Nervous About ChatGPT? Try ChatGPT With a Hammer
Last March, just two weeks after GPT-4 was released, researchers at Microsoft quietly announced a plan to compile millions of APIs—tools that can do everything from ordering a pizza to solving physics equations to controlling the TV in your living room—into a compendium that would be made accessible to large language models (LLMs). This was just one milestone in the race across industry and academia to find the best ways to teach LLMs how to manipulate tools, which would supercharge the potential of AI more than any of the impressive advancements we’ve seen to date.
The Microsoft project aims to teach AI how to use any and all digital tools in one fell swoop, a clever and efficient approach. Today, LLMs can do a pretty good job of recommending pizza toppings to you if you describe your dietary preferences and can draft dialog that you could use when you call the restaurant. But most AI tools can’t place the order, not even online. In contrast, Google’s seven-year-old Assistant tool can synthesize a voice on the telephone and fill out an online order form, but it can’t pick a restaurant or guess your order. By combining these capabilities, though, a tool-using AI could do it all. An LLM with access to your past conversations and tools like calorie calculators, a restaurant menu database, and your digital payment wallet could feasibly judge that you are trying to lose weight and want a low-calorie option, find the nearest restaurant with toppings you like, and place the delivery order. If it has access to your payment history, it could even guess at how generously you usually tip. If it has access to the sensors on your smartwatch or fitness tracker, it might be able to sense when your blood sugar is low and order the pie before you even realize you’re hungry.
Perhaps the most compelling potential applications of tool use are those that give AIs the ability to improve themselves. Suppose, for example, you asked a chatbot for help interpreting some facet of ancient Roman law that no one had thought to include examples of in the model’s original training. An LLM empowered to search academic databases and trigger its own training process could fine-tune its understanding of Roman law before answering. Access to specialized tools could even help a model like this better explain itself. While LLMs like GPT-4 already do a fairly good job of explaining their reasoning when asked, these explanations emerge from a “black box” and are vulnerable to errors and hallucinations. But a tool-using LLM could dissect its own internals, offering empirical assessments of its own reasoning and deterministic explanations of why it produced the answer it did.
If given access to tools for soliciting human feedback, a tool-using LLM could even generate specialized knowledge that isn’t yet captured on the web. It could post a question to Reddit or Quora or delegate a task to a human on Amazon’s Mechanical Turk. It could even seek out data about human preferences by doing survey research, either to provide an answer directly to you or to fine-tune its own training to be able to better answer questions in the future. Over time, tool-using AIs might start to look a lot like tool-using humans. An LLM can generate code much faster than any human programmer, so it can manipulate the systems and services of your computer with ease. It could also use your computer’s keyboard and cursor the way a person would, allowing it to use any program you do. And it could improve its own capabilities, using tools to ask questions, conduct research, and write code to incorporate into itself.
It’s easy to see how this kind of tool use comes with tremendous risks. Imagine an LLM being able to find someone’s phone number, call them and surreptitiously record their voice, guess what bank they use based on the largest providers in their area, impersonate them on a phone call with customer service to reset their password, and liquidate their account to make a donation to a political party. Each of these tasks invokes a simple tool—an internet search, a voice synthesizer, a bank app—and the LLM scripts the sequence of actions using the tools.
We don’t yet know how successful any of these attempts will be. As remarkably fluent as LLMs are, they weren’t built specifically for the purpose of operating tools, and it remains to be seen how their early successes in tool use will translate to future use cases like the ones described here. As such, giving the current generative AI sudden access to millions of APIs—as Microsoft plans to—could be a little like letting a toddler loose in a weapons depot.