Why USA needs to invest more in AI

The requested FY2020 allow all non-defence AI-related R&D programs within the entire U.S. national — across the DOE, NIH, NSF, NIS, IARPA, etc. — is simply $973 Million.

This is not enough — simply sixth of 1 % of the federal R&D budget dedicated to AI. Funding for a technology that will be therefore transformative to the U.S. economy so important to our national and physical security, should scale proportionately to the remainder of the U.S. R&D budget.

The Donald Trump administration has shown nice leadership in championing the reason behind AI: developing steering for the implementation the OPEN Government Data Act, driving the event of the Federal Data Strategy, issuing an Executive order on Maintaining American Leadership in AI, change the National AI R&D strategy, and urging government agency to develop a concept for Federal engagement in AI Standards. However, they came up short on this budget.

As proved by the Center of Data Innovation’s extensive research, USA continues to be winning the AI race against China currently. However, there’s proof that China is catching up. China’s government disbursal, its aggressive and well-coordinated industrial policy is permitting them to shrink our lead, which can quickly evaporate while not an acceptable continuing investment.

Great strides are however to be created in AI elementary analysis. Deep learning and reinforcement learning could in the future exhaust their utility and open the house for paradigm-shifting theories. They want to rethink several elementary ideas behind what we tend to characterize nowadays as machine intelligence. Today’s supervised deep learning wherever most personal investment is created is information-hungry, power-hungry, lacks skilfulness, is incapable of thinking rationally, and solely learns by the observation that makes it liable to bias.