The World Artificial Intelligence Conference (WAIC) kicked off in Shanghai recently, with over 500 companies and agencies participating in the event on the theme of “Intelligent World, Indivisible Community.” WAIC gathers the world’s top scientists to talk about the future challenges and breakthroughs of artificial intelligence. At the opening ceremony, leaders from the industry, academia, and research circles expressed their views on the development of AI, including examining the development of AI from the macro, pointing out the challenges and future trends, and proposing executable from the micro for the further development of AI. 

The 2020 edition of the WAIC showcased Shanghai’s endeavour to become a rising AI centre of global influence. The event included many talks, dialogues and announcements from government leaders, top scientists and well-known entrepreneurs. The main takeaway from the event was that AI is good for the common values ??of mankind that benefit the world and experts proposed new solutions for the development and application of artificial intelligence worldwide. 

For the business perspective, the event had Baidu CEO Robin Li Yanhong, Tencent Chief Operating Officer Ren Yuxin, Tesla CEO Elon Musk, United Nations Digital Cooperation High-level Group Co-Chairman Ma Yun. From Academia, there was Turing Award winner Andrew Yao Turing Award winner Yoshua Bengio. 

Baidu CEO Shares AI’s Three Development Stages

Speaking at the conference, Baidu’s CEO Robin Li said that AI development would go through three stages. As the artificial intelligence technology platform is open, more and more applications can be easily developed, benefiting all aspects of the economy and society. Li believes that the main operating system software in the future will not be an operating system based on PC or mobile phone, but an operating system based on artificial intelligence and a deep learning framework, which will undertake various intelligent applications on the top. In his view, AI development will go through three historical stages. 

As part of the first stage, the algorithms have seen rapid iteration and innovation. Through cloud computing and big data movement, it has gradually developed into a new technology platform. But technological exploration has not evolved into an industrial or economic benefit. The second stage involves the data transformation of the economy. With the expansion of the Internet, the effective data generated by humans has increased exponentially, and the development of cloud computing has provided the capabilities required for massive computing, plus the basic environment created by the general digitalisation of the economy and society and the Internet, and artificial intelligence. Finally, AI can begin to exert its magic in a wide range of economic fields, which is the full popularization of industry applications and commercialization.  

The third stage is the intelligence of society. Artificial intelligence will penetrate from the economic field to a wider social field. Intelligent cooperation and institutional innovation throughout society and the world will be the main characteristics of this stage. The final collaboration and reform will have a more profound impact on the economy and eventually enter an intelligent society.  

Robin Li also announced two small goals of Baidu: first, the number of intelligent cloud servers will reach 5 million by 2030. Second, the training of more than 5 million AI professionals for the whole society in the next five years. He believes that the current stage of artificial intelligence is also a stage that is prone to confusion. This is a necessary stage before most disruptive technologies accelerate their popularity, as experienced by the Internet around 2000. He believes that AI is a technological wave comparable to the industrial revolution, and will definitely change every industry.

Tencent’s Ren Yuxin Describes Three Development Areas of Artificial Intelligence

Tencent COO, Ren Yuxin, shared Tencent’s thinking and exploration in the field of artificial intelligence with three development areas. The first is the new growth. The normalization of the epidemic makes it very challenging to resume work and resume production. In order to ensure the smooth flow of logistics, capital flow and information, AI has played a major role. For example, Shanghai Youtu Artificial Intelligence Laboratory provided logistics company Sinotrans with OCR (Optical Character Recognition) technology instead of manually entering logistics documents. The Tencent security team helped small and micro enterprises obtain “zero-touch” online credit services. According to Yuxin, AI technology is deeply embedded in all walks of life and has become ubiquitous. The increasingly perfect new infrastructure and industrial Internet will bring new volume to all walks of life and bring new growth to economic development.

The second is gaming. Tencent is supporting Shanghai to build a global esports capital. Gaming provides an ideal experimental field for AI. In the course of AI development, the iconic AlphaGo was born in the ancient Chinese game scene of Go. After solving the Go problem, multiplayer tactical competitive games like MOBA have become an important scenario for us to study the complex decision-making capabilities of AI. At present, Tencent has tried to use the technical advantages of AI in the gaming field to build a simulation environment for car manufacturers to improve Test efficiency and safety of autonomous driving.

The third is the new generation. In Ren Yuxin’s view, the new infrastructure will inspire the wisdom of a new generation of young people, and the Industrial Internet is destined to become a stage where digital aborigines bring their dreams. In the first four months of this year, the number of digital jobs increased in Tencent-owned WeChat ecosystem by nearly 600,000, which has become a new stage for the employment of college graduates. Ren said that the AI world is waiting for the global young professionals to build together.

Musk: Tesla Is Very Close To L5 Autonomous Driving

At WAIC 2020, Elon Musk said he is very confident in the future L5 of autonomous driving and thinks it will be realized soon. “Tesla is very close to 5G autonomous driving. We can use Tesla’s existing hardware and only need to improve the software. I am confident that we will complete the basic functions of the L5 level this year.”

He believes that there is currently no underlying challenge to achieve L5 autonomous driving, but it is necessary to integrate the system and continue to solve many details. In his view, nothing is more complicated than reality, and any simulation is a subset of the complexity of the real world. Musk pointed out that cognition may be the weakest area of ??artificial intelligence. But in the future, cognition will perform better. He elaborated that we can think of AI as a game. As long as there are clear game rules, artificial intelligence will definitely play better than humans in any game, he said. 

Musk also explained why Tesla should develop more AI chips. He said that there are no systems with reasonable cost and low power consumption on the market. If we use a traditional GPU, CPU or other similar products, it will consume hundreds of watts of power, and the trunk will be occupied by the huge cooling system of the computer GPU. Moreover, it is important to know that energy consumption is important for the mileage of electric vehicles. He also revealed that Tesla cautiously launched the second system of the chip a few months ago. Musk also introduced the Tesla training system and said the company developed a new bus and cooling system for the development of more efficient computers so that visual data can be processed more efficiently.

Andrew Yao Spoke On The New Direction Of Artificial Intelligence

Andrew Yao, A.M. Turing Award Winner- 2000 and Professor and the Dean of Institute for Interdisciplinary Information Sciences at Tsinghua University, shared his insights on the current state of AI coming from academic research. According to Yao, the theoretical research done now will give us tremendous progress in the future. In his speech, he mainly talked about three points. First, academia is very important. The challenges facing AI can now be analysed through theory, find ways to find solutions. 

Second, AI is definitely an interdisciplinary industry, Yao stated. According to him, there are many examples to show that some of the huge results achieved in AI are often due to cooperation between disciplines that seem to be completely out of bounds. This may require decades of effort because if there are no research results achieved by scientists in other disciplines, it is impossible for AI to achieve such rapid development.

According to Andrew Yao, the connection between machine learning and topology, some concepts, technologies and complexity theories in topology can allow us to obtain a new theory in AI, which is impossible only by looking at AI. The connection between artificial intelligence and privacy, such as letting the results of calculations do not need to reveal who the data belongs to, or even the need to reveal the data, he believes that this goal can actually be achieved through cryptography, which is a new field of AI.

Yoshua Bengio’s Talk On Machine Learning To Empower Contact Tracing

Yet another A.M. Turing Award co-winner, Yoshua Bengio, presented a talk on how ML can be used for contact tracing. Yoshua Bengio said that the problem with standard data tracking is that it only considers the binary information about whether a person’s nucleic acid test is positive or not. According to Bengio, standard tracing (manual or digital) is binary and brittle. In reality, many clues should be integrated before taking a decision about the risk level of a person. This includes symptoms which are available many days before tests, age, biological sex, prior medical conditions, and risk level of all the contacts. ML can be used to predict the infection status probability distribution, given these clues. This provides early warning signals, well before standard tracing would raise a flag. These signals and other information from the app can empower manual contact tracers to make informed decisions and extend their reach. He proposed that the machine learning on mobile phones can be used to strengthen the tools of manual contact tracing and expand the prediction range.

The video recordings of the various talks and panels can be found here.

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