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!

Ed Rex believes we're heading towards a world where artificial intelligence will master creativity. Professor Lynda Gratton explains how people should prepare for it. "The secret to creativity is knowing how to hide your sources." This quote is often attributed to Albert Einstein but also to philosopher C.E.M. Joad, among others: a well-hidden source indeed. But the idea behind the phrase raises questions: if creativity is copied, is it original?

Putting aside contested definitions of what artificial intelligence actually is (Digital Journal has discussed this recently), the new Google projects are orientated towards pushing forward the concept of the thinking machine or, as the head of research Emmanuel Mogenet puts it, "teaching machines common sense." The projects form the core part of Google's significantly expanded European research center. Mogenet is head of Google's Zürich Search Team. He is credited with developing the application Shake (software), before being headhunted by the U.S. tech firm. The Google projects focus on three research areas: machine learning, natural language understanding and computer perception.

Diabetes can result in a common complication--known as diabetic retinopathy--that damages the back of the eye, known as the retina. The chronic disease can cause vision loss if left untreated. But, if caught early, patients can significantly reduce the risk of blindness. Google's DeepMind, which specializes in artificial intelligence, is partnering with Britain's National Health Service (NHS) to do just that. The NHS is Britain's publicly funded healthcare system--free to use, treating a million patients every 36 hours, and consisting of one of the largest workforces in the world.

Panasonic just created a new 10 million budget for its new corporate shopping list in the areas of Artificial Intelligence (AI) and Machine Learning Technologies (MLT). The Japanese technology giant is looking at these two streams as the future beacon of change and development, for its handset business unit which currently faces an immensely competitive market in the country. The 10 million budget has been set aside for either creating joint-ventures with other enterprises, or perhaps even acquiring a few smaller ones. Pankaj Rana, Panasonic's head of mobility division in India, South-Asia, Middle-East, and Africa commented, "The budget is in tune of 10 million to start with and as we see progress on this front and things go in right direction, then there will be no constraint on the budget part. We can spend as high as possible. Some part of this budget has been generated from the India business, while some portion has been allocated from Japan."

Even the also-rans fared better, TechRepublic notes. Despite tougher demands, only four competitors failed to score (versus half in the 2015 challenge). Nearly half of the entries managed over 40 points, which would have been good enough to get third place a year ago. TU Delft and other entrants aren't about to replace people any time soon. Human workers typically pick 400 items per hour, and they won't suffer the 16.7 percent failure rate of the Picking Challenge leader.

Google DeepMind has announced a brand new research project in partnership with the NHS. The collaboration will see Google's artificial intelligence division working with London's Moorfields Eye Hospital to develop a machine learning system that will detect the early signs of degenerative eye conditions which humans might miss. Though this is the second project DeepMind is embarking on with the NHS, it's the first time the company has used machine learning in purely medical research and DeepMind co-founder Mustafa Suleyman says he thinks "one day this work will be a great benefit to patients across the NHS." Moorfields says that analysing complex eye scans is a time consuming process and traditional analysis tools have been unable to explore them fully. It hopes the research with DeepMind will lead to earlier detection and more effective treatment for patients and ultimately help to avoid cases of preventable eye disease.

A preliminary investigation has begun for a fatal car crash involving a Tesla Model S.According to the National Highway Traffic Safety Administration, the electric model sedan had Autopilot mode engaged when a driver was killed. SAN FRANCISCO -- Some Tesla owners have used the cars' Autopilot feature to take their hands off the wheel -- and film themselves doing anything but driving. YouTube videos uploaded since Tesla introduced the self-driving feature in October show drivers playing games, pretending to sleep, and in general, not holding the steering wheel. This kind of distracted driving is what Tesla Motors Inc., under federal investigation after a man using Autopilot died from injuries sustained in a May crash, says drivers should not do -- though Tesla founder Elon Musk has teased the possibility that one day, Autopilot could completely replace the role of a human driver. The Autopilot feature is designed to allow Teslas to cruise highways without drivers steering, braking or accelerating.

In the construction industry, many projects involve remodeling or refurbishing existing buildings, and such jobs often face delays or cost overruns when hidden problems emerge. "Renovation projects live and die by the quality of information," according to Martin Fischer, a Stanford professor of civil and environmental engineering. New software can analyze this point cloud to automatically extract details to plan a remodel or other purposes. Newer buildings often have computerized blueprints and records, including details such as the number of rooms, doors and windows, and the square footage of floors, ceilings and walls. But such information may not exist for older buildings, necessitating the time-consuming and difficult task of collecting these details manually.

A new Guardian report shows where AI is headed next, in a joint venture between Google's Deep Mind and the British NHS … The British team behind Google's AI efforts is teaming up with the UK's National Health Service and London's Moorfields Eye Hospital to build a machine learning system capable of recognizing potentially sight-threatening conditions by simply identifying symptoms from a digital scan of the eye. The core of the research will see about a million eye scans (all coming from anonymous patients) being analysed by an AI-fuelled computer, which Deepmind researchers will use to train a special algorithm. The algorithm will then allow the machine to spot early signs of eye conditions, such as wet age-related macular degenerations and diabetic retinopathy; diabetes, in fact, apparently makes it "25 times more likely to go blind", as per Mustafa Suleyman, Deepmind's co-founder. "If we can detect this, and get in there as early as possible, then 98% of the most severe visual loss might be prevented," Mustafa said. And indeed, allowing a computer to do most of the hard work would help immensely in increasing both the speed and the accuracy of a diagnosis, potentially helping the sight of thousands to be saved.