Following my 2017 blog entry, Reinvention, where I had looked back to recount my jump from industry back to academia. Here is a video from the CSAIL 60th anniversary celebration where I finish telling my personal academic story about a career reinvention.
If you watch it to the end, you can see the three big lessons about how to do research that I learned during my PhD - and how I learned those lessons.
Continue reading "Reinvented"There is a false dichotomy between two alternatives facing us in the burgeoning AI industry today: "open" versus "closed."
This dichotomy is being promoted by both sides: Closed-AI advocates (oddly, including the company named "Open AI") justifiably warn about the misuse risks posed by unregulated use of AI and the geopolitical risks posed by exfiltration of weights of large-scale pretrained models, but then they falsely imply that the only solution to these risks is to lock their AI behind an opaque service interface, with no visibility to the internals provided to outsiders. On the other hand, open-AI advocates (including Yann LeCun, one of the giants of our field) correctly point out the huge community benefits that come from transparency and competition, but then they make the mistake of assuming that benefits will be guaranteed if they throw their trained models over the wall to the public, releasing full model weights openly.
Both sides are bankrolled by massive monetary investments and project the polished air of billion-dollar confidence. But the ugly truth is that the AI industry is built around an extraordinary uncertainty: although the industry has become expert in the science of creating AI, we are pitifully unequipped to meet the challenge of understanding AI. This unprecedented state of affairs is a direct outgrowth of the nature of modern machine learning: our clever training processes have created systems that contain orders of magnitude more complexity than has ever been created in software before, but no human has examined it. Beyond a superficial level, we do not currently understand what is good or bad or smart or stupid inside these systems.
The long-term risk for humanity comes from our ignorance about the limitations, capabilities, and societal impacts of AI as we continue to develop it. Neither the open nor closed models on their own offer a credible path to cracking this problem. Thus we ask: what is the right kind of openness? What ecosystem will lead to a healthy AI industry, built on strong science, transparency, accountability, and innovation?
In the series of articles that follow, we will survey the benefits and drawbacks of both the open and closed models. Then we will examine a third, pragmatic path that brings together the benefits of both approaches. Our proposal does not foreclose either open nor closed corporate strategies for any individual company or product, but it provides a framework of standards and services that will create healthy incentives for companies to pursue vigorous innovation, meaningful transparency, and safety in the public interest.