Potato raises $4.5M to create AI scientists

The article “We Still Can’t Predict Much of Anything in Biology” by Claus Wilke, circulated among our team this week, validating what our experts in synthetic biology already know: data is the key to moving biology forward. We know this as a fact. The idea that we can simply model biological systems today ignores reality. We’re still a long way from tuning a few parameters and producing the biology we want. At the Oregon Biosciences Annual Conference, I had a chance to listen in on a roundtable discussion on AI in health and biology. The most important takeaway was clear: data must expand dramatically, and its structure matters as much as its volume. The form of the tensor defines what the model can learn, and the inclusion of negative data, where “no” is as valuable as “yes”, is essential for progress in biological prediction. This is where SynPlexity plays a critical role. Our platform leverages Broad Mutational Scanning (BMS) to generate the high-dimensional, information-rich datasets that discovery biology needs. We expect more advanced analytical tools will evolve to fully capitalize on BMS, but the fundamental truth remains: biology’s complexity demands more and better data.

🚀 The Dream of Science: Two Centuries Later In a fascinating journey through the history of innovation, the article explores how the utopian visions of 19th-century scientists are materializing in the current era. Two hundred years ago, pioneers like Mary Shelley and Charles Babbage dreamed of thinking machines and advances that would transform humanity. Today, with artificial intelligence and biotechnology at the forefront, those dreams are not only a reality but also pose new ethical and technical challenges. 🔬 Origins of the Scientific Vision - Imagine a world where fiction inspires science: the article highlights how "Frankenstein" from 1818 anticipated debates on the creation of artificial life. - Babbage and his analytical engine foreshadowed modern computers, laying the foundations for the digital era we live in. 🤖 Contemporary Advances that Fulfill the Dream - Generative AI, like advanced language models, performs calculations impossible in the 19th century, processing data at unimaginable speeds. - In biotechnology, genetic edits like CRISPR revive ideas of immortality and human enhancement, balancing progress with ethical risks. - Space exploration, with missions to Mars, echoes the ambitions of Jules Verne, driven by algorithms and autonomous robotics. ⚠️ Ethical and Technical Challenges on the Horizon - While we celebrate these achievements, questions arise about privacy, biases in AI, and the environmental impact of accelerated technology. - Experts call for responsible governance so that the scientific dream does not turn into a dystopia. This summary illustrates how science has evolved from speculation to practical application, inviting reflection on our role in shaping the future. For more information visit: https://enigmasecurity.cl #Science #Innovation #ArtificialIntelligence #HistoryOfTechnology #Biotechnology #TechnologyEthics If you're passionate about the intersection between history and technology, consider donating to Enigma Security to support more content like this: https://lnkd.in/er_qUAQh Connect with me on LinkedIn to discuss these topics: https://lnkd.in/ehaea5vE 📅 Fri, 10 Oct 2025 06:30:05 GMT 🔗Subscribe to the Membership: https://lnkd.in/eh_rNRyt

🚀 The Dream of Science: Two Centuries Later In a fascinating journey through the history of innovation, the article explores how the utopian visions of 19th-century scientists are materializing in the current era. Two hundred years ago, pioneers like Mary Shelley and Charles Babbage dreamed of thinking machines and advances that would transform humanity. Today, with artificial intelligence and biotechnology at the forefront, those dreams are not only a reality but also pose new ethical and technical challenges. 🔬 Origins of the Scientific Vision - Imagine a world where fiction inspires science: the article highlights how "Frankenstein" from 1818 anticipated debates on the creation of artificial life. - Babbage and his analytical engine foreshadowed modern computers, laying the foundations for the digital era we live in. 🤖 Contemporary Advances that Fulfill the Dream - Generative AI, like advanced language models, performs calculations impossible in the 19th century, processing data at unimaginable speeds. - In biotechnology, genetic edits like CRISPR revive ideas of immortality and human enhancement, balancing progress with ethical risks. - Space exploration, with missions to Mars, echoes the ambitions of Jules Verne, driven by algorithms and autonomous robotics. ⚠️ Ethical and Technical Challenges on the Horizon - While we celebrate these achievements, questions arise about privacy, biases in AI, and the environmental impact of accelerated technology. - Experts call for responsible governance so that the scientific dream does not turn into a dystopia. This summary illustrates how science has evolved from speculation to practical application, inviting reflection on our role in shaping the future. For more information visit: https://enigmasecurity.cl #Science #Innovation #ArtificialIntelligence #HistoryOfTechnology #Biotechnology #TechnologyEthics If you're passionate about the intersection between history and technology, consider donating to Enigma Security to support more content like this: https://lnkd.in/evtXjJTA Connect with me on LinkedIn to discuss these topics: https://lnkd.in/eVFXiMyf 📅 Fri, 10 Oct 2025 06:30:05 GMT 🔗Subscribe to the Membership: https://lnkd.in/eh_rNRyt

The recent article “We Still Can’t Predict Much of Anything in Biology” by Claus Wilke captures a truth our team at SynPlexity works with every day…biology is hard, and predictive modeling alone won’t solve it. The path forward depends on data: not just more of it, but the right kind, with the structure and context needed to make learning possible. That’s where SynPlexity comes in. Our platform uses Broad Mutational Scanning (BMS) to accelerate biological discovery by systematically mapping how sequence variation drives function. This approach creates the dense, structured data that AI models need, data that includes both “yes” and “no,” success and failure. While many in the field hope to model biology directly, the reality is that biology still resists simple parameter tuning. SynPlexity’s mission is to change that by building the datasets that make predictive biology possible. To model biology, we first have to measure it — at scale. https://lnkd.in/gMJyQh83

AI is reshaping science, from drug discovery to data analysis. But if one day an algorithm makes a breakthrough worthy of a Nobel Prize, would the honor go to the machine or remain reserved for humans?   👉 Read more: https://lnkd.in/gzHCePG8   #ArtificialIntelligence #NobelPrize #FutureOfScience #HumanVsMachine

🚀 We’re thrilled to announce that ELLIS unit Jena together with Friedrich Schiller University Jena and the Max Planck Institute for Biogeochemistry has joined ELSA – European Lighthouse on Secure and Safe AI ELSA is a European Network of Excellence uniting 26 leading academic and industry partners across 8 countries. The network builds on the pan-European initiative ELLIS – European Laboratory for Learning and Intelligent Systems and drives world-class research in foundational, secure, and safe AI by design. By joining this community, we’re contributing to high-impact research, software, and benchmarks that shape the future of trustworthy AI in Europe and beyond. 🌍🤝 ELLIS - European Laboratory for Learning and Intelligent Systems 👉 Learn more: elsa-ai.eu #ELSAAI #NetworkOfExcellence #EuropeanResearch #AI #TrustworthyAI

Biology and AI : as Open Source software has changed dramatically the Software industry over the last 25years, we are starting to see in Biology various initiatives like OpenBind aimed at creating high quality datasets (open source ) upon which we can start developing and nurturing AI/ML models correctly: Build First. ‘Clean’ Data once again is key in Hcare too and it’s the foundation to better understand the disease mechanisms changing today’s taxonomy for many complex biological processes ….please have a look at https://openbind.uk/

The open-science group Frontiers has rolled out FAIR² Data Management, an AI-driven system meant to rescue the huge amount of research data that never sees the light of day. The company estimates that almost 90% of scientific datasets stay unused, slowing discoveries in fields like medicine, climate research, and technology. FAIR² brings everything into one place—curation, compliance, peer review, and formatting—so data can be easily found, reused, and credited. Built on the FAIR principles (Findable, Accessible, Interoperable, Reusable), the platform uses an AI Data Steward developed by Frontiers’ venture Senscience to handle tasks that once took months. Pilot projects range from COVID-19 variant data to biodiversity studies, showing how careful curation can make research more open, trustworthy, and ready for both people and machines to use. https://lnkd.in/ecFe36YR

There's been a lot of time spent discussing what jobs AI might take away, but the loftier goal and true promise of AI lies in accelerating science in fields like physics and chemistry which is why we invested in William (Liam) Fedus, Ekin Dogus Cubuk and the Periodic Labs team's seed round.

FACS researcher Yilang Peng blends his curiosities about people and science, using machine learning to better understand human behavior and decision-making. https://lnkd.in/errNfiKw #WeAreFACS