“The world is experiencing the third wave of artificial intelligence (AI), and this AI wave is very different from the Internet wave. The focus of the Internet wave was driven by business models, while this AI wave is driven by technology.” At the 2017 New Business Summit, Dr. Hongjiang Zhang, Venture Partner at Source Code Capital, shared his thoughts on the essence of this AI wave, as well as the challenges and opportunities that it will bring. Dr. Zhang believes that the machine learning, which forms the core of AI, has already shifted from traditional mathematical modeling to relying on big data. This wave of AI will bring more possibilities. By leveraging troves of big data from various industries and the reserves of AI talent, it is possible for China to lead the world in AI technology, which is an opportunity for Chinese start-ups. As for how AI will affect people’s lives, Dr. Zhang believes that we should be ready for AI to not only assist humans in the future, but also replace or surpass some functions, as some new AI algorithms are already equipped with “god’s view.” AI has already surpassed humans in the capabilities relating to perception, such as speech and image recognition. In cognitive capabilities, however, AI still has a long way to go. In his speech, Dr. Zhang also reminded founders and investors that as any company can brand itself as an AI company these days, it presents the risk of creating a bubble. If the company only has algorithms and a few experts, but does not own, or has difficulties obtaining data, and lacks application scenarios, then it will not grow in scale. The full transcript of Dr. Zhang’s speech is as follows: Mr. Feng, distinguished guests, good morning. It is my pleasure to be here at the Forum organized by 36Kr. I would like to spend some time today talking about the essence of the current wave of artificial intelligence, as well as the challenges and opportunities it will bring. 1. Data as the new religion If we look at the development of AI over the past 60 years, the core of the AI we talk about today is really a branch of AI – machine learning. More specifically, it is the technology of using neural networks to conduct machine learning, which is the technology of deep learning. What are the differences between this wave of AI and previous ones? I believe that the AI wave hitting China today is more aggressive than the ones hitting anywhere else in the world. The reason may be that AlphaGo has defeated humans for the first time and that has led us to gain a newfound respect for AI. The second wave of AI was from the mid-1980s to the early 1990s, but it disappeared so quickly that for a long time, people like me were embarrassed to say that we studied artificial intelligence. An important reason behind its quick disappearance is that although its neural network was very popular, it also died down quickly. There were two reasons: one was that the neural networks back then did not have the support of big data, and two was the lack of support for computational resources. Today, we not only have new deep learning algorithms, but more importantly, we are equipped with high quality, categorized big data and extremely strong computational resources. From a technology perspective, today’s deep learning is fundamentally different from previous AI methods, especially when comparing to the expert system methods. Today’s algorithms are in fact driven by data, and not solely reliant on the rules of thumb. Let us take a look at the two drivers of the current AI wave. First, Computational resources have grown by leaps and bounds in the past 30 years. In the development over the past few decades, we saw exponential growth in the performance of supercomputers and an exponential reduction in their unit price. Second, data, another important support for AI, has begun to explode. According to research by IDC, the data created by the entire human race will increase tenfold between 2013 and 2020, equivalent to an annual growth rate of around 40%. The data we generate every day has exceeded tens to the 19th power (or ten quintillion bytes). A large amount of today’s data is created by the Internet. This data is not only large in quantity, but has also been labeled. For example, your phone will record the time, location, and other information about a photo that you took. It is thanks to the explosive growth of this type of data that we can provide better training data for artificial intelligence. I want to share some of my own experiences. Today’s smartphones can take photos, recognize faces, and not only beautify these faces, but also tell you how many people are in the photo and who they are. That was my dream 20 years ago – to have a mobile device that can recognize the people in a photo. After 20 years of development, this was finally realized in smartphones. Why it took so long to achieve this is mainly due to the two drivers previously mentioned: computational resources and big data, plus the newest machine learning algorithms. When we first started to work on this problem in the 1990s, we only had a few hundred photos and a little over 100 people in our entire database. Only when industry giants like Google, Facebook, and Microsoft started to use hundreds of millions of photos to train deep neural networks, and use the method of deep learning to conduct the recognition (around five years ago), did we see an increase in the recognition rate and become able to recognize people in hundreds of millions of photos. So what I want to stress is, when the scale of your training data begins to increase, the accuracy of your learning should start to experience great growth as well. The more resources and data used in training, the...
