Human brain bionics will make a major breakthrough
Human brain bionics is the creation of human beings. By defining the structure and working principle of the brain, it is possible to build a large human brain neural network composed of small neurons to realize the reconstruction of the human brain. Our brain is not isolated, we need to interconnect with the machine, and the other direction of human brain bionics is the human-machine interface. Through a series of technical means, the brain is connected to the machine, so that we can run with our brain and control machine.
Since 2013, there have been two major events in human brain bionics. In 2013, the EU, China, and the United States all proposed big plans. The research institutions in the United States and the United Kingdom also had significant technological advances in human-machine interfaces, allowing us to connect spacecraft through the brain. In 2014-2016, IBM, as a major technology company, released brain-like micro-processing chips, brain-like computers and other artificial neurons, a series of human brain bionic important products, marking a series of cutting-edge advances in human brain biomimetic technology. In 2016, the human-machine interface can make people with physical disabilities gain visual and tactile functions, which is a major development of human brain bionic technology. In 2017, brain-like chips and brain-like computers will have small-scale commercial operations and applications.
Machine learning will be applied in depth in areas with large data volumes and urgent needs.
The development of machine learning has three carriages, hardware, algorithms, and data. Hardware is the backbone of support, algorithm is the soul of machine thinking, and data is the nourishment of machine operation. Just as human beings are learning a series of textbooks or materials, the three carriages mark the continuous development of machine learning and form the most important paradigm of machine learning.
For the collection, labeling, analysis and processing of large-scale data, this is a statistical algorithm. It can be said that the most important feature of machine learning at present is that there is no data and no intelligence. We can look at the areas in which machine learning can be applied, such as in a specific area where data is concentrated on a large scale. On this basis, we can find the pain points of the industry, and which areas it needs to use technology. The IBM Watson Machine Learning Platform breaks through the important ways doctors used to diagnose cancer. Using the Watson platform to diagnose cancer patients, the Watson platform has studied large-scale cancer medical textbooks and cancer cases. By studying these data and materials, it provides accurate diagnosis and treatment, which is the most important for smart medical care. application. At present, the IBM Watson platform has entered the domestic hospital.
Intelligent voice assistant will become a breakthrough in the development of natural language understanding
It is mentioned that voice assistants have to mention natural language understanding techniques. The main application of natural language understanding technology is to help people and machines to communicate better. Machines are intelligent, but not isolated. They need to be integrated with people and society. This is the main aspect of natural language understanding technology application.
Intelligent voice assistant is a good application scenario, because the intelligent voice assistant has a wide penetration field and can connect all the data services in the society with the social population. Amazon, Apple, Microsoft, Samsung, and Baidu all launched their own voice assistants. We can learn about the weather conditions of the day through Apple's Siri, and we can immediately know what the temperature and smog index is today, and suggest how to dress. We don't need to use mobile phones and search engines to query a wide variety of information, and this information can only be obtained with one sentence of ours.
At present, voice assistants have applications in smart homes and assisted driving, and future voice assistants will become large platforms, large integrations, and large services. The voice assistant concentrates all the services including eating, drinking, swimming, purchasing, entertainment, travel, etc. on this platform. We only need some language communication, and we can get all the platform information without the need of the current hand operation. , touch operating system and app. Intelligent voice assistants may become the most important big platform in the future.
Machine vision will continue to penetrate into the production and living areas
The human eye is limited. The distance and precision of the eye are limited. How can we break through such limitations? Since the birth of mankind, various tools have been invented constantly. From the beginning of iron and bronze to the current computer information technology, this is to help humans break through the current visual limits and enable effective communication between humans and the natural environment.
The current machine learning technology is mainly used in the production and life fields, including assembly, high-precision inspection, quality inspection and product traceability in production, especially for small differences in parts. The auto parts we produce have high requirements for their high precision, and they can't be inferior. This is difficult for the human eye to distinguish. We need to test through the machine, and we need to eliminate some defective products and assemble some fine products into finished products. This is the most important application of machine vision. In the field of life, the most widely used is the driverless car, Baidu, Tesla are studying the driverless technology. Traditional imaging brings radar technology, using radar to control the surrounding environment. These visual technologies can use a series of means such as intelligent sensors and intelligent video monitoring equipment to sense changes in the environment, so that they can be analyzed through the platform of deep learning in the background. Provide the most important and correct decision support for the car. We see visual technology in heart surgery, video surveillance and face recognition, and the future will be the most important visual extension of humanity.
AR will take the lead in VR to enter the fast development lane
In 2016, there were several major events in the field of augmented reality and virtual reality. In August, Pokemon games became popular around the world, causing a lot of attention. This means that the virtual reality and augmented reality large-scale commercial expansion to the consumer-grade spring is coming. At the beginning of December, Magic Leap, the most famous augmented reality company in the United States, was caught in the news of false propaganda. All the special effects were produced by video companies, and all the doubts evolved into virtual reality and augmented reality.
The experience behind augmented reality and virtual reality is completely different. VR-virtual reality is completely isolated from physical space and requires an immersive experience. AR—Augmented reality is a fusion of the physical environment of the outside world. The virtual image is displayed in a physical environment where people and the physical environment are completely blended. These two technologies currently have certain obstacles, the equipment cost is too high, and the current good equipment needs about 3,000 US dollars. Such expensive products can not enter the ordinary people's home, and can not expand the user-level application. At the same time, the user experience needs to be improved. After wearing these devices, users may have some dizziness, which is also a question for the next development of our AR and VR.
Through the popularization of smart phones, AR and VR technologies are grafted on cheap products for development, and after proprietary devices reach a certain level, they will expand into better applications. From the development of Pokemon games, the integration of augmented reality and mobile phones, including wearable devices, is better, and we can wear glasses for augmented reality applications. Therefore, in 2017, augmented reality will once again surpass virtual reality. Through the integration of wearable devices and smart terminals, especially the convergence of mobile phones, the continuous enrichment of application content can be rapidly developed.
Blockchain accelerates platformization, open source, and integration
The block records the data according to the time window, and then connects the time blocks in a chain. The blockchain is different from the traditional database. The traditional database is a centralized connection, and the blockchain needs distributed access to reach a consensus when it is stored. When we want to tamper with the data, we need distributed nodes to confirm at the same time. One node is not sure that we can't tamper with the data. This is the biggest benefit brought by the blockchain technology. The credibility is very high and no intermediateization is needed. There is no need for a clearing center in the banking sector, and data can be stored through nodes. Ethereum cooperates with Microsoft, IBM open source, blockchain technology and Internet of Things, cloud computing, big data and other technologies. The security of IoT devices has great challenges, but as long as there is blockchain technology, the security of IoT data storage can be guaranteed. Under the premise of the integration of the two, IoT technology can be better developed.
Digital Twins will create an informational physical space for residents' lives
Digital twinning is a digital, networked, and intelligent mapping relationship between physical space and virtual space. It records the trajectory of an individual's life cycle in both physical and digital spaces. We can record or observe the operational characteristics of the material in cyberspace to predict future trends.
Digital twinning is an accurate mapping of physical space and virtual space. Digital twinning technology has entered the industrial level and has important applications in product design and simulation. Next, digital hygiene expands into the realm of life, and medical and educational and travel can build a digital predictive model of the individual's life cycle. As a researcher today, I will publish a research report and open several meetings. The digital prediction model will tell you whether it has an impact on health and will tell you whether you should reduce the workload. We can get more scientific guidance in the future, which is the most important application of digital hygiene technology in the life field, predictive record analysis and decision support throughout the life cycle.
The artificial intelligence industry ecology is accelerating
We now see that the industry and the industrial industry are completely different, and it is a building block innovation. In the past, large enterprises have done all the things in the industrial chain. Now the industrial chain needs division of labor and cooperation. All artificial intelligence enterprises can carry out division of labor and cooperation, collaborative innovation, and jointly promote the development of artificial intelligence industry.
The artificial intelligence industry chain is mainly composed of infrastructure, core algorithms, application platforms (open source platforms) and solutions. The three modes of upstream to downstream, downstream to upstream, and intermediate to both ends are creating artificial intelligence industry ecology from different paths. In the upstream to downstream mode, there are traditional chip companies Intel is developing artificial intelligence algorithms, and Science and Technology News Development develops artificial intelligence products. The middle to the two-end mode, there is a Google company open source platform, developed CPU algorithm, and a series of important applications. Many leading companies are expanding from their own end to other ports, and such expansion has a division of labor. In October of this year, the most important artificial intelligence companies in the United States, including Google and Facebook, formed an artificial intelligence industry alliance. They lobbied the country to carry out artificial intelligence legislation. This is the most important development trend we see in the artificial intelligence ecosystem relying on alliance cooperation.
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