AI Revolution or Evolution

An interesting IEEE Spectrum article “AI and Economic Productivity: Expect Evolution, Not Revolution” by Jeffrey Funk questions popular claims about the rapid pace of AI’s impact on productivity and the economy. He asserts that “Despite the hype, artificial intelligence will take years to significantly boost economic productivity”. If correct, this will have serious implications for public policymaking who have chosen due be proactive. The article raises good points, but many of the examples do not look like real AI, at least as a dominant component. Putting “smart” in the name of a product doesn’t make it AI, and automation doesn’t necessarily use AI. 

On a broader note, we should care about the technology language we use and be aware of the usual practices in commercialization. As discussed in previous blog posts, expanding too far the meanings of terms like AI, machine learning, and algorithms makes rational discourse more difficult. Some of us remember marketing of expert systems and relational databases: companies do a disservice to society by claiming each breakthrough technology actually is in their products. Here we go again — today about anything counts as AI depending on the point you want to make and the products you want to sell. 

Another issue raised by the article is from the emphasis on startups as the leaders of economic impact, as opposed to the results of innovations from established industry and government labs. Technologies have adoption curves, going from early adopters through the laggards, of about seven years. If you add to that the difficulties of making a startup succeed, a decade or so is probably the minimum timescale for large impact on the economy. A better perspective on revolution versus evolution could come from longitudinal evaluations looking at trends. In that case, a good endpoint for a hypothesis about dramatic impact on productivity might be the 2030-2035 timeframe. 

A problem with using a vague or broad notion of AI is that policymakers could miss the revolutionary impact of data science, which can, but may not, involve real AI. Data science probably has the best chance of dramatically impacting society and the economy in the short and long terms and has the advantage of not having to involve designing and manufacturing physical objects, and thus not always having to wait for consumers to adopt new products. Data Science is already affecting society and employment with obvious, and not so obvious, revolutionary impacts on our lives.

PCAST and AI Plan

Executive Order on The President’s Council of Advisors on Science and Technology (PCAST)

President Trump issued an executive order on October 22 re-establishing the President’s Council of Advisors on Science and Technology (PCAST), an advisory body that consists of science and technology leaders from the private and academic sectors. PCAST is to be chaired by Kelvin Droegemeier, director of the Office of Science and Technology Policy, and Edward McGinnis, formerly with DOE, is to serve as the executive director. The majority of the 16 members are from key industry sectors. The executive order says that the council is expected to address “strengthening American leadership in science and technology, building the Workforce of the Future, and supporting foundational research and development across the country.” For more information, see the Inside Education article about the first appointments.

Schumer AI Plan

Jeffrey Mervis has a November 11, 2019, article in AAAS News from Science on a recommendation for the government to create a new agency funded with $100 billion over 5 years for basic AI research. “Senator Charles Schumer (D–NY) says the initiative would enable the United States to keep pace with China and Russia in a critical research arena and plug gaps in what U.S. companies are unwilling to finance.”

Schumer gave his ideas publicly in a speech in early November to senior national security and research policymakers following a recent presidential executive order. He wants to create a new national science tech fund looking into “fundamental research related to AI and some other cutting-edge areas” such as quantum computing, 5G networks, robotics, cybersecurity, and biotechnology. Funds would encourage research at U.S. universities, companies, and other federal agencies and support incubators for moving research into commercial products. An additional article can be found in Defense News.