Tesla is one of those companies that people love to love. The electric-car maker's story is one of innovation and genius, with a dose of erudite bravado coming from its intrepid CEO, Elon Musk. Musk has championed Tesla's technologies, including the driver-assist feature called Autopilot. Much has been said about Autopilot's virtues -- its ability to keep the car in one lane, avoid collisions and use cameras and radar to detect its surroundings -- but the technology is not perfect. Japanese automakers, by comparison, are unwilling to follow Tesla's aggressive strategy of getting such features into drivers' hands quickly.

In the not-so-distant future, children will be amazed by stories that we allowed relative strangers to steer one tonne machines, relying on human skill and instinct over the capabilities of computers. The field of artificial intelligence has a transformative capability over many things that govern our daily lives. Consider self-driving cars: about 33,000 people are killed in automobile accidents in the US every year and most fatalities can be traced back to humans. Companies across technology and automobile industries are pouring millions of dollars into using AI to drive new breakthroughs in this field. Already, cars by Tesla have limited self-drive capabilities and we will soon see this rapidly increasing.

Note: The title of this article is a play on the term "Rube Goldberg machine". Acccording to Wikipedia, a Rube Goldberg machine is a contraption, invention, device, or apparatus that is deliberately over-engineered to perform a simple task in a complicated fashion, generally including a chain reaction. Keep that in your mind. Everyone (that cares about ML) knows about supervised / unsupervised / semi-supervised learning pipelines. I have now come across an entirely new class of ML pipelines that I shall call "Rube Goldberg Machine Learning" pipelines.

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.

JUDY WOODRUFF: Today, the Obama administration revealed new information that sheds light on the reality of modern warfare, the number of civilians accidentally killed in U.S. airstrikes. JOHN YANG: Today's release is the first time the White House has said how many terrorists and innocent civilians it believes have been killed by airstrikes, including by drones. Between 2009 and 2015, the administration says it launched 473 airstrikes in Pakistan, Yemen and Africa. It estimates that as many as 2,581 combatants, and as many as 116 noncombatants were killed. Now, these numbers do not include airstrikes in Iraq, Afghanistan or Syria, what the administration calls areas of active hostilities. A new executive order has also been issued, with the aim of decreasing the number of civilian deaths.

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.

Trader Navinder Singh Sarao, who is resisting market manipulation charges, at Westminster Magistrates' Court In Robert Harris's 2011 novel The Fear Index a secretive hedge fund builds a computer capable of making its own trading decisions. Gobbling up information, the machine starts to confuse its human creators by building huge stakes and making a handsome profit from a market panic. As they assess the outcome, one of the protagonists notes: "The beauty of it is that it was but 0.4 per cent of total market volatility. No one will ever notice, except us." As markets increasingly rely on computer algorithms, reality is imitating fiction: artificial intelligence is becoming a bigger part of investing and it is also helping regulators ensure that traders do not get away with bad behaviour.

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.