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.

We're all familiar with the story – sophisticated cyber-attacks are increasing in number and complexity, adding to the workload of already beleaguered security professionals, and helping fuel a surge in cybersecurity job postings. But these positions are incredibly tough to fill because of the insufficient supply of experienced professionals and lack of new talent. In fact, a Stanford University study shows the cybersecurity skilled personnel gap stands at 200,000 unfilled jobs. Between the challenge of choosing the right tool amongst a myriad and the challenge of finding skilled security personnel, you have to wonder: how can companies defend themselves in this increasingly complex threat landscape? Machine learning is an excellent place from which to start.

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.

A serious problem in the Turing test for computer intelligence is exposed in a study published in the Journal of Experimental and Theoretical Artificial Intelligence. If a machine were to'take the Fifth Amendment' – that is, exercise the right to remain silent throughout the test – it could, potentially, pass the test and thus be regarded as a thinking entity, authors Kevin Warwick and Huma Shah of Coventry University argue. However, if this is the case, any silent entity could pass the test, even if it were clearly incapable of thought. The test, devised in 1950 by pioneering computer scientist Alan Turing, assesses a machine's ability to exhibit intelligent behaviour indistinguishable from that of a human. Also known as the'imitation game', it requires a human judge to converse with two hidden entities, a human and a machine, and then determine which is which.

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.

Once in a while I use AirBnB. There are a couple of features that I (intuitively) use to judge if an apartment is save to book; ratings, images of the flat and the user avatar. Apparently, these avatars play an important part in the overall service and usage of AirBnB. A recent study finds that "Attractive Airbnb hosts are more likely to get bookings, even with bad reviews". With the easy availability of image recognition services, even the everyday researcher can do a small analysis.

According to the official statement, "Sparkling Water was designed to allow users to get the best of Apache Spark -- its elegant APIs, RDDs and multi-tenant Context -- along with H2O's speed, columnar-compression and fully-featured machine learning algorithms." Plus, this newly updated tool empowers enterprise customers to use H2O algorithms in conjunction with, or instead of, MLlib algorithms on Apache Spark. Matt Aslett, Research Director, Data Platforms and Analytics at 451 Research forecasted that Sparkling Water could attract both Spark and H2O users, helping them to mix and match algorithms as needed. Enterprises are looking to take advantage of a variety of machine learning algorithms to address an increasingly complex set of use cases when determining how to best serve their customers. H2O.ai updates Sparkling Water machine learning API for Apache Spark https://t.co/ZWEYFoi4a6

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.

One of the most hotly contested aspects of taking on artificial intelligence (AI) has always been the potential for machines to take over jobs that have historically belonged to humans. The debate's first arena was in manufacturing, where machines are now doing most of the grunt work normally reserved for people -- assembling products, painting parts and finished products, welding, bolting, and more. The result has been interesting. While AI has resulted in fewer people jobs in manufacturing, the jobs that exist now are far safer and generally pay better than ever before. In the end, AI may actually replace the tedious, boring legal work, allowing people to do the parts they like and excel at, such as arguing. But don't tell those recent law school grads who are looking forward to a couple of years reviewing contracts for peanuts.