Erik Aronesty

SPACE2: Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope. Web server: https://lnkd.in/gJT3wgh8 Paper: https://lnkd.in/gwEs93Fq Group your antibodies by what epitope they bind to on Tamarind Bio! Abstract: The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes.

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This is just incredible. Neal Ghosh's demonstration of how #VentureIQ takes deeply technical and complicated content across a range of industrial use cases and ages/stages through an AI-generated conversational podcast (a la #notebookLM) is really mind-blowing. Check out his post, below, and if you haven't seen VentureIQ, yet, let's get you set up to learn more. You can visit VentureIQ.ai or DM any of us over here to book a demo!

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Had a great time with Grant El-Deir on the @venturalabs podcast yesterday. We covered Bittensor, Opentensor Foundation, DCG, MicroStrategy and more. Check it out and let me know if I'm smart (comments) or dumb (DM only pls). #crypto #bittensor #bitcoin #laboreconomy #artificialintelligence #machinelearning https://lnkd.in/gu6aFziu

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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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Very insightful work by Anthropic’s interpretability team. And an amazing paper, with outstanding writing and figures. The idea is very simple: interpret LLMs by leveraging sparse autoencoders as surrogate models of the MLP of transformer blocks, which allow one to disambiguate the superposition of features captured by a single neuron. A simple idea, but a very careful and complex execution, as it is often the case in our line of work. The paper goes into many details and provide a large array of insights, although the gist of the implementation remains obfuscated due to the closed source nature of Claude. Too bad, because this is the kind of work that we need to better understand and eventually trust LLMs. This is demonstrated by the authors in the section ‘Influence on Behavior’, where they show that clamping some features to either high or low value during inference is “remarkably effective at modifying model outputs in specific, interpretable ways”. Hopefully this kind of work is going to be replicated and generalized to open-weights models, such that we have new ways to steer their behavior. https://lnkd.in/eVym7f_f #interpretability #xai #explainableai #steerableai #anthropic #claude #anthropic

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ThermoMPNN rapidly predicts point mutations to proteins for changes in stability. This neural network achieves state-of-the art performance on the Fireprot benchmark, and returns predictions in a less than a minute. Try it out on Tamarind Bio: https://lnkd.in/gSCbrpDH Paper:https://lnkd.in/gCXQwUTS

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A bill pending in the California Senate that would essentially make many start-ups, open source projects as well as some research illegal in California and force it to move to other states. We need to stop this nonsense. Usually I don't post about politics as issues are often nuanced, complex and hard to fully address in a short post. This is an exception. The bill has zero benefit for anyone, except maybe very large tech companies that can use it to crush stat-ups. And it doesn't mitigate any potential downside of AI. However it would kill many open source efforts. It would stop some research. And it would force start-ups to do their model development in other states (or other countries). If you can, please support the effort to stop this. https://stopsb1047.com/

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Agreed Srini Pagidyala. This is where we need to to understand the limitations of LLM or Generative AI. Accurately representing the real world requires interaction with it. I feel that it requires more brain functions added to the generative part. The generative part is like our brain creating abstract thoughts based on experiences (data). Now some entity should course correct those thoughts to match with the real world. Our human brain does interact with the real world. Point here is that LLM should have the capability to collect relevant world data (from the context it is generating for) and course correct it’s content. Well a far fetch for today’s LLMs

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Today's dispatches from quantum: ⚆ Quantum computing for protein structure prediction as demonstrated by framework that accurately predicts Zika Virus NS3 Helicase ⚆ QAOA gets a makeover — QIRO bypasses locality issues, matches classical heuristic performance ⚆ Central New Mexico Community College to offer first of its kind quantum computing bootcamp amidst concerns for related US programs Plus, IBM releases execution modes, QTunisia hosting certificate-awarding workshop for all levels, and Massachusetts Institute of Technology technique for atom-proximity has big implications for quantum. We're THE quantum computing newsletter, bringing you daily insights into the latest news & research. With leaders in academia, founders, and subscribers from top companies like Xanadu, SandboxAQ, and Qunnect, you're in good company. Join now at www.thedailyqubit.com and never miss a single qubit. #quantumcomputing #quantumphysics #innovation

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"United Independents, the group behind the Independent National Convention set for Denver in September, has launched a new software platform for indie candidates called Electify.us, which came out of a partnership with incubator Wolfram Labs — the first tool to emerge from its program." I am so proud to share that Chief Product Officer Dana Cichon has launched the first political tech platform to come out of our accelerator program. What are you waiting for? We have the formula for breaking into the digital campaign space. Are you are the next success story? Read more in Campaigns & Elections -->> https://lnkd.in/eHhqa-Xr #techaccelerator #startups #incubator #VC #SaaS #software #political #campaigns

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Google's 6th generation Trillium TPUs are now powering arguably the largest RNA-focused AI foundation model on the market. During Google I/O 2024, Canadian "techbio" company Deep Genomics announced that it is using Google Cloud’s next generation Tensor Processing Units (TPUs) in the development of its AI foundation model.  Deep Genomics is the creator of BigRNA, the first AI Foundation model for decoding RNA biology and designing therapeutics (as the company claims). I will soon be publishing my interview with Dr. Brendan Frey, Founder and Chief Innovation Officer at Deep Genomics. Brendan is an incredibly interesting guy who, among a litany of major contributions, was an early pioneer in the development of ML & AI to accurately determine the consequences of genetic mutations. Stay tuned for the upcoming interview publication by subscribing to our newsletter (link in the comments) or by following Where Tech Meets Bio (Substack Newsletter). #techbio #artificialintelligence #foundationmodel #RNA #biopharmatrend Image credit: Deep Genomics

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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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Science-fiction is inherently speculative. Author George #Orwell of ‘#1984’ predicted concepts such as doublethink, little earbuds and society’s obsession with the black mirror screens that dominate our lives — and according to the BBC, novelist and short story author JG Ballard, of “High-Rise” and “Crash” fame, has predicted the current tool and cultural fixation of #GenerativeAI in the modern society — only he didn’t just speculate them within a story, he is actually one of the first use-cases of a generative AI used in a creative context — Ballard was so fascinated with the growing technological advancement of computers that he used one to compose poems, essentially the precursor to what we have now with ChatGPT. Today, the debate about the creative use of generative AI and its value on creative industries is an extremely fraught and controversial topic, but if controlled in a space that doesn’t replace human labor or try to equate itself to traditional art forms, it is interesting to see what computers can do and how they are creatively limited in comparison. Ballard’s two poems, The Yellow Back Novels and Machine Gun City are generated, and both include notes on how Ballard achieved producing them. Regardless of quality (Ballard himself probably wouldn’t describe these two poems as his best work), it is interesting to see primitive versions of the technology that we utilize on the daily today, and how these moral conversations that concern them would continuously evolve.

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Mathematical Explanation of How DLSFH Addresses Perturbative Nonrenormalizability @SSRN The Dodecahedron Linear String Field Hypothesis (DLSFH) leverages string theory's approach to circumvent the issue of perturbative non-renormalizability in gravity. https://lnkd.in/dRKX-Mtx

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♟️ Don't play quantum games -- you'll win and lose at the same time. Today's dispatches from quantum: ⚆ Riverlane collaborates with global leaders to reduce the environmental impact of quantum computing through the Quantum Energy Initiative ⚆ A new recurrent quantum embedding neural network method combines quantum computing with NLP to improve memory efficiency and detection accuracy in vulnerability detection ⚆ An experimental demonstration of the odd-cycle game shows a quantum advantage over classical strategies using entangled ions with high detection efficiency ⚆ Queen Mary University of London upgrades quantum research capabilities with the state-of-the-art ProteoxMX dilution refrigerator ⚆ EV Group and Fraunhofer IZM-ASSID develop advanced bonding and debonding technologies to improve semiconductor integration for quantum systems ⚆ NBC News takes a tour of Google's Quantum AI Lab ⚆ Alberto Prado from Unilever discusses the transformative impact of quantum computing and the importance of data management at the Ai Summit London ⚆ A feedback-based quantum algorithm introduces an operator to encode solutions for quadratic constrained binary optimization problems, improves performance and reduces resource usage ...and more. 📰 Stay quantum-savvy. The Daily Qubit offers daily insights into the rapidly evolving world of quantum computing. Join leaders in academia, founders, and industry experts in staying informed. Sign up at www.thedailyqubit.com and never miss a qubit.  #quantumcomputing #quantumphysics #innovation

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The release of OpenAI Strawberry (o1) is another reason why scaling inference efficiently is important. Inefficient workload utilization drives up cloud costs and reduces gross margins for AI companies. Cedana improves gross margins from 20%-400% by automatically and safely suspending GPU workloads when not in use and instantly resuming them upon user demand. https://lnkd.in/ekBJKQuB

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The rate of open source LLM innovation is continuing at breakneck speeds. Just out today - the newest version of the much lauded Llama series of LLM models produced by the outstanding team at Meta lead by the consistently impressive Yann LeCun. I'm looking forward to seeing how well it can be run on the Jetson series mobile GPU processors from Nvidia that I learned about that the GTC a few weeks ago. As the hardware and models they can run improve their efficiency, we're going to see more and more excellent real-world physical system implementations of LLMs. I'll put a link to the Jetson chip info site in comments (i'm told links in posts are a no no). #machinelearning #AI #jetson #LLM #llama #robotics #Nvidia #Meta #opensource

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The April 25th seminar was viewed by over 16,000 people regarding 'Meta Llama 3 Fine tuning, RAG, and Prompt Engineering for Drug Discovery' to learn about the basics of advanced LLMs as they apply to a scientific application. https://lnkd.in/gy-FH6aj This week, the use of LLM agents to further automate and improve the performance of Generative AI was explored. In specific, an ACS publication used Ontological Knowledge Graphs and multiagent strategies with Llama-2 models to improve materials design. An innovative CrowChem AI Agent autonomously planned and executed the syntheses of an insect repellent, three organocatalysts, and guided the discovery of a novel chromophore better than using GPT-4 alone. Video discussion: https://lnkd.in/gMdWHf45 “Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles - such as a software engineer, product manager, designer, QA (quality assurance) engineer, and so on - and have different agents accomplish different subtasks.”  - Andrew Ng, Founder of Deeplearning.ai, April 17, 2024.

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The fantastic BioGlyph product and the team of Fernando Garces, McClain Kressman, and Avi Peltz will enable Dotmatics Luma customers to create, enumerate, and assess of a whole host of antibody formats within a beautiful visual interface. This is a key part of the Lab-in-a-Loop concept: 1) computational simulation and ML models inform the selection of molecules, 2) those molecules are made via experiments in the wet lab, 3) results data from the wet lab are used to adapt ML models and optimize the overall process. Dotmatics Luma is at the center of this cycle, enabling scientists to orchestrate every piece while collecting all data in an open and harmonized way so it can accelerate decision-making for critical areas of research. Multispecific Antibodies (MsAbs) is one of the fastest growing of those areas. I'm very excited to see what our customers will achieve with this! https://lnkd.in/e9JpFna8

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