In 1948 a young man by the name of Alan Turing penned a report entitled "Intelligent Machinery." The opening sentence "I propose to investigate the question as to whether it is possible for machinery to show intelligent behavior" (1) had instantly set the stage for what we today would call AI, or Artificial Intelligence. And ever since that time the world has looked towards the future with glossy stares and dreams of such a day. Turing, in 1935, was the pioneering mind behind the modern computer, though most people recognize the name based on the human computer test called the Turing Test. The test was introduced by Alan in a 1950 paper titled "Computing Machinery and Intelligence," and his goal was to "test if a machine's ability could exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human."

There's been much talk about how artificial intelligence will benefit society, but what about the potential impacts that AI has when the system is poorly designed and creates problems? This is a question several researchers and OpenAI, a non-profit artificial intelligence research company, tackled in a recent paper. The paper was written by researchers from Google Brain, Stanford University and the University of California, Berkeley, as well as John Schulman, research scientist at OpenAI. It's titled Concrete Problems in AI Safety, and it looks at research problems around ensuring that modern machine learning systems operate as intended. Researchers have started to focus on safety research in the machine learning community, including a recent paper from DeepMind and the Future of Humanity Institute that looked at how to make sure that human interventions during the learning process would not induce a bias toward undesirable behaviors in machine learning robots.

After several months of beefing up the Watson Health Unit, IBM on Wednesday announced it has recruited 16 other entities involved in the health care sector to from a new Watson Health medical imaging collaborative. The global collaborative aims to advance cognitive imaging in a range of medical specialties, from eye care to the treatment of heart and brain disease. The group plans to use Watson to analyze previously "invisible" unstructured imaging data, found in places such as radiology and pathology reports, as well as broad swaths of data collected from sources like population-based disease registries. "There is strong potential for systems like Watson to help to make radiologists more productive, diagnoses more accurate, decisions more sound, and costs more manageable," Nadim Michel Daher, a medical imaging and informatics analyst for Frost & Sullivan, said in a statement. "This is the type of collaborative initiative needed to produce the real-world evidence and examples to advance the field of medical imaging and address patient care needs across large and growing disease states."

Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. The Internet of Things genie is out of the bottle and growing at an accelerating pace. According to Gartner, 6.4 billion connected things will be in use worldwide in 2016, up 30 percent from 2015. This number will soar to more than 20 billion by 2020.

The method can also take an arbitrary Penalty object, i.e., an instance that implements methods projection regularization method (see file penalty.py) Whether to use a direct multiclass formulation (True) or one-vs-rest (False). Maximum number of steps to use during the line search. Constant used in the line search sufficient decrease condition. For example, eta 2. will decrease the step size by a factor of 2 at each iteration of the line-search routine.

In the previous part of this series, I began discussing the field of advanced evolutionary artificial intelligence. AI has seen some stunning advancements in recent times, but we are still quite a while away from achieving the holy grail - general artificial intelligence. That is, an AI so developed that it could perform any cognitive task that a human can. To achieve this, we must look further than applying neural networks to specific tasks, we must look for algorithms that evolve and mutate to adapt to situations. What we're talking about are genetic algorithms; effectively the mathematical counterpart to Darwinian evolution.

"Mutation, it is the key to our evolution. It is how we have evolved from a single-celled organism into the dominant species on the planet. This process is slow, and normally taking thousands and thousands of years. Until few weeks back, it never occurred to me in so many years that above Darwinian quote from my all-time favourite sci-fi movie hints something about one of the most compelling theories in computer science I ever came across. Yes, I said – "Computer Science".

Last week I wrote a post on a Russian Robot that escaped it's testing grounds. In an obvious attempt to cover up the fact that the Soviets Russians had lost control of their Sentient Artificial Intelligence, they said that it was just a small glitch in the system. Well, apparently it's more then a small glitch since the robot has escaped yet again, even after being erased. The Promobot IR77 has been fitted with artificial intelligence meaning that it learns from its experiences and surroundings and can remember everybody it meets. FYI, Promobot IR77 is just a code name for X-1 Hunter-Killer.

A longer version of this article appeared in Recode two years ago, before Amazon Echo, Tesla Autopilot, Google Photos and the AI hype. Since then, it has inspired a conference that is now a yearly event, and we're all becoming data natives (or at least really well-assimilated data immigrants). A few years ago, YouTube was abuzz with viral videos of toddlers pinching magazines with their fingers as they would an iPad. These children were heralded as members of a new generation of digital natives: People who grew up surrounded by computers, shaped by always-on technology and the Internet. We are now witnessing a new revolution -- that of data natives who expect their world to be "smart" and seamlessly adapt to their taste and habits.

Martin Wilson says artificial intelligence will have a huge impact on the future of digital asset management. Are you aware that AI (artificial intelligence) applications are already used in almost every industry? If not that's probably because in popular culture for a system to be artificially intelligent it needs to be able to'think', like we humans do. Many computer scientists prefer the term machine learning for exactly this reason. Often you won't read about'artificial intelligence' in a company's marketing blurb, even if their products use machine-learning technologies.