US chipmaker Nvidia Corp showed off on Monday a smaller and more efficient artificial intelligence computer for self-driving cars, saying it would power Baidu's mapping and autonomous vehicle technology. Chinese web services company Baidu will deploy Nvidia's new Drive PX 2 as its in-vehicle car computer for its self-driving system, Nvidia said in a press release as it unveiled the computer at the GPU Technology Conference in Beijing. As more carmakers develop plans for self-driving technology to roll out in their vehicles in the next decade or less, Nvidia is trying to lower the barriers to entry, providing powerful computers to help automakers enter the market. Earlier this month, Nvidia and Baidu announced a partnership to develop a full self-driving car architecture from the cloud to the vehicle using both companies' expertise in artificial intelligence (AI). Nvidia said its new Drive PX 2 computer uses 10 watts of power and is half the size of the original version, launched in January.

It's a problem we can all relate to: sitting in traffic and waiting for a green light. While waiting, you may have even pondered how you would try to improve traffic efficiency--surely there's got to be some way for everyone to get to work on time. But ponder no longer, because a team of engineers from Tsinghua University in China has handed the problem over to machines. The team's recent study makes use of deep reinforcement learning algorithms to optimize traffic signaling, and its promising results suggest there may be a way to arrive on time after all. Let's be clear: traffic is a complex problem to solve, and traffic control engineers have long worked on improving efficiency.

Artificial Intelligence (A.I.) is the most important thing on the planet right now. The potential of A.I. for both positive and negative disruption is absolutely huge. We need to get ready for humans and machines co-existing in the workplace. The challenge of A.I. presents is how we codify human behaviour into elements that can be performed by machines. Everyone needs to think through what is it that technology can do really well and really cheaply, and get the humans to focus on the stuff that the technology can't do.

IBM (NYSE: IBM) and the United States Tennis Association (USTA) today announced that IBM Watson is coming to the US Open Tennis Championships for the first time via a new cognitive concierge feature being piloted within the tournament's official mobile apps to enhance the fan experience at the Billie Jean King National Tennis Center. The US Open mobile app Guest Services companion will pilot a Watson-enabled discovery tool that allows fans to input natural language questions and receive immediate responses about a range of tournament topics, such as transportation and directions, food and drink options, on-site services and facilities, as well as other topics. By tapping into the Natural Language API from the Watson platform, the AI-infused app will enable fans to ask questions in natural language and get the information they need to plan and navigate their tournament experience. For example, fans can ask questions like, "Where can I get a soda?" or "Where are the taxi stands?" Due to Watson's machine learning capabilities, the app also continuously learns from previous interactions helping further refine its responses over time.

So what is machine learning? Put simply, it is the science of enabling computers to learn and take action without being explicitly programmed. This is achieved through complex algorithmic models applied to data. From this are derived data-driven predictions or decisions. What has this to do with information security?

Artificial Intelligence has always been a hot topic. The problem of creating a machine that can think, make its own decisions and at the same time lacks human limitations, has always aroused controversies. We all remember what the infamous Skynet has done to our planet in Terminator series, right? We can also recall this nasty feeling of a cold shiver running down our spines when Neo has woken up from the Matrix. After watching such films, we can wonder why our scientists want to build intelligent machines in a first place. One would ask: why putting humanity at risk?

Over the last few months we have seen NVIDIA's Pascal GPUs roll out among their consumer cards, and now the time has come for the Tesla line to get its own Pascal update. To that end, at today's GTC Beijing 2016 keynote, NVIDIA CEO Jen-Hsun Huang has announced the next generation of NVIDIA's neural network inferencing cards, the Tesla P40 and Tesla P4. These cards are the direct successor to the current Tesla M40 and M4 products, and with the addition of the Pascal architecture, NVIDIA is promising a major leap in inferencing performance. We've covered NVIDIA's presence in and plans for the deep learning market for some time now. Overall the deep learning market is a rapidly growing market, and one that has proven very successful for NVIDIA as the underlying neural networks map well to their GPU architectures.

Sure, the Google self-driving cars cruising around Mountain View, California and the Uber fleet expected to roll out any day now in Pittsburgh, Pennsylvania are expected to stop at red lights, avoid pedestrians and obey speed limits. Mountain View startup Drive.ai, which unveiled its technology recently, wants to focus on that aspect of self-driving cars. The company's artificial intelligence system uses software and hardware to make autonomous vehicles smarter, enabling them to not only navigate obstacles, but also to communicate both with people inside and outside the car. "Vehicles of the future will communicate transparently with us," co-founder and president Carol Reiley wrote in a news release, "they'll have personality, and they'll make us feel welcome and safe, even without a human driver." To that end, the startup plans to sell self-driving kits that include some sort of communication device that can be mounted on a car's roof.

Artificial intelligence is nothing new. Researchers have been trying to solve the puzzle of creating human-like intelligence in machines for decades. But it's only in recent years that they've started to make real progress towards realizing this incredible goal. Just in the last few years we've seen the emergence of chat bots, self- driving cars, smart appliances, and smarter search engines. They all rely heavily on artificial intelligence.

Artificial intelligence is nothing new. Researchers have been trying to solve the puzzle of creating human-like intelligence in machines for decades. But it's only in recent years that they've started to make real progress towards realizing this incredible goal. Just in the last few years we've seen the emergence of chat bots, self-driving cars, smart appliances, and smarter search engines. They all rely heavily on artificial intelligence.