The Friday afternoon news dump, a grand tradition observed by politicians and capitalists alike, is usually supposed to hide bad news. So it was a little weird that Elon Musk, founder of electric car maker Tesla, and Sam Altman, president of famed tech incubator Y Combinator, unveiled their new artificial intelligence company at the tail end of a weeklong AI conference in Montreal this past December. But there was a reason they revealed OpenAI at that late hour. It wasn't that no one was looking. It was that everyone was looking. When some of Silicon Valley's most powerful companies caught wind of the project, they began offering tremendous amounts of money to OpenAI's freshly assembled cadre of artificial intelligence researchers, intent on keeping these big thinkers for themselves. The last-minute offers--some made at the conference itself--were large enough to force Musk and Altman to delay the announcement of the new startup.

Every day we create billions of bits of data. Ever faster and more powerful computers can use that big data to learn, predict events and carry out key tasks. Surveillance, voice recognition and driving vehicles are all areas where people are becoming superfluous. The BBC's Colm O'Regan explores the process known as "deep learning" - a branch of machine learning used to develop artificial intelligence.

Artificial intelligence is once again in the media spotlight. But what is it exactly? And how does it relate to developments in machine learning and deep learning? Below we spell out the various interpretations of AI and look back on how the technology has developed over the years. "The fundamental challenge is that, alongside its great benefits, every technological revolution mercilessly destroys jobs and livelihoods – and therefore identities – well before the new ones emerge."

From forecourt to scrapyard, a new car in the UK lasts an average of 13.9 years, which is why if you got one today, it might very well be the last car you buy. Over the next decade, accelerating autonomous driving technology, including advances in artificial intelligence, sensors, cameras, radar and data analytics, are set to transform not only how we drive (or, indeed, are driven), but the notion of car ownership itself. "Autonomous driving has become the next major battlefield for the car industry," says Luca Mentuccia, automotive global MD at Accenture. The six levels of automation, defined under international standards by the Society of Automotive Engineers, range from "no automation" to "full automation", explains Sven Raeymaekers, of tech investment banker GP Bullhound. "If you look at the most recent predictions, the majority of car manufacturers estimate the first highly to fully automated vehicles [AVs] will hit the market between 2020-2025," he says.

Deep learning is taking on dermatology. An algorithm trained in image recognition has matched dermatologists in its ability to identify certain types of skin cancer based on photographs of skin lesions. "I'm certain this is how melanomas are going to be identified in the future," says Richard Weller, a consultant dermatologist at the Royal Infirmary of Edinburgh in the UK, who was not involved in the work. Researchers led by Andre Esteva and Brett Kuprel at Stanford University trained a neural network on more than 129,000 images of skin lesions associated with 2000 different diseases. They then pitted it against 21 certified dermatologists on new sets of images to find out whether deep-learning algorithms could reliably pick out cancerous moles and lesions.

Computers can classify skin cancers as successfully as human experts, according to the latest research attempting to apply artificial intelligence to health. The US-based researchers say the new system, which is based on image recognition, could be developed for smartphones, increasing access to screening and providing a low-cost way to check whether skin lesions are cause for concern. "We hope that this is a first step towards early detection," said Andre Esteva, an electrical engineering PhD student from Stanford University and co-author of the research. According to the World Health Organisation, skin cancer accounts for one in every three cancers diagnosed worldwide, with global incidence on the rise. In the UK alone, 131,772 cases of non-melanoma skin cancer were recorded in 2014.

The Genetic and Evolutionary Computation Conference (GECCO) presents the latest high-quality results in genetic and evolutionary computation since 1999. Topics include: genetic algorithms, genetic programming, ant colony optimization and swarm intelligence, complex systems (artificial life/robotics/evolvable hardware/generative and developmental systems/artificial immune systems), digital entertainment technologies and arts, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, evolutionary numerical optimization, real world applications, search-based software engineering, theory and more.

This 3-hour course (video slides) offers developers a quick introduction to deep-learning fundamentals, with some TensorFlow thrown into the bargain. Deep learning (aka neural networks) is a popular approach to building machine-learning models that is capturing developer imagination. If you want to acquire deep-learning skills but lack the time, I feel your pain. In university, I had a math teacher who would yell at me, "Mr. Görner, integrals are taught in kindergarten!"

A very common task in data analysis is that of grouping a set of objects into subsets such that all elements within a group are more similar among them than they are to the others. The practical applications of such a procedure are many: given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user's mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some sort of structure that might indicate that the data is separable. The k-means algorithm takes a dataset X of N points as input, together with a parameter K specifying how many clusters to create. The output is a set of K cluster centroids and a labeling of X that assigns each of the points in X to a unique cluster. All points within a cluster are closer in distance to their centroid than they are to any other centroid.

Imagine training a neural network and watching its representations wander through this space. You can see how your representations compare to other "landmark" representations from past experiments. If your model's first layer representation is in the same place a really successful model's was during training, that's a good sign! If it's veering off towards a cluster you know had too high learning rates, you know you should lower it. This can give us qualitative feedback during neural network training.