The path to general AI — and possibly superintelligence — is being paved before our eyes. And with the proliferation of an AI-driven society, the social and economic value of such technology is also on the rise. In turn, harnessing and leveraging such technology needs to extend beyond the interests of venture capitalists, investment groups and entrepreneurs — and also be a priority on a geopolitical scale.
When the global economy starts to feel the shift ushered in with mass-adoption of AI, the United States needs to be leading the charge as opposed to chasing the pack.
If the U.S. is to compete on a global level, they’ll face an arms race of sorts from a litany of nations that are already doubling-down on the massive advantages that come with national AI proficiency. In fact, 18 different countries have launched national AI strategies, with government funding ranging from $20 million to almost $2 billion.
A first step in the right direction was taken by the Trump administration recently when the president signed an executive order launching the American AI Initiative. This policy will funnel federal funding and resources toward AI-specific research while also implementing U.S.-led international AI standards. Additionally, the program will call for new research into increasing AI literacy in American workers.
Unfortunately, there are no specifics around what exactly this new program actually looks like in practice, and there is no additional research being dedicated toward AI development. There are no timelines for implementation of these initiatives, only a vague goal of roughly six-ish months before a detailed plan is rolled out. Jason Furman, a Harvard professor who helped draft the Obama administration’s report on AI, said that the plan had “all the right elements” but was also “aspirational with no details and is not self-executing.”
How can the private sector build on what the federal government has put in place?
Yet, the importance of government involvement in AI R&D cannot be overstated. If we remain on the path we’re on, one where large technology companies and VC firms are funding the bulk of AI research, the country would only see pockets of growth around the largest technology companies and the regions of the country would continue to stagnate. We would not be able to work on major moonshot projects and collectively pool our resources for the greater good across all regions of the U.S. All innovations would be tightly controlled by technology companies and adoption rates would not move up and actually make a difference in the way we utilize AI. This would result in a marginal talent pool, and new developments would be those of technology innovators — not problem-solvers. Everything would be driven by its contribution to business and not its contribution to society at-large.
So, government involvement matters, yet the administration can’t be solely responsible for catalyzing the change needed by the American workforce — it falls on us as well. So that begs the question…
How can the private sector build on what the federal government has put in place?
The program focuses on five key pillars: Research and development, infrastructure, governance, workforce and international engagement. Like Furman said, those concepts are well and good, but they remain vague and still clearly undefined. But, even if the administration’s program isn’t hitting the ground running, that doesn’t mean that you and I can’t push the ball in the right direction. So, how can we as a workforce help execute on the program? What do we need to do to enact the ideals that the federal government is focused on in AI?
Focus on building AI-literacy in American workers
Until the American workforce itself is concerned with being AI-first, we will see challenges in implementation, adoption and deployment, and AI literacy will be largely confined to the areas in which it’s already being heavily used (automation, customer service, insights, engagement, etc.).
Additionally, these industries aren’t even using AI to actually solve problems or improve society, they are largely using it as an autopilot. And if AI is being used simply to automate processes for tech companies, then we’re missing out on the opportunity to use it to its full advantage to solve actual sociological issues around hunger, poverty and healthcare.
And the focus needs to extend beyond the workforce and into the classroom. All STEM programs in American schools need AI-based coursework. Universities need AI-based programs and intelligence labs, such as MIT, for example, where roughly 25% of faculty conduct research on intelligence in labs like the MIT-IBM Watson AI Lab, the Robust Robotics Group and the Laboratory for Information and Decision Systems (LIDS).
Our academic institutions and research centers would continue to strive as centers of excellence around the world, meaning that the best and brightest minds would continue to be attracted and would keep our talent pool stocked. Our universities would increase enrollment for AI/digital experts, as those roles would be the golden mature standard.
Startups need to swarm and work closely with federal AI strategy
While I hate to use cliches, this is a “teamwork makes the dream work” situation. Aligning the startup community with government strategy would allow innovation and social good to walk hand-in-hand when it comes to AI development.
The importance of government involvement in AI R&D cannot be overstated.
This would lead in new space technologies, create new innovation for society in food, energy and health, and create a lifestyle that balances efficiency and leisure. It also would allow American corporations to go after dispersion and breakthrough innovation. From a government perspective, this means continuing to provide open and structured data sets for the public to use while still protecting the sensitive information that keeps our citizens safe. Providing these data sets is the first step, but making others aware through education campaigns is also important
Make AI all-inclusive
Much the same way that IT experts, coders and web/app developers had to learn to work side-by-side with business owners, marketers and production-level employees across the business ecosystem over the last two-and-a-half decades, we must bridge the “gap” between AI experts, technologists and leading technology companies and solutions owners, general SMBs and corporate America to develop an inclusive and universally understandable AI strategy.
The advancement of machine learning models, specifically deep learning, relies on the ingestion of data — structured or unstructured. The sharing of this data, from people involved in day-to-day problems and solutions to technologists who are concerned with the big picture, is the key to developing innovative and inclusive AI solutions. A better AI future built on diverse data sets requires both parties to work collaboratively.
Data is officially the most valuable commodity on earth and the countries that win the race to harness and use it to its maximum value and efficiency are going to position themselves favorably around the globe. And if America is to win the race, it will take the buy-in of the collective public, private and government entities in our country. If we are to move past improving our viewing patterns on Netflix and start solving the brass-tax issues in our country’s society, it will come as a result of the convergence of government, society and business.