Lila Sciences develops an AI-enabled Scientific Superintelligence platform – Combined with autonomous laboratories – That can perform the entire scientific method.
In a conversation with MobiHealthNewsMolly Gibson, president of Future Science at Lila Sciences, explained that this technology extends beyond traditional AI applications, such as protein modeling, by generating hypotheses, designing and learning from results.
She also emphasized potential risks, such as creating pathogenic organic products, and described how Lila works actively to reduce them.
MobiHealthNews: Can you tell me about the technology behind Lila Sciences?
Molly Gibson: Lila Sciences is a scientific super intelligence with autonomous laboratories. We build the opportunity to expand knowledge by performing the scientific method. So you take various aspects of science, biology and microbiology and more, and you use a computer to see how they can work together.
Historically, we have over the past five to 10 years, because we started using generative AI in science, really applying it to these parts of the science that the human brain is not connected. Things such as modeling proteins, or the molecular structure of a protein therapeutic, is something that our human brain is not connected to be able to do. We have applied AI to those places and in those narrow domains we were able to show very quickly that AI can do better than people.
The thing we have not shown before is that AI can actually do part of the reasoning of the scientific method that people are traditionally best suited to do. So the ability to generate a new hypothesis over the world, design an experiment, to test that hypothesis, to enter the lab and actually carry out that experiment and to learn from it. That is what human scientists have traditionally done.
We now believe that AI will be able to do all those components to run the entire wheel of science, and that is really what we believe in – Expand knowledge and the ability to build scientific super intelligence.
MHN: Is this comparable to a quantum computer?
Gibson: We use traditional computer use, GPU computing. So you can think about it as a similar type of progress, but not really quantum, not from the perspective of the types of calculations we do. It comes more of the way we integrate AI into the scientific method.
MHN: How will AI and super intelligence scientific research change?
Gibson: It is going to change the process with which we generally do scientific research. I think it will ultimately influence the role of a scientist. Scientists will always play a very important and important role in the scientific discovery, but some things that scientists do today will be done by AI.
But what I really believe is that it will make the role of a scientist much more fun, exciting and collaborative. The pace of the discovery will increase.
You could imagine that the role of a scientist is much more to guide AI to be more creative, to expand the searches with which we can explore, but [their role] is helped by AI. So I think it will change the nature of what it means to be a scientist.
MHN: So this is a tool for scientists; Is it not going to replace scientists?
Gibson: Yes, it is a tool for scientists. It will replace some of the things that scientists do today, but that does not mean that scientists will replace.
Nowadays we have such brilliant scientists designing plate cards for how experiments are conducted, and those are things they should be free of. When they are trained as scientists, they actually want to remain a scientist; They want to stay in that profession, and often I see so many scientists trying to get away from the bank today. How do we let Ai take those steps while they can do the nice parts?
MHN: How accurate is the Superintelligence computer?
Gibson: It really depends on what you are looking at. Nowadays there are many places where it is incredibly accurate. Our ability to design proteins, for example, is one of those places where it is really remarkable what we can do.
There are other places where there are unexplored spaces, and as we come to more and more uncertain spaces, it becomes less and less accurate. So just like any other type of computer system or any intelligence, it is honestly becoming less accurate because it becomes less certain and in more specific places that are investigated more, it is more accurate. And that is just how exploring new spaces is. If we actually go to new places, it won’t know much until it starts to explore.
MHN: Is this comparable to the Stargate project of President Trump and what they are trying to achieve – healed diseases by improving AI systems?
Gibson: There is some resemblance about many of the AI efforts. I will say what I think is very special about lilac is the focus on science and our ability to really understand. It was built by scientists, it is run by scientists and is also run by AI scientists. But we understand the problems of science and how we can actually do science.
There are these components of the real world you have to contend with when you make scientific discoveries, and that is what we are really building. We build AI -Science factories with which you can actually go to the lab, perform experiments and expand knowledge. So we do not stop when building the central AI system; We really build the fully integrated stack, end-to-end, for scientific discovery.
MHN: Do you think the technology will ultimately cure diseases?
Gibson: I really believe we will see healings. I think there is a lot of reach about what that looks like and what a cure really means. What I believe deeply is that AI will make human condition and health dramatically better. Whether it will heal a disease or whether it will enable us to live in a world without obesity, whether it allows us to fight with crises for mental health care – all those things will be improved with these types of systems. The exact definition of healing disease is often discussed, but today I think it is just the advantage that we know that life will be better if we have expanded scientific knowledge.
MHN: What are you nervous about as a risk? Do you pay attention to everything while you promote this technology?
Gibson: From my perspective, many of the risks we see are things that we cannot predict today. And so what we are working on is trying to identify how we follow it. How do we recognize them before they happen? How do we prepare for those moments when intelligence has risen to new levels?
What we work on building is that a safety framework that enables us to say: “Okay, this model or these models can improve our ability for a non-scientist to do advanced scientific methods. What are the risks associated with it? How do we follow?
We have had to test some of these things for decades. With the arrival to even synthesize DNA, we had to contend with the idea of synthesizing pathogenic agents, and we learned from it.
Now we are only trying to implement what is new in that case with AI, and it is really only to keep the same safety procedures in place for all the biological systems that we have today, but also fight with any form of malignant intention or bad intention, such as, only errors by the AI system.
MHN: Right. AI has a lot of potential, but you have to be careful, because what if AI wants to create something that destroys us?
Gibson: I think this is the debate, right? And I think we should be very careful in the end, but avoid building the thing that will improve the world … I think you just have to do it carefully. Just like in any other industry, when you make self -driving cars, there is so much advantage, but we have to do it carefully.
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