Humanity is now developing our greatest contribution to the expansion of intelligence on the planet: the flowering of artificial intelligence. It would be a shame if all we used it for were Amazon shopping and Facebook birthday reminders.
Luckily, machine learning and artificial intelligence aren’t just a for-profit undertaking. Universities, companies, nonprofits, and governmental agencies are already busy developing interesting tools and applications that direct machine learning toward the common good. Though still in their early days, these initiatives just may represent our best bet for addressing our most challenging ecological and societal problems. Welcome to the world of “Mission-Driven AI.”
OpenAI focuses on what may well be the most important mission of them all: finding a safe, beneficial route to AGI, or Artificial General Intelligence — which is to say, intelligence that matches, or exceeds that of the human mind. These folks are the closest I’ve seen to an overtly social mission in the development of artificial intelligence.
It’s worth mentioning a couple of other players in this field. In researching this article, I ran across the Prague-based organization, GoodAI, though I don’t know much about their work. And even though they aren’t directly involved in machine learning development themselves, another organization, AI4All, works to promote diversity and inclusion in the field of artificial intelligence. They focus on high school students, and partner closely with universities to broaden access to education in the field of artificial intelligence.
Speaking of universities, there are many that play important roles in AI research. Stanford, Carnegie Mellon, MIT, Berkeley, and the University of Washington are just a few of the top names in the US. In addition, in Canada, there is the University of Montreal, the University of Toronto (and the affiliated Vector Institute), the University of Alberta, again, just to name a few.
How truly mission-driven these programs are is hard to say, aside from the fact that, in most if not all cases, their research is openly shared.
When it comes to the application of mission-driven machine learning, it may be helpful to draw on a whitepaper I wrote many years ago, called “Movement as Network.” I wrote the paper as a way to reframe the environmental movement, but people found the ideas applicable to other social change movements and organizations as well. In Movement as Network terms, there are three primary types of mission-driven work: ‘resource organizations’ provide utility-like services across the network, ‘solution organizations’ solve specific problems, and ‘people organizations’ build political and social power.
Here’s how this division might apply to the mission-driven application of AI:
There are utility functions in machine learning with broad applicability to a range of users. Speech recognition and chatbot technologies are perfect examples.
General Utility Machine Learning:
University AI Development
Mission-Driven AI Applications
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