Robot PK: Baidu “Apollo” Self-Driving Robot Vehicle vs Tesla Bot Robot
Robot PK: Baidu “Apollo” Self-Driving Robot Vehicle VS Tesla BOT Robot
The Concept of “Robot” is on FireRobin Li just released the Baidu “Apollo” self-driving robot vehicle at Baidu World Conference. Two days later, the Silicon Valley “maniac” on the other side of the ocean was also tacit to announce that Tesla was developing a Tesla BOT on Tesla AI Day.Baidu Robot VehicleRobin Li released the “car robot”, which looks like a car, can achieve L5 level unmanned automatic driving, equipped with an artificial intelligence dialogue system. Once an order is received, the “car robot” can take you to the destination.Musk’s humanoid robot is 1.73 meters tall and weighs 56.7 kilograms. It has a human hand, made of ultra-light materials, and can scan useful information. Its moving speed is 8 km / h, which looks like the robot in science fiction movies.On Tesla AI day, for the display of the Tesla BOT robot, Musk asked a dancer to “disguise” the robot. The dancer dressed in robot clothes, starting with mechanical action, and later turning into a smooth dance. In the conference, Musk introduced the concept of the robot. The prototype will not appear until next year.Tesla BotRobin Li’s Baidu “Apollo” self-driving robot vehicle was officially released at the Baidu World Conference on life. In terms of building the robot’s thinking and logic, the two guys have different perspectives. Robin Li’s is an AI robot built by an AI company, while Musk’s is an AI robot built by a car company.However, the underlying logic of R & D is based on artificial intelligence technology. In terms of core technology data processing, the driving ability of the Tesla robot is “L2 +” level. It is equipped with 7NM process AI chip Dojo D1 as the basis to train its L2 level automatic driving FSD. Baidu’s robot has a L5 automatic driving ability and is equipped with Baidu’s self-developed chip — 7NM AI chip Kunlun 2.Robin Li is also a strong believer in technology. Musk continues to innovate from cars to rockets and complete the industrial puzzle through high-tech products. Robin Li is ambitious, patient, and able to endure loneliness. From chip to car robot, step by step breakthrough, continuous high-intensity input.For the relationship between robots and people, the two “Heroes think alike”, that is, let technology serve people. Robin Li said Baidu’s “Apollo” self-driving robot vehicle “ is capable of automatic driving, intelligent assistant, loyal companion, and self-learning. The technologies and products made by artificial intelligence must serve and help people, instead of hurting and controlling them.Musk said that Tesla BOT is “made by and for the human world” and “friendly and non-threatening”. Tesla launched this robot in the hope that it can replace human beings to complete some boring, dangerous, and repetitive work. In the future, all the things that human beings don’t want to do will be handed over to the Tesla robot.Data, Algorithms, and Processing are Three indispensable Elements of AIData is the starting point. The performance of an AI system depends more on the training data than the code.Data Ranks the Second-Highest Obstacle in AI AdoptionAn Alegion survey reports that nearly 8 out of 10 enterprises currently engaged in AI and ML projects have stalled. The research also reveals that 81% of the respondents admit the process of training AI with data is more difficult than they expected before.It is not a unique case. According to a 2019 report by O’Reilly, the issue of data ranks as the second-highest obstacle in AI adoption. Gartner predicted that 85% of AI projects will deliver erroneous outcomes due to bias in labeled data, algorithms, the R&D team’s management, etc.The Data Limitations in Machine Learning Include But not Limited to:Data Collection:Issues such as inaccurate data, insufficient representatives, biased views, loopholes, and data ambiguity affect ML’s decision and precision. Especially during Covid-19, certain data has not been available for some AI enterprises.Data Quality:Since most machine learning algorithms use supervised approaches, ML engineers need consistent, reliable data in order to create, validate, and maintain production for high-performing machine learning models. Low-quality labeled data can actually backfire twice: during the training model building process and future decision-making.Efficiency:In the process of machine learning project development, 25% of the time is used for data annotation. Only 5% of the time is spent on training algorithms. The reasons for spending a lot of time on data labeling are as follows:The algorithm engineer needs to go through repeated tests to determine which label data is more suitable for the training algorithm.Training a model needs tens of thousands or even millions of training data, which takes a lot of time. For example, an in-house team composed of 10 labelers and 3 QA inspectors can complete around 10,000 automatic driving lane image labeling in 8 days.EndOutsource your data labeling tasks to ByteBridge, you can get the high-quality ML training datasets cheaper and faster!Free Trial Without Credit Card: you can get your sample result in a fast turnaround, check the output, and give feedback directly to our project manager.100% Human ValidatedTransparent & Standard Pricing: clear pricing is available(labor cost included)Why not have a try?Source:https://baijiahao.baidu.com/s?id=1708703504326707519