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Jens Norskov, a professor at Stanford University and director of the SUNCAT Center, is also full of expectations for collaboration: "This is an excellent time to extensively promote the use of databases and machine learning technologies in the discovery of new materials. Our partnership can combine theory, computation, and experimentation, making unprecedented collaborative efforts. We are very excited about developing cutting-edge technologies for fuel cell catalysts

The attitude of the executives is positive, but Toyota Group President Akio Toyoda's attitude is likely to be anxious. Do you see the difference? For Tesla (and most other battery manufacturers), as they are part of the industry chain, they are more committed to commercializing technology as soon as possible. For example, the Tesla battery expert Jeff Dahn mentioned in the above article, his job is to prevent adverse reactions harmful to the positive electrode under high voltage conditions and achieve commercialization of new technologies as soon as possible. Subsequent mass production battery tests have shown that, After the new technology, the reaction between the electrolyte and the positive electrode is almost stagnant under high voltage, allowing high-performance NMC lithium-ion batteries to operate under high voltage. In other words, Jeff Dahn perfectly achieved his goal of investing technology in the industry.

The development approach of TRI is more like that of a university research institution than that of a commercial company. In addition to the battery material research project, TRI is also responsible for Toyota's autonomous vehicle project. 36 Kr has previously reported that, unlike the fully autonomous driving technology being developed in the industry, TRI focuses more on improving the human driving experience through artificial intelligence. Dr. TRICEOGillPratt has specifically explained the differences between two development directions: "These two research directions can be described as' parallel 'and' serial '. The former means that machines replace humans, while the latter is a way for machines to collaborate with humans, Toyota will adhere to the concept of "people-oriented" and maintain human driving pleasure. What we are concerned about is how to prevent autonomous driving from taking away driving pleasure, "he said." If autonomous driving can avoid accidents, it should also bring more driving pleasure

The path to achieving "machine human collaboration" like TRI is through artificial intelligence and deep learning technology. There is nothing wrong with the different development ideas. However, Toyota seems to be too conservative. On the second generation autonomous vehicle released by TRI in March 2017, Toyota carried seven laser special types (one 64 line, six 16 line), ordinary special types and GPS antennas on the front, rear, left and right sides, and three cameras on both sides of the front. Although the automatic driving industry tends to achieve accurate judgment of the road conditions around the vehicle body through the comprehensive judgment of the data of multiple sensors, that is, the principle of redundancy design, Toyota's solution gives the impression of "redundancy surplus". Both the high cost (the total value of all sensors is $123000) and the complex data fusion show that Toyota's autonomous vehicle is far from commercialization. To put it more bluntly, Toyota seems to have not figured out the sensor options for developing autonomous driving technology. Instead, it has started piling up hardware, at least giving the public an image of a "responsible international enterprise". But how can this plan be commercialized?

Today, Toyota, which is the first to announce the development of battery materials using artificial intelligence and machine learning technology, is once again taking an unusual path. Will Toyota break through this time, or will it once again fall behind temporarily? What's your opinion?

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