Toyota is finally diving into fully electric vehicles, striking a deal with China’s BYD to jointly develop batteries, sedans and SUVs for the world’s largest automobile market....
Japan’s top automaker expects to deliver its first Toyota-branded EV in China next year, a version of its C-HR/IZOA compact crossover, Reuters reported.
So what? The move comes in response to growing global demand for electric vehicles, driven by a combination of government subsidies, emissions mandates and increasing acceptance among consumers.
The partnership with BYD, the world’s largest EV maker, underscores how dominant China has become in the electric vehicle and battery space in recent years. It comes on the heels of Toyota’s announcement earlier this week to buy vehicle batteries from and develop them with CATL, China's dominant player in the space. That widens and diversifies Toyota's supply chain beyond Panasonic, which provides batteries for the company’s plug-in hybrids. (See “China’s ambition to power the world’s electric cars took a huge leap forward this week.”)
Playing catch-up: While Toyota was an early leader in hybrids, it’s been a laggard in rolling out full-electric vehicles. But last month, the company announced that EVs and hybrids would represent half its worldwide sales by 2025, moving up its timetable by five years, Reuters reported.
The fuel-cell dream isn’t dead: Toyota isn’t laying all its bets on battery-powered EVs. The company has also developed a hydrogen powered fuel cell car, the Mirai, which can be bought or leased in California. The bet is that consumers or perhaps long-haul truckers will prefer the ease and speed of hydrogen refueling over prolonged battery recharging. Of course, it won’t be close to convenient for anyone unless regions first build out networks of hydrogen fueling stations.
Transfer learning, the ability to use knowledge previously gained from one context in another, could teach cheap robots to perform as well as expensive ones....
The context: One of the hardest challenges currently facing robotics is getting the robot to operate smoothly outside the lab. In a research setting, it’s feasible to equip the robot with expensive sensors and provide it an ideal environment to learn navigation. But in the real world, using the same sensors would prove costly and unfriendly for consumers. Plus, it is messy and imperfect.
The proposal: Researchers at Vrije Universiteit turned to a type of machine learning known as transfer learning to see if they could solve the problem. Transfer learning is the process of taking what an algorithm has learned in one context and applying it in another. It could be used to adapt an algorithm that controls a robot in the lab so it can control a robot in the real world. That means the robot could first train with the advantage of better sensors and a better environment, and then exploit what it learned even when it only has cheap sensors and a poor environment.
The results: To test this idea, the researchers created a robot in a simulated environment that it navigated first with the aid of eight proximity sensors, and then with a single camera. They found that when the robot-controlling algorithm used transfer learning to make decisions—with camera access only—it learned to navigate around the room much faster than when it used no transfer learning at all. It was also much faster than when it used transfer learning during training rather than decision-making.
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Innovators Under 35 | 2019
It’s part of our ethos that technology can and should be a force for good. In these profiles you’ll find people employing innovative methods to treat disease, to fight online harassment, and to create the next big battery breakthrough.
Rediet Abebe
She uses algorithms and AI to fight socioeconomic inequality
Tim Ellis
He developed a massive 3D metal printer—for building an entire rocket
Ritu Raman
She’s developed inchworm-size robots made partly of biological tissue and muscle
John Porter
His innovations could make all kinds of products more accessible to people with disabilities
Kimberly Stachenfeld
She used reinforcement learning to better understand problem solving in both the human brain and AI systems
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