R Street Institute
R Sheets Technology and Innovation

R Sheet on Competition in Artificial Intelligence

by Caleb Watney AND Charles Duan
Nov 12, 2018
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Authors

Caleb Watney
Caleb Watney
Former Resident Fellow, Technology & Innovation
Charles Duan
Charles Duan
Former Senior Fellow

Key Points

AI can power innovations across the entire economy by augmenting human decision-making, automating rote tasks and finding new patterns in datasets.

To ensure a competitive ecosystem, Congress should focus on reducing barriers to entry that exist in the supply of labor, the supply of data and access to hardware.

Congress can increase the supply of skilled AI analysts by reforming our immigration system to allow more high-skill AI talent, and by allowing companies to deduct the cost of training AI talent.

Congress can increase the supply of high-quality datasets by opening up more government data for public use, and by clarifying the fair-use exemption for training data.

Congress can help ensure access to specialized AI hardware by avoiding political destabilization of international supply chains, and by maintaining our healthy ecosystem around distributed platforms.

Background

Artificial Intelligence (AI) is developing rapidly, and countries from around the globe are beginning to articulate national strategies for handling the political ramifications. Powering innovations like driverless cars, autonomous drones, full-sequence genetic analytics and powerful voice-assistant technology, the future certainly looks full of potential. However, unsettled questions about who will reap these benefits and when they will be achieved leave storm clouds on the political horizon.

Formal definitions for AI vary but generally the term can be used to refer to the broad suite of computer algorithms being used to automate or improve aspects of human decision making. In the most current iteration, this is largely being accomplished via machine learning (ML), whereby an algorithm uses statistical techniques to find patterns from a dataset and progressively improve prediction ability at a given task (email spam filters are a great example). In this understanding, AI exists on a spectrum rather than a binary, with increasing sophistication in the ability to apply various models to solve the problem at hand, indicating higher levels of intelligence.

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