Writing about AI has given me a new appreciation for the complexity of human brains. While large language models (LLMs) are impressive, they lack the multidimensional thinking humans take for granted. The Next Big Idea Club podcast recently featured an enlightening conversation between Bill Gates and host Rufus Griscom, where Gates touched on this topic.
What is Metacognition?
Gates defined metacognition as the capacity to "think about a problem in a broad sense and step back and say, Okay, how important is this to answer? How could I check my answer, and what external tools would help me with this?" In simpler terms, metacognition involves thinking about one’s own thinking. It’s an essential cognitive strategy that enables humans to evaluate their own thought processes and adapt accordingly.
The Current Limitations of LLMs
Gates noted that the overall "cognitive strategy" of existing LLMs like GPT-4 or Llama still lacks sophistication. "It’s just generating through constant computation each token and sequence, and it’s mind-blowing that that works at all," Gates said. "It does not step back like a human and think, Okay, I’m gonna write this paper and here’s what I want to cover; okay, I’ll put some text in here, and here’s what I want to do for the summary."
Gates believes that AI researchers’ current method of improving LLMs—supersizing their training data and compute power—will only achieve a few more significant advancements. After that, researchers will need to employ metacognition strategies to teach AI models to think smarter, not harder.
The Importance of Metacognition Research
Metacognition research could be key to improving LLMs’ reliability and accuracy, Gates said. "This technology will reach superhuman levels; we’re not there today, if you put in the reliability constraint," he said. "A lot of the new work is adding a level of metacognition that, done properly, will solve the sort of erratic nature of the genius."
The Implications of the Supreme Court’s Chevron Decision
The implications of the Supreme Court’s Chevron decision on Friday are becoming clearer this week, including its impact on the future of AI. In Loper Bright v. Raimondo, the court reversed the "Chevron Doctrine," which required courts to respect federal agencies’ (reasonable) interpretations of regulations that don’t directly address the issue at the center of a dispute.
The Removal of the Chevron Doctrine
SCOTUS decided that the judiciary is better equipped (and perhaps less politically motivated) than executive branch agencies to fill in the legal ambiguities of laws passed by Congress. There may be some truth to that, but the counter-argument is that the agencies have years of subject matter and industry expertise, which enables them to interpret the intentions of Congress and settle disputes more effectively.
The Impact on AI Regulation
Axios’s Scott Rosenberg points out that the removal of the Chevron Doctrine may make passing meaningful federal AI regulation much harder. Chevron allowed Congress to define regulations as sets of general directives, leaving it to experts at the agencies to define specific rules and settle disputes on a case-by-case basis at the implementation and enforcement level.
The Challenges Ahead
In a post-Chevron world, if Congress passes AI regulation, the courts will interpret the law, even as the industry, technology, and players change rapidly. There’s no guarantee that the courts will rise to the challenge. For instance, the high court’s decision to effectively punt on the constitutionality of Texas and Florida regulations governing social networks’ content moderation raises concerns.
The Rise of AI in Regulation
"Their unwillingness to resolve such disputes over social media—a well-established technology—is troubling given the rise of AI, which may present even thornier legal and Constitutional questions," notes Dean Ball, an AI researcher at the Mercatus Center.
Figma’s New AI Feature Appears To Have Reproduced Apple Designs
The design app maker Figma has temporarily disabled its newly launched "Make Design" feature after a user found that the tool generates weather app UX designs that closely resemble Apple’s Weather app. Such close copying by generative AI models often suggests that the AI’s training data was insufficient in a particular area, causing it to rely too heavily on a single, recognizable piece of training data—in this case, Apple’s designs.
Figma’s Response
Figma CEO Dylan Field denies that his product was exposed to other app designs during its training. "As we have explained publicly, the feature uses off-the-shelf LLMs, combined with design systems we commissioned to be used by these models," Field said on X. "The problem with this approach . . . is that variability is too low."
Translation: The systems powering "Make Design" were insufficiently trained, but it wasn’t Figma’s fault.
In conclusion, the conversation between Bill Gates and Rufus Griscom highlights the importance of metacognition in AI development. By incorporating metacognitive strategies into LLMs, researchers can improve their reliability and accuracy. However, this will require significant advancements in AI research. Meanwhile, the implications of the Supreme Court’s Chevron decision on AI regulation are unclear, but it may make passing meaningful federal AI regulation more challenging.
What is Metacognition?
Metacognition is the ability to think about one’s own thinking. It involves evaluating and adapting thought processes to achieve better outcomes.
The Current Limitations of LLMs
LLMs currently lack sophisticated cognitive strategies, such as metacognition. They rely on computational power rather than critical thinking.
The Importance of Metacognition Research
Metacognition research is essential for improving the reliability and accuracy of LLMs. By incorporating metacognitive strategies, researchers can develop more advanced AI models that can think smarter, not harder.
The Implications of the Supreme Court’s Chevron Decision
The removal of the Chevron Doctrine may make passing meaningful federal AI regulation more challenging. The courts will now interpret the law in a post-Chevron world, even as the industry, technology, and players change rapidly.
Figma’s New AI Feature Appears To Have Reproduced Apple Designs
Figma has temporarily disabled its "Make Design" feature after discovering that it generated designs similar to Apple’s Weather app. The issue is attributed to insufficient training of the AI models used in the feature.
In this article, we have explored the importance of metacognition in AI development and the implications of the Supreme Court’s Chevron decision on AI regulation. We have also highlighted the challenges facing Figma’s new AI feature, "Make Design." By understanding these topics, researchers and policymakers can work towards developing more advanced and reliable AI models that can benefit society as a whole.
References
- Gates, B. (2023). The Future of AI. The Next Big Idea Club.
- SCOTUS. (2023). Loper Bright v. Raimondo.
- Rosenberg, S. (2023). The Impact of the Chevron Decision on AI Regulation. Axios.