In 2016, artificial intelligence will shine. In 2017, China may become the main force in the artificial intelligence industry. However, the hype of the “spiritualization†needs to be vigilant. Let us take a look at what investments are worth focusing on. The development of artificial energy and machine learning in 2016 is a year in which the development of the main military industry in China or AI need to be vigilant. The MIT Technology Review released a forecast for the development of artificial intelligence in 2017. , Technically optimistic about reinforcement learning, generation of confrontational networks, and emphasis on speech recognition in applications. When it comes to China, this year may be the main force of the artificial intelligence industry, but “hybrid†speculation needs to be vigilant. The 2016 that was just in the past is the year of the outbreak of artificial intelligence and machine learning, but 2017 will present more content to the world. Let's take a look at which five areas are worth looking forward to... Enhance learning AlphaGo's victory over Li Shishi is not only the historic success of the artificial intelligence industry, but also the depth of reinforcement learning technology is known and shines. Depth-enhanced learning is not a process in which a machine learns through a program or a set of cases. It is learned through experimental design and positive reinforcement. The concept of positive reinforcement has existed for decades (such as biology, education, etc.), and its perfect combination with deep neural networks makes it possible for machines to complete the high-burning task of playing chess. Through continuous experiments and analysis of the previous game of chess, AlphaGo explored a way to become a master of Go. The next step for reinforcement learning is to apply it to more real life scenarios. Several recently released simulation environments should motivate this trend to help establish algorithms that allow machines to acquire more skills through reinforcement learning. 
In 2017, we may see an attempt to strengthen learning in the areas of autonomous driving and industrial robotics. Google claims to have applied deep reinforcement learning to optimize its own data center, but this program is still in the experimental stage, and it also takes a lot of time to simulate, so it is still in the wait and see. "Duel" Neural Network Architecture At the NIPS2016 conference in Barcelona, ​​a new type of machine learning tool was highlighted, called Generation Network. This achievement was invented by OpenAI scientist Ian Goodfellow. Generating a countermeasure network (GAN) consists of a network that is trained to generate new data and another network that is used to distinguish between accurate data and erroneous data. When these two networks work simultaneously, they can produce very realistic synthetic data. This method can be used to generate physical scenes for computer games, make pixelated videos clearer, or make designs more stylish. Yoshua Bengio is a world-class expert in machine learning and a doctoral supervisor at Goodfellow University in Montreal. He said at this academic conference that the generation of confrontational networks enables computers to learn through untagged data. Getting rid of the mark is considered the key to making the machine smarter. The outbreak of artificial intelligence in China in 2017 may be the year when China began to evolve into a major force in the artificial intelligence industry. Chinese tech companies no longer stop plagiarizing foreign companies, but are actively making significant strides toward artificial intelligence. Baidu has established its own artificial intelligence laboratory for some time, and has made achievements in areas such as voice recognition, natural language processing, and advertising optimization. Other giant companies are also aligning with Baidu: Tencent began recruiting talents after setting up its own AI lab in 2016; Didi is also planning to set up a lab for research and development of unmanned vehicles. Chinese investors are now paying close attention to entrepreneurship in the artificial intelligence field and spending large sums of money. The Chinese government has also released a strong signal that it is committed to investing 15 billion U.S. dollars by 2018 to jointly promote the development of artificial intelligence. Language Recognition If you ask an AI researcher what is the next goal that needs to be overcome, they will most likely speak language-related. Because the advancement of language and image recognition can promote machine analysis and generate your own language. The realization of machine language is a long-term goal. People are full of delusions and expectations for the interaction and communication between machines and humans. Better language understanding can make the machine more useful, but considering the complexity, subtlety, and infectiousness of language, the challenges faced by the scientific community are enormous. Don't expect to have any substantial conversation with your mobile phone in a short period of time, but there are already many visible developments and 2017 will continue to bring good news in the language field. The counteraction of the tune In addition to some real scientific and technological advances, 2016 has also experienced some "out of the air" speculation. Although many people have enough confidence in the technological value of AI, the reports around AI are overwhelming and out of control. Some AI researchers are inevitably harassed frequently. NIPS's academic seminar was held at the opening conference for a fake company called RocketAI, in order to ironically confuse many academic circles around AI's impractical and blindly exaggerated phenomenon. Although the effectiveness of this activity itself is open to question, the problems it reflects are real. One of the most real problems is that if technological breakthroughs are not as fast as imagined, then the so-called “spiritualism theory†will lead people to the edge of disappointment, and you will see a large number of startup companies with high valuations dying. The source of investment is drying up. Maybe 2017 will reflect a number of “spiritualized†recoils, and it may not be a bad thing to return to the original.
In 2017, we may see an attempt to strengthen learning in the areas of autonomous driving and industrial robotics. Google claims to have applied deep reinforcement learning to optimize its own data center, but this program is still in the experimental stage, and it also takes a lot of time to simulate, so it is still in the wait and see. "Duel" Neural Network Architecture At the NIPS2016 conference in Barcelona, ​​a new type of machine learning tool was highlighted, called Generation Network. This achievement was invented by OpenAI scientist Ian Goodfellow. Generating a countermeasure network (GAN) consists of a network that is trained to generate new data and another network that is used to distinguish between accurate data and erroneous data. When these two networks work simultaneously, they can produce very realistic synthetic data. This method can be used to generate physical scenes for computer games, make pixelated videos clearer, or make designs more stylish. Yoshua Bengio is a world-class expert in machine learning and a doctoral supervisor at Goodfellow University in Montreal. He said at this academic conference that the generation of confrontational networks enables computers to learn through untagged data. Getting rid of the mark is considered the key to making the machine smarter. The outbreak of artificial intelligence in China in 2017 may be the year when China began to evolve into a major force in the artificial intelligence industry. Chinese tech companies no longer stop plagiarizing foreign companies, but are actively making significant strides toward artificial intelligence. Baidu has established its own artificial intelligence laboratory for some time, and has made achievements in areas such as voice recognition, natural language processing, and advertising optimization. Other giant companies are also aligning with Baidu: Tencent began recruiting talents after setting up its own AI lab in 2016; Didi is also planning to set up a lab for research and development of unmanned vehicles. Chinese investors are now paying close attention to entrepreneurship in the artificial intelligence field and spending large sums of money. The Chinese government has also released a strong signal that it is committed to investing 15 billion U.S. dollars by 2018 to jointly promote the development of artificial intelligence. Language Recognition If you ask an AI researcher what is the next goal that needs to be overcome, they will most likely speak language-related. Because the advancement of language and image recognition can promote machine analysis and generate your own language. The realization of machine language is a long-term goal. People are full of delusions and expectations for the interaction and communication between machines and humans. Better language understanding can make the machine more useful, but considering the complexity, subtlety, and infectiousness of language, the challenges faced by the scientific community are enormous. Don't expect to have any substantial conversation with your mobile phone in a short period of time, but there are already many visible developments and 2017 will continue to bring good news in the language field. The counteraction of the tune In addition to some real scientific and technological advances, 2016 has also experienced some "out of the air" speculation. Although many people have enough confidence in the technological value of AI, the reports around AI are overwhelming and out of control. Some AI researchers are inevitably harassed frequently. NIPS's academic seminar was held at the opening conference for a fake company called RocketAI, in order to ironically confuse many academic circles around AI's impractical and blindly exaggerated phenomenon. Although the effectiveness of this activity itself is open to question, the problems it reflects are real. One of the most real problems is that if technological breakthroughs are not as fast as imagined, then the so-called “spiritualism theory†will lead people to the edge of disappointment, and you will see a large number of startup companies with high valuations dying. The source of investment is drying up. Maybe 2017 will reflect a number of “spiritualized†recoils, and it may not be a bad thing to return to the original.
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