Ross Walker

Manufacturing autologous cell therapies using paper batch records doesn't scale. Imagine having to create, review and store a full paper batch record for every single therapeutic dose! We need step-change in efficiency for the quality control process and digitally enabled technologies are key unlocking this.

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Dynamic DNA structures and formation of memory “DNA topology is much more dynamic than the static, right-hand double helix, as presumed by most researchers in the field,” said Professor Bredy. “There are actually more than 20 different DNA structure states identified to date, each potentially serving a different role in the regulation of gene expression.” The team found that G-quadraplex DNA (G4-DNA) accumulates in neurons and dynamically controls the activation and repression of genes underlying long-term memory formation. #innovation #medicine

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👉NEW: Getting Started Guide for Quantum Computing with MATLAB - download here:

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2023 was a breakout year for investment in generative AI startups, with equity funding of almost $22B across 426 deals. The 5 largest rounds all went to companies focused on core generative AI infrastructure: * OpenAI, AI poster child and maker of ChatGPT ($10B corporate minority) * Inflection AI, which focuses on human-computer interfaces ($1.3B Series B) * Anthropic, an AI model developer and research outfit ($1.8B across 2 corporate minorities) * Databricks, a data integration and analytics platform ($504M Series I) * Aleph Alpha, Germany-based LLM developer ($500M Series B) When coupled with the pace of change and the compression of time and cost, makes anyone wonder, given the apparent speed of the commoditization of these LLM engines, what will the ROIC on funds deployed actually look like over time?

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A simple illustration of an LLM's mathematical creativity (higher temperature) https://lnkd.in/eSKbSPem My code is at https://lnkd.in/eK_BR2hv ... I'm using Quarto. Why? Quarto notebooks can render either R or python/Jupyter. So I can illustrate in my stronger language, R, where I much prefer tidyverse/ggplot, then switch to python simply because that's how the API ref doc examples are written (see https://lnkd.in/eee6AeT3)

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The Precipex paper of the week this week really takes me back. We would have been all over this back at Tripos (now Certara) when we were doing protein ligand docking with genetic algorithms. Super cool idea and execution - definitely worth a look. It's also another example of what we keep seeing in this space: generative AI opening up whole new programming paradigms. Even if you don't care a whit about drug discovery, the approach presented here could be applied anywhere you have an unknown or intractable fitness landscape to optimize.

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"Sequence modeling and design from molecular to genome scale with Evo" by Eric Nguyen, Michael Poli, Matthew Durrant, Armin Thomas,  Jeremy Sullivan, Madelena Ng, Ashley Lewis, Aman Patel, Aaron Lou, Stefano Ermon, Tina Hernandez-Boussard, Patrick Hsu, Brian Hie et al. "The genome is a sequence that completely encodes the DNA, RNA, and proteins that orchestrate the function of a whole organism. Advances in machine learning combined with massive datasets of whole genomes could enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on 2.7M prokaryotic and phage genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multiscale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity." Blog: https://lnkd.in/dDpfHZbd Paper: https://lnkd.in/dpaswkHR

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Bend is a new high-level programming language for generating highly parallel code. The code can run on multicore CPUs or on GPUs. Bend looks and feels like Python, but it automatically detects opportunities for parallelism. #bend #programminglanguages https://lnkd.in/eUmzAuWw

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Today Random processes shape science and math: Researchers propose a unified, probabilistic framework by Santa Fe Institute SFI Professor David Wolpert, a physicist, and former SFI Omidyar Fellow David Kinney, a philosopher and cognitive scientist, propose a unified, probabilistic framework to describe math, the physical universe, and even describe how humans reason about both. To illustrate, say you answer a mathematics question now, and in the faraway future, some mathematicians answer the same question. The correctness of your answer depends on how your probabilities match theirs. (In physics, your correctness would involve physical experiments, not future physicists.) Randomness pervades every part of the question-answer process, from question selection to the answer given. This results in an important benefit of the proposed framework: a novel, formal justification for two common-sense shortcuts in science and math—believing more strongly in ideas supported by multiple lines of evidence, and believing more strongly in ideas that best explain something you already believe.

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Excellent report from General Dynamics Information Technology John P. Curran regarding adoption and trends of AI in the Govt. I used Meta to generate summary below and included link to original article. Main AI use cases are chat and creating/updating computer code. Note also the 23% failure rate of AI projects due to user expectation misalignment. This can be overcome with thorough protoyping and establishing use case metrics. - Successful AI project completion timelines are narrowing, with an average of 14 months from pilot to production. - Future demand for AI projects is building around chat and coding-related functions. - 54% of surveyed AI experts said projects currently under development involve improving computer code. - 59% of projects involve "research and understanding", which may relate to creating chat functions. - 23% of AI projects fail due to misalignment between AI solutions and user expectations. - Partnership efforts are required for successful AI projects. Data diversity is crucial, and incorporating diverse data sources can lead to better AI outcomes. Trends: - AI adoption is increasing across the Federal government, with a growing focus on chat and coding-related functions. - Agencies are leveraging cloud-native services, tooling, and other capabilities to accelerate AI project development. - There is a growing interest in translating older code to newer languages, such as Python or cloud-based analytic tools. - Domain-specific chat functionality is becoming a priority, with many agencies planning to implement chat services in the next two years. Statistics: - 325 AI experts surveyed across the Federal government - 14 months: average time from pilot to production for AI projects - 54%: percentage of projects currently under development that involve improving computer code - 59%: percentage of projects that involve "research and understanding" - 23%: percentage of AI projects that fail due to misalignment between AI solutions and user expectations. Bill Casola John Grim Leslie Anne McIndoe Kim Giffin Michael S. Freeman III Tobias Quaranta https://lnkd.in/eqvmAVFG

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Quantum science has revolutionized the way we look at the world, not just for the scientists, but for all seekers who want to understand the secrets of the cosmos. This #WorldQuantumDay, gratitude to the tireless work and ingenuity of the professors, scientists and researchers who have made it possible for non-science people to get a peek into the wonders of the quantum world. Remarkably, as greater understanding of the quantum phenomena unfolds, it finds striking parallels in the ancient philosophy of Advaita Vedanta. The two disciplines are widely different and have completely different approaches separated by centuries, but with the same destination – the quest for the ultimate secrets of the universe. Unfortunately, recent misinterpretations of quantum phenomena and the over-enthusiasm of certain quarters has led to a great number of pseudoscientific claims about quantum physics’ relation to Vedanta. My book #Vediquant offers a fresh and unbiased approach towards the parallels between the ancient philosophy of Vedanta and modern quantum science, and celebrates the similarities between them without claiming their influence on each other, and most importantly, keeping pseudoscience at an arm’s length. For the seekers of the ultimate truth of the universe, this will be a refreshing read. https://lnkd.in/gNTyH3_z   World Quantum Day Rupa Publications India #Vediquant #Vedanta #AdvaitaVedanta #quantumphysics #quantumscience #quantum #universe #life #cosmos 

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This is the code repository for Microsoft's #VSCode, the dominating code editor of today, with well over 10 million monthly active users: 2026 contributors 120,000+ commits (code changes) 167,000+ closed issues (fixed bugs) Some issues are solved within minutes. I filed an issue number 210624, referencing similar symptoms from a 9-month old issue. Some take 3 years (the 114425 in 'resources'). There is no point I'm trying to make. Just a little appreciation for the scale and pace of work on major software products.

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Serendipity. Tonight, some physics reading led me to an interesting 2019 post by Stephen Wolfram, reflecting on the life of Murray Gell-Mann (link in the 1st comment), which led me to the Particle Data Group's site for the 2023 Review of Particle Physics (the downloadable PDF for the 2022 edition is 2,270 pages, the booklet PDF form is 278 pages)

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