This 1947 photo shows an early airplane autopilot system. This 1947 photo shows an early airplane autopilot system. Cars are more maneuverable because of tire design changes. Jet engines are less likely to fail mid-flight thanks to better propulsion mechanics. Clinical diagnoses are more accurate thanks to improvements in medical imaging.

Today on In Case You Missed It: An Osaka University robotics lab produced a dog robot that can run six miles per hour while being the most uncoordinated robot you've seen (that works), and the Soundstage app lets users set up a recording studio to rock out with a VR headset on. The Sunday breakfast machine is worth seeing, as is the winning entry in Amazon's robot Picking Challenge. As always, please share any interesting tech or science videos you find by using the #ICYMI hashtag on Twitter for @mskerryd.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

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.

Alternatively, you can get the measure of one at Dyson's new "Demo" store in London, which not-so-coincidentally opens its doors today. The first of these interactive spaces debuted in Paris way back in 1999. Over the past couple of years, though, they've come to Tokyo, Jakarta, and now London's shopping-mad Oxford Street. The best way I can describe it is like walking into a fancy infomercial. Products sit on white plinths, illuminated by swish LED spotlights developed by Jake Dyson, son of company frontman James.

Do you get what overfitting means in machine learning? If you don't, then you better learn about it if you want to use or leverage machine learning. Because overfitting can ruin the effectiveness of machine learning. I wrote this blog because I found existing explanations of overfitting to be too technical. I hope this one is more consumable by non specialists. Machine learning involves a fairly complex workflow, see Machine Learning Algorithm!

Two weeks after Twitter acquired Magic Pony to advance its machine learning smarts for improving users' experience of photos and videos on its platform, Google is following suit. Today, the maker of Android and search giant announced that it has acquired Moodstocks, a startup based out of Paris that develops machine-learning based image recognition technology for smartphones whose APIs for developers have been described as "Shazam for images." Moodstocks' API and SDK will be discontinued "soon", according to an announcement on the company's homepage. "Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription," the company noted. Terms of the deal were not disclosed and it's not clear how much Moodstocks had raised: CrunchBase doesn't note any VC money, although when we first wrote about the company back in 2010 we noted that it had raised 500,000 in seed funding from European investors.

Combining machine learning method -- a type of artificial intelligence -- with a special MRI technique may help physicians predict who is more likely to develop Alzheimer's disease, a study says. Machine learning is a type of artificial intelligence that allows computer programs to learn when exposed to new data without being programmed. "With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," said principal investigator Alle Meije Wink from VU University Medical Centre in Amsterdam. "But at that point, the brain tissue is gone and there's no way to restore it. It would be helpful to detect and diagnose the disease before it's too late," Meije Wink explained.

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.

Google's autonomous cars may look cute, like a yuppie cross between a Little Tikes Cozy Coupe and a sheet of flypaper, but to make it in the real world they're going to have to act like calculating predators. At least, that's what a handful of scientists at the Institute of Neuroinformatics at the University of Zurich in Switzerland believe. They recently taught a robot to act like a predator and hunt its prey--which was a human-controlled robot--using a specialized camera and software that allowed the robot to essentially teach itself how to find its mark. The end goal of the work is arguably more beneficial to humanity than creating a future robot bloodsport, however. The researchers aim to design software that would allow a robot to assess its environment and find a target in real time and space.