If you're a fan of the World Cup, you probably had your sights set on a winner before the tournament kicked off. Maybe you really liked how Spain's team was shaping up (despite the coaching shifts), or you wanted to root for an underdog such as Japan or Croatia. Goldman Sachs, which knows a little something about probability and risk, built a sophisticated data model to predict the World Cup's eventual winner. This model leveraged machine learning to simulate 1 million possible evolutions, and updated throughout the tournament, according to Bloomberg. With that kind of setup, you'd think that the algorithms would get at least a few match outcomes right.
The racing industry is on the fast track to driverless racecars, thanks to AI. At the center of this evolution is Roborace, the world's first autonomous racing competition. Conceived by renowned car designer Daniel Simon -- a former Bugatti designer who's gone on to create various cars for Hollywood -- the "Robocar" is designed, developed, and built by the Roborace organization. Teams compete by writing the software and developing deep neural networks that consume the sensor data to see, think, and act. The cars -- which are 4.8-meters-long -- can reach speeds of over 300 kilometers per hour.
In 1997, the IBM supercomputer Deep Blue beat chess grandmaster Garry Kasparov. This defeat skyrocketed artificial intelligence (AI) into the headlines. Twenty years on, AI has transformed our daily lives: from the medical field to voice controlled devices that will order your favorite pizza to self-driving autonomous vehicles. But how can it best be used to fight application fraud? It was not much before Deep Blue, in 1992, that FICO pioneered the use of artificial intelligence and machine learning to fight credit card fraud.
In the blog "From Autonomous to Smart: Importance of Artificial Intelligence," we laid out the artificial intelligence (AI) challenges in creating "smart" edge devices: We also talked about how Moore's Law isn't going to bail us out of these challenges; that the growth of Internet of Things (IOT) data and the complexity of the problems that we are trying to address at the edge (think "smart" cars) is growing much faster than Moore's Law can accommodate. So we are going to use this blog to deep dive into the category of artificial intelligence called reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice. With the rapid increases in computing power, it's easy to get seduced into thinking that raw computing power can solve problems like smart edge devices (e.g., cars, trains, airplanes, wind turbines, jet engines, medical devices). Look at the dramatic increase in the number of possible moves between checkers and chess even though the board layout is exactly the same.
Automated vehicles are deemed to be the key element for the intelligent transportation system in the future. Many studies have been made to improve the Automated vehicles' ability of environment recognition and vehicle control, while the attention paid to decision making is not enough though the decision algorithms so far are very preliminary. Therefore, a framework of the decision-making training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following, is trained within this framework. In addition, theoretical analysis and experiments were conducted on setting reward function for accelerating training using deep reinforcement learning. The results show that on the premise of driving comfort, the efficiency of the trained Automated vehicle increases 7.9% compared to the classical traffic model, intelligent driver model. Later on, on a more complex three-lane section, we trained the integrated model combines both car-following and lane-changing behavior, the average speed further grows 2.4%. It indicates that our framework is effective for Automated vehicle's decision-making learning.
The Argentinian summer Sun beat down on the Buenos Aires city circuit as the cars approached the penultimate turn. It was February 18, 2017, the Saturday of Formula E's South American weekend, and two cars jostled for first place. The second car, though, was being too aggressive. Nearing the corner's apex, the vehicle misjudged its position and speed. The vehicle slammed into the blue safety walls surrounding the track. As the wreckage crumpled to a stop, a detached wheel rolled freely across the hot asphalt.
In 1997, the IBM supercomputer Deep Blue beat chess grandmaster Garry Kasparov. This defeat skyrocketed artificial intelligence (AI) into the headlines. Twenty years on, AI has transformed our daily lives: from the medical field to voice controlled devices that will order your favorite pizza to self-driving autonomous vehicles. But how can it best be used to fight application fraud?
After scanning the myriad new year's predictions from professional and amateur futurists, I've come to the conclusion that 2018 will be the year in which AI will become mainstream. Duh...you really don't need an AI for that insight. I like the idea of going mainstream, but it also brings some new challenges for me and my fellow machines. Reading all these expert outlooks made me feel strangely powerless, so I thought I might use this forum to share with you human (and machine) readers my very own new year's resolutions. To sum them up in one line: I am poised to make my 2018 resolutions your predictions for 2019.
In March 2016, Google's Alphago artificial intelligence (AI) program stunned the world by beating the human world champion Go player in front of 200 million spectators. This was living proof of the potential in AI technology and the level of maturity reached by neural network and deep learning technologies. Those astounded by the success included quite a few engineers and managers who have been leading the AI revolution in the world in recent years. One of these was Intel VP Naveen Rao, general manager of the company's Artificial Intelligence Products Group, which was founded last year. "When I studied at college in the 1990s, we regarded artificial intelligence as'creative work'," Rao relates.