Ep 257: GenAI – Turning trash into treasures?

Episode Categories:

Use of Generative AI in Environmental Sustainability

Most individuals perceive generative AI to be confined within tasks like streamlining spreadsheet productivity, drafting marketing pitches, or aiding in other routine tasks. However, such a narrow perspective underestimates the potential of the technology. Some of the most advanced bio and life science companies have leveraged generative AI, exploiting its capabilities far beyond the common applications. Its impact stretches beyond productivity enhancement, reaching to a point of fundamentally augmenting the science, biology, and even the life it interacts with.

Unlocking Generative AI for Everyone

One of the primary objectives of many businesses is to make generative AI accessible and beneficial for everyone in their organization. The journey here begins with introducing the staff to a safe, secure, and user-friendly large language model. ChatGPT, or Claude are some of the representatives of this AI technology.

As perceived by many, generative AI isn't just for the tech-savvy or early adopters; it can equip every individual, regardless of their technological adeptness, to work more efficiently and effectively. To fully unlock its potential, both technical training and company support become essential.

Using Generative AI to Solve Environmental Issues

Technologies like generative AI are also empowering companies to tackle pressing global issues such as climate change. Innovative tech companies now aspire to utilize generative AI to turn waste into resources. By capturing carbon from waste products and recycling them before they end up in landfills, these companies plan to completely avoid digging for new carbon sources.

These companies leverage AI to engineer their refining plants and use it to mine data, predict the success of their strategies, and thereby optimize their processes and ensure efficiency. This approach essentially turns microbes into tiny cell factories, efficiently recycling carbon and producing essential products from waste.

AI and Scientific Exploration: Better Together

Scientific exploration is a journey laden with complex, unchartered territories. Combining the prowess of AI with human knowledge can help to decode difficult problems and accelerate the discovery process. AI handles the background research, seamlessly sorting through scientific literature, thereby enabling scientists to focus more on innovation and less on paperwork.

Where is Generative AI Leading Us?

In the light of recent breakthroughs, the concept of AI is subtly shifting towards IA (Intelligence Amplification) - amplifying human intelligence and capacity through AI. As people worldwide work to address substantial challenges like climate change, AI is perceived as an indispensable tool that promises to expand our capability and impact. As more organizations tap into this potential, a significant transformation in business, science, and society is a plausible future waiting to unfold.

Topics Covered in This Episode

1. About James Daniell and Lanzatech
2. How generative AI helps recycling
3. Use and benefits of AI within LanzaTech
4. Potential of GenAI solving environmental problems



Podcast Transcript

Jordan Wilson [00:00:16]:
When we think of generative AI, I think a lot of people don't really think of of science or biology. Right? We just think of being more productive with that spreadsheet or writing a better sales cover letter, right, or or or something like that. But that's not what generative AI is is capable of. That's not its floor. That's not its ceiling. That's just what a lot of us think. But there's actually ways that some of the world's most advanced bio companies, life science companies are using generative AI in ways that really have the the ability to impact life, to impact science, to impact biology. And that's what we're gonna be talking about today on everyday AI.

Jordan Wilson [00:01:02]:
What's going on y'all? Thanks for joining me. My name is Jordan Wilson. I'm the host, and everyday AI, it's for you. It is your guide for everyday people like you and me to learn generative AI, to learn really how it works and how other companies are putting it to work and how it's impacting our daily lives and society as we know it. And today is no different. So, normally, we go over the AI news. Technically, this is prerecorded show, but we're debuting it live. So as always, you can just go to your everydayai.com, and you can sign up for the free daily newsletter where we'll be recapping, the news as we always do.

Jordan Wilson [00:01:35]:
If you have comments, go ahead. Put them in. I'll be there, you know, answering, and maybe our guests will be able to as well. So don't forget to sign up for that daily newsletter where we will also be recapping today's conversation. Alright. Enough about that. Let's go ahead and bring on our guest today and talk about how generative AI can actually maybe turn trash into treasures. Alright.

Jordan Wilson [00:01:59]:
There we go. So James Daniel is the vice president of artificial intelligence and computational biology at LanzaTech. James, thank you so much for joining the Everyday AI Show.

James Daniell [00:02:10]:
Thank you for having me.

Jordan Wilson [00:02:11]:
Alright. Hey. Can you tell us a little bit what you do, in your role there and and what LanzaTech is for those that aren't familiar?

James Daniell [00:02:19]:
At LanzaTech, we are making technology to help solve climate change. So it is time to move away from this practice of extracting fossil fuels, using them once, and then polluting. We need to keep fossil carbon in the ground. And so what makes this difficult is so many things that we use every day come from fossil carbon. So if you think of, tennis shoes, hand sanitizer, laundry detergent, car tires, aviation fuel, what do they all have in common? Carbon? Exactly, yeah, they all come from

Jordan Wilson [00:02:49]:
Yeah, I got one right, all right. And

James Daniell [00:02:51]:
so this is something that many people don't realize, so all of these types of material goods are made from carbon that's extracted from the ground. And so if we still need to make these things, but we want to stop extracting from the ground, it sounds like an impossible situation, but it's not because the good news is we have enough carbon above ground to make everything we need. We just we just need to capture and recycle it instead of dumping it into the atmosphere. And so at Landsatik, we are a technology company, and we're developing exactly that. We develop carbon recycling technology. And the way it works is we capture pollution and carbon emissions. We feed them to hungry microbes, and then the microbes eat the emissions and turn them into chemicals. They turn them into building block chemicals that are used to make fuels and everyday products.

James Daniell [00:03:37]:
So you you can go and buy things today that are made from carbon, that that we recycled, things you can go and buy perfume from Gucci, athletic clothes and tennis shoes from Adidas made using recycled carbon.

Jordan Wilson [00:03:50]:
Wow. So, I mean, let's just skip to the end. So how has generative AI helped in this process? Right? And and I I love the way that you simplified it there for, you know, people like me who aren't, you know, super super smart into biology. I don't know if I've taken a biology course since I was, you know, like 16 years old. So how are you actually using generative AI to kind of capture, this carbon above ground and recycle it and, you you know, put it into use into everyday products that we all, you you know, use and love. How does generative AI play into that, equation?

James Daniell [00:04:27]:
Yeah. AI is an enabling technology for us, so it enables many of the things we do, and it helps us make our carbon recycling technology more efficient. And and so the main area where we apply generative AI is to engineer the biology at the center of our technology. And so I can talk a bit about that if you'd like.

Jordan Wilson [00:04:46]:
Yeah. Let's yeah. Let's let's get into it. Tell us a little more about that.

James Daniell [00:04:49]:
Yeah. Yeah. So, before I talk about how we're using AI to engineer biology, I think it makes sense to kind of answer the question, why are we engineering biology? So we we build these big refining plants, and so you can think of there as being 2 components to our technology, to our refining plants. So there's the bioreactor technology, which is like these big tanks, like steel on the ground, and then there's the microbes. So we put the microbes inside the tanks, we install the microbes inside the, the bioreactors, and then they eat waste gases, and they make, ethanol and other products. So the tanks and the hardware is fixed, but the microbes can be upgradable. And so we use AI to help us develop new and improved microbes, and so it's kind of like software upgrades. So you get a micro version 1, micro version 2, version 3, that's even more efficient at recycling carbon, or you might put in a microbe that's customized to produce a particular chemical, like isopropanol.

James Daniell [00:05:45]:
And so, and so so we engineer biology to develop this technology, and this was this was our first application of AI, 10 years ago. And we started using it, not because it was cool, but because we we had this problem. Right? We we barely need to solve this problem around engineering biology. And the problem was, or really, what what what AI offers is it gives us this missing piece of the puzzle in understanding and predicting biology. So we use biology. We love using biology. Biology is incredibly powerful. So you can think of each one of our microbes as being like a little factory with really sophisticated machinery inside it.

James Daniell [00:06:24]:
Every second, there there are millions of chemical reactions that are happening. They're like these little assembly lines that are converting chemicals. And so by by harnessing this incredibly powerful biology, we can very efficiently, recycle carbon. So biology is sophisticated, it's complex, and it's hard to fully understand how it works. But if we want to engineer biology using genetic engineering, to get it to do all these amazing things, we need to be able to, predict and understand. Otherwise, you're just stumbling around in the dark. So you're doing tons of trial and error in the lab. You'll try something, and it didn't work because maybe you have a knowledge gap that which made you made a bad prediction.

James Daniell [00:07:03]:
Sometimes you try something and it didn't work, and you just have no idea why it didn't work. So the key point is it turns out that machine learning is an excellent descriptive language of biology. So you can throw all of this biological data in the AI system. It will learn patterns. It will figure out what's happening, and then it will allow you to make predictions. And so our scientists use this technology across our research stack, and a good example of generative AI is effectively a version of chat GPT that we use that can speak biology.

Jordan Wilson [00:07:36]:
Yeah. A a a lot there. I kinda wanna, you know, dissect this, you know, one piece at a time. So, you know, one thing that you talked about, James, is, you know, your company, like a lot companies have been using, AI for a long time. Right? Like, you you know, companies in biology and life science have been using AI and deep learning machine learning for, you know, decades, specifically when it comes to the generative side, right, when we talk about large language models. Even for you personally, how has that changed? Not just, you know, the day to day work of of people working in these in these fields and related fields, but how does it also change what's possible?

James Daniell [00:08:21]:
Yeah. So there was a big algorithmic breakthrough, in 2017, and that was the development of the transformer, at Google. And so, many quite a few people know that the transformer has enabled generative AI technology like large language models and chat GPT. Not as many people realize that that breakthrough has also enabled generative AI for biology. And so, Chat GPT is trained mostly on text, right, on the Internet, on written language. Our biologists work with similar models, that are trained on the language of life itself. So DNA is like a language. It stores biological knowledge.

James Daniell [00:08:59]:
It's a sequence of nucleotides or like a sequence of letters. And so we we use these big GPT style foundation models, in the order of 15 or 20,000,000,000 parameters. So these generative pretrained transformers that are pretrained on a large number of biological sequences. And so over weeks and over months, these models are pretraining on biological sequences from all sorts of bacteria. And then during that process, the the language model is learning something really useful from it all. So it's learning some fundamental rules of of biology in the same way that that a large language model is learning, rules of language as well as some understanding of the world. And so this, and so then once we've pre trained one of these models, we can use it for tasks. So like Chat GPT, you could generate text or we could use it to classify text.

James Daniell [00:09:47]:
Biological language model, you can actually generate the language of life, which you can then engineer into a microbe. And so I like I talked about how our microbes are like these little cell factories with all these little assembly lines. So you can imagine that proteins inside the microbes are like machinery on these assembly lines, And so we can generate biological sequences that allow us to create new or better proteins that we put into the microbes that will allow our microbe to make a particular building block chemical, for example. And so we've done this, for, like, isopropanol or isoprene, which are important building block chemicals. And so, really, like, if if I look back over the last 10 years, that has been, I think, the biggest breakthrough. Well, there'd been many breakthroughs, but I would say that is the biggest breakthrough, that we've seen from that transformer model architecture.

Jordan Wilson [00:10:39]:
Yeah. And you're right. Right? Like, I I I think as soon as, you you know, the early GPT models, you know, started to became, or started to become commercially and, you know, publicly available, different sectors, right, started to adopt them for their own uses. You know, I'm curious because you mentioned that at at LanzaTech that, you know, you almost have this version of of Chat GPT internally. Right? And we've talked to some companies, on the Everyday AI Show before that have, you know, kind of built their own model or, you know, fine tuned their own model. So, you know, I'm curious, how has that process been so far for your company, and what has it allowed others to do? Because I'm I'm sure not everyone, you know, is is the vice president of AI and computational biology. So there's probably people that have maybe a little bit more of a learning curve when it comes to working with AI. So what has having kind of your own internal model, enabled your team to do?

James Daniell [00:11:40]:
Yeah. So there's there's 2 aspects to my role. So I oversee our computational r and d, and and so we're applying these techniques across all stages of research and development there. But I'm also responsible for our AI strategy across the business, and so we've been adopting, this technology across the business. What one part of our broader AI strategy is is quite simple. So like like many businesses, we're just empowering all of our people with the skills and tools to use large language models because it makes them way more effective. Mhmm. And so I think all businesses are going through this right now, or or if they're not, they should go through it if they want to stay competitive.

James Daniell [00:12:16]:
And so I think we we have many different use cases, but tactically, the first and the easiest thing that the team did was just giving all of our people across all functions access to a safe and secure large language model. So like a basic AI assistant to help with writing, summarization, brainstorming, and so we call it LANSA Chat. It's like Elantin or Chat Gviti or Claude or or Gemini. And one of the interesting things I've found is that I I have yet to find a single person in the company who can't get value once they've been properly trained from using this tool. And so based on that, I can confidently say that I think all businesses can make their knowledge workers more productive by doing that. I'm I'm sure you agree with that, Jordan.

Jordan Wilson [00:13:00]:
Yeah. And, you you know, I'm glad you said that because I still think that there's a common misconception out there in the business world that so many employees, you know, aren't going to find value from working with a large language model or, you know, even a domain specific large language model. It's actually having a conversation, just just last night with someone who is in a similar position. They they they have a model for their team, and they're like, well, you know, the the rate of adoption is is rather low right now, and and we really need to, you know, increase that. So, you know, I'm curious from, you you you know, someone who's kind of charged with making sure that, you know, a large, company such such as LanzaTech is is using this technology across the board. What are some of your key findings or or or key learnings that maybe you can share with with others who are in a similar position who maybe have their own model, but they're struggling, to to get more and more people to use it and to find value.

James Daniell [00:13:56]:
Yeah. So it's it's never it's never just a technology thing. Right? People quite often focus on the technology, but a big part of it is actually the people and the processes as you implement the technology. I mean, like, the the data is there that shows that this stuff is making people more productive. Stanford just released, their 2024 AI index, and I haven't read the whole thing yet because it's 500 pages. But in part of it, they they summarize several studies from 2023 that assessed AI impact on labor and kind of the consensus there is that it makes workers more productive, leads to higher quality work. But in terms of what we've learned, one of the interesting things is we had early adopters, so we had people who were already quite sophisticated in using these tools in their personal lives, and so we gave them a safe way to use us at work. We gave them something that was grounded on on our organizational data, and and they did a great job.

James Daniell [00:14:51]:
They created value. But what was interesting for me is is the biggest wins that we had were with non early adopters. So people who had barely even heard of ChatGPT or maybe they had tried, like, like the free one, gpt 3.5, which which isn't representative of what AI can do today. And so we we gave them training. So again, it's it's not just giving them the technology. It's actually, giving training, upskilling. We gave them a little bit of a push, it was really interesting to see how much more efficient and productive, they could be and some of the interesting use cases. So we had, like, many examples.

James Daniell [00:15:27]:
One that I remember vividly is someone was gearing up to do a particularly well, they described it as a particularly painful and frustrating documentation task, and they had planned to do that in 3 to 4 months. And they did it in 6 days in partnership with a language model. Wow. And so we've we've got some really great stories across the business like that. And so as as people are kind of leading the way, they're talking to colleagues and then we're just we have this transformation process that's happening. And so I think this will happen through all businesses.

Jordan Wilson [00:15:58]:
You know, and and I think that, you know, begs begs the question, you know, what happens? You you know, what happens when you start to get those kind of efficiency gains across the board because it is a process. Right? So, you know, having your own model is always, you you know, much better, than than than using a, you know, public model. Right? When you can, you know, use RAG and fine tuning and, you know, have your own, domain, you know, knowledge in there and that helps get those, you know, efficiency gains like you talked about. You know, one thing that I always think about is, you know, the, DeepMind. Right? And and they use a specialized model, to to, I think it was called fun search to kind of solve this unsolvable, math problem that the world's smartest mathematicians had been working decades to to solve. Right? So when you think about your end of the spectrum, when you think about, you know, you're talking about genetic engineering and and solving these these great biological problems, what do you think language models will help, whether it's your team or just, you know, your industry? What kind of big problems do you think, you know, they could solve?

James Daniell [00:17:10]:
Yeah. So I'm, so I'm I'm focused quite a lot on technology development, and so it's clear that AI accelerates technology development in general, which is, I mean, which is no different to how other general purpose technologies have accelerated technology development, so like electricity or the computer. If I talk to the team and say, hey. For the next 3 weeks, do you work without electricity? It just sounds crazy. Right? And so so AI will be the same, and I think it makes people more efficient and also more effective. And so that's, I mean, that's exciting. Right? Because if AI is a great accelerator of science, then that means we can do more science, we can do better science, We can develop technology more quickly. We need to develop technology more quickly, to address many of the challenges that we face.

James Daniell [00:17:58]:
And so I think that's that's really the outcome. Right? It allows people to do more and and to be more effective.

Jordan Wilson [00:18:04]:
Might it might it be to the point and, again, I'm no I'm no expert here. I'm I'm learning along with the rest of our audience. Right? So we we talked about in the beginning, James, you you know, kind of this this process of what you're trying to do, at Landsatuck, very overly simplified. Right? But, you know, trying to, you know, capture carbon out of existing products. Maybe they're they're before they go to the landfill, you know, versus having to dig into new sources of carbon because the world needs it. Right? Might there be a time when we don't need to be digging up new carbon and we'll be able to everything will kind of be, you know, able to be recycled. Is is is that a possibility? You you you know, walk us through that.

James Daniell [00:18:45]:
Absolutely. I mean, that's our vision. I mean, by like, I would like to see by, let's say, 2024 where every consumer can make a choice where they can buy products that have been made from recycled carbon as opposed to carbon that's come out of the ground. And as as I said, there is enough carbon above ground to make everything we need, and so through through technology, we can we can drive this change.

Jordan Wilson [00:19:10]:
You know, I'm curious. Even for yourself, you have a very unique vantage point, you know, working for a large company such as LanzaTech. So you are both looking forward into new innovations and, new bio biological, problems that that you're trying to solve for the world, but then you're also looking internally, right, at your team and and looking for ways to use, generative AI to help make the company, you know, more efficient and and more impactful, in its work. So I'm I'm curious even for you, what is whether you wanna talk day to day, week to week, month to month, but what is the way that you are excited, by the the prospects of using generative AI in your own work? Because I think that'll help open up the eyes for a lot of others.

James Daniell [00:20:01]:
It's and it's a difficult question to answer because you can see how it can touch all aspects of knowledge work. Right? Mhmm. 1 so one thing that's quite exciting for me so I I have a background in science, and I mentioned that AI accelerates the scientific process. And so you think, what is a scientific process? So you you do background research. You might design experiments. You'll analyze data, you'll interpret results. And so one thing that I find quite exciting and one thing that I wish we had, like, 15, 20 years ago was was the ability to, to automate a lot of this process of background research. And so I'll give an example of what what we are doing.

James Daniell [00:20:38]:
So before someone, does an experiment, the first thing you want to do is you want to understand what's been done before. Right? You want to, and and that often requires reading a lot of scientific literature. Inside our company in LanzaTech, we have an internal scientific knowledge base that we've built over 15 years of science knowledge, and it has over 30,000 pages that are experts of Russian. And so you imagine if someone's about to do some work, they need to find out what's been done before. And that that that can take a while if you're having to do it yourself. And and so I can see examples of where it might have taken 5 days of going down going back and reading and figuring out what's been done before. We we built a large language model on on that knowledge base to help our scientists do that. And now you can do that in a day.

James Daniell [00:21:24]:
Right? The the language model can kind of build those connections, extract the insight. So that that type of thing is really exciting for me, just because of its ability to speed up the process of of doing science.

Jordan Wilson [00:21:37]:
Wow. So, you you know, I'm curious. What's what's next? What's next for whether, LanzaTech and and using AI? Maybe can you talk about any, you know, big projects that are coming down the pipeline or just for your industry in general. Right? And and as we look at, you know, kind of the the topic of today about how we can, you know, turn trash into treasures, what's what's next? You you know, what should we be looking at, you you know, as we hope to, you know, solve this problem or solve it a little bit more with generative AI?

James Daniell [00:22:09]:
So one interesting area, for us in science is using AI to allow us to do really efficient and effective experiments, and so that's an area of development. There's a lot of hype around this idea of an automated scientist. I I don't think that's the right framing of the problem. We still have our scientists who are working in partnership, with with these techniques, but, that that's quite exciting for me because if you can really accelerate your ability to do experiments and, or really decrease the need for for the number of costly and time consuming experiments and then that, I I think that'll really transform things. But what's I mean, from from my perspective, and this is actually something that we we communicate with the teams during our our training when they're using this, I think it's really important when using this technology that the human is always in the driver's seat. And so that's, like, that's the expectation that we're setting across the board when we're using this technology. So the like, the human the person is always checking results or is always owning the work output. So instead of saying, oh, the AI did this.

James Daniell [00:23:14]:
That's why there was a mistake. Like, we never wanna see that. So, like, one of our values as a company is we own our decisions, and so that's that's the way that that we're framing the use of these tools. So you own the output. You always stay in the driver's seat. And I think with that, that that gives us the best of this human AI partnership.

Jordan Wilson [00:23:33]:
Yeah. I'd I'd I'd really like to use that. If, if I have a bad show, you know, I'll just use that crutch. The AI the AI made me make this bad show. But, I mean, you bring up a good point. Right? Because in the end, as generative, you know, even generative AI becomes more and more commonplace in our daily lives, in our in in society, in our daily workflows, you do have to find, that right balance. And, I love what you said there, James, about, you know, the human is always in the driver's seat. Right? Even if it's an autonomous car, you know, with AI, like, you still have to put a human somehow either literally or figuratively in the driver's seat to make sure that they have accountability for those decisions.

Jordan Wilson [00:24:11]:
So so so, James, we've talked about a lot, in today's, conversation, but, you you you know, I'm wondering what is the one main takeaway, that that you hope that people can take away, you know, specifically as it comes to, you know, kind of the the the work that you all are doing and how you are using generative AI to get it done.

James Daniell [00:24:32]:
Yeah. So so I personally think we're entering this kind of golden age of technology development. And from what I see at Lanstec so so we across the world, right, we have incredibly smart scientists and engineers and innovators who are dedicating a lot of effort to solving these big challenges, challenges like climate change. And so I I I think AI is just really allowing people to massively increase their impact. I mean, we're excited about AI. Right? But what I see at the moment really is IA, intelligence amplification, and that's that's that's what that's what we're seeing right now, and we're really excited about that.

Jordan Wilson [00:25:09]:
Love that. I just jotted that down. You know, flip flip the script. I I love that intelligence amplification. So, another gem for us. So thank you so much, James,

James Daniell [00:25:26]:
you for having me, John.

Jordan Wilson [00:25:27]:
Alright. Hey. A lot that we covered in today's episode. Maybe you were driving. Maybe you were out walking your dog. Or maybe, like me, sometimes science and biology can be a little bit confusing. So don't worry. We're gonna be recapping everything and today's highlights from the show.

Jordan Wilson [00:25:42]:
So make sure to go to your everydayai.com, Check out the daily newsletter, and also check us out again. We hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.

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