From drawing machines to early digital art and now AI, we've wondered whether the fundamentally human seeming endeavor of ART MAKING can be done by machines. At Idea Channel, we think the answer is unequivocally, yes! Perhaps the better question would be, will we ever let machines make art on their own? Will we ever be able to scrub away the influence and bias of the humans that created the machines making said art? And if so, how will we respond to the art being made by machines?

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.

After Apple's WWDC keynote last month, some reporters asked me if Apple's new additions to Siri are reactionary. They assumed that since Amazon, Google and Microsoft have upped the intelligence of their voice assistants, Apple was forced to make Siri more competitive. But to think that Apple's Siri improvements are reactionary shows a lack of understanding about the company's work in artificial intelligence (AI). Apple has been working on speech and voice AI solutions for decades. In fact, in 1992, I got involved with the earliest version of its voice technology research, which was tied to an early AI and machine learning engine.

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.

Clifford Chance (CC) has become the latest firm to sign a deal with an artificial intelligence (AI) provider, with the magic circle firm partnering with Kira Systems. The deal means CC's lawyers will be able to use the AI software for tasks such as document review in M&A due diligence. The firm said it has already used AI technology in several other applications but declined to provide details. The Kira deal, which is aimed at reducing costs for clients, was led by the firm's head of innovation and business change Bas Boris Visser. He said CC's clients are "under substantial pressure to reduce legal spend", adding: "At the same time, they need more support to manage the increasing risks and complex issues that their companies are facing."

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.

The Otto Group Product Classification Challenge made Kaggle history as our most popular competition ever. Alexander Guschin finished in 2nd place ahead of 3,845 other data scientists. In this blog, Alexander shares his stacking centered approach and explains why you should never underestimate the nearest neighbours algorithm. I have some theoretical understanding of machine learning thanks to my base institute (Moscow Institute of Physics and Technology) and our professor Konstantin Vorontsov, one of the top Russian machine learning specialists. As for my acquaintance with practical problems, another great Russian data scientist who once was Top-1 on Kaggle, Alexander D'yakonov, used to teach a course on practical machine learning every autumn which gave me very good basis. Kagglers may know this course as PZAD.

Tokyo, June 27, 2016 --- Hitachi, Ltd. (TSE:6501; 'Hitachi') today announced the development of technology using artificial intelligence that automatically creates effective advice for raising the happiness of workers based on the behavioral data of each individual on a daily basis and the commencement of an internal trial with 600 participants from sales & marketing. More precisely, a name tag type wearable sensor collects the massive amount of individual behavioral data which is then analyzed using Hitachi AI*1Technology/H (hereafter referred to as H), and used to create and deliver personalized advice on actions automatically, such as on communication in the workplace or time allocation that will contribute to raising individual happiness. This advice is delivered daily, and individual workers can check the daily advice on their smartphone or tablet, and choose to apply the advice in their daily activity. Hitachi will integrate the results from this trial into the solution which it will provide to corporate and other organizations globally, to support increased productivity through a more active organization resulting from the increased happiness of workers. In recent years, increasing'happiness' has become one of society's most important issues.