The next time you order food using the delivery app Just Eat in London, don't be surprised if it's brought to your door by a small, six-wheeled robot. London-listed Just Eat Plc. will join German retail chain Metro AG, logistics company Hermes Group, and U.K. food delivery startup Pronto Technology Ltd., in trialing delivery using self-driving robots. Starship Technologies, the company that makes the droids, said Wednesday that Just Eat and Pronto will be using the robots in London, while Metro and Hermes will deploy them in Dusseldorf, Germany, and Bern, Switzerland, as well as another undisclosed German city. Created by two Skype co-founders, Starship, which is headquartered in London and has its engineering research and development office in Tallinn, Estonia, has been testing its robots in 12 countries over the past nine months. This will be the first time businesses will be using the technology to deliver real orders to paying customers.

The White House and New York University's Information Law Institute, with support from Google Open Research, Microsoft Research and the MacArthur Foundation will host a major public symposium to address the near-term impacts of AI technologies across social and economic systems. The focus will be the challenges of the next 5-10 years, specifically addressing four themes: social inequality, labor, healthcare, and ethics. Leaders from industry, academia, and civil society will share ideas for technical design, research and policy directions. This event will be live-streamed. Check back on July 7th for a live feed.

Google DeepMind, the British artificial intelligence research arm of Google, will collaborate with Britain's National Health Service (NHS) to tackle sight loss in humans. A retina-scanning system that will be able to detect the early signs of eye disease will be developed in partnership with with Moorfields Eye Hospital in London. About a million anonymized scans taken from patients who attended Moorfields between January 1, 2007 and February 29 this year will be used for analysis by Google's Artifical Intelligence (Al) computer. Scientists are hopeful that they will be able to recognize conditions such as macular degeneration, a gradual deterioration of the light-sensitive tissue lining at the back of the eye, and diabetic retinopathy, when high blood sugar levels damage the retina over time. Early treatment can be critical in both conditions.

Last year, Amazon kicked off its inaugural Picking Challenge to encourage teams to create robots able to perform the task of an Amazon stock picker. This year the competition was expanded to include not only picking items from a shelf and placing them in a container, but the reverse as well – and a team from the Netherlands has claimed victories in both. This year's pick task, which carries over from last year but has been made more difficult, requires robots to grab target items from a shelf and place them in a container. Conversely, the stow task involves the robot removing items from a box and placing them back on the shelf. They sound like very simple tasks for a human, but for robot competitors it requires a sophisticated array of sensors, moving parts and artificial intelligence.

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!