The Case for Cyborgs

2023 was the year AI went mainstream. The big question in 2024 is: how far can this generation of AI technology go? Can it take us all the way to superintelligence—an AI that exceeds human cognitive ability, beyond what we can understand or control? Or will it plateau?

This is a tricky question to answer. It requires a deep understanding of machine learning, a builder’s sense of optimism, and a realist’s level of skepticism. That’s why we invited Alice Albrecht to write about it for us. Alice is a machine learning researcher with almost a decade of experience and a Ph.D. in cognitive neuroscience from Yale. She is also the founder of re:collect, which aims to augment human intelligence with AI. 

Her thesis is that this generation of AI will peter out performance-wise fairly quickly. In order to get something closer to superintelligence, we need a different approach: the augmentation, rather than replacement, of human intelligence. In other words, we need cyborgs. 

I found this piece illuminating and well-argued. I hope you do, too. —Dan

---

In the year-plus since the world was introduced to Chat-GPT, even your Luddite friends and relatives have started talking about the power of artificial intelligence (AI). Almost overnight, the question of whether we will soon have an AI beyond what we can understand or control—a superintelligence (SI) that exceeds human cognitive ability across many domains—has become top of mind for the tech industry, public policy, and geopolitics.

I’ve spent much of my career straddling human cognition, cognitive neuroscience, and machine learning. In my Ph.D. and postdoctoral work, I studied how our minds compute the vast amounts of information we process on a second-by-second basis to navigate our environments. As a data scientist, machine learning researcher, and founder of re:collect, I’ve focused on what we can learn about humans from the data they generate while using technology, as well as how we can use that knowledge to augment their abilities—or, at the very least, make their interactions with technology more bearable. 

Even with this experience, I know that it can be daunting to understand all the terms about AI that people throw around. Here’s a simple explainer: generally speaking, the goal of AI is to create an artificial intelligence that matches human-level intelligence, while SI is an intelligence beyond what even the most intelligent human is capable of. Sometimes, SI is imagined as a machine that outpaces human intelligence. But it can also be a human that’s been augmented—or even a group of humans working collectively, like a team at a startup. 

The fear that SI may become a “runaway intelligence,” like the Singularity Ray Kurzweil has written so much about, comes from the theory that these intelligent systems might discover how to create even more intelligent systems—which could lead to machines that we can’t control, and that may not share our cultural or moral norms. 

Different pathways to superintelligence. Source: the author. 

But we can’t plan for runaway SI yet. We still haven’t reached the first step—creating a machine that matches human intelligence, typically measured by the Turing Test benchmark. This first step is a big one—replicating human intelligence in machines has proven to be a much harder task than some early computer scientists anticipated. 

Up until about four years ago, we had reached artificial narrow intelligence (ANI), which could be used to accomplish specific tasks—like image recognition—as well as or better than humans. With ANI, it wasn’t possible to generalize to a broader range of potentially novel situations or tasks—an ability that would make it artificial general intelligence (AGI). And depending on whom you ask, large language models (LLMs) like GPT-4, which powers ChatGPT, have allowed for us to create an AGI. 

However, moving beyond AGI to SI is a different challenge. Thus far, LLMs have tended to improve with more compute and access to larger-scale training data, there’s no guarantee this trend will continue. In fact, I believe we will soon plateau.

If that happens, I propose an obvious but somewhat overlooked path to SI: cyborgs, or a closer integration between humans and machines that can augment our already amazing intelligence. In this article, I’ll lay out my case for cyborgs as a path to SI and how I believe we can get there.