Machines and Molecules

Laura Möller is a seasoned expert in venture capital and entrepreneurship, with a focus on artificial intelligence and technology transfer. She holds leadership roles as Director of the Künstliche Intelligenz Entrepreneurship Zentrum (K.I.E.Z.) and UNITE in Berlin, and is the founder of Paola Ventures. With over a decade of experience, she has built expertise in supporting start-ups and fostering innovation in AI-driven ventures. She holds a Master’s degree in European Studies from Humboldt-Universität. Laura’s broad network and hands-on experience make her a vital asset in connecting entrepreneurs and investors, advancing Berlin’s tech ecosystem. In this episode of Machines and Molecules, Laura Möller, Director of KIEZ Accelerator, discusses supporting AI-driven startups, particularly those rooted in scientific research. She highlights KIEZ’s individualized approach, offering startups access to a strong network of venture capitalists, grants, and expert guidance. Laura emphasizes the challenge for AI and science-based startups in turning cutting-edge technology into practical business solutions.She also shares KIEZ’s vision of uniting accelerators and networks to create interdisciplinary teams of AI and domain experts and bridge gaps between research and commercialization. Laura stresses the need to open the funnel and fully utilize the potential of all researchers in academia, not just those who choose the entrepreneurial path. Laura believes fostering entrepreneurial education early, would be a gamechanger to the European startup ecosystem. 00:00 - 03:40 Introduction to Laura Möller and KIEZ Accelerator 03:40 - 05:45 Individualized Support Offered by KIEZ to Startups 05:45 - 07:40 Long-term Vision of KIEZ 07:40 - 10:45 Common Challenges Faced by AI and Science-Based Startups 10:45 - 15:22 Strategies for Securing Funding After the KIEZ Accelerator 15:22 - 19:50 Enhancing Access to Government Funding in the EU 19:50 - 28:50 Comparison of EU and US Funding and other Factors for Startup Success 28:50 - 32:50 3rd M-Word and Laura’s Mission

Dr. Martin Steinegger is an expert in computational biology and bioinformatics, specializing in large-scale sequence data analysis. He earned his Ph.D. from the Technical University of Munich in collaboration with the Max Planck Institute for Biophysical Chemistry, focusing on methods to cluster and assemble metagenomic sequencing data. Currently an Associate Professor at Seoul National University, his research group develops novel computational methods to analyze microbial communities using machine learning and big data algorithms. Martin is the creator of MMseqs, a highly efficient software suite for protein sequence searches and co-author of AlphaFold2. His work in pathogen detection and metagenomics has made a significant impact on bioinformatics, with a strong commitment to open science and open-source tools. Together with Martin we discuss the importance of productizing research code and the key factors for creating reusable software. He emphasizes the need for user-friendly interfaces, intuitive outputs, and software that doesn't crash. Martin talks about his background in software engineering and how it influenced his approach to developing tools in bioinformatics. Hence, he gives an overview about all the different tools he has developed over the years and for what they are used. Furthermore, he explains the significance of protein structure in understanding protein evolution and function, and highlights the role of his tools MMSeqs and AlphaFold in protein sequence and structure analysis. Martin shares his personal journey from starting in a lower-level school to pursuing higher education and research, driven by his passion for computers and learning. 00:00 - 01:32 Introduction 01:32 - 06:25 Productization of research code 06:25 - 08:20 Testing of software 08:20 - 12:28 Overview about the tools Martin has developed 12:28 - 15:40 The relevance of protein structure 15:40 - 18:00 Structural vs. Statistical approaches 18:00 - 21:00 AlphaFold collaboration and insights 21:00 - 31:10 Martin's personal journey and motivation 31:10 - 33:41 Machines and Molecules theme - 3rd M-Word Season 2, episode 5 - Category Knowledge

In this episode we host Carlos Härtel, an expert in innovation and strategy within the applied sciences. Carlos holds a Habilitation in Mechanical Engineering from ETH Zürich and a PhD in Chemical Engineering from the Technical University of Munich. Currently, he is a Venture Partner at Spacewalk VC, investing in deep tech startups, and a Senior Advisor at Carbon Removal Partners, focusing on carbon withdrawal technologies. He also chairs the Board at Science|Business, fostering collaboration between industry, research, and policy. Previously, Carlos served as CTO at Climeworks, a leader in carbon dioxide air capture technology, and held significant roles at General Electric, including CTO and Chief Innovation Officer for Europe. Additionally, he was a Non-Executive Director at Futurice, a digital consultancy, and Chairman at EUROGIA2020, promoting low-carbon energy technologies. Carlos joins us in our Ecosystem category. We dive into CO2 capturing technologies and their role in climate innovation. Key takeaways include the cost-effectiveness of capturing CO2 at industrial sources, the advanced direct air capture methods Carlos developed at Climeworks, and the challenges of ocean-based CO2 capture. Drawing from his experience, Carlos furthermore discusses the critical factors for advancing hard technical innovations in climate and deep tech. He highlights how market demand drives innovation, addresses the chicken-and-egg dilemma of reliability versus adoption, and emphasizes the role of government policies in creating market demand. His insights reveal what’s needed to overcome barriers and implement complex solutions effectively.

In this episode, we are excited to host Dr. Stanislav Mazurenko, a leading expert in protein engineering and artificial intelligence. With a Ph.D. in applied mathematics and cybernetics from Lomonosov Moscow State University and extensive postdoctoral research at Loschmidt Laboratories, Stanislav now leads research at RECETOX. We delve into molecular dynamics simulations, statistical models, and the application of machine learning to protein design. Key takeaways include the essential role of proteins, the complexities of simulating their dynamics, and the optimization benefits provided by machine learning. Stanislav highlights the synergy between automation and machine learning, and underscores the importance of learning from mistakes in scientific research.

In this episode, we're joined by Matteo Aldegni, Director of Machine Learning Research at Bayer, where he leads a team specializing in Machine Learning applications for chemistry and drug discovery. Matteo delves into the intricacies of Molecular Dynamics, the cornerstone of computer-aided molecular design, shedding light on how it paved the way for modern Machine Learning techniques. From the innovative sampling methods inspired by Molecular Dynamics to the transformative potential of Bayesian optimization, Matteo provides insights into the cutting-edge advancements driving molecular design. Join us as we explore the intersection of first-principles molecular models and Machine Learning, as well as Matteo's vision for the future of molecular design, including the concept of self-driving laboratories and Machine Learning force fields.

In this episode, we welcomed Richard Socher, AI expert and serial entrepreneur, serving as CEO and founder of you.com, a pioneering chat-search assistant, and as founder and managing partner of AIX Ventures, guiding and supporting AI startups. Focusing on this wealth of experience, we delved into the core drivers of AI startup success and investment strategies. Richard emphasized AI's versatility, its seamless integration across diverse use cases, and its immense potential, distinguishing it from other ventures and technologies. Regarding the paramount aspects of startup building and investment decisions, he stressed the importance of assembling top talent, maintaining focus, and avoiding overextension. Having a solid Plan A and a thoughtful approach to pivoting if needed are crucial for success.

A decade ago, Martin Rahmel co-founded a startup with the idea of dissolving catalysts in water for reuse. Today, he leads the Chemical Invention Factory (CIF) in Berlin, a hub for green chemistry startups. CIF's story began simply with providing a brick-and-mortar lab to decrease cost for chemistry startups, now blossoming into a ecosystem of innovation with 10Mio Euros of funding. Martin shares the trials of translating science into business, recalling a year without sales before breaking through with their first client, Lonza. His insights on the mindset needed for startup success are invaluable, emphasizing perseverance and confidence. Beyond CIF, Martin's passion extends to restoring river systems, showcasing his dedication to environmental change. Join us as we explore how self-reflection and an objective perspective are key to driving scientific and sustainability advancements.

This episode features Max Welling, a visionary at the intersection of science and machine learning. Starting under Nobel laureate Gerard 't Hooft, Max shifted from the enthralling world of physics to machine learning, inspired by the methods of his early studies. His journey through prestigious labs, including Turing Award winner Geoff Hinton's, illustrates his belief in intuition-driven research. For a long time now, Max has been pioneering ML methods using a broad toolkit of approaches. These days, he is blazing a trail in applications of ML in climate change and scientific advancements. His resilience, shaped by youthful dreams of aviation and overcoming his disappointment in not becoming a fighter jet pilot, fuels his relentless pursuit of knowledge and innovation. 

In this enlightening episode, we delve deep with Finnish machine learning grandmaster Aapo Hyvärinen, as he discusses his latest book, “Painful Intelligence”. Hyvärinen, a trailblazer in unsupervised learning, sheds light on the profound connections between human suffering, the pursuit of goals, and the mechanics of learning. His discussion moves from super technical realms of non-gaussian latent variable models to the philosophical, exploring how the connection between machine learning and mindfulness meditation. As we venture into the future of AI, Hyvärinen raises thought-provoking questions about possibilities for the emergence of a survival instinct of AI agents.

In this episode, we explore the challenges faced by scientists like Patrick Torbey, the founder of Neoplants, as they transition from academia to entrepreneurship. Patrick shares his struggles in finding a co-founder with business acumen, emphasizing the rare intersection of academic and business worlds. He discusses how building a startup, like his venture that engineers plants to purify air rapidly, means strong improvements in terms of speed and resources compared to academia. Patrick underscores the importance of scalability and reproducibility in the journey from lab to market, and how the consumer can be a very harsh critic

In the latest episode, Alan Aspuru-Guzik introduces us to the transformative potential of self-driving labs, where complex chemical equipment merges with machine learning. However, it's not about chasing the perfect algorithm – it's about making things work. Old workhorses like Bayesian optimization he augments with a wide range of innovative twists. Motivated by David King's chilling take on climate change, Aspuru-Guzik urges us to venture into uncharted territory, balancing imagination with impact. Because, in the end, it's about doing what can truly add value.

In this captivating episode, delve into the intriguing world of enzymes, nature's microscopic machines, as Uwe Bornscheuer recounts his early fascination with their intricate design and function. Traverse through time as we explore both historical and contemporary methods of enzyme engineering and discover the parallels between enzyme optimization and techniques used in machine learning and operations research. Uwe's driving forces are a potent mix of impact on the world and the joy of nurturing people. And for a lighter note, Uwe reveals his 'M-Word' – his (not so secret) escape from the lab. Don't miss it!

In this enlightening episode, Kristof Szalay unveils the journey of Turbine - from a mere idea to a pharmaceutical game-changer. By simulating how proteins send messages in cells, Turbine is on the cusp of slashing drug discovery timeframes and ushering in groundbreaking drugs. Kristof highlights how Turbine transitioned from academia's incremental approach to the realm of engineering moonshots. The discussion delves into the sweet spot between innovation management and unfettered freedom in realizing one's vision. Dive in to learn why a "just do it" approach carries you far in audacious projects.

This episode is a deep dive with Tina Klüwer, a beacon in Germany's AI landscape. From trying to understand human thought by replicating it in machines to founding an AI startup in 2014, Tina's overflowing enthusiasm has always been an asset. We discuss the nuances between academia and startup culture and tackle pressing topics like societal biases mirrored in AI. We touch upon her role in explaining to Germany's policymakers what AI can and can't do, as well as the question of whether machines have developed consciousness.

In this episode, Ingmar chats with François-Xavier Briol (FX for short), a lecturer at University College London. FX specializes in probabilistic numerics and its applications in the natural sciences. In the episode, we nerd out about model misspecification, which addresses the challenges posed by the idea that "all models are wrong, but some are useful." We also discuss personal growth and the importance of collaboration in FX's academic career.

In this first episode of Machines and Molecules, Ingmar talks with Dr. Mikio Braun talks about machine learning before it was a hype, Math and persevering through hard phases in your work (read: PhD thesis). We discuss what you learn when you move from academia to industry and the probability that humanity will be terminated by AI.