这篇文章来自 nvidia.com。原始 url 是: https://blogs.nvidia.com/blog/2018/05/10/investing-artificial-intelligence-u-s-leadership/
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I am fortunate today to be in Washington with leaders from three dozen companies, including many of our close partners, to discuss with administration officials how the U.S. can continue to lead the world in research, development and the adoption of artificial intelligence. I’d like to thank the Office of Science and Technology Policy for convening this important meeting.
AI is becoming the world’s most important computational tool — applicable to a wide variety of industries including transportation, energy and healthcare. But AI is enormously demanding in terms of computation — it requires processing hundreds of millions of data points to extract insight. Therefore, it’s important for us to discuss how to improve our nation’s computing infrastructure to support AI and maintain leadership in this space.
In our meetings, I hope to reiterate some of the themes I shared a few months ago with the House Subcommittee on Information Technology — that we need to increase funding for research, give university researchers access to GPU computing clusters, open access to datasets, and train more AI developers and data scientists.
There’s simply no replacement for the federal government significantly increasing support for fundamental research to bolster university research. Funding drives research. Research, in turn, drives innovation, from startups to multinationals.
Government also has a role in providing the infrastructure to support research. Universities need access to large-scale, state-of-the art, GPU-accelerated computing systems to do cutting-edge research. But most lack the expertise to procure and run them. The government should provide better access to universities for future computing systems — all of which need to support high performance computing and AI workloads.
Data is the lifeblood of AI. Developers and researchers need access to high-quality data. Federal agencies should disclose what datasets are available, including anonymized healthcare, weather, satellite and industrial datasets.
Simulation for Safe Autonomous Vehicles
Safety is essential for autonomous vehicles and it’s our highest priority. For the U.S. to lead, we need to ensure safety and time to market. Developing a safe vehicle requires traveling billions of miles, which is extraordinarily challenging. Computer simulation is an ideal methodology to test and validate AI for self-driving cars, enabling us to accelerate development and improve safety under a wide variety of road and weather conditions.
Simulation together with AI will greatly advance autonomous vehicle technology to achieve the highest levels of safety. Simulation should be part of the virtual “drivers test” of autonomous systems. This will help reduce the terrible toll of 37,000 American fatalities each year.
States often have different regulations for transportation infrastructure. The federal government should make recommendations for all 50 states to share unified autonomous vehicle guidelines and smart infrastructure, including street lights, sensors and construction zones.
The government should partner with industry to train more developers and data scientists. Academia can’t do this by itself. NVIDIA trains tens of thousands of developers and data scientists each year, partnering with educational leaders such as Coursera and Udacity.
In my recent testimony before Congress, I said that AI represents the biggest technological and economic shift in our lifetime. The stakes are huge — trillions of dollars in opportunity for American companies, and life-saving breakthroughs. I look forward to continuing to work with our partners in Washington and throughout the country to strengthen our leadership, foster innovation and drive advances that will lead us to a brighter future.