On March 13, 2004, a gaggle of engineers and a few thousand spectators congregated outside a California dive bar to watch 15 self-driving cars speed across the Mojave Desert in the first-ever Darpa Grand Challenge. Before the start of the race, which marked the first big push toward a fully autonomous vehicle, the grounds surrounding the bar teemed with sweaty, stressed, sleep-deprived geeks, desperately tinkering with their motley assortment of driver less Frankencars: SUVs, dune buggies, monster trucks, even a motorcycle. After the race, they left behind a vehicular graveyard littered with smashed fence posts, messes of barbed wire, and at least one empty fire extinguisher. What happened in between--the rush out of the starter gate, the switchbacks across the rocky terrain, the many, many crashes--didn't just hint at the possibilities and potential limitations of autonomous vehicles that auto and tech companies are facing and that consumers will experience in the coming years as driverless vehicles swarm the roads. It created the self- driving community as we know it today, the men and women in too-big polo shirts who would go on to dominate an automotive revolution. In 2001, eager to keep soldiers away from harm in combat zones, the US Congress demanded that a third of the military's ground combat vehicles be uncrewed by 2015. But defense industry stalwarts weren't innovating quickly enough on the sensor and computing technologies that would enable autonomous driving.

Autonomous cars have been a staple of science fiction for years, appearing in films like I, Robot, Demolition Man and Minority Report. Google is nearing the final stages of testing for its autonomous car programme, Tesla drivers can enjoy an'Autopilot' feature for hassle-free motorway driving, and Pittsburgh residents can hail an Uber that drives itself. But how do driverless cars work? When can we expect to try one out for ourselves? We answer all these questions, and more, below. The tech giant finally acknowledged the truth in rumours that it was building driverless technology in June, when Cook told Bloomberg that it was "a core technology that we view as very important". But he declined to give a steer on how the tech would manifest itself in Apple products. Yesterday he painted a clearer picture of its potential on a conference call following the company's quarterly results.

With no significant atmosphere or particles in the air to scatter sunlight, light on the moon is distributed much differently than it is here on Earth, giving rise to extreme dark patches offset by ultra-bright regions. This phenomenon, coupled with the presence of moon dust, presents a challenge for future lunar rovers and even human exploration, according to NASA. To work around this, scientists have created a'lunar sandbox' that simulates the conditions on the moon, allowing them to develop algorithms that can guide their robots safely around the environment. The Lunar lab is a 12-foot square sandbox in which researchers build the moon's terrain using statistically-generated features based on spacecraft observations. It contains eight tons of the human-made lunar soil simulant JSC-1A.

Amazon has a problem, and that problem is humans. Amazon needs humans, lots of them. But humans, as we all know, are the most unreasonable part of any business, constantly demanding things like lights and air. So Amazon has turned to robots (over 100,000 of them) for doing tasks like moving things around in a warehouse. But it's proving to be much more difficult to get the robots to do some other tasks.

We introduce a notion of algorithmic stability of learning algorithms---that we term \emph{argument stability}---that captures stability of the hypothesis output by the learning algorithm in the normed space of functions from which hypotheses are selected. The main result of the paper bounds the generalization error of any learning algorithm in terms of its argument stability. The bounds are based on martingale inequalities in the Banach space to which the hypotheses belong. We apply the general bounds to bound the performance of some learning algorithms based on empirical risk minimization and stochastic gradient descent.

The mystery behind the disappearance of Malaysia Airline Flight MH370 continues with no closure for the families of 239 people who were on board the jet. On Monday, Malaysia Airline's CEO Peter Bellew told The Australian newspaper that advances in science and technology, including artificial intelligence, can help pinpoint the resting place of the missing jet. The Boeing 777-200 went missing March 8, 2014, while on its way from Kuala Lumpur to Beijing. After over three years of search in a remote part of the southern Indian Ocean where the plane was believed to have crashed, no concrete clues as to what happened to the jet have been yielded. The multimillion-dollar hunt for MH370 was suspended this year after search vessels failed to find it.

Along with other tech trends, Natural Language Processing became another buzzword in the past years. But not everyone really understands what NLP is and how it can be used to improve efficiency of the process and impact your business in a positive way. In this article I will be briefly explaining what natural language processing is, how it is used, a few benefits on-site search get from doing it and I will mention a some cool startups that are doing natural language processing today. Let's start with the basics. Natural language processing (NLP) is the ability of a computer program to understand human speech as it is spoken.

In terms of the future of smart machines, the Internet of Things (IoT), and artificial intelligence (AI), it more or less comes down to "Like It or Not, This Is Happening." The quoted statement is a section in the first chapter of the book What to Do When Machines Do Everything from Cognizant Technology Solutions. "What to Do When Machines Do Everything" offers deep insight on how emerging technologies like artificial intelligence and the Internet of Things will change our labor force and production industries. The authors do not sugar-coat the inevitable future. In chapter one, they state how this next stage of technology and business is the same as what we have experienced in the past.

Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding model for hierarchical relation learning, with an aim towards improved performance on text data for commonsense knowledge representation. Our first model jointly learns ordering relations and non-hierarchical knowledge in the form of raw text. Our second extension exploits the partial order structure of the training data to find long-distance triplet constraints among embeddings which are poorly enforced by the pairwise training procedure. We find that both incorporating free text and augmented training constraints improve over the original order-embedding model and other strong baselines.

Cartman – a budget-priced robot from Australia – has triumphed in an annual contest to create a machine that can identify, pick up and stow warehouse goods. The bot was designed from scratch to take part in 2017's Amazon Robotics Challenge and used a radically different design to past winners.