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Ep 147: The AI Revolution in Biology – How it’s changing

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  • 17 Nov, 2023
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Overview

The ever-evolving landscape of technology has paved the way for groundbreaking advancements in various fields. Artificial Intelligence (AI), in particular, has emerged as a transformative force, pushing the boundaries of what is possible. While AI is often associated with fields like finance and marketing, its impact on biology is now becoming increasingly apparent. In a recent episode of the Everyday AI Show, key insights were shared regarding the AI revolution in biology, shedding light on its potential to revolutionize humanity and create new opportunities.

Unlocking the Power of AI in Biology:

The integration of AI in biology is bringing forth a new era of scientific discovery and innovation. AI models are now being harnessed to develop new protein sequences with enhanced functions, ultimately revolutionizing the pharmaceutical, agriculture, and food industries. By analyzing vast amounts of unstructured data, AI models can uncover hidden patterns and generate insights that were previously unimaginable.

The Intersection of AI and CRISPR:

One of the most captivating aspects of the AI revolution in biology is its potential in gene editing. CRISPR, a protein complex inspired by microbial defense mechanisms against viruses, has been at the forefront of genetic engineering. However, its current applications are considered crude, and there is a pressing need for more precise and less disruptive gene editors. This is where AI can truly shine.

By leveraging AI algorithms, researchers and scientists can search for diverse gene editors that have the ability to make substantial changes in genetic disorders and engineering applications. The vast potential of AI and gene editing is limited only by our imagination, promising breakthroughs in medicine and agriculture that were once considered unattainable.

Addressing Ethical Considerations:

As with any revolutionary technology, ethical considerations must be at the forefront of AI in biology. The rapid pace of advancements necessitates careful regulation and responsible use. While government bodies are demonstrating interest in AI applications in biology, a combination of industry self-regulation and governmental oversight is crucial to navigate this emerging field.

Furthermore, education and public awareness play an essential role in fostering responsible AI integration in biology. By generating more interest, disseminating knowledge, and highlighting the potential benefits, society can make informed decisions about the ethical implications of AI adoption in the biological realm.

The Future of Medicine, Food, and Agriculture:

The synergy between AI and biology holds tremendous potential for transforming the medical, food, and agricultural sectors. Imagine a future where doctors can input a patient’s genetic information and symptoms into an AI model, which can then generate personalized therapeutics with minimal side effects. This level of precision medicine can revolutionize patient outcomes and treatment strategies.

Likewise, AI can optimize agricultural practices, leading to the production of healthier, more sustainably grown crops. By harnessing AI’s intelligent algorithms, farmers can improve productivity, reduce waste, and minimize the environmental impact of their operations.

Conclusion:

The alliance between AI and biology is ushering in a new era of scientific discovery and innovation. Through the integration of AI models, the shackles of traditional scientific limitations are being broken, offering vast opportunities to reshape industries and create a better future. As business leaders, decision-makers, and innovators, now is the time to embrace the AI revolution in biology, harness its potential, and explore the uncharted frontiers of technology in pursuit of remarkable advancements that will benefit humanity as a whole. Stay informed, stay curious, and be part of this transformative journey.

Topics Covered in This Episode

1. Impact of AI on Biology
2. Advancement in AI and Biology
3.  AI Models and Protein Sequences
4. Potential Uses of AI and Gene Editing

Podcast Transcript

Jordan Wilson [00:00:19]:

Everything is biological. So how can we use AI to shape the world that we live in? You know, maybe the the medicines that we all need, the food we eat, how does biology and artificial intelligence shape that? We’re gonna be answering those questions and more today on Everyday AI. Welcome. My name is Jordan Wilson, and I am your host. Everyday AI is a daily livestream, podcast, and free daily newsletter. Make sure to go check out that free deal daily newsletter. And we help everyday people like you And like me, learn what’s going on in the world of AI and how we can also leverage it. Right? And and and so many times we talk about, you know, software that we use, Business growth strategy.

Daily AI news

Jordan Wilson [00:01:07]:

So that’s why I’m extremely excited because I’m gonna learn so much today. And I and I know that you will too, but I’m extremely excited to talk about how, you know, AI is impacting the world that we live in and, you know, the biological makeup of just about everything. So, stick around for that. We’re gonna get started. But before we do, as we do every single day, let’s take a look at what’s going on in the world of AI news. There’s a lot. There’s some interesting things today. So, it’s kind of speaking of biology.

Jordan Wilson [00:01:34]:

Well, AI is apparently like climate change. So that’s the comparison that Google CEOs, Sundar Puchai recently made. So during the APAC CEO, summit in San Francisco, Sundar Pichai spoke about the global responsibility to create frameworks for AI regulation, comparing it to the shared responsibility for addressing Climate change. You know? AI will continue to proliferate globally, making it necessary to create global frameworks for regulation. So Pichai said Countries have a shared responsibility to build those global frameworks. So I was personally very you you know, when I read the headline for this story, I was like, wait. What does this all mean? But kinda makes sense. Right? It seems like individual countries right now are, you know, kind of going about AI regulation on their own, Accords, and, this is probably the first time I’ve seen a real call for global, regulation.

Jordan Wilson [00:02:27]:

So pretty interesting stuff there from the Google CEO. Speaking of big companies, Meta is now debuting some AI powered creator tools That seemed to be a Runway competitor. So if you’re in the creative content space, you probably know Runway, fantastic, text to video, image to video tool, but, the Facebook parent company just launched 2 new AI based features for video editing that Can be used to post to Instagram or Facebook. One is called Emu Video, which generates 4 second long videos with just a prompt. And the other one is called EmuEdit, which allows you to edit short videos with a text prompt, you know, to say, hey. You know, erase this from this video, And it does it. I’m extremely interested in this one. Meta technically has been teasing this for, Probably more than 9 months.

Jordan Wilson [00:03:20]:

And, you know, Runway has really grown in popularity, so I’m excited to see what this new Emu will do. And I love that Emu commercial, you know, the Life insurance one. Alright. So last but not least, Google is delaying its new keystone large language model to better catch up to OpenAI. So Gemini has reportedly been delayed. So right now, Bard uses the model Palm 2, which is not really Great to tell you the truth when compared to GPT 4. So Google is facing difficulties in catching up to OpenAI As seen through this delay in releasing their new large language model, Gemina. And this also impacts the Google versus Microsoft cloud race with enterprise customers because Microsoft has seen some great success recently, in this field due to their partnership with OpenAI.

Jordan Wilson [00:04:07]:

So, Google is kind of taking a step back, when they were reportedly going to be, debuting Gemini this fall, apparently, it’s gonna be delayed a little bit longer. Alright. So some big AI news, and we always have more. So make sure to go to your everyday AI .com. Sign up for that free daily newsletter. We’re gonna have more news of what’s happening in the world today, of in artificial intelligence and a lot more. But I’m excited now to to talk about the AI revolution in biology and how it’s changing. I don’t think we’ve had a guest On the Everyday AI Show that can help us look at the world in in this way.

About Anna Marie and Ginkgo Bioworks, Inc.

Jordan Wilson [00:04:38]:

So that’s why I’m extremely excited for today’s, show. So let me help. Bring on to the show, and please help me welcome. Here we go. I think there we go. Alright. So, Anna Marie Wagner is the SVP head of AI at Ginkgo Bioworks. Anna Marie, thank you for joining us.

Anna Marie Wagner [00:04:56]:

Thanks for having me. I’m really excited

Jordan Wilson [00:04:58]:

for this. Oh, I am as well. Biology. Right? Like, It’s it’s a certain thing that I think we we kind of take for granted because outside of, you know, biology and, you know, your high school or college classroom, I think Unless you’re in the field, you kind of stop thinking about it. But maybe, Anna Marie, just tell us a little bit about what you do, at at Ginkgo Bioworks.

Anna Marie Wagner [00:05:18]:

Yeah. Sure. And I totally agree. I think we I just think there’s, like, this massively wasted opportunity in the way that we teach biology To kids, like, it is it is this in incredible, like, alien technology that is capable of just such Incredible applications. My my favorite example, I I use this one a a lot, but it’s like there’s a there’s a protein in our bodies called ATP synthase, but it’s like a 21,000 RPM motor. It’s like 10 nanometers wide. It’s just like it is bananas, and it all runs on on code. Right? It’s like a c t and g, not zeros and one, but Like codable nanoscale physical technology.

Anna Marie Wagner [00:05:51]:

It’s very cool. So what ginkgo does in that sort of cool world of biology is recognizing that Most of the stuff around us is biological. Right? Like, you mentioned it earlier. We are biological. Our food is biological. Honestly, like, most of the Stuff we use has biological origins. Right? Like, even the plastics come from petrochemicals, which came from fossils, which were animals and plants at one time. Right? So most of our stuff can be made with biology.

Anna Marie Wagner [00:06:18]:

And our view was the way that the industry is organized right now To enable all of those applications is a is a bit broken because you have all these companies that are focused on individual products. And what that does is it loses the opportunity To build the type of kind of horizontal infrastructure that’s been so enabling in, like, the tech industry, for example. Right? You’ve got these massive data centers that are allowing Even tiny companies to access large amounts of compute really cost efficiently. Like, we don’t have that same infrastructure in the biological field today. And so that’s really what Ginkgo is is building. Right? Like, we are building. It’s it’s a huge wet lab run by robots and a lot of software that tries to lower the cost of doing, like, physical biological experiments, like making DNA, sticking it sticking it in cells, testing those cells to see what they do. And then in the process of doing that, we generate a ton of data about biology.

Anna Marie Wagner [00:07:11]:

Right? So what does this gene sequence do? Can we actually start understanding that? And that’s put us in a really interesting position to start leveraging generative AI to to try to get us closer to being able to answer these questions around. You know, if I wanted to design some new piece of biology that does something interesting and important, how would I do it, and and on a first principles basis?

Jordan Wilson [00:07:33]:

Alright. And I’m gonna maybe oversimplify this and, so we can put this conversation, you know, frame it a little bit. So, is it essentially that, you know, what you’re doing, at Ginkgo is, you know, collecting And organizing data from all different aspects of the biological world and then creating models and For everyone else to use. Is that kind of how it goes?

Anna Marie Wagner [00:07:59]:

That’s that’s part of it. Yeah. But importantly, we also do that sort of reinforcement learning step. So it’s like, the reality is that there’s a lot of data out there, but there’s there’s a lot more we don’t know about biology than what we do know Biology. And one interesting thing that I I think I I like to make people think about is if you think about large language models and human language, There’s a really high bar because we invented human language, and so you’re teaching a model to do something We invented. Like, we’ve got high standards for that. We did not invent biology. Like, we are just students of biology.

Anna Marie Wagner [00:08:37]:

Like, we call it drug discovery, not drug engineering for a reason. Like, we are out there discovering drugs. And what we wanna do is we wanna get to a point where we understand this space enough that we can start applying engineering principles to it in the same way that we can Build, you know, human language in the same way we can write computer code. All these things we invented, we can engineer with them. We’re not really there yet with biology, That’s that’s what Ginkgo is trying to do. And so, yes, we have the model side, and then we have this kind of data generation side because there’s still a lot we need to go test and learn To make these models good.

The future of AI in biology

Jordan Wilson [00:09:11]:

And and, hey, as a reminder, everyone, who’s joining us live, thank you as always. Please get your questions in now. It’s always so sad when a great question comes in right at the end of the show. So what do you want to know, about the AI revolution in biology? I think there’s so much that we can learn here. Maybe, Annemarie, let’s let’s kinda start at the end. What does this look like? So, you know, everything that’s going on in the world with, you know, AI in Biology and and what you’re doing at Ginkgo. What does it look like in the end? Is it, you know, the ability for other companies to Use these models that you create to help create, you know, better medicine, you know, healthier food, better crops. Like, what does it ultimately look like, if this kind of marriage between AI and biology in the long term is successful.

Anna Marie Wagner [00:09:57]:

Yeah. Yeah. So I think all those things that you mentioned, those are things I hope happen actually in the, Like, relatively near future. Like, that that should happen, you know, in my career, I I hope. Right? And and where I’d like to see that go is, Like, imagine a chat GPT prompt. Like, I I as a researcher should be able to type in, I want to develop a or so, like, maybe maybe I’m a doctor. Here’s my patient. I’m gonna upload their their genome, and these are their symptoms.

Anna Marie Wagner [00:10:23]:

I you know, I’ve I’ve I we’ve identified a cancer. Here’s the pathology of the cancer, And I would like to I would like to make a therapeutic for it. Please generate a therapeutic that kills the cancer and doesn’t have any side effects for this patient. And and you should have a model that is smart enough and has seen enough biology that it understands, okay, well, here’s how I’m gonna target that cancer cell and kill it, And and here are all the other proteins that this person has that play important functions, and I don’t wanna touch those. I don’t wanna kill those healthy cells, I don’t wanna interfere with any of their other biology, and so I’m gonna have a totally personalized therapy for that person. Like, that Would be and and I should as a doctor, I should be able to type that in in English, and and have have some cool, you know, medicine, programmable medicine come out the other side. Like, That is is what I think this this type of technology enables. But then remember, this is like every field.

Anna Marie Wagner [00:11:17]:

Right? So my my favorite sci fi example is, I wanna be able to sit down at a computer. Let’s say I I wanna buy a new house. Instead of buying a new house and or buying a plot of land and building it, like, I wanna be able to go into a CAD and design a house like the Sims or something, and then have it, like, print out DNA for that house into, like, a seed that I can plant on the ground and, like, water and grow. Like, I mean, biology is amazing. It can do these things. We just don’t understand how to how to manipulate that DNA in order To take advantage of all of those capabilities.

Challenges of AI in biology

Jordan Wilson [00:11:52]:

I oh, gosh. I mean, the, Like, obviously, I’m I’m not a scientist, and I’m not, you know, I’m not one that even understands biology very well. I think, yeah, it’s been 15 years, but The the thought that that could actually happen, right, is amazing to me. But so in the long term, you you know, or you said, hey. Even in your career, you’d like to think that These things are are very obtainable. Some of them, you you know, being able to use kind of this, kind of this new wave of AI, and biology to create better medicines, more personalized, you know, like in your example that, you know, doctor being able to sit down and type in real English. What are the the the challenges to getting there? Right? Because, obviously, there’s a lot of data. You know? Making these models is not easy, but, you know, specifically from, your vantage point, what are the biggest challenges until we can get there in those various fields?

Anna Marie Wagner [00:12:46]:

Yeah. Yeah. So I think the challenge is twofold. 1 is related to what I mentioned earlier, which is we didn’t invent biology. Right? We’re we’re intelligence of biology, and therefore, we don’t fundamentally understand all of the rules of biology, and therefore, we rely on the study of real world data. And so then the question is, how easy is it to get the data? And and there is a huge amount of data that is publicly available now, which we can which we we can and are using, but there’s also a huge amount of data that we don’t have. And bio to to get biological data, remember, these are physical experiments. Like you’re you’re printing DNA.

Anna Marie Wagner [00:13:24]:

You’re you’re moving liquids around. You’re growing cells. You’re measuring them. And so, like, the one analogy I like to give is is As you can think about programming biology like programming a computer. Right? It’s ACTG, not 0 and 1, but you can think about it similarly. But a big difference is that flipping a bit is effectively free today in computers. Flipping a bit in biology, like switching an a to a t, It’s not free. It is very, very, very expensive.

Anna Marie Wagner [00:13:51]:

Even just printing that code, like, print writing the code is a few cents per ad Base pair. Right? And so the issue we have, and one of the things Ginkgo is really focused on, is that it’s just really expensive to create and High quality labeled training data for AI models. And so it’s it’s and until we, you know, bring that cost down a few more orders of magnitude, I I think that will be our biggest limiting

Real world examples of AI in biology

Jordan Wilson [00:14:15]:

Interesting. Yeah. It’s I I guess unless you’re in your position, you really don’t know or understand how the whole process works. So thanks thanks for, Letting us know a little bit about this. I’m gonna go straight to questions because we already have some good questions, and and we’ll get back because I have I have more questions. But, a good a good one here from doctor Harvey Castro. Thanks for joining us. Just asking, what are some top examples of kind of AI revolution or maybe some, some new AI advancements, in biology.

Jordan Wilson [00:14:40]:

So, yeah, some some some real examples. We talked some some theoreticals and things you’re working toward. What’s kind of out in the wild, maybe some things that you’ve you’ve worked on?

Anna Marie Wagner [00:14:48]:

Yeah. So there are a couple of cool examples that we’ve worked on at Ginkgo. So, one one is so there’s a class of proteins called enzymes, And enzymes are proteins that basically catalyze chemical reactions. So, it makes chemistry happen. It it serves a function. And almost every program we work on for a customer at some point involves enzyme engineering or protein engineering. And so we’ve Already incorporated many AI models and trained them on our datasets, to help answer protein engineering questions. And so these might be things like help Me engineer this protein to better catalyze that reaction.

Anna Marie Wagner [00:15:26]:

So I have to I I have to use less of it to get, you know, the the impact I want or help make it more Stable so it doesn’t break down when it’s sitting on my shelf or it doesn’t break down when it’s in a high temperature reaction or something. And so we we have AI models that we deploy today that can Help us take, you know, just a generic sequence from the wild and or even from really no starting point and develop a new sequence that performs a better protein functions. So that’s 1. Another really cool one is on the biosecurity side. So we obviously think a lot about The other side of the a AI opportunity, which is the risk. So, you know, there is bio like, we are susceptible to biology. Right? So, you know, if you hear about folks kind of doomsdaying the future of AI. It’s usually around the intersection of AI and biology.

Anna Marie Wagner [00:16:12]:

Like, what happens if you can make, a bioweapon with AI? And so we’ve invested a lot in building biosecurity infrastructure. You know, how do you have effectively bio radar that is monitoring the world for new Biological threats and then identifying, and this is where the AI piece comes in. If I find a new piece of biology I’ve never seen before, Can I answer a few questions? Can I answer what it does, and should I be worried about it? Can I answer where did it come from? Like, is it engineered or not? And can I answer what do I do about it? Like, how do I make a vaccine or a therapy that that, protects us And so we we did some really cool work with IARPA, which is, like, the CIA’s innovation agency on, one of those questions, which If you find a piece of DNA, can you tell whether it’s engineered or not? So it’s sort of the equivalent of, like, if kids are cheating on a test By using OpenAI, can you tell that AI wrote their exam? It’s kinda the same thing for biology. Like, if I found a piece of biology, can I tell if somebody engineered it, ad Or was it just like mother nature threw something at us, that we don’t like? So it’s a really, really neat application of AI today.

Jordan Wilson [00:17:20]:

And and I’m I’m I’m glad, Tanya had this question just now. So saying, can you reiterate how you are going about retraining these models? Because, yeah, I think, You know, the example that you gave, it’s it’s easy with human language or probably much easier, right, to to train different, AI models. So Talk talk maybe a little bit about how that works. So you said it’s it’s expensive. It’s, you know, complex. But, yeah, maybe just just take us a little bit more behind how you’re actually Training, and and retraining these models that deal with biology, which, yeah, we didn’t create.

Anna Marie Wagner [00:17:52]:

Yeah. Yeah. Well, we’re we’re able to leverage You have the state of the art technologies and and art model architectures that are out there, and and there are a lot of parallels between The architecture of human language and the architecture of biology. Right? So you can think about, you know, an amino acid or a or a nucleotide, in in in biology as the equivalent of, like, a letter. Right? And and then you see these tokens, right, which are combinations of letters that are Reused frequently. Like, you see the same thing in biology. Like, a token might be a sequence of amino acids that tends to show up in a lot of different places. Like, it’s a recurring theme.

Anna Marie Wagner [00:18:27]:

It’s a backbone or and then those are assembled to make things that have meaning. Right? Like an entire protein, and and then you assemble proteins to make me. And so you can use a lot of that same, you know, kind of architect model architecture. You’re just feeding it a different a fundamentally different language, But it’s still doing the same thing where it’s recognizing, okay. Well, when I see this type of token, in that context, well, the next token is usually Like, I’ve got I’ve got a good odds that that token is that. And then and then the really tricky part comes in when you start wanting to build task specific applications on top of that with, kind of multimodal daily. What happens when I’m now importing? Oh, okay. Well, now I wanna understand function different kinds of functions, and I’m now importing different types of data and measurements, and and then you get a little bit more complicated.

Anna Marie Wagner [00:19:16]:

But that’s the the basics of it.

Jordan Wilson [00:19:18]:

No. I love it. And and and maybe it’s also important to, You know, hit rewind on this a little bit because, artificial intelligence is not new in biology. Right? I’m sure, you know, deep learning and, you know, neural networks have have been widely used for many years. But how how do by

Anna Marie Wagner [00:19:35]:

the way, The AI stole all their terminology from biology because biology is amazing. Like, a neural network is, like, the brains. It’s biology. It’s all it’s coming close.

Jordan Wilson [00:19:45]:

That’s very true. Yeah. I’ve got alright. Hey. Call the AI out. You know? Hey. In AI, whoever took that, Give it back to biology. Right?

Anna Marie Wagner [00:19:52]:

Give it back.

GenAI advancements in biology

Jordan Wilson [00:19:53]:

But but maybe talk a little bit about how with, advancements in generative AI, right, large language models even, How is this even changing AI in general in biology? And, yeah, maybe give us a quick little history lesson and, you know, what the new advancements in generative AI mean for your space.

Anna Marie Wagner [00:20:11]:

Yeah. So I I think the big one for us so historically in at the intersection of AI and biology, what we saw was that the Focus was really on the architecture, and and and there was sort of this acceptance that, well, because we’re not gonna have very much data, We need to have really good models that can deal with really small amounts of high quality data. And and that was sort of the way that the industry, Thought about AI historically. I think what changed with, you know, the advent of, like, the transformer architecture was this ability to take in large amounts of unstructured data and get useful insights out of it. Because, again, historically, The biology field is very, very focused on small amounts of structured data tell us something useful. And and, yeah, NL can kind of help us on the margin, but it was really around the quality of a specific experiment that you would run to answer a specific question. Now we can advance the state of the art by taking in these massive amounts of unstructured datasets that, like, I can’t understand. There’s nothing I can really do with it, But these AI models can now start identifying the patterns and the functionalities that are that are present in that unstructured data, and that’s become a much more useful foundation.

AI and gene modification

Jordan Wilson [00:21:28]:

And and, you you know, if if you are a little newer or not as, you know, AI geeky as as maybe me. So, you know, kind of what we’re talking about here is, and Structured data is data that you can easily categorize. Right? And and, yeah, like, many different sectors have been using, you know, structured data for decades, but now with large language models and generative AI, it allows us to better use the unstructured data, which is, you know, data that maybe can’t easily be Categorize because it needs a human, to interpret. So, it’s it’s it’s really cool. So maybe, one thing actually, Another great question because this this was on my mind too. So, asking, so, Taylor, thank you for the question. So saying, has Ginkgo or other companies, I thought about using AI with CRISPR. I I think that’s how it’s pronounced.

Jordan Wilson [00:22:13]:

I’m not sure. For gene modification. But, yeah, I’m super interested in in in, you know, how biology even Biology and AI come together for gene modification. So, yeah, what’s Yeah. No.

Anna Marie Wagner [00:22:24]:

I I’m really excited about the gene editing space in general. So maybe just a quick Science history for for folks that have studied biology. So what is CRISPR? CRISPR is a protein complex. Like, it’s a series it’s a it’s a biological complex that has emerged in microbes as they defend each other to defend themselves from from viruses. And and so what what’s interesting, like, if you think about the dirt in your There are billions, trillions of of little bugs living in there that are constantly battling each other for space and territory and are getting infected by viruses, and so they’re evolving very quickly interesting genetic tools to protect themselves. And so CRISPR is one of those tools. And and as we so we have a really large a couple billion, member, what we call a metagenomic database. So that is If you sequence all of these little microbes that live in random places, and and kind of understand what types of proteins they’re making, we we’ve got a very large proprietary collection of that, and so we’ve looked in that to see, okay, well, what else is in there that looks like a gene editor? Because CRISPR, honestly, we discovered a little bit by accident, Much like many of our medicines today, it was a bit of an accidental discovery, like, penicillin, like, bread got moldy, and now we have antibiotics.

Anna Marie Wagner [00:23:40]:

It’s great. Like, that is that is the last couple of centuries in biology. And so when we now we have the ability to use AI to be much more targeted about, like, what type of a gene edit do we wanna because CRISPR is honestly a little bit crude. So instead of, like, breaking all of the DNA apart and sticking stuff in and you’ve got all sorts of edits in places you don’t want, ad Can you find gene editors that are much more precise, much less disruptive, much safer, that can make bigger changes maybe? You know? So Different types of of, you know, genetic disorders that you might want to treat, or engineering applications of gene editors have different requirements. And and right now, the biological field tends to use kind of the same hammer for every job. And and what we wanna do is, like, sometimes you need a hammer, sometimes you need a screwdriver, And and those are different those are different tools, and and AI allows us to search the, you know, like, you know, 3 3,700,000,000 years of evolution to find that type of diversity so that we can use better tools.

Jordan Wilson [00:24:44]:

And and What would this ultimately be be used for? Right? Like, if if we can with the help of AI, you know, if AI and biology can cook up in the lab and, you know, make better DNA for us. Like, what does that mean? Is that, you know, like like, what Woozy is asking here about, you know, antiaging, or or what does that ultimately mean If we can use AI and biology to help alter DNA.

Anna Marie Wagner [00:25:08]:

Yeah. It I mean, I think we are only limited by our imagination. Like, this is, like, Biology follows rules, but it feels like magic to us because we don’t understand the rules. And so I think what AI does is it, you know, shows a little bit more of the how and the why behind the magic. And so, yeah, I mean, antiaging seems like it’s totally within the realm of what we could choose to do with biology. I do think you shift pretty quickly into the world of What should we do with biology? Right. I I I think that that will actually, to me, be the much more interesting conversation that we’re having 10 years from now. It’s not what can we do, it’s What are we as a society comfortable to doing? But I do think this intersect like, we we are we are at that inflection point to the vertical part of the Exponential curve Do the types of experiments we are doing in our labs that help us train AI models, which are getting exponentially cheaper to run and more advanced.

Anna Marie Wagner [00:26:16]:

And so, Like, that’s the intersection of a lot of exponential technologies. This stuff is going to move really fast, and I think we’re gonna have a hard time keeping up with that speed. And the more interesting questions will be, how do we choose to how do we choose to use it?

AI regulation in biology

Jordan Wilson [00:26:33]:

Yeah. Because then I yeah. And another great question here. That that leads to how is AI being regulated in biology because, yeah, it does seem like Once you achieve, you know, certain breakthroughs, there’s probably some ethical choices to be made or some deep conversations as a society. But at least, You know, right now and in the near future, how is AI being regulated in biology?

Anna Marie Wagner [00:26:55]:

Yeah. It fits and starts. So there’s there’s, like, the basic infrastructure of, like, drugs still have to go to clinical trials and get FDA approval before patients get treated with it. Right? So there’s, like, basic stuff of before things at the market. There is a regulatory process. On AI specifically, what you see in, like, the executive order that came out a couple weeks ago is, like, the the, you know, the government is certainly very interested in how AI is being applied to biology. And so in the same way that they’re saying, hey. Please Check-in with us if you’re training a really, really big human language model.

Anna Marie Wagner [00:27:27]:

They’re also saying check-in with us if you’re training a model on biological data. And so we are collaborating very closely, obviously, with with our partners, across, both private sector and and the government. There’s also a lot of self regulation that’s happened in this space. So for many years, we’ve been part of a, consortium of, companies that have the capability of doing DNA synthesis. So this is like writing biological code, where we self regulate, and we’ve said, alright. We need to make sure any piece of DNA we’re printing is not unknown pathogen. Like, you you can’t call us up and make anthrax. Like, that should not be possible.

Anna Marie Wagner [00:28:04]:

Right? And so there’s some self regulation that’s happened. But I think, again, I I do think this is one of the hardest questions because this technology is gonna move so fast. It’s gonna be really hard for regulation to keep up. And I think there’s a there’s a delicate balance here of, you know, sort of recognizing both the and and and the potential risks of this technology, but also recognizing that it will be this technology that allows us, It allows us to respond to the risks as well. And so making sure that responsible parties are able to, you know, advance the state of the art and be in a position where we can respond bad actors, or accidents. Like, that is that is really important.

Anna Marie’s final takeaway

Jordan Wilson [00:28:45]:

And, you know, as as we wrap up here because, Annamarie, we’ve We we’ve talked about everything. I’m glad we got to the ethical, piece, but we’ve talked about how AI is being used, how it could be used, some of the ethical concerns. But, in In the biology field and and how it impacts their lives. What’s that one thing that you want people, to know that will help, them understand, this this field that it’s kinda hard to understand.

Anna Marie Wagner [00:29:17]:

Yeah. I mean, I think, like, my main shtick right now is, Like, I think biology is the future kinda computer science. Like, for the last 20 years, everyone was like, oh, man. I wish I studied computer science because that’s the future. Like, today, that thing is biology. Biology is programmable. Like, it is the next frontier of major scientific discipline that we will be able to proactively forward engineer. And so making helping kids fall in love with biology, helping adults who are interested in, you know, shifting gears Care about biology and recognizing the impact it can have both for good and and for bad, is is my my top mission.

Anna Marie Wagner [00:29:55]:

And so if If I can get a couple more people interested in the field of biology through this, then, mission accomplished.

Jordan Wilson [00:30:02]:

Alright. Well, hey. There’s so much to be interested in, so much we couldn’t even get to, but, hey. That’s why every single day we put out a daily newsletter. So there’s gonna be a lot more there. But, Anna Marie, thank you so much for joining the Everyday AI Show and helping us better understand this AI revolution that’s going on in biology. Thank you so much for joining us.

Anna Marie Wagner [00:30:21]:

Thanks for having me.

Jordan Wilson [00:30:22]:

Hey. And as a reminder, like I said, please do go to your everyday AI.com. There’s gonna be a lot more. We’re gonna share a little bit more on what Ginkgo is working on, as well as maybe some topics we didn’t get as long to dive into. So make sure you go check that out, and we hope to see you back for more everyday AI. Thanks y’all. Appreciate

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