Eric Schmidt, former CEO of Google, on AI – recommends a testing/certification ecosystem: https://www.wsj.com/tech/ai/how-we-can-control-ai-327eeecf
What’s still difficult is to encode human values. That currently requires an extra step known as Reinforcement Learning from Human Feedback, in which programmers use their own responses to train the model to be helpful and accurate. Meanwhile, so-called “red teams” provoke the program in order to uncover any possible harmful outputs. This combination of human adjustments and guardrails is designed to ensure alignment of AI with human values and overall safety. So far, this seems to have worked reasonably well.
Red teams have so far shown some promise in predicting models’ capabilities, but upcoming technologies could break our current approach to safety in AI. For one, “recursive self-improvement” is a feature that allows AI systems to collect data and get feedback on their own and incorporate it to update their own parameters, thus enabling the models to train themselves. This could result in, say, an AI that can build complex system applications (e.g., a simple search engine or a new game) from scratch. But, the full scope of the potential new capabilities that could be enabled by recursive self-improvement is not known.
Another example would be “multi-agent systems,” where multiple independent AI systems are able to coordinate with each other to build something new. Having just two AI models from different companies collaborating together will be a milestone we’ll need to watch out for. This so-called “combinatorial innovation,” where systems are merged to build something new, will be a threat simply because the number of combinations will quickly exceed the capacity of human oversight.
Short of pulling the plug on the computers doing this work, it will likely be very difficult to monitor such technologies once these breakthroughs occur. Current regulatory approaches are based on individual model size and training effort, and are based on passing increasingly rigorous tests, but these techniques will break down as the systems become orders of magnitude more powerful and potentially elusive. AI regulatory approaches will need to evolve to identify and govern the new emergent capabilities and the scaling of those capabilities.
I recently attended a gathering in Palo Alto organized by the Rand Corp. and the Carnegie Endowment for International Peace, where key technical leaders in AI converged on an idea: The best way to solve these problems is to create a new set of testing companies that will be incentivized to out-innovate each other—in short, a robust economy of testing. To check the most powerful AI systems, their testers will also themselves have to be powerful AI systems, precisely trained and refined to excel at the single task of identifying safety concerns and problem areas in the world’s most advanced models. To be trustworthy and yet agile, these testing companies should be checked and certified by government regulators but developed and funded in the private market, with possible support by philanthropy organizations. (The philanthropy I co-founded, Schmidt Sciences, and I have helped fund some early AI safety research.) The field is moving too quickly and the stakes are too high for exclusive reliance on typical government processes and timeframes.
One way this can unfold is for government regulators to require AI models exceeding a certain level of capability to be evaluated by government-certified private testing companies (from startups to university labs to nonprofit research organizations), with model builders paying for this testing and certification so as to meet safety requirements. Testing companies would compete for dollars and talent, aiming to scale their capabilities at the same breakneck speed as the models they’re checking. As AI models proliferate, growing demand for testing would create a big enough market. Testing companies could specialize in certifying submitted models across different safety regimes, such as the ability to self-proliferate, create new bio or cyber weapons, or manipulate or deceive their human creators. Such a competitive market for testing innovation would have similar dynamics to what we currently have for the creation of new models, where we’ve seen explosive advances in short timescales. Without such a market and the competitive incentives it brings, governments, research labs and volunteers will be left to guarantee the safety of the most powerful systems ever created by humans, using tools that lag generations behind the frontier of AI research.
Eric Schmidt is the former CEO and executive chairman of Google and cofounder of the philanthropy Schmidt Sciences, which funds science and technology research.