AI News, The Race for Artificial Intelligence: China vs. America artificial intelligence

China battles the US in the artificial intelligence arms race

This time that came in the shape of a meeting with Kai-Fu Lee, former president of Google China and now a leading venture capitalist in Chinese technology.

This has a startling story to tell: for the first time since the industrial revolution, he argues, China will be at the forefront of a huge economic transformation —

Mr Lee describes a world of cut-throat business activity and remorseless imitation, which has already allowed Chinese businesses to defeat leading western rivals in their home market.

It could, for example, work far better in introducing autonomous vehicles than the west’s safety-conscious approach.

So China has been able to jump to universal digital payment systems, while western businesses still use outdated technology.

At present, he thinks China is equal to the US in the first, vastly behind in the second, a little ahead in the third, and, again, far behind in the fourth.

But five years from now, he thinks, China might be a little ahead in the first, less far behind in the second, well ahead in the third and equal in the last.

China is far behind the US in production of semiconductors, ahead in the number of potential users and has about half the number of AI experts and roughly half the number of AI companies.

Historical experience suggests that the rents created by a lead in an important technology are valuable, though often impermanent.

But the economic and social impact of AI is a bigger issue and one that is relevant to every country As Mr Lee stresses, advances in AI offer gains.

This is not just in personal convenience, but in improving medical diagnostics, tailoring education to individual students, managing energy and transport systems, making courts fairer, and so on and so forth.

It seems reasonable to fear that AI will accelerate the hollowing out of the middle of the earnings distribution, possibly even the upper middle, while increasing concentrations of private wealth and power at the top.

Yet perhaps the most important consequence will be in the intensity of influence and surveillance made possible by AI-monitored mobile devices and sensors.

The Race Is On: Assessing the US-China Artificial Intelligence Competition

A revanchist Russia might be the scourge of the Western defense community, but Vladimir Putin has arguably issued the clearest articulation of AI’s massive potential: “Whoever becomes the leader in [AI] will become the ruler of the world.” But how do we assess who is leading?

simple manufacturing process consists of three elements: raw materials, production, and manufactured goods.

Likewise, current AI systems typically take large amounts of input data, process it using machine-learning techniques, and output trained algorithms.

For example, numerous photos of cars can be processed using machine learning to create an algorithm that recognizes cars in other photos.

The government and private sector use those algorithms in applications from autonomous vehicle vision to detecting terrorist activity.

China AI competition provides a clear picture of both where that competition stands today and its future trajectory—as well as the consequent policy implications.

For example, merino wool is softer than regular wool, adapts to changes in temperatures, and is moisture wicking.

Data may come in different forms such as audio or imagery data and may capture different types of information (e.g., financial transactions and Netflix ratings).

To quote William Bruce Cameron: “Not everything that counts can be counted, and not everything that can be counted counts.” On this dimension, China has strong structural advantages in data collection.

China is also one of the most surveilled countries in the world with 176 million security cameras in operation (and expected to grow to 626 million by 2020).

Chinese surveillance efforts likely focus on highly populated, developed areas, but semi-autonomous and autonomous military vehicles must operate off-road in areas with comparatively limited GPS data and poor terrain mapping.

After a Chinese video game publisher implemented AI-enforced restrictions on minors, underage gamers found a variety of ways of defeat the system, including using photos of sleeping relatives.

Unlike the traditional production process, AI production leaves the raw material intact and certain AI processes may not require raw material.

The supercomputer competition matters to AI because some applications for AI require processing extremely large amounts of data that conventional computers cannot handle.

China has made significant efforts to develop quantum computers that may ultimately better the capabilities of traditional computers (so-called “quantum supremacy”).

China has invested billions of dollars in quantum technologies, has broken several technological world records, and is integrating its quantum computing and AI research.

The strength of the US university system offers strong advantages in developing novel AI tools and creating the best AI talent.

The United States currently has a larger AI talent pool, but China’s larger population and major investments in AI training programs give it the potential to surpass the United States.

The outputs for AI are the trained algorithms and supporting programs that can improve marketing strategies, inform intelligence analysis, aid object detection in autonomous systems, and enhance decision making.

Innovative firms require flat organizations and high tolerance for criticism that China may have difficulty replicating due to a highly hierarchical business culture.

The raw materials in AI are datasets, processed through machine-learning techniques powered by computers, resulting in trained algorithms for numerous applications.

The United States should also investigate ways to develop artificial data, such as artificial imagery data, that can be used for training machine-learning algorithms.

The United States should especially leverage its comparative advantage in organizational innovation to support and encourage the work of organizations like the Defense Advance Research Projects Agency, as well as innovative American firms working on AI.

Success will likely require a serious communications effort to improve perceptions of the military in Silicon Valley and new efforts to draw in top talent.

A recent McKinsey study noted: “Survey respondents report the rapid adoption of AI and expect only a minimal effect on head count.

Yet few companies have in place the foundational building blocks that enable AI to generate value at scale.” Defense organizations especially should be better prepared for realizing the value of AI.

The United States government could adopt policies to support AI venture capital, increase government investment and support for AI businesses, and encourage development of AI talent.

For example, it could hold AI circuses: public displays of advanced robotics with representatives from AI-related university programs, companies, and government organizations.

Zachary Kallenborn is an independent national security researcher / analyst, specializing in CBRN weapons, CBRN terrorism, drone swarms, and emerging technology.

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