of them, however, seems to be satisfactory in terms of ability to remove multivariate, non-linear and non-binary biases. Therefore, in this paper I propose the concept of Fair Adversarial Networks as an easy-to-implement general method for removing bias from data. This paper demonstrates that Fair Adversarial Networks achieve this aim.

This paper will discuss the role of an artificially-intelligent computer system as critique-based, implicitorganizational, and an inherently necessary device, deployed in synchrony with parallel governmental policy, as a genuine means of capturing nation-population complexity in quantitative form, public contentment in societal-cooperative economic groups, regulatory proposition, and governance-effectiveness domains. It will discuss a solution involving a well-known algorithm and proffer an improved mechanism for knowledgerepresentation, thereby increasing range of utility, scope of influence (in terms of differentiating class sectors) and operational efficiency. It will finish with a discussion of these and other historical implications. Introduction The world created by humans to manage their daily affairs is growing in complexity beyond the comprehension capability of the vast majority of them. The political classes are vulnerable to implementation of policy that proves incorrect and damages the credibility of the state over the long term.

The new goal is to seek high levels of human control AND high levels of automation, which is more likely to produce computer applications that are Reliable, Safe & Trustworthy (RST). Achieving this goal, especially for complex poorly understood problems, will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility. The traditional belief in computer autonomy is compelling for many artificial intelligence (AI) researchers, developers, journalists, and promoters. The goal of computer autonomy was central in Sheridan and Verplank's (1978) ten levels from human control to computer automation/autonomy (Table 1). Their widely cited one-dimensional list continues to guide much of the research and development, suggesting that increases in automation must come at the cost of lowering human control. Shifting to HCAI could liberate design thinking so as to produce computer applications that increase automation, while amplifying, augmenting, enhancing, and empowering people to innovatively apply systems and creatively refine them.

As an activist, Nandini Jammi has become accustomed to getting harassed online, often by faceless social media accounts. But this time was different: a menacing tweet was sent her way from an account with a profile picture of a woman with blonde hair and a beaming smile. The woman went only by a first name, "Jessica," and her short Twitter biography read: "If you are a bully I will fight you." In her tweet sent to Jammi last July, she said: "why haven't you cleaned your info from Adult Friend Finder? It's only been three years."

"Between 12 to 18 million Americans every year will experience some sort of diagnostic error," said Paul Cerrato, a journalist and researcher. "So the question is: Why such a huge number? And what can we do better in terms of reinventing the tools so they catch these conditions more effectively?" Cerrato is co-author, alongside Dr. John Halamka, newly minted president of Mayo Clinic Platform, of the new HIMSS Book Series edition, Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. At HIMSS20, the two of them will discuss the book, and the bigger picture around CDS tools that are fast being transformed by the advent of artificial intelligence, machine learning and big data analytics.

On Wednesday the European Commission launched a blizzard of proposals and policy papers under the general umbrella of "shaping Europe's digital future". The documents released included: a report on the safety and liability implications of artificial intelligence, the internet of things and robotics; a paper outlining the EU's strategy for data; and a white paper on "excellence and trust" in artificial intelligence. In their general tenor, the documents evoke the blend of technocracy, democratic piety and ambitiousness that is the hallmark of EU communications. That said, it is also the case that in terms of doing anything to get tech companies under some kind of control, the European Commission is the only game in town. In a nice coincidence, the policy blitz came exactly 24 hours after Mark Zuckerberg, supreme leader of Facebook, accompanied by his bag-carrier – a guy called Nicholas Clegg who looked vaguely familiar – had called on the commission graciously to explain to its officials the correct way to regulate tech companies.