This week's news is preliminary, but a U.S. house committee panel passed some new federal regulations which suggest sweeping change in the US regulatory approach to robocars. Today, all cars sold must comply with the Federal Motor Vehicles Safety Standards (FMVSS). This is a huge set of standards, and it's full of things written with human driven cars in mind, and making a radically different vehicle, like the Zoox, or the Waymo Firefly, or a delivery robot, is simply not going to happen under those standards. There is a provision where NHTSA can offer exemptions but it's in small volumes, for prototype and testing vehicles mostly. The new rules would allow a vendor to get an exemption to make 100,000 vehicles per year, which should be enough for the early years of robocar deployment.

They all have to be tested before they roll out into the world, not only to meet government regulations but to verify adherence to a host of voluntary standards, like WiFi, Bluetooth, ZigBee, Thread and others. That is a lot of testing. And that's why TUV Rheinland recently opened a huge Silicon Valley test facility in Fremont, Calif. It's important for testing to be near the design teams, says TUV Rheinland's Sarb Shelopal, the company's global director of wireless and IoT testing. Distance, he says--and Silicon Valley's traffic--is a big deal when companies are trying to move fast.

The best models around are based on generative adversarial networks. Clune says that GAN is composed of two neural networks playing a game of cops and robbers--or cops and forgers, rather. These neural networks are commonly referred to as "deep neural networks"--they take data and combine them through a series of many transformations. For instance, GAN is often given images of tumors and then asked to predict whether they are cancerous. The high number of transformations is what makes a neural network "deep."

At first glance, COMPAS appears fair: White and black defendants given higher risk scores tended to reoffend at roughly the same rate. New laws and better government regulation could be a powerful tool in reforming how companies and government agencies use AI to make decisions. Last year, the European Union passed a law called the General Data Protection Regulation, which includes numerous restrictions on the automated processing of personal data and requires transparency about "the logic involved" in those systems. However, existing federal laws do protect against certain types of discrimination -- particularly in areas like hiring, housing and credit -- though they haven't been updated to address the way new technologies intersect with old prejudices.

Whether it's done consciously or subconsciously, racial discrimination continues to have a serious, measurable impact on the choices our society makes about criminal justice, law enforcement, hiring and financial lending. It might be tempting, then, to feel encouraged as more and more companies and government agencies turn to seemingly dispassionate technologies for help with some of these complicated decisions, which are often influenced by bias. Rather than relying on human judgment alone, organizations are increasingly asking algorithms to weigh in on questions that have profound social ramifications, like whether to recruit someone for a job, give them a loan, identify them as a suspect in a crime, send them to prison or grant them parole. But an increasing body of research and criticism suggests that algorithms and artificial intelligence aren't necessarily a panacea for ending prejudice, and they can have disproportionate impacts on groups that are already socially disadvantaged, particularly people of color. Instead of offering a workaround for human biases, the tools we designed to help us predict the future may be dooming us to repeat the past by replicating and even amplifying societal inequalities that already exist.

Erik Brynjolfsson, MIT Sloan School professor, explains how rapid advances in machine learning are presenting new opportunities for businesses. He breaks down how the technology works and what it can and can't do (yet). He also discusses the potential impact of AI on the economy, how workforces will interact with it in the future, and suggests managers start experimenting now. Brynjolfsson is the co-author, with Andrew McAfee, of the HBR Big Idea article, "The Business of Artificial Intelligence." SARAH GREEN CARMICHAEL: Welcome to the HBR IdeaCast from Harvard Business Review. It's a pretty sad photo when you look at it. A robot, just over a meter tall and shaped kind of like a pudgy rocket ship, laying on its side in a shallow pool in the courtyard of a Washington, D.C. office building. Workers – human ones – stand around, trying to figure out how to rescue it. The security robot had just been on the job for a few days when the mishap occurred. One entrepreneur who works in the office complex wrote: "We were promised flying cars. Instead we got suicidal robots."

Motivation: Text mining has become an important tool for biomedical research. The most fundamental text-mining task is the recognition of biomedical named entities (NER), such as genes, chemicals and diseases. Current NER methods rely on pre-defined features which try to capture the specific surface properties of entity types, properties of the typical local context, background knowledge, and linguistic information. State-of-the-art tools are entity-specific, as dictionaries and empirically optimal feature sets differ between entity types, which makes their development costly. Furthermore, features are often optimized for a specific gold standard corpus, which makes extrapolation of quality measures difficult. Results: We show that a completely generic method based on deep learning and statistical word embeddings [called long short-term memory network-conditional random field (LSTM-CRF)] outperforms state-of-the-art entity-specific NER tools, and often by a large margin. To this end, we compared the performance of LSTM-CRF on 33 data sets covering five different entity classes with that of best-of-class NER tools and an entity-agnostic CRF implementation. On average, F1-score of LSTM-CRF is 5% above that of the baselines, mostly due to a sharp increase in recall. Availability and implementation: The source code for LSTM-CRF is available at https://github.com/glample/tagger Text mining is an important tool for many types of large-scale biomedical data analysis, such as network biology (Zhou et al., 2014), gene prioritization (Aerts et al., 2006), drug repositioning (Wang and Zhang, 2013) or creation of curated databases (Li et al., 2015).

A silent communications system in which nonaural carriers, in the very low or very high audio-frequency range or in the adjacent ultrasonic frequency spectrum are amplitude- or frequency-modulated with the desired intelligence and propagated acoustically or vibrationally, for inducement into the brain, typically through the use of loudspeakers, earphones, or piezoelectric transducers. The modulated carriers may be transmitted directly in real time or may be conveniently recorded and stored on mechanical, magnetic, or optical media for delayed or repeated transmission to the listener.

If you hadn't heard, Elon Musk is worried about the machines. Though that may seem a quixotic stance for the head of multiple tech companies to take, it seems that his proximity to the bleeding edge of technological development has given him the heebie-jeebies when it comes to artificial intelligence. He's shared his fears of AI running amok before, likening it to "summoning the demon," and Musk doubled down on his stance at a meeting of the National Governors Association this weekend, telling state leaders that AI poses an existential threat to humanity. "Until people see robots going down the street killing people, they don't know how to react because it seems so ethereal. AI is a rare case where I think we need to be proactive in regulation instead of reactive. Because I think by the time we are reactive in AI regulation, it's too late," according to the MIT Tech Review.

The US Department of Justice has shut down AlphaBay and Hansa, two of the biggest criminal marketplaces in the world. AlphaBay, the largest marketplace of its kind on the internet, has been linked to several deaths. It was known as "the new Silk Road" when it was operating, and went offline in mysterious circumstances earlier this month. Many of its users initially thought its admins had pulled an exit scam, but it turns out its closure was the result of a massive international operation. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.