I first got speaking to Jason via Twitter and then we met at a few networking events. We quickly realised we shared an overtly cynical attitude to the vacuous tripe that emerges from Silicon Valley's startup culture, and want to resist the adoption of that culture in the Northern Ireland tech scene. When it comes to big data and machine learning, I know no one more qualified than Jason. He wrote a book about machine learning which has helped me immensely in coming to grips with the topic, even though I can't even begin to understand the mathematics behind it all. Like myself, Jason is not native to Northern Ireland, but he's been here so long he might as well be part of the furniture.

As manufacturers increasingly turn to industrial robots, workers in developing countries will be hit hardest, a new UN report warns. Developing countries could soon lose'about two thirds of all jobs' So far, automation has largely been introduced to automotive, electrical, and electronics industries, as shown on left. For jobs which involve performing predictable physical activities, the researchers say the feasibility of automation is roughly 78 percent. Machines equipped with artificial intelligence are ever creeping into the workforce, and for humans, this could soon mean job displacement and a'universal basic income,' according to Elon Musk (pictured) The views expressed in the contents above are those of our users and do not necessarily reflect the views of MailOnline. By posting your comment you agree to our house rules.

It is ALREADY too late to ban killer robots: Researchers say autonomous weapons'have and will become possible' University at Buffalo researchers claim'society is entering into a situation where systems like these have and will become possible.' Lethal armed robots which could target and kill humans autonomously should be banned before they are used in warfare, campaigners have said. No comments have so far been submitted. Why not be the first to send us your thoughts, or debate this issue live on our message boards. By posting your comment you agree to our house rules.

Using that information, water authorities or companies can target areas or homes where people are wasting water, and send specialized educational materials to let people know how they can cut down on water waste. Some of OmniEarth's customers have already started seeing results, with some reporting a 15 percent reduction in water use, just by using the conservation messages. Many people, Fentzke said, may not even realize that they're wasting water. There could be bad meters, leaks, or bad settings on automatic sprinkling systems that are contributing to the problem. "We've found it to be very effective in targeting people who may not even know that they're inefficient," Fentzke said.

With the elections done, let's take a look at one of the most prolific source of political tweets during the campaign period -- bots. Yes, you may well have been retweeting or replying to bots. This isn't so terribly novel, but notably, they can seem as passionate as you are, and they have been given an AI upgrade. A study published by researchers at the University of Southern California's Information Sciences Institute recently examined just how influential these bots were (and are). And according to the authors, Alessandro Bessi and Emilio Ferrara, their presence is profound.

Beleaguered action camera maker GoPro is recalling its new Karma drone just weeks after it launched, the company announced late Tuesday as many Americans were glued to coverage of the Presidential election. The recall affects all approximately 2,500 Karma units GoPro has sold thus far, representing a major blow as the holiday shopping season draws near. GoPro says it issued the recall after it was discovered that the Karma lost power during flight in "a very small number of cases." No injuries or property damage has been reported as a result of the malfunctions, the company says. Those who have purchased a Karma will receive a full refund.

Digital tools using machine learning to analyze a person's spoken or written words could be instrumental in aiding mental health clinicians in assessments determining whether that person is suicidal, researchers have found. A new study published in the journal Suicide and Life-Threatening Behavior found machine learning is 93 percent accurate in correctly identifying a suicidal person, and is 85 percent accurate in determining differential diagnosis of mental illness. The study, led by researchers at the Cincinnati Children's Hospital Medical Center, looked at 379 patients who were recruited from three different sites – two academic medical centers and a rural community hospital. "Death by suicide demonstrates profound personal suffering and societal failure," writes lead author Dr. John Pestian, who is also a professor of biomedical informatics and psychiatry at Cincinnati Children's. "While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers."

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. Thereby we will see that a neuron and a convolutional net are very similar information processing machines. While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century. This blog post is complex as it arcs over multiple topics in order to unify them into a coherent framework of thought. I have tried to make this article as readable as possible, but I might have not succeeded in all places.

As healthcare systems strive to realize IOM's vision for continuous improvement in care delivery, many are recognizing that they have outgrown their data management and reporting capacity. Those that have turned to new machine-learning approaches have found they can expand capacity and capabilities while reducing administrative burden on clinicians. Here's an example of how one health system used machine-learning tools to improve care delivery for intestinal surgery: Until recently, the health system's surgical services team used traditional methods of hospital data analysis to inform their creation of order sets, protocols, and provider and patient education materials spanning the pre-op, intraoperative and post-op phases of care. Then they applied a "machine intelligence" platform that pairs machine learning algorithms with topological data analysis (TDA)--a mathematical process that uses shape as an organizing principal for understanding complex data. By giving visible form to their data, the health system was able to replicate and validate years of analytical insights in a matter of days.

The idiom "never judge a book by its cover" warns against evaluating something purely by the way it looks. And yet book covers are designed to give readers an idea of the content, to make them want to pick up a book and read it. Good book covers are designed to be judged. And humans are quite good at it. It's relatively straightforward to pick out a cookery book or a biography or a travel guide just by looking at the cover.