I believe that anyone who has seen the movie "Artificial Intelligence" was deeply impressed by the cute-looking, kind and soft-hearted robot, David, who longed for the love of human mother Monica. David was a robot made by a robot company that could love people. He replaced Monica’s son Henry, who is terminally ill and falls into a vegetative state. When Henry wakes up, David is faced with the situation of being destroyed. He turns into a real human boy, and seeks to gain the love of his mother, Monica. Robot David's persistent love makes everyone who has seen this movie stunned. With the development of artificial intelligence, humanoid robots are gradually moving from the screen to reality. Recently, Tesla CEO Elon Musk announced that he will release the humanoid robot "Optimus", also known as "Tesla Humanoid Robot", at Tesla Artificial Intelligence Day on September 30.” (Tesla Bot). "Optimus Prime" is 5 feet 8 inches (about 1.72 meters) tall, weighs 125 pounds (about 56.7 kilograms), has a load of 20kg (with an additional 5kg on the arms), and can travel at a maximum speed of 8 km/h. Musk said Optimus Prime can basically do anything humans don't want to do, such as perform some dangerous, repetitive or boring work. Musk himself has high hopes for this robot, and he even said that "Optimus Prime" will "change the world" and may be more famous than Tesla. Although the humanoid robot does not have the ability to love like David in the movie, it is still a major step towards the landing of the humanoid AI robot.
The development of humanoid AI robots involves technologies such as hardware machinery, sensors, chips and software AI artificial intelligence algorithms. AI artificial intelligence and intelligent algorithms are the soul of humanoid robots. These include technologies such as autonomous driving, visual navigation, voice interaction, and natural language processing. Integrating numerous technologies into a humanoid robot faces enormous challenges.
Hardware resources are limited, and algorithm integration is difficult
Since humanoid robots are similar in shape to humans and have a small space volume, both power support and chip hardware support are limited. No matter how good an algorithm is, it needs resources to run. In a limited resource environment, it is very challenging to make all the algorithms of the robot run in coordination with each other. In an interview with Lex Fridman, an MIT Ph.D. and a well-known YouTube technology blogger, Musk said at the end of last year that if you have a huge server room with 10,000 computing machines, it is not complicated to do a neural network, but now it is extracted to make it in the Running on a low-power humanoid computer or car, this is actually very difficult, with a lot of complex software work to do. Achieving all of this requires both real-world AI and very good manufacturing skills.
The robot's environmental adaptability is weak
The current artificial intelligence algorithms are based on the experience of various aspects of human society learned from big data, and guide robots to complete corresponding actions or tasks. Due to the limitations of data and the ever-changing real-world scenarios, humanoid robots need to respond reasonably to each scenario during the actual landing process, so as to avoid threats to human society. Therefore, the adaptation of the robot to the environment or the update of the robot's soul algorithm needs to be carried out continuously. In other words, humanoid robots need continuous learning, lifelong learning. in order to better serve mankind.
For the hardware side. The acceleration of hardware research and development is required. Humanoid robots involve the integration of multiple technologies such as automatic driving, visual navigation, and sensor technology. The mechanical industry chain mainly involves the core components of robots (servo motors, reducers, control systems, drives, etc.) and The research and development of algorithm running chips, and the development of these technologies determines the speed of the robot landing.
The algorithmic cornerstone of humanoid robots is data. To use humanoid robots in different scenarios, it is necessary to update and upgrade the algorithms inside the robot with data from different fields, so that the robot has the ability to adapt to different fields. This requires different vertical data. Vertical data needs a professional team to collect, clean and label. MagicData has vertical domain data of various scenes, including speech data in multiple languages and dialects, as well as text data for natural language processing and image data for computer vision, and can also customize annotation data according to customer requirements.
The essence of artificial intelligence is to mine a large amount of empirical information from massive data to guide robot learning. Compared with AI algorithms, data is the cornerstone of artificial intelligence.