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MR. DE VYNCK: Hello. That was a long quote from you, Alex.

MR. DE VYNCK: My name is Gerrit De Vynck. I am a tech reporter with The Washington Post based in San Francisco, and I'm joined by Alex Wang, who you just saw that great intro about.

It's really cool to see so many people here. Thank you so much for coming. This event was really oversubscribed, and I think, you know, we both go to a lot of events that are both in San Francisco usually, and it's cool to come here to the other side of the country and see, you know, just the level of interest and--like in NSF, it's kind of everyone--everything everyone's been talking about. Maybe we're even a little bit sick about it, but it's cool to say, okay, it's not just us. It's not just the weird, you know, hippie commune out in the West Coast that's talking about this stuff. So thank you all for coming.

Alex, I think we can just kind of jump right into it, but I just want to make sure for people who maybe haven't heard of Scale or maybe have heard of you but don't really know what you do. Can you just in a couple sentences explain, you know, your company? Like how are you different from a company like OpenAI or Google or Microsoft that's also doing AI right now?

MR. WANG: Yeah, that's a great question. So I started the company back in 2016. I was studying artificial intelligence at MIT and became--this was the year when DeepMind came out with AlphaGo, when Google released TensorFlow, and it became very clear that artificial intelligence, even at that time, was going to be one of the most important technologies certainly of my lifetime.

And I started Scale really to build the foundations and power the development of AI with the most critical piece, the data.

Since then, since starting the company in 2016, over the past seven years, we've been a part of every major advancement in artificial intelligence. We've worked with many of the large autonomous vehicle efforts, including General Motors, Toyota, and many of the large automakers. We've worked closely with the U.S. government and the DoD on many of the initial AI programs. I'm sure we'll talk about that here in a bit. And then we've worked very closely with the entire emergence of generative AI and large language models. We've worked with OpenAI since 2019, innovated on many novel methods and use of data for artificial intelligence, and work at this point with the majority, vast majority of the AI ecosystem, players like Meta, Microsoft, and many others.

MR. DE VYNCK: So it's kind of like picks and shovels of like this AI gold rush. Like you're selling the tools to help these companies develop these chatbots and LLMs and that kind of thing?

MR. WANG: Yeah. I think our differentiated view is perhaps that, you know, this entire industry needs platforms, and it needs infrastructure to enable it to be successful. And so our view is, the best way to enable that all to happen is to power it in infrastructure platform that everybody can use and the entire industry can benefit from.

MR. DE VYNCK: And you mentioned the government. Obviously, your quotes, you know, your congressional testimony talking a lot about military use, talking a lot about China, geopolitics. You have a $250 million deal with Pentagon. I mean, that's a serious amount of money. I mean, why--you know, coming from San Francisco, it's not something that we hear a lot. We don't talk that much about even government tech and especially military tech. A lot of people out there are still uncomfortable with that. Why did you choose to kind of position your company this way to, you know, really aggressively sell to the Pentagon? What's behind that choice?

MR. WANG: Yeah. So I grew up in Los Alamos, New Mexico. Fr those of you watched "Oppenheimer," it was literally filmed in many places from my childhood. And both my parents worked for the National Lab in Los Alamos, working on fusion physics and weapons technology, and so I grew up in this hotbed of, you know, these incredibly brilliant scientists who had made it their career and made the decision to dedicate their lives towards building very advanced technology to ensure that we maintain U.S. leadership.

And as artificial intelligence became a more and more real technology over the past decade, it became pretty clear that this technology was one of the very few technologies that had the ability to impact the balance of power globally, and in particular, the sort of--you know, China published their strategy, "Made in China 2025," of which chips and AI are some of the key tenets. You know, they talk specifically about how they believe a future world will be primarily dominated by chips and semiconductors, and they need to invest heavily into that technology to enable future technologies such as AI.

And so, in particular, I went to China in 2018. I visited China--one of our investors organized this trip--to understand the Chinese tech ecosystem, and in one of the companies, a Chinese facial recognition company, you walk in the lobby, and there's a giant screen that shows like a video feed of the lobby and your face immediately gets recognized as you walk in, and real time, you see your face be recognized. It recognized who you are, your major demographics, like all this like very dystopian--it was a very dystopian tech demo. And this was back in 2018 and basically realized that other countries were going to be--particularly China--was going to be very, very dedicated in using artificial intelligence to power their country's ambitions, let's say, and this was--you know, this is well reported. They use facial recognition technology very actively to suppress Uyghurs and for in building a global surveillance state.

And it became pretty clear that, you know, if you believe that AI is going to be one of these critical technologies, there had to be American companies who could help bridge the gap between Silicon Valley and D.C. and could help bridge the gap between this incredible wellspring of technology and innovation that was happening in San Francisco, happening in Silicon Valley, and bring that technology to the U.S. government to actually empower America to stay ahead, to stay in a leadership position.

MR. DE VYNCK: Right. I mean, we just heard from Leader Schumer and he was--you know, he said we are ahead--or the U.S. is ahead, but the gap is narrowing was his characterization of it. You know, you and I were just talking backstage about chips and kind of, you know, how we're--you know, a lot of time when we talk about AI, we think about software. We think about things that are, you know, learning themselves. But at the end of the day, hardware is a huge part of this. And so, I mean, do you think that characterization of, you know, ahead but the gap is narrowing is accurate, and how do you think about this race or this arms race, so to speak?

MR. WANG: Yeah. You know what? What I would probably say is certainly the U.S. is ahead. The technologies were invented and innovated and developed predominantly in the United States. London is--you know, the UK as well has been a key, a key innovation hub for the technology. And so we're ahead today.

I think China has incredible ambitions to catch up from a technological perspective, and they've demonstrated in the past, in both software and other AI technologies, a clear ability to, you know, catch up and in some cases even surpass U.S. tech capabilities.

If we look the last generation of artificial intelligence, computer vision technology, so being able to understand images and videos for technologies like facial recognition or self-driving cars, China was behind. You know, these technologies were created and developed in the United States. China recognized that, immediately created very large domestic industries to fuel this AI development in facial recognition, in autonomous vehicles and so on. And now if you look at where is the cutting-edge computer vision technology being built, it's actually in China. You know, they successfully caught up and got ahead.

And so my fear is in this--you know, in this current wave is that in large language models, in cutting-edge generative AI, and AI technologies, the same might happen yet again.

You know, we saw--it was reported earlier this year that China has bought $5 billion worth of high-end GPUs, predominantly NVIDIA GPUs. That's an incredible investment. It's a very--that's a very large and decisive investment by Chinese tech giants to catch up to American technology.

And in the backdrop of everything that's happening now in AI is the scaling laws, and this is, I think--you know, it's sort of a little bit behind the scenes, but this is the underlying trend that's defining everything, which is, you know, simply put, we're using just dramatically more compute, dramatically bigger models, and dramatically more data to build dramatically more powerful algorithms.

So in the past four years, there's been a thousand-fold increase in the amount of data used to power large-scale AI systems. So, you know, in 2019, the models were about 2 billion parameters in size, and now they're about 2 trillion parameters in size.

Many companies are on the record for over the next three years, roughly three years, for another hundred-fold scale-up in computational capacity for these--for these algorithms.

So over the course of, you know, that seven-year span, it's a hundred thousand-fold increase in amount of computational power applied to training these large generative models. And that--you know, there's very few industries where you see a--over a seven-year period, a hundred thousand-fold increase in resources. And so this creates a lot of--this creates a lot of pressure in how countries think about this technology, and in particular, it creates a lot of pressure on the supply chain.

So kind of as you mentioned, this depends a lot on hardware. It depends a lot on high-end GPUs, particularly GPUs manufactured and produced by NVIDIA.

And, you know, we saw recently increased sort of export controls on chips. I think this is going to be an increasingly hotbed issue for the U.S. versus other countries, and today, a hundred percent of high-end GPUs are manufactured in Taiwan at TSMC.

MR. WANG: So there's a very clear geopolitical tension that only increases--it will increase literally a hundred-fold over the next three years, which is that today there's an entire--the choke point of the entire AI industry and all AI progress comes in these fabs in Taiwan at TSMC. And so if--you know, there's a lot of ways this plays out. There's many scenarios, but in one such scenario where China deems that they're falling dramatically far behind, it makes it far more likely that they'd choose to invade Taiwan and either all the fabs in Taiwan blow up and TSMC blows up and set back AI progress across the board or they seize them and then use that production solely for their own purposes.

MR. DE VYNCK: I mean, there's a lot of people in Silicon Valley, prominent AI leaders, powerful AI leaders who are, you know, talking about AI algorithms beginning to outthink humans in years rather than decades. And I think, you know, I've been very skeptical about this, but these are very smart people who have serious chops and have huge amounts of followings within the industry. And some of them say, you know, the worst thing you could do is attach something like that to a military system. And so, I mean, how do you engage with that, or how do you think about that belief that AI will, you know, outstrip human ability to control it imminently? Like do you take that seriously at all, or like where does--what do you think of that?

MR. WANG: If you look at the existing technology that we have as well as the technology that's coming down the pipe and sort of like all of the research and understanding of where this technology is going, I don't think that's a reasonable fear as of now.

I do think that this technology is incredibly powerful, both for use of ensuring that democratic powers stay on top and that the United States maintains a leadership position, and there's real misuse cases, and there's things that we need to be concerned about the technology being used for.

Our view is that AI--you know, if you look at the history of warfare, it's punctuated by technology, technological advancements that create asymmetric advantage.

MR. WANG: That's sort of the--you know, summarize centuries and centuries of warfare--and artificial intelligence is one of a small handful of technologies. It's not the only technology, but it's one of a small handful of technologies that has the potential to shift that balance of power going forward.

You know, we talked about this almost exactly one year ago with Eric Schmidt, and I think that the--you know, the CCP is very clear about their ambitions. They're very clear that they believe--you know, there's some writings that they have where they talk about AI as a potential leapfrog technology for the PLA versus the U.S. DoD. They believe that, you know, if they overinvest into AI and the United States by--in parallel underinvests in artificial intelligence, because we're going to overinvest into maintaining our existing systems, they could actually develop far superior capabilities than us in the United States.

So broadly speaking, if you zoom all the way out, I think this is--this is one of the key technologies for military power and hard power over the next--over the coming years, and we need to be--we need to be thinking about it as such.

MR. DE VYNCK: Do you draw any like red lines for yourself, though? Because, you know, obviously, you're providing infrastructure for the government. You're providing tools for the government to, you know, crunch their data, to get smarter, to get faster. But if there was a bid for, say, a couple years, your own tech is advanced, and there's a bid for some kind of maybe cyber weapon that would go and disrupt an enemy nation's energy infrastructure at a time of war and you had the capacity to build something like that, would you bid for an offensive weapon like that?

MR. WANG: Yes. So our view--you know, the DoD has actually spent a lot of time thinking about these questions. And I think the ethics of the use of artificial intelligence has been one of the primary pillars of their exploration and their effort. The DoD published their ethical AI principles a number of years ago, long before the technology was even as powerful as it is today, let alone even more powerful, to do a lot of, I think, preemptive thinking about what happens as this technology becomes more and more powerful. And I think they're very thoughtful, and I think, in general, our view is that we should build technologies that adhere ultimately to the DoD's ethical AI principles.

There's yet an additional piece which is the--let's say how do you enforce that we actually adhere to these principles, right? And our view is that there has to be a lot of advancements in testing and evaluation of AI systems.

I think the greatest fear of many military commanders I've spoken to is that there will be some decision that's made, rightly or wrongly, to deploy a very immature AI system that could then create dramatic risks of our soldiers on the battlefield. And so I think we need to be thinking about what does it mean to actually have mature AI technology versus hype-driven AI technology, and how do we ensure that any technology that we deploy goes through the proper rigorous testing and evaluation of, you know, red teaming and deep, deep sort of principles-based assessment to ensure that we have, you know, actually effective systems?

MR. DE VYNCK: Right, right. And, I mean, like are you--do you think we're there? Like, you know, because there's also some autonomous weapon systems that are already out there, you know, that other countries are using. There's, you know, drones that are able to kind of like detect certain targets and make decisions sort of on their own, based on their own programming without a human necessarily in the loop. And so, in some ways, it feels like this stuff is already getting out of our hands.

MR. WANG: You know, our view and in our conversations with most of the leaders in the DoD is that humans are always quite necessarily in the loop. The technology as it stands today is primarily useful as a decision aid, not a decision-maker, and, you know, there's a lot of a very, very advanced military analysis on this matter. I think, you know, if you were to sum it up overall, it's that there's--today one of the key problems impacting our military is that there's too much information but too little intelligence.

MR. WANG: You know, there's an inundation of information coming from all sorts of different sensors and platforms and the ability to synthesize that into core intelligence that can help military leaders and commanders understand what they should do. That's the missing gap. That's very different from, I think, fully autonomous weaponry or fully autonomous operations. I think it's more about decision aid and helping human decision-makers and human operators be able to operate more effectively.

MR. DE VYNCK: You know, a lot of your business model requires contract workers to kind of like assess technology, label things. This is something that obviously not just you, but the entire AI industry, there's a lot of humans that are behind it. And some colleagues of mine earlier this summer reported--you know, went and spoke to some contractors of yours in the Philippines who, you know, weren't getting all the money that they believed that they were entitled to. And, you know, you don't need to talk specifically about your situation, but if you talk--if you look at the industry as a whole, there's still a lot of human involvement, right? And, you know, where in terms of that contract workforce--like is that something that you think for years and years and years as AI continues to get smarter, we will need, you know, hundreds of thousands of humans to be involved in that painstaking work, or is that something that is only really at the beginning of the tech development, and then down the road, it might not be necessary anymore?

MR. WANG: Our view is that humans will always be very, very critical towards the development of AI technology. And so there will always be humans in the loop. There will always be humans involved in the actual development of the algorithms that are used.

You know, back in 2019, we actually worked very closely with OpenAI to innovate and develop some of the--today, very cutting-edge techniques to enable humans to provide input and preferences into the models to be able to guide their behavior. We know we developed this technique called "reinforcement learning with human feedback," RLHF, that has now become a cornerstone of the entire AI industry in ensuring that we build very helpful and harmless AI models. There have been--you know, OpenAI has published some of their research on this. They've reported that, you know, through use of reinforcement learning with human feedback, they're able to achieve an improvement in the helpfulness of the models equivalent to 100-fold increase in model size. Simply put, what that means is, there's a--there's almost a quantum leap forward in the ability to build AI systems that actually adhere to human intent, adhere to human principles, because of this technology that we've developed with them.

MR. DE VYNCK: Just got a minute left, and I want to ask you the same question that we asked Senator Schumer, which is, you know, you've testified a lot. You've talked a lot about, you know, concerns, risks. You've mentioned even in this conversation about, you know, guardrails and testing and evaluation. I mean, what--but if you just zoom out and think about AI in general and how quickly this technology is moving, what keeps you up at night?

MR. WANG: I think global proliferation of the technology is the most concerning trend today. If you look at what's happened just in the past year since ChatGPT, you've seen it become a primarily domestic technology to being an incredibly international technology. Some of the most advanced open-source models were developed in Paris and France. There's been very large open-source models being developed in UAE and in the Middle East, and then China, as I mentioned, has bought $5 billion worth of high-end chips to put--you know, put their own hat into the ring of AI development.

And the technology is at risk of real misuse. You know, some of the risks that keep me up at night, the most are misuse in cyberattacks and misuse in bioweaponry, and these are some of the use cases of technology that I think could really negatively impact humanity and could have very, very negative consequences for us all.

MR. DE VYNCK: All right. Well, thank you very much for joining us, Alex.

MR. WANG: Thanks, Gerrit.

MR. DE VYNCK: Dont go anywhere. Well have another guest very soon.

The rest is here:
Transcript: The Futurist Summit: The Battlefields of AI with Scale AI ... - The Washington Post

Prime Minister Kakar will be visiting China this week to attend the scheduled Belt & Road moot where one wonders what our endeavour would really be? Well, one assumes that his contingent would have its strategy worked out, however, it would be important to take note that the outlook on what the developing countries would like to extract from this connectivity stands significantly changed from its formative years. Gone are the days when mere investments regardless of their long-term impact on the respective external accounts would be acceptable. This is what we not only have to mindful of, but also put on the table how the previous such projects need to revisited to ease the fall-outs from the same. No better time to renegotiate the same in a rather polarised global environment that is making it essential for countries to put their own interests and priorities before mere plain political alignments. Only last month the conference on international science, technology and innovation hosted by the Forum saw countries calling instead for a revised focus on instead moving towards evolving a community of innovation. Meaning, that a shift is the sought by the belt & road partners from infrastructure and taxing power plants towards cooperation and technology transfers in science, technology and innovation. Climate change has today become a climate crisis and to address this challenge of global warming the transition needs to be away from simple physical connectivity to digital, economic, social and cultural connectivity - And science, technology & innovation permeate through all forms of connectivities.The idea being for the B&R to have a budget for resources of research and development to help financially weaker countries supplement their smaller spends in this area. This also goes on to help the overall objective to ensure that connectivity takes place smoothly by encouraging the movement of goods & services - by trains, road and sea - in good quality, efficiently and transparently with the help of modern day technology. Also, important now would be the free flow of people across borders to become truly entrenched in a common cum shared work place. For example, while every country may have its own data on vaccination or verified skills of its workers and professionals, but this now needs to developed collectively for all partners to access and benefit from. The other important platform that is under discussion is to achieve From AlphaGO to Chat GPT. Referring to how to capitalise on artificial intelligence (AI) in a responsible manner. Implying that a time has come to try and deeply understand the new scientific technological tools in a way that the new inventions and innovations are implemented in a parallel understanding of the human mind per se. This would be critical in ensuring that current day transformation are beneficial and not damaging for countries and people across the board. Another very important area that Pakistan can capitalise on, given its very young population, is to play its due role in garnering a significant share in offering solutions to the developed countries with an ageing population problem. The reality is that though one cannot exactly predict all climate change impacts, on the other hand one can tangibly calculate the consequences of an aging phenomenon problem. As we know that AI, if used constructively, is today unearthing solutions to many deeper challenging faced by the countries in almost all spheres: economic, security and social development.One hopes that these are the kind of debates that the prime ministers team will be involving itself in and also at the same time looking at steps on how all of these new strategies can be prudently taken up by Pakistan, so that we draw upon the real underlying positives of B&R and do not end missing the boat again by restricting ourselves to projects that perhaps are better undertaken by domestic industrial captains than the imported ones. More importantly, in Thomas Edisons words, Vision without execution is hallucination!

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Belt & Road - Latest - The Nation

How AI and the Metaverse Will Impact the Datasphere

The dataspherethe infrastructure that stores and processes our datais critical to many of the advanced technologies on which we rely.

So we partnered with HIVE Digital on this infographic to take a deep dive on how it could evolve to meet the twin challenges of AI and the metaverse.

If the second decade of the 21st century is remembered for anything, it will probably be the leaps and bounds made in the field of AI. Large language models (LLMs) have pushed AI performance to near-human levels, and in some cases beyond. But to get there, it is taking more and more computational resources to train and operate them.

The Large-Scale Era is often considered to have started in late 2015 with the release of DeepMinds AlphaGo Fan, the first computer to defeat a professional Go player.

That LLM required a training compute of 380 quintillion FLOP/s, or floating-point operations per second, a measure of computer performance. In 2023, OpenAIs GPT-4 had a training compute 55 thousand times greater, at 21 septillion FLOP/s.

At this rate of growthessentially doubling every 9.9 monthsfuture AI systems will need exponentially larger computers to train and operate them.

The metaverse, an immersive and frictionless web accessed through augmented and virtual reality (AR and VR), will only add to these demands. One way to quantify this demand is to compare bitrates across applications, which measures the amount of data (i.e. bits) transmitted.

On the low end: music streaming, web browsing, and gaming all have relatively low bitrate requirements. Only streaming gaming breaks the one Mbps (megabits per second) threshold. Things go up from there, and fast. AR, VR, and holograms, all technologies that will be integral for the metaverse, top out at 300 Mbps.

Consider also that VR and AR require incredibly low latencyless than five millisecondsto avoid motion sickness. So not only will the metaverse contribute increase the amount of data that needs to be moved644 GB per household per daybut it will also need to move it very quickly.

At time of writing there are 5,065 data centers worldwide, with 39.0% located in the U.S. The next largest national player is the UK, with only 5.5%. Not only do they store the data we produce, but they also run the applications that we rely on. And they are evolving.

There are two broad approaches that data centers are taking to get ahead of the demand curve. The first and probably most obvious option is going BIG. The worlds three largest hyperscale data centers are:

The other route is to go small, but closer to where the action is. And this is what edge computing does, decentralizing the data center in order to improve latency. This approach will likely play a big part in the rollout of self-driving vehicles, where safety depends on speed.

And investors are putting their money behind the idea. Global spending on edge data centers is expected to hit $208 billion in 2023, up 13.1% from 2022.

The International Data Corporation projects that the amount of data produced annually will grow to 221 zettabytes by 2026, at a compound annual growth rate of 21.2%. With the zettabyte era nearly upon us, data centers will have a critical role to play.

Learn more about how HIVE Digital exports renewably sourced computing power to customers all over the world, helping to meet the demands of emerging technologies like AI.

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How AI and the Metaverse will Impact the Datasphere - Visual Capitalist

Hong Kong based Insilico Medicine, a pioneer in AI-based drug discovery, has made significant strides in recent years. Two of their candidates have reached clinical trials, with INS018-055 leading the pack as the first AI-discovered drug designed by generative AI to enter phase 2 clinical trials for idiopathic pulmonary fibrosis (IPF). Back in 2014, when the company began, AI for drug discovery was relatively unheard of, but now it's an indispensable part of the drug discovery process. Insilico's partnerships with major pharmaceutical firms like Janssen underscore the growing importance of AI in this field. Dr Alex Zhavoronkov, Founder and CEO of Insilico Medicine, sheds light on the industry's evolving response to AI in drug discovery, partnerships, regulatory reforms etc. and also shares the company's future plans.

Insilico Medicine has garnered attention for its innovative utilisation of artificial intelligence (AI) in drug discovery. Could you provide insights into how the industry's response to AI-based drug discovery has evolved since your inception in 2014?

In the early days, when I presented at conferences on how generative AI technology could be applied to chemistry, there was a lot of scepticism. I had discovered through my research that generative adversarial networks (GANs) combined with deep reinforcement learning (the same AI learning strategy used in AlphaGo) could generate novel molecules that could be used to treat disease. Since that time, AI drug discovery has undergone enormous acceleration, fueled both by advances in AI technology and in massive stores of data. While there are still no AI-designed drugs on the market, there are a number of companies with these drugs in advanced clinical trials, including our own lead drug for idiopathic pulmonary fibrosis, the drug with an AI-discovered target and designed by generative AI now in Phase II trials with patients.

Although the pharma industry has moved cautiously, the inherent risks in drug discovery (99 per cent of the drugs fail in the early discovery phase and 90 per cent of the drugs fail in clinical trials) and the validation of AI developed drugs to reach advanced trials, means that pharma companies are more actively pursuing partnerships and developing their own internal AI programmes. We have major partnerships with Exelixis, Sanofi and Fosun Pharma to develop new therapies, for instance.

Recently, your two candidates INS018_055, ISM8207 have entered phase II and phase I respectively. Can you share the significance of reaching these stages in the drug development process, and what key milestones do you hope to achieve during these trials?

To our knowledge, Insilicos lead drug for IPF INS018-055 - is the first drug for an AI-discovered target and designed by generative AI to reach Phase 2 clinical trials with patients.

AI was used in every stage of the process. Insilico Medicine used its AI target-discovery engine, https://insilico.com/pandaomics, to process large amounts of data including omics data samples, compounds and biologics, patents, grants, clinical trials, and publications to discover a new target (called Target X) relevant for a broad range of fibrosis indications. We then used this newly discovered target as the basis for the design of a potentially first-in-class novel small molecule inhibitor using its generative AI drug design platform, Chemistry42.

Insilicos molecule INS018_055 - demonstrated highly promising results in multiple preclinical studies including in vitro biological studies, pharmacokinetic, and safety studies. The compound improved myofibroblast activation, a contributor to the development of fibrosis, with a novel mechanism and was shown to have potential relevance in a broad range of fibrotic indications, not just IPF.

The current phase II study is a randomised, double-blind, placebo-controlled trial to assess the safety, tolerability, pharmacokinetics and preliminary efficacy of 12-week oral INS018_055 dosage in subjects with IPF divided into 4 parallel cohorts. To further evaluate the candidate in wider populations, the company plans to recruit 60 subjects with IPF at about 40 sites in both the US and China.

If our phase IIa study is successful, the drug will then go to phase IIb with a larger cohort. This is also the stage where our primary objective would be to determine whether there is significant response to the drug. The drug will go on to be evaluated in a much larger group of patients typically hundreds in phase III studies to confirm safety and effectiveness before it can be approved by the FDA as a new treatment for patients with that condition. We expect to have results from the current phase II trials next year.

Advancing ISM8207 is also significant both because it is the first clinical milestone reached in our partnership with Fosun, and also because it is the first of our cancer drugs to advance to the clinic, and cancer represents the largest disease category in Insilicos pipeline. This drug is a novel QPCTL inhibitor, designed to treat advanced malignant tumours, and works by blocking the tumour cells dont eat me signal. We entered into phase I clinical trials to assess the drugs safety in healthy volunteers in July 2023.

You have had quite successful partnerships with Exelixis, Fosun etc. Can you provide insights into Insilicos approach to forming strategic partnerships? How do you approach deal making?

We have the advantage of being able to produce and advance new, high quality small molecules that have been optimised to treat diseases much more quickly than traditional drug discovery methods. Thats because our generative AI system can optimise across 30 parameters at once based on desired criteria when generating molecules, rather than the traditional method of screening libraries to find a potential compound, and then working to optimise it for each desired property in a linear fashion. As we speed up the drug discovery process on these high-quality molecules we now have 31 in our pipeline we look to find partners who have specific disease expertise and clinical experience to advance these molecules into later stage clinical studies, and, we hope, to market where they can begin helping patients.

Our most recent partnership with Exelixis is a perfect example. We just announced an exclusive global licence agreement with Exelixis with $80 million upfront granting Exelixis the right to develop and commercialise ISM3091, an AI designed cancer drug and potentially best-in-class small molecule inhibitor of USP1 that received IND approval from the FDA in April 2023. This company is expert in cancer and cancer drug development and discovery, and has an expert drug hunting team. Because its an extremely innovative company, they already have substantial revenue coming from best-in-class cancer therapeutics and they are strengthening this pipeline and making bets on innovative cancer drugs.

If we were to look at one of your AI-designed drugs versus a traditionally designed drug candidate, is there a telltale signature?

Our AI-designed drugs will often have a novel structure or work via a novel mechanism compared to existing drugs. By optimising across these 30 different parameters to design molecules with just the right structure and properties to provide the best likelihood of treatment without toxicity and minimal side effects, we are essentially designing ideal new drug-like molecules from scratch. There may be other drugs that are designed to act on those same targets, but ours are optimised through structure or mechanism to be most efficacious, first-in-class, or best-in-class.

Until recently perhaps, big pharma was somewhat sceptical or resistant to AI. What has been responsible for this growing appetite to embrace AI as a fundamental part of the drug discovery process?

There are a number of reasons pharma is now embracing AI. Traditional drug discovery is an incredibly slow and expensive process that fails in clinical trials 90 per cent of the time. AI improves all three of those roadblocks improving speed, lowering cost, and optimising molecules to have the greatest likelihood of clinical trial success. Our AI engine known as PandaOmics can sift through trillions of data points quickly to identify new targets for disease that humans might not find. Then, our generative AI Chemistry42 platform can design brand-new molecules that are optimised to interact with those targets without causing adverse effects, scoring them based on which are likely to work the best. Finally, using our InClinico tool, we can predict how these drugs will likely fare in clinical trials to reduce the time and money lost on failed trials.

There is also now significant validation that this method of developing new drugs is producing very high quality new drugs for hard-to-treat diseases and even diseases that were considered undruggable. And a number of these AI-designed drugs are now in later stage clinical trials.

Finally, the technology is itself progressing and improving with additional use and data via reinforcement learning and expert human feedback. The better the AI gets, the harder it is to ignore.

How sceptical are regulatory bodies towards AI-driven drug discovery? How are regulations evolving to support such developments?

Data privacy and protection are critical to any businesses utilising AI, as is compliance with all international laws and regulations. I expect that these measures will become more stringent in coming years and they are essential to building and maintaining public trust. Insilico Medicine uses only publicly available data and employs privacy by design and by default. We facilitate security of our systems by thorough security analysis on each phase of development. All Insilico data hubs are contained in Amazon Web Services (AWS) or Microsoft Azure cloud.

In addition, there are several checks and balances in place to ensure continuous data integrity, protection and privacy. For example, clients data is not used in any internal environments of the platform, and a firewall is separated for the clients access to the platform versus everyone elses access. All data is encrypted, and data privacy is managed according to Insilico Medicines privacy policy.

What does the future hold for Insilico over the next few years?

Were eager to see our clinical stage programmes progress, and the continued advancement of our lead drug for IPF. Its a terrible, chronic condition with a very poor prognosis and patients are in desperate need of new treatment options.

I also hope that our latest deal with Exelixis marks a trend of pharma companies partnering earlier in the drug development process with highly optimised AI-designed molecules as we continue to expand our pipeline, so that we can truly accelerate the process of delivering new treatments to patients in need.

We will also continue to expand the capabilities of our end-to-end generative AI platform, through new data, reinforcement learning, and expert human feedback; and augment those capabilities with our AI-powered robotics lab as well as incorporating the latest technological tools into our platform, including AlphaFold and quantum computing both of which weve published papers on.

Ayesha Siddiqui

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The better the AI gets, the harder it is to ignore - BSA bureau