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.

Therefore, SendPulse will add Italian, French, German, Turkish, Simple Chinese, Indonesian, Korean, Japanese, Arabic, and by the way, Spanish (LatAmerica), Brazilian Portuguese localization. Currently we've used pre-launch product for English and Russian audience. In total we will be have 13 foreign language groups. The language detection are based on geolocation. The modeling technology is based on math method, behavioral analyses to find lookalike audiences (digital twins) to create on behavior models in consuming content, clicks, design, social demographics - the simple comparison will be Facebook Artificial Intelligence for native advertising.

Top Gun was released 30 years ago and it looks as if the Maverick of tomorrow will be made of microchips. Developed by a University of Cincinnati (US) doctoral candidate, an Artificial Intelligence (AI) called ALPHA has consistently beaten other AIs and a retired United States Air Force Colonel in a high-fidelity, air-combat simulator using what's known as a genetic-fuzzy system that relies on off-the-shelf PC processors to do what was thought to be the reserve of supercomputers. Unmanned Combat Aerial Vehicles (UCAVs) have made great strides in recent years, going from items of speculation to the decks of aircraft carriers. But however well they've done in taking off, landing, and carrying out assigned aerial missions, there's still been a big gap between what a human pilot can do and what a combat drone can hope to achieve. Until recently, experienced humans have found it easy to beat UCAVs in simulations after learning their tricks and weaknesses.

'We have arrived,' the personal assistant announced as the automated share car pulled up outside Turing & Partners, one of London's best-known AI-powered law firms. Elon Turing looked up from his notes that were projected before him and got out at the curb on Gresham Street, not far from the Bank of England. It was very warm, as it always was these days, though there was a heaviness in the air that promised a thunder storm. Elon nodded to the car, which zipped away into the pollution free morning and disappeared into a sea of auto-taxis, self-driving buses and other share cars. He took a deep breath and marched up to the front door of the building. The door scanned his face and opened, greeting him politely as he walked into the cooled lobby. At the reception desk was Albert.

It's 2016, and businesses big and small, far and wide have finally stopped using the term Big Data. The consensus seems to be converging on the idea that data alone doesn't solve problems. You still need to understand, analyze, and test test test data using hypotheses to prove intuitions and make solid decisions. Things that should be happening regardless the size of your data. But instead of developing creative uses for the data that we have, we're all now looking to'cognitive computing' and'artificial intelligence' to save us.

One million anonymised eye scans from Moorfields Eye Hospital will be used to train an artificial intelligence (AI) system from Google. Machine learning algorithms will scour the images for signs of diseases such as macular degeneration and diabetes-related sight loss. Moorfields is teaming up with Google's AI division DeepMind during the scheme. Previously, DeepMind faced criticism over a little-known data sharing agreement with three London hospitals. An agreement to share patient data from the Royal Free, Barnet and Chase Farm hospitals over the past five years and continuing until 2017 was revealed by the New Scientist in May.

Google DeepMind has announced its second collaboration with the NHS, working with Moorfields Eye Hospital in east London to build a machine learning system which will eventually be able to recognise sight-threatening conditions from just a digital scan of the eye. The collaboration is the second between the NHS and DeepMind, which is the artificial intelligence research arm of Google, but Deepmind's co-founder, Mustafa Suleyman, says this is the first time the company is embarking purely on medical research. An earlier, ongoing, collaboration, with the Royal Free hospital in north London, is focused on direct patient care, using a smartphone app called Streams to monitor kidney function of patients. The Moorfields collaboration is also the first time DeepMind has used machine learning in a healthcare project. At the heart of the research is the sharing of a million anonymous eye scans, which the DeepMind researchers will use to train an algorithm to better spot the early signs of eye conditions such as wet age-related macular degeneration and diabetic retinopathy.

Famous eye hospital Moorfields has agreed to give GoogleHow AI is fuelling the car industry. Read more ... »'s DeepMindHow AI is fuelling the car industry. Read more ... » access to one million anonymous eye scans as a part of a machineHow AI is fuelling the car industry. Read more ... » learningHow AI is fuelling the car industry. Read more ... » studyMicrosoft scans photos to guess what your feelings are.

Machine learning is great and it does some amazing things, but even though we refer to the techniques as "neural networks" the way these systems learn is different from the way people learn. The biggest difference is that these algorithms/systems have insatiable appetites for clean data. You have to present one of these systems with huge numbers of pictures of kittens before it has any hope of labeling kittens reliably. As opposed to a child, who can be shown three pictures of kittens, and who at that point would probably perform as well as the exhaustively trained neural net. In all fairness, if we examine what these (deep) neural nets are learning we can see that the contest is not really fair.