By super nervous
On February 11, President Trump signed an executive order outlining THE United States’ ai development initiatives, which emphasized that the United States must and must take a leading position in the field of ARTIFICIAL intelligence worldwide.
After that, American technology leaders began to ridicule: “They do not welcome American talents to study in the United States and weaken foreign technology enterprises. How can you lead others?”
The executive order comes less than a week before Trump’s second State of the Union address, a measure of its significance. He also talked in his State of the Union address about working with lawmakers on an infrastructure plan to invest in cutting-edge industries of the future.
Mr Trump stressed that this was not an option, but a must. Mr Trump is preparing a series of executive orders aimed at improving the overall competitiveness of the US in key technology areas including AI, 5G and quantum computing.
But many in the tech world believe that continued U.S. leadership in A.I. is far from assured.
Prediction: China will comprehensively surpass the United States in just a few years
In 2018, China surpassed the United States in terms of investment in AI startups, with nearly 50 percent of all AI investment funds going to Chinese startups.
While the US still leads in terms of the number of deals, the number of AI startups in the US has been steadily declining over the past few years.
China is now challenging the US for supremacy in the number of patents and papers it produces in artificial intelligence. To be sure, the quality of some of these papers may still lag behind that of the US, but China has been catching up, and the rate of improvement over the past few years has been staggering.
Three big ai giants: China/America/Others
Based on what we have discussed above, we propose to divide the world into three broad categories: The West, China and the rest of the world. Obviously, this segmentation is very subjective, but we think it frames the conversation around AI policy in a useful way.
Let’s take a closer look at the key factors that will determine the ongoing global AI arms race. When considering any problem that can be solved using machine learning, there are three building blocks to consider: data, talent, and funding.
1. Data
According to IDC, more than 5 billion consumers now generate massive amounts of data every day, and that number will grow to 6 billion by 2025.
As data volumes increase, iot devices will increasingly drive growth, now expected to generate over 90 Zettabytes (*) of data per year by 2025.
* Zettabytes are usually used to indicate the total size of a network hard disk or the storage capacity of a large storage medium. 1 ZB=1024 PB 1PB=1024 EB, 1EB=1024 TB.
There are two key factors: data collection and data use.
First, in terms of data collection, while the growth of smartphones is slowing globally, the development of the Internet of Things is just beginning. As of 2018, there are at least 7 billion IoT devices, growing to 21.5 billion by 2025, more than all other categories combined.
Every person in society will be connected to dozens, if not hundreds, of smart devices throughout their lives, potentially recording everything from road traffic to apartment temperatures. As long as we have the will to share and store data, it can be exploited.
Source: State of the IoT 2018
Second, there is data use. There is a growing concern about privacy and a growing policy and consensus to limit data collection and prevent abuse. But at this stage of machine learning, privacy protections affect the amount of data available for training models.
This, in turn, means that countries that are not yet too concerned about privacy (following the example of China), for example, have begun large-scale security deployments with AI-powered security cameras and have succeeded in catching criminals, are gaining an edge in data.
That said, in other areas, such as driverless cars or machine translation, Europe and America have better data sets, experimental space and regional policies.
2. The talent
People represent a second key resource, because they can instruct machines how to solve problems.
The reality is different from what we saw in the report:
Europe and the United States, especially the United States, has a natural talent advantage because it is still one of the most ideal places to work and live, and it is easier to attract talents from all over the world. An open, inclusive and creative living environment facilitates the discovery and cultivation of innovative ideas.
The United States also has the world’s largest established research university system for basic research.
In recent years, however, China has built a first-class research university system and continues to invest heavily. China has accelerated the development of talent in the natural sciences and engineering and published more papers in journals than the United States.
Although the United States has a greater lead in certain areas of research, when it comes to translating research results, China is nowhere near as fast as it is in practice.
We measured the efficiency of translating research results by the number of AI startups established in each country and the number of engineers joining the field.
The US has the most start-ups, thanks to big tech companies such as Google, Microsoft and Facebook investing in existing ecosystems.
However, China ranks second here as a result of accelerating investment by Chinese tech giants in ai companies.
Taken as a whole, Europe ranks third.
3. The investment
According to CB Insights, investment in Chinese startups accounted for 50 percent of global AI startup investment in 2017. Compared with 2016, it is up 11.6 percent.
In 2018, the two companies that raised the most money, SenseTime and Face ++, were both from China. China today is ahead of all competitors in terms of early stage investment.
It is clear that both countries are equally well positioned in terms of the amount of money available, the robustness of the ecosystem and availability in multiple areas.
Despite President Trump’s announcement of his American AI Initiative, it seems that the shape of ARTIFICIAL intelligence is almost set.
Let’s use a model of strategic investment to assess the viability of Trump’s American AI Initiative.
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First, consider the overall size of the project and assess their likelihood of achieving the milestones;
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Secondly, consider the growth cycle of the project, the use efficiency of funds to the project at the present stage;
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Finally, determine the strategic focus of the project and whether it will generate steady growth by targeting key areas that are likely to yield the best returns.
Now, applying this framework to evaluate President Trump’s AI strategy, it’s safe to conclude that this Trump initiative doesn’t really change anything, given how vague and generic the WORDING of the AAI is.
Bottom line: Trump is so unreliable
Many people see AI as a new arms race, with countries competing fiercely for hardware, software and business start-ups. We believe that ai cooperation can deliver better results for all.
Interestingly, Europe and the United States in particular are more likely to benefit from global cooperation than from independent development over the last 50 years, because they are freer to think and create what historically has attracted talent to the United States.
The path to sustainable AI development in the United States may depend on:
The role of the US government should therefore be focused on helping to build a business environment more conducive to co-operation, rather than trying to stifle innovation and co-operation by imposing unnecessary restrictions.