When many of us think about artificial intelligence, we have a sort of nebulous idea about digital entities trying to trick us into believing that they’re human. But in some ways, it’s a lot more complex than that. The Turing test is only one aspect of how we interact with LLMs and sophisticated neural networks today. Sometimes we know that we’re listening to the product of voice cloning combined with other technologies, but that doesn’t impact how we use the result.

Also, there’s an emerging field that’s getting a lot of attention now in light of all of this new potential. It’s called computational biology.

What is Computational Biology?

ChatGPT define con computational biology as a field that “applies mathematical models, computer science techniques, and statistical methods to the study of biological systems.” So if, for instance, you analyze the patterns on snail shells with reinforcement learning, that would be an example of computational biology. Likewise, if you’re trying to model human personas or behaviors, you could put that in the same category, too. Essentially, we’re trying to use technology to simulate biological systems, or alternately, diagnose them, and identify changes.

A Renaissance Man

Let’s ask this question: how old is the discipline of computational biology?

When we dig a little, we see that you can trace the origin of this field of study to none other than Alan Turing, the same person who’s widely cited for developing that Turing test concept and suggesting that computers will be able to present like humans, which, to a large extent, they’re now doing. Alan Turing wrote an article called “The Chemical Basis of Morphogenesis” in 1952, about natural patterns emerging from less dynamic states. So in addition to being so instrumental to the emergence of AI, he was a pioneer in computational biology, too.

A Modern Experiment

Let’s talk about some of the modern applications of new systems that you could think of as dynamic computation of biology – where again, the technologies are trying to mirror the behaviors, sentience and cognition of people, albeit, in this case, either fictional people playing roles, or people who are long dead.

In a recent presentation, Yufei Chen, an undergraduate from MIT, showed us an AI panel discussing AI ethics – it was composed of Marie Curie, Lord Voldemort from Harry Potter, and Sheldon Cooper, the imperious nerd from the Big Bang theory, played by Jim Parsons.

Oddly, the last two characters were based on the acting roles, not the actual people.

Throughout the presentation we got a look at how sophisticated these new systems can be, as Chen suggested we should “rethink entertainment,” citing statistics that streaming grows at 20% per year with a market cap of $150 billion by the end of this year.

Multi-Agent AI and Other Capabilities

Obviously, this kind of system runs on the principle of voice cloning combined with LLMs.

It demonstrates RAG or retrieval-augmented generation, where one component takes the knowledge from the knowledge base, and gives it to the generative engine, which then, well, runs with it.

It also demonstrates the ensemble approach that Marvin Minsky suggested in his “Society of Mind” book, where he famously theorized that the human brain is like hundreds of computer computers, all connected and working together. In the same vein, he noted, a society is full of individuals, collaborating and working together on various results.

In this case, we have an AI agent for each voice, and another AI agent as a mediator to make the conversation flow. You start to see the backbone of how these systems are going to work. Chen adds thoughts on capabilities like memory and multimodal function, suggesting we can fine-tune the personalities to get better results, and that AI will eventually do things like play video games with us.

So, in a sense, this kind of computational biology work leads toward more interactive applications. I’ve written about the idea, promoted by our own John Sviokla, of robust AI tutoring. I’ve written about the possibilities of AI companionship, and various types of agent services. We’re moving toward a new world at a tremendous rate, and computational biology is sure to be one of our central focus points going forward.