While running a digital marketing agency, Neerav Parekh regularly updated his clients on their campaign performance with reports and charts that were carefully put together. However, the clients were quickly snowed under the blizzard of data, and inevitably demanded that account managers personally visit them and take them through these reports. This was a laborious process and, rather than plod through it repeatedly, Parekh, a serial entrepreneur, turned to artificial intelligence (AI), the science of trying to make computers think and act like humans, for a solution. His product, Phrazor, is aimed at automating the process of interpreting data and communicating insights. Having used Phrazor for his agency, Parekh has now sought to extend the reach of his product.

Recently Elon Musk made internet headlines by claiming that the probability we live in "base reality" is one in billions. Instead, we are much more likely to be living in a historical ancestor simulation created by an advanced future civilization some 10,000 years from now. "The strongest argument for us being in a simulation, probably being in a simulation is the following: 40 years ago, we had pong, two rectangles and a dot," the SpaceX and Tesla founder said. "That is what games were. Now 40 years later we have photorealistic 3D simulations with millions of people playing simultaneously and it's getting better every year. And soon we'll have virtual reality, augmented reality, if you assume any rate of improvement at all, the games will become indistinguishable from reality." It is probably not Musk's most original idea.

BMW CEO Krueger has made the boldest promise of any automotive brand about selfSelf-Driving Cars and the Looming Privacy Apocalypse. When he inherited the post, the company had enjoyed 10 hugely successful years following the Strategy NumberCube26: Driving the next wave of innovation. Read more ... » One plan created by his predecessor, Norbert Reithofer. Krueger arrived right as BMW and the overall industry found itself at a crossroads. First, the tremendous growth in sales and profits in China has disappeared so much so that BMW was forced to provide financial relief for dealers who had never before experienced sluggish demand in new car sales.

Earlier this week, I found myself answering a question from a new colleague at Finning International that relates both to the research I do in the iSchool at the University of British Columbia, as well as the analytics, engineering & technology work that I lead at Finning. The questions were simple: 1) What is artificial intelligence? As I sat to reflect last evening, it dawned on me that taking time to craft a clear answer to these questions might be extremely beneficial for many. Analytics, data science, and predictive intelligence are hot topics in many communities and business areas. And yet, despite this interest, few folks I have talked to have a clear understanding of the history of the discipline; one, that frames much of the work currently going on within the space.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Way, way back in 2006, before artificial intelligence had any of today's momentum, Jules the robot made a big splash when videos of him conversing and appearing quite sentient hit the internet. Jules creator, as seen in the video, shipped him off to a university in the UK and since then people are demanding to know what has become of Jules? His trademark Frubber skin and expressions were a real achievement in the valley of uncanny and people fell in love with Jules. However, now those people are wondering what has become of Jules in the almost ten years since the making of the video below. Yes, we know Jules resides at an academy in the UK, but where are the new videos tracking his progress?

We are a team of scientists, engineers, designers, and developers with offices in London and Boston and together we've developed the DESKGEN platform for CRISPR genome editing. Founded in 2012, Desktop Genetics is venture backed by some of the UK's leading life science thought leaders and supports customers in the UK, USA, Japan, and beyond. As a Machine Learning (AI) Scientist, you'll work to translate the latest CRISPR research into software that powers the experiments of thousands of labs. The application of artificial intelligence to genomics is a rapidly evolving field currently at the forefront of research. The work is highly interdisciplinary and successful candidates will have experience or advanced training in aspects of computer science, data science, engineering, biology, statistics, mathematics, or machine learning.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

An Amazon warehouse is a flurry of activity. Towering hydraulic arms lift heavy boxes toward the rafters. And an army of stubby orange robots slide along the floor like giant, sentient hockey pucks, piled high with towers of consumer gratification ranging from bestsellers to kitchenware. Those are Kiva robots, once the marvel of warehouses everywhere. Amazon whipped out its wallet and threw down US 775 million to purchase these robot legions in 2012.

Japan is one of the world's most advanced countries in the field of artificial intelligence and robot industries. At the Group of Seven summit Japan hosted in May, world leaders looked amused and fascinated as they watched a robot performing at the International Media Center. At the same time, because of the technological advances and advent of robots, the leaders are concerned that many people in their countries will lose their jobs and cry out for assistance from governments. This kind of downside to innovation has always been a part of modernization. Innovation brings fundamental change, but the impact of AI will be on a far larger scale than ever before. The most visible sector in which robots are bringing considerable change in Japan is the carmaker industry.