Elon Musk has hired a new director of AI research at Tesla, and it may signal a plan to rethink the way its automated driving works. This week, Musk poached Andrej Karpathy, an expert on vision, deep learning, and reinforcement learning, from OpenAI, a nonprofit that Musk and others are funding that's dedicated to "discovering and enacting the path to safe artificial general intelligence." Karpathy, who will apparently report directly to Musk, is a rising star in the world of AI, having studied at Stanford with Fei-Fei Li, a leading AI expert who is now the chief scientist of Google Cloud. Li is famous in tech circles for having developed a data set of images that helped inspire a breakthrough in machine vision. Many have pointed to Karpathy's expertise in computer vision as a key asset for Tesla, and that's true.
Rockets, electric cars, solar panels, batteries--whirlwind industrialist Elon Musk has set about reinventing one after another. Thursday, he added another ambitious project to the list: Future Tesla vehicles will run their self-driving AI software on a chip designed by the automaker itself. "We are developing customized AI hardware chips," Musk told a room of AI experts from companies such as Alphabet and Uber on the sidelines of the world's leading AI conference. Musk claimed that the chips' processing power would help Tesla's Autopilot automated-driving function save more lives, more quickly, by hastening the day it can drive at least 10 times more safely than a human. "We get there faster if we have dedicated AI hardware," he said.
Volvo is adjusting the timeline on its ambitious Drive Me autonomous program until it can find the right sensors. "The development in sensor performance and processor capabilities is going so much faster than we expected in 2013," program director Marcus Rothoff recently told Automotive News Europe. "Because advancements are being made at such a rapid pace, we want to make this decision as late as possible." In addition to the sensors that enable Level 4 autonomy (the car can drive itself, but a steering wheel and pedals are still present), the Swedish automaker is having issues with the wiring. Rothoff said that laying copper has been "a really huge" challenge that was unanticipated at the project's outset in 2013.
Another application of the technology could be important in ensuring that automated cars – particularly level 2 and 3 autonomous vehicles – can safely assess the condition of the driver (for example – are they awake and sober?) before switching from automated to manual driving mode. "Emotional intelligence is how you understand yourself and the people around you, and it is just as important as cognitive, or rational intelligence, to how we make decisions," el Kaliouby tells me. "People who don't have that often really struggle with operating effectively in a world where other people live." So while it's true that machines have been, by our own standards, largely sociopathic until now – unable to comprehend feelings and take them into account – that could be set to change. "I'm a big believer that in the next three to five years, this is going to be ubiquitous, and our devices will have'emotion chips' and a variety of sensors for the state of people around it – it's a natural evolution from the mouse and keyboard we used to use, to touch interfaces, and most recently the way devices have become conversational.
Whether they drive themselves or improve the safety of their driver, tomorrow's vehicles will be defined by software. However, it won't be written by developers but by processing data. To prepare for that future, the transportation industry is integrating AI car computers into cars, trucks and shuttles and training them using deep learning in the data center. A benefit of such a software-defined system is that it's capable of handling a wide range of automated driving -- from Level 2 to Level 5. Speaking in Tokyo at the last stop on NVIDIA's seven-city GPU Technology Conference world tour, NVIDIA founder and CEO Jensen Huang demonstrated how the NVIDIA DRIVE platform provides this scalable architecture for autonomous driving. "The future is surely a software defined car," said Huang.
Rinspeed has been dreaming up insane vehicles for years -- from scuba cars plucked from James Bond's garage to modded self-driving rides. Even if they never make it to the public, the concepts are at least fun to check out, and the Rinspeed Snap is no different. Essentially a modular vehicle in two parts, the Snap is made up of interchangeable pods that attach to a rolling chassis, which houses data-processing computers and the EV power train. When the latter starts ageing, you simply slide a new one under your existing pod, theoretically extending the lifecycle of the vehicle at a fraction of the cost of buying a new car. And, if you get bored of the top half, you can swap that out too.
Artificial intelligence is everywhere now. The auto experts at WheelArea have had to update their maintenance tips for car owners page with each advance in the auto industry, and over the years they say they've witnessed an incredible increase in vehicles' reliance on computers–including, recently, the arrival of A.I. assistants in onboard computers and GPS systems. Next up: self-driving cars, which are already being developed by several tech companies. Imagine the rigor involved with becoming a certified technician for self-driving systems; according to one program advisor, it's hard enough these days to become a certified automotive specialist, and that will likely become even more difficult with the increased complexity of on-board systems. The increased technological dependence in most modern vehicles' systems means that much more training and specialization for even a simple routine job, that more technicians are going back to school than ever before.
Artificial Intelligence (AI) is starting to change how many businesses operate. The ability to accurately process and deliver data faster than any human could is already transforming how we do everything from studying diseases and understanding road traffic behaviour to managing finances and predicting weather patterns. For business leaders, AI's potential could be fundamental for future growth. With so much on offer and at stake, the question is no longer simply what AI is capable of, but where AI can best be used to deliver immediate business benefits. According to Forrester, 70% of enterprises will be implementing AI in some way over the next year.
Automobile companies and technology firms are racing to deploy autonomous vehicles (AVs). But they could face one key obstacle: consumer distrust of the technology. Unnerved by the idea of not being in control--and by news of semi-AVs that have crashed, in one case killing the owner--many consumers are apprehensive. In a recent survey by AAA, for example, 78% of respondents said they were afraid to ride in an AV. Such numbers are a warning sign to firms hoping to sell millions of AVs, says Jack Weast, chief systems architect of Intel's autonomous driving group in Phoenix.