This article originally appeared on the International Business Times. Machines are capable of understanding speech, recognizing faces and driving cars safely, making recent technological advancements seem impressively powerful. But if the field of artificial intelligence is going to make the transformative leap into building human-like machines, it'll first have to master the way babies learn. "Relatively recently in AI there's been a shift from thinking about designing systems that can do the sort of things that adults can do, to realizing if you want to have systems that are as flexible and powerful and do the kinds of things that adults do, you need to have systems that can learn the way babies and children do," developmental psychologist Alison Gopnik, a researcher at the University of California at Berkeley, told International Business Times. "If you compare what computers can do now to what they could do 10 years ago, they've certainly made a lot of progress, but if you compare them to what a 4-year-old can do, there's still a pretty enormous gap."

Microsoft principal software development engineer Jennifer Marsman talked about the applications of machine learning at Microsoft's Ignite NZ conference. From teaching computers to make predictions to helping blind people "see", machine learning technology has already made incredible advancements in a short timeframe. Microsoft's Jennifer Marsman's interest is machine learning and helping to make the technology understandable to the average person. The Detroit-based principal software development engineer was in New Zealand last week for Microsoft's Ignite New Zealand conference, where she gave talks about applications of machine learning. It can be easy to let our imaginations run too wild when it comes to the future of technology, so Marsman to gave examples of machine learning's relevance in real life.

This is the text version of a talk I gave on November 11, 2016, at the Direction conference in Sydney. When John Allsopp invited me here, I told him how excited I was discuss a topic that's been heavy on my mind: accountability in automated systems. But then John explained that in order for the economics to work, and for it to make sense to fly me to Australia, there needed to actually be an audience. Let's start with the most obvious answer--the military. This is the Predator, the forerunner of today's aerial drones. Those things under its wing are Hellfire missiles. These two weapons are the chocolate and peanut butter of robot warfare. In 2001, CIA agents got tired of looking at Osama Bin Laden through the camera of a surveillance drone, and figured out they could strap some missiles to the thing. And now we can't build these things fast enough. We're now several generations in to this technology, and soldiers now have smaller, portable UAVs they can throw like a paper airplane. You launch them in the field, and they buzz around and give you a safe way to do reconaissance. There are also portable UAVs with explosives in their nose, so you can fire them out of a tube and then direct them against a target--a group of soldiers, an orphanage, or a bunker–and make them perform a kamikaze attack. The Army has been developing unmanned vehicles that work on land, little tanks that roll around with a gun on top, with a wire attached for control, like the cheap remote-controlled toys you used to get at Christmas. Here you see a demo of a valiant robot dragging a wounded soldier to safety. The Russians have their own versions of these things, of course. I imagine it asking you who you are in a heavy Slavic accent before firing its many weapons into your fleeing body. Not all these robots are intended as weapons. The Army is trying to automate transportation, sometimes in weird-looking ways like this robotic dog monster.

We introduce an Embarrassingly Parallel Search (EPS) method for solving constraint problems in parallel, and we show that this method matches or even outperforms state-of-the-art algorithms on a number of problems using various computing infrastructures. EPS is a simple method in which a master decomposes the problem into many disjoint subproblems which are then solved independently by workers. Our approach has three advantages: it is an efficient method; it involves almost no communication or synchronization between workers; and its implementation is made easy because the master and the workers rely on an underlying constraint solver, but does not require to modify it. This paper describes the method, and its applications to various constraint problems (satisfaction, enumeration, optimization). We show that our method can be adapted to different underlying solvers (Gecode, Choco2, OR-tools) on different computing infrastructures (multi-core, data centers, cloud computing). The experiments cover unsatisfiable, enumeration and optimization problems, but do not cover first solution search because it makes the results hard to analyze. The same variability can be observed for optimization problems, but at a lesser extent because the optimality proof is required. EPS offers good average performance, and matches or outperforms other available parallel implementations of Gecode as well as some solvers portfolios. Moreover, we perform an in-depth analysis of the various factors that make this approach efficient as well as the anomalies that can occur. Last, we show that the decomposition is a key component for efficiency and load balancing.

It's one thing to want to protect endangered animals, but another entirely to keep track of them. Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow. Cute they may be, but spotting them in large bodies of water is easier said than done. Since marine researchers want to do so to keep tabs on population sizes, conservation status, and their important habitat areas, that poses a bit of a problem. Fortunately, this is where Dr. Amanda Hodgson of Australia's Murdoch University comes in.

This talk was given at a local TEDx event, produced independently of the TED Conferences. The extraordinary, wonderful, and terrifying implications of computers that can learn Jeremy is the CEO of Enlitic, which uses recent advances in machine learning to make medical diagnostics faster, more accurate, and more accessible. The company's mission is to provide the tools that allow physicians to fully utilize the vast stores of medical data collected today, regardless of what form they are in - such as medical images, doctors' notes, and structured lab tests. He is a serial entrepreneur, business strategist, developer, and educator. He is also the youngest faculty member at Singularity University, where he teaches data science, and is a Young Global Leader with the World Economic Forum.

The UK's national security boss, Robert Hannigan, should come clean on surveillance and stop attacking technology companies, privacy experts have said. Intelligence agencies must use the debate sparked by Edward Snowden's surveillance revelations to overhaul their attitude to privacy and oversight, said the group speaking at Dublin's Web Summit in November. "What's urgently required is a real cultural shift amongst our politicians and among our civil servants in Whitehall as to the value of privacy: the fact that it's a public and social good, and it's a collective good as well," said Bella Sankey, policy director at civil liberties organisation Liberty. Sankey, speaking alongside the former MI5 intelligence officer and whistleblower Annie Machon, criticised Hannigan for his attack on technology companies, in which he claimed were "in denial" about the misuse of the internet by terrorists, and that "privacy has never been an absolute right". "Given everything we've learnt in the past 18 months, he chose not to address at all the very serious things that GCHQ stand accused of: blanket surveillance of the UK population with public knowledge and without parliamentary knowledge, [and] receiving warrantless bulk intercepts from the NSA on US and people around the world," said Machon.

Machines are capable of understanding speech, recognizing faces and driving cars safely, making recent technological advancements seem impressively powerful. But if the field of artificial intelligence is going to make the transformative leap into building human-like machines, it'll first have to master the way babies learn. "Relatively recently in AI there's been a shift from thinking about designing systems that can do the sort of things that adults can do, to realizing if you want to have systems that are as flexible and powerful and do the kinds of things that adults do, you need to have systems that can learn the way babies and children do," developmental psychologist Alison Gopnik, a researcher at the University of California at Berkeley, told International Business Times. "If you compare what computers can do now to what they could do 10 years ago, they've certainly made a lot of progress, but if you compare them to what a four year old can do, there's still a pretty enormous gap." Babies and children construct theories about the world around them using the same approach scientists use to construct scientific theories.

Populism is sweeping the nation, but it's likely just getting started. Donald Trump's win is a wake-up call that voters are angry with a system that's made middle-class jobs tougher to come by, and increased inequality. As pronounced as the trend already is, it's only just the beginning, experts say. Looming technological advances will wipe out more jobs, broadening the base of disenfranchised, unemployable and frustrated citizens. Meanwhile, elites with the skills to flourish in the digital economy will get richer.

It's been more than a decade since Jurassic Park hit theaters, but fans of the film have not yet given up hope that such a park could one day exist. Now, a Japanese firm is one step closer to making this a reality with an animatronic dinosaur park called Dino-A-Live where visitors see realistic replicas first hand. The theme park would contain full-sized dinosaur robots, and the firm set some of them lose in a Tokyo hotel to announce the park - to the horror of those in attendance. A Japanese firm announced an animatronic dinosaur park called Dino-A-Live where visitors see realistic replicas first hand. This fictitious Jurassic Park is the brainchild of Kazuya Kanemaru, who is the CEO of ON-ART Corp. Kanemaru and his team unveiled fully-painted man-operated robotic models of raptors, an allosaurus and a tyrannosaurus rex, in a performance at a hotel hall in Tokyo.