Ever since algorithms began recognizing patterns faster and better than humans, computers have been making doctors' lives easier and diagnoses more accurate. But widely used tools like automated cell counters, which can quickly point to diseases like malaria and leukemia by getting a head count on different kind of blood cells, are beginning to look quaint next to the deep learning and neural networks coming online. Today, hospitals can outfit their existing computer systems with a $1,000 graphics processor and speed-boost their capacity up to 260 million images per day. That's basically equivalent to all the MRIs, CT scans, and other images that all the radiologists in America look at each day. Unleashing that kind of AI on the medical world's mountains of patient data could speed up diagnoses and get patients on the path to recovery much sooner.

I often get asked on how to get started with Machine Learning. Most of the time, people have troubles understanding the maths behind all things. And I have to admit, I don't like the maths either. Math is an abstract way of describing things. And I think the way machine learning is described is too abstract to understand it easily. I probably try to describe things with foo code or a bit of JS to explain what I'm talking about.

Artificial Intelligence, Machine learning and Predictive Analytics are at a perfect storm and many companies leverage these technologies to transform their organization. These technologies existed for a decade, but they evolved rapidly – machine learning today is not like machine learning of the past. In these session, we will look into these technologies, understand what SAP is to offer and how SAP customers are using these technologies. The SAP Community Calls (known previously as "Mentor Monday Webinar Series") is a part of the SAP Mentors Program, and hosted by SAP Mentors, to share relevant and interesting SAP topics and knowledge with all SAP community members. In case of any questions please contact sapmentors@sap.com.

It's scary enough making a doctor's appointment to see if a strange mole could be cancerous. Imagine, then, that you were in that situation while also living far away from the nearest doctor, unable to take time off work and unsure you had the money to cover the cost of the visit. In a scenario like this, an option to receive a diagnosis through your smartphone could be lifesaving. Universal access to health care was on the minds of computer scientists at Stanford when they set out to create an artificially intelligent diagnosis algorithm for skin cancer. They made a database of nearly 130,000 skin disease images and trained their algorithm to visually diagnose potential cancer.

A number of research organizations are working to create artificial intelligence systems capable of developing machine-learning software. Several research organizations, including Google Brain and DeepMind, are working to create artificial intelligences (AI) that can in turn develop machine-learning software. In many cases, the results coming from machines programming other machines match or exceed work done by humans. If self-programming AI techniques become practical, they could increase the pace at which machine learning is adopted throughout the economy without requiring more machine-learning experts, who already are in short supply. One set of experiments from DeepMind suggests self-teaching methods could alleviate the problem of AI software needing to consume massive amounts of data on a specific task.

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.