Factory workers are about to get super-human strength thanks to Nasa's'Robo-Glove'. The glove helped scientists control Robonaut 2, a humanoid that provided engineering and technical assistance on space mission just like Star Wars' R2-D2. But now it has been given power-boosting technologies thanks to a partnership between General Motors and medical technology company Bioservo, who hope that it will help workers on factory floors. The Robo-Glove wraps around a worker's hand essentially, takes on the hard work for them. An assembly operator in a factory may use 15- to 20 pounds of force to hold a tool during an operation.

Apparently, a crash related to Tesla's autopilot feature was material, before it wasn't. On Tuesday, Fortune reported that Elon Musk and Tesla Motors may have withheld a material fact from shareholders when it failed to disclose that a driver had died using the semi-self driving "autopilot" feature in one of the company's vehicles. The fatal accident, the first known case related to the autopilot feature, occurred 11 days before Musk and Tesla sold 2 billion shares in an offering on May 18. Yet the company made no mention of the crash in its offering documents. The news of the accident didn't come out until last week, when it was reported by federal highway authorities--six weeks after the offering.

DeepMind, Google's London-based artificial intelligence company, has started training neural networks to recognise the signs of eye disease in medical images. A partnership with Moorfields Eye Hospital in London has given the company access to about a million anonymised retinal scans, which DeepMind will feed into its artificial intelligence software. The project will target two of the most common eye diseases – age related macular degeneration and diabetic retinopathy. More than 100 million people around the world have these conditions. The information that Moorfields is providing includes scans of the back of people's eyes, as well as more detailed scans known as optical coherence tomography (OCT). The idea is that the images will let DeepMind's neural networks learn to recognise subtle signs of degenerating eye conditions that even trained clinicians have trouble spotting.

Your job may end up being done by a computer in the not-so-distant future, according to participants in a White House-hosted discussion on Tuesday. The discussion addressed how the future of automation can provide many social and economic advantages, but could also be a major disadvantage for those who would lose their jobs to robots. "There are fantastic things to be very excited about," said Martin Ford, author of two books on the future of automation. He cautioned that increased automation could have major detriments for the labor market. "Almost any kind of work that is on some level routine and predictable is very highly susceptible to [automation]."

Staging is serious business in presidential politics. Every detail--from the sign on the lectern to the demographics of everyone behind the candidate to (on one bizarre occasion this cycle) the raw slabs of meat dressing the set--is meant to convey who the candidate is and what the candidate stands for. So you can learn a lot juxtaposing the optics of the campaign speeches Hillary Clinton and Donald Trump gave last week on the future of the economy. While Clinton spoke from the center of a tech hub in Denver, surrounded by millennials tapping away on MacBooks, Trump addressed a crowd inside a scrap metal factory in a Pennsylvania steel town, standing before a wall of crushed aluminum cans. Before either candidate spoke, they'd cast two opposing visions.

Everything we know about the face recognition systems the FBI and police use suggests the software has a built-in racial bias. That isn't on purpose--it's an artifact of how the systems are designed, and the data they are trained on. Law enforcement agencies are relying more and more on such tools to aid in criminal investigations, increasing the risk that something could go wrong. Law enforcement agencies haven't provided many details on how they use facial recognition systems, but in June the Government Accountability Office issued a report saying that the FBI has not properly tested the accuracy of its face matching system, nor that of the massive network of state-level face matching databases it can access. And while state-of-the-art face matching systems can be nearly 95 percent accurate on mugshot databases, those photos are taken under controlled conditions with generally coöperative subjects.

It is Wednesday, July 6. A 300-pound robot is the new security guard at Uber's inspection lot in San Francisco. Here's what else is happening in the Golden State: When undercover CHP officers shot at suspects in a moving car this weekend, they used a tactic that's been outlawed in many major cities because experts believe it's too dangerous. They fired at a moving car. "Only a fool thinks a … bullet is going to stop a 3,800-pound car. Nobody is really shooting at the vehicle, they're shooting at the driver," said Sid Heal, a retired Los Angeles sheriff's commander and chairman of strategy development for the National Tactical Officers Assn.

In last month's blog I talked about keeping your team innovative. One of the key tenets of an innovative team is the concept of moonshots or "10x Thinking" or audacious goals. In a "moonshot economy," while a breakthrough can mean the start of a new industry leader, it can also mean the death of an industry stalwart. First, let's take a look at some previous moonshots and how they've impacted various industries and the world. The first is, of course, the great Space Race.

The NHS is pairing up with Google's artificially intelligent image software DeepMind for a new medical research partnership that could play a "big role in tackling avoidable sight loss." Specialists at the NHS-funded Moorfields Eye Hospital in London will use DeepMind, the internet giant's machine learning project, to research whether the technology can help detect and prevent eye diseases and blindness. DeepMind will be applied to one million anonymous eye scans to look for early signs of eye conditions that humans might miss such as macular degeneration and retinal conditions caused by diabetes. The end goal of the research is to create a more efficient method by which to analyse data and come to an earlier diagnosis for patients. The number of people suffering from sight loss in the UK is predicted to double by 2050, and the project marks Google's first machine learning collaboration with healthcare specialists.

Google-owned DeepMind has expanded its collaboration with the UK's National Health Service (NHS), announcing a research partnership today with Moorfields Eye Hospital NHS Foundation Trust in London -- its second publicly confirmed foray into working with the NHS. But this time the project is being explicitly badged as medical research, and DeepMind will be applying AI machine learning algorithms to the data -- so that's also a first. Although the company has been public about its ambitions to apply AI to health data before now. The Moorfields partnership is focused on two specific sight-loss causing conditions: diabetic retinopathy and age-related macular degeneration (AMD), which DeepMind notes collectively affect more than 625,000 people in the UK and more than 100 million people worldwide. The stated aim is to investigate whether machine learning algorithms can automate the analysis of the digital eye scans that are typically used to diagnose the two conditions.