Michael Gee

Breaking down our latest newsletter section by section, starting with our expanded focus in Deep Tech: --- We have built expertise in the robotics sector, and will continue to double (and triple) down on investing in this category, but we have been increasingly finding that robotics is naturally steering us into two exploding adjacent areas: Aerospace, and Advanced Manufacturing (which we define as industrials, semiconductors, materials science, and energy production). Stepping back, this expansion makes perfect sense. Robotics is an inherently horizontal platform - meaning it can be applied productively across multiple industries. This quality, paired with the fact that robotics has long shown the highest TRLs of any deep tech technology, makes it a kind of “gateway” to other sectors in the hard sciences. Now with the recent breakthroughs in AI for robotics, growing semiconductor demand, and the resurgence of aerospace and defense tech, we are seeing a dramatic uptake of robotics and relevant technologies in both advanced manufacturing and aerospace. As an added bonus of the expansion, many of the founder (and funder) networks across these landscapes have natural overlaps and linkages. For AlleyCorp Deep Tech this means that we can be extremely helpful, knowledgeable, and engaged across all three sectors right away, and continue to be the best-in-class investing team across these categories.  --- More in our newsletter: https://lnkd.in/ewiTuYq6

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Please media types writing about the cost of AI: recognize there is a difference between huge GPU/TPU clusters needed to train foundation models vs. inference costs to run most apps on top of foundation models. The two are not synonymous, so you can dial back some of the doom and gloom about AI startups and your commingling of the two different AI business types.

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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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Excerpt from an Angel Squad memo that we invested in recently: “[Company] is led by a team of scientists and engineers with PhD-level expertise in Machine Learning, Engineering Simulations, and Computational Materials Science. The team is headed by CEO, [Name], who previously led AI development at two early-stage startups. [Name] is joined by CTO, [Name], who has deep experience in machine learning and materials science, and Chief Science Officer, [Name], a doctoral researcher at the [School]. “ Investing at the earliest stages of value creation often means taking big bets on a founding team. At Angel Squad and Hustle Fund, we love founders with deep expertise and super domain knowledge, particularly in deep tech verticals.

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The most impactful technology augments human capability. Take assembly line work as an example: Robots enhance speed and precision but still require human oversight. This dynamic mirrors our evolving relationship with AI, especially within the service-as-software paradigm shift. As Jaya and I wrote in our recent blog post, AI's evolution from a basic tool into an autonomous agent isn't about replacing jobs. It's about reshaping them. This shift is part of a $4.6 trillion opportunity where AI not only boosts efficiency but also creates new roles, blending tech with human insights. AI doesn’t just replace—it reinvents how we work, creating more value than ever.

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To build a multi-agent system, start simple, validate your design, then gradually scale. When I spoke to Chi Wang, the creator of AutoGen, he explained why. Deploying one or two agents at first allows builders to evaluate and refine the core design and interaction patterns before introducing additional complexity. This method also streamlines debugging and optimization because it makes it easier to trace issues back to specific agents. Our full conversation here: https://lnkd.in/gEKmExu4

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How does a venture capitalist pick winning startups? Ben Hemani of Bison Ventures joined us on the podcast this week to discuss the intersection of deep tech, biotechnology, and climate sustainability. Key insights: 1️⃣ Evaluating founders and teams in early-stage ventures is an art and a science. 2️⃣ How Ben and his team are navigating the current funding climate. 3️⃣ The powerful potential of cross-disciplinary approaches in tech and biotech. (Plus, why Ben and I both love a good platform company) This episode is packed with actionable advice for entrepreneurs and investors alike. I hope you enjoy our conversation as much as I did! #VCInsights #TechInnovation #Biotechnology #StartupFunding

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In just a couple of days, thanks to the work of Osma Suominen (and the Annif tool - suggested by Juho Inkinen), there are some very compelling results (> 0.95 recall for policy area, and F1 of nearly 0.74 for subject areas), demonstrating the power of open data on Hugging Face and the excellent international Library community! In follow-up discussions with Marci Harris and Leonard Park, my team at DreamProIT plans to add Committee referrals to the data set. This could allow a model to recommend committees, based on prior bill referrals... See https://lnkd.in/gww39QeG

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Open source has always been a major part of my career. I spent 13 years building MongoDB and its open source developer ecosystem, and at Viam all of the software that runs on a user's device, our APIs, and our SDKs are open source. Now, I am excited to share that Viam is now an official member of the Open Source Robotics Alliance. Let’s talk more about why open source is important to us:  Security - If machines are inside your house, your office, etc. security and privacy are paramount, and you'd want to know exactly what’s happening with that data. Open source not only makes companies responsible to write good software, but lets the community easily vet it. Innovation - I have said before, no one wants to contribute to a closed source ecosystem. We want developers to bring their ideas to life using Viam. To do so, open source is necessary, as it’s harder to contribute to closed source, and not nearly as rewarding. It keeps corporations in check - When betting your business on a technology, you are responsible for making sure there are some checks in place. By making everything on-machine open source, it means that if something happens, there are ways for users to move forward using open source without Viam. This is all just the tip of the iceberg. Open source has the power to democratize robotics, remove barriers to interoperability, and make automation much easier than it is today. Read more here: https://lnkd.in/eTc8p_Dk Open Robotics #opensource #openrobotics #viam #mongodb #robotics #opensourceroboticsalliance #OSRA

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The biggest opportunity for application layer AI is when it is combined with Walled Gardens Solly and I are hunting for startups that can stand the test of time and that benefit from structural advantages before they write one line of code Link below for more info

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In today's episode, the renowned RLHF thought-leader Dr. Nathan Lambert digs into the origins of RLHF, its role today in fine-tuning LLMs, emerging alternatives to RLHF... and how GenAI may democratize (human) education! Nathan:  • Is a Research Scientist at the Allen Institute for AI (AI2) in Seattle, where he’s focused on fine-tuning Large Language Models (LLMs) based on human preferences as well as advocating for open-source AI. • He’s renowned for his technical newsletter on AI called "Interconnects".  • Previously helped build an RLHF (reinforcement learning from human feedback) research team at Hugging Face. • Holds a PhD from University of California, Berkeley in which he focused on reinforcement learning and robotics, and during which he worked at both Meta AI and Google DeepMind. Today’s episode will probably appeal most to hands-on practitioners like data scientists and machine learning engineers, but anyone who’d like to hear from a talented communicator who works at the cutting edge of AI research may learn a lot by tuning in. In today’s episode, Nathan details:  • What RLHF is and how its roots can be traced back to ancient philosophy and modern economics.  • Why RLHF is the most popular technique for fine-tuning LLMs. • Powerful alternatives to RLHF such as RLAIF (reinforcement learning from A.I. feedback) and direct distilled preference optimization (dDPO). • Limitations of RLHF. • Why he considers AI to often be more alchemy than science. Many thanks to Amazon Web Services (AWS) and Crawlbase for supporting this episode of the "Super Data Science Podcast with Jon Krohn", enabling the show to be freely available on all major podcasting platforms as well as the video version we publish on YouTube. This is Episode #791! #superdatascience #machinelearning #ai #llms #rlhf #reinforcementlearning

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For NY Tech Week we'll be demo-ing Substrate at the South Park Commons Science Faire on Thursday: https://lnkd.in/e5JwDKKX There are so many incubator-shaped things out there these days, but there's nothing quite like SPC. It's like a chill, small-batch, locally-sourced alternative to YC, where you can either hang out as a member forever - or choose the VC-funded adventure. The -1 to 0 phase [0] they talk about at SPC is real, and it made a real difference for us to have a home at SPC. It takes a lot to quit your job and do something new, especially when it isn't entirely clear what you're doing. (1) Do it – take the leap! and (2) consider joining SPC. Though the core vision has remained the same, many of the important turns in our squiggle at Substrate (including the graph framework) came from those early days at SPC. If you're here for tech week and can't catch us at SPC on Thursday, DM me if you want to meet up! [0] https://lnkd.in/e6KEMr9V

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Is AI hype overshadowing true technical innovation? The true measure of AI and machine learning isn't found in the buzz but in the substance of the work behind it. From my own experience, I've noticed a trend: projects that look great in a demo but fall short when showing a proper enterprise use case, i.e. they fall short when it comes to substantive deployment. The key takeaway? As AI continues to evolve, the real game-changers will be those who focus on the bedrock of AI – the fundamentals. I’ve discussed this and other AI-related stuff in our AI-searched podcast with my colleague Tanya Ariana Bendis So go, check it out. https://lnkd.in/dKp39pN9

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pumped to announce sacra pipeline, which syncs sacra research and data directly into your database or data warehouse—so you can work with it natively using your existing tooling. our mission at sacra is to get our research & data on growth and pre-ipo tech companies into the hands of every platform and fund that needs it. sacra pipeline makes that stupidly easy by piping our data to you from day 1. just connect your database (postgres, mysql, mongodb) or data warehouse (snowflake, redshift, databricks) and your product and data teams can start cranking immediately. to check it out, see our launch vid below and email us at founders@sacra.com to get started 👇 🔌 for platforms, this means exposing sacra data in customer-facing features without building data pipelines 🧑‍💻 for funds, it means analyzing companies in sql and creating prospect lists without switching tools. 🔁 for ai developers, it means ingesting and continuously syncing data for RAG as we add to and update our research ultimately, our goal is "easy in, easy out" data. stupidly easy to sign up, get started, and get value - without barriers or commitments. major thanks to Danny Tharma who made it happen here at sacra! to get started with sacra pipeline, email us at founders@sacra.com. 🚀

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RethinkX's recent publication on The Rise of Human Robots and The Future of Work is quite interesting. Tony Seba and his team including Adam Dorr and Bradd Libby, do an excellent job dissecting the emerging humanoid robot technology market. Here's a brief summary, with a more in-depth summary from Cern Basher, CFA, an expert on the future financial impacts of humanoid robots, linked in the comments below: The rise of humanoid robots is poised to disrupt human labor across industries, much like how automobiles disrupted horses. This convergence of technologies creating a "labor engine" will enable robots to eventually perform virtually any task better and cheaper than humans. Initially, robots will complement human workers by taking on simpler tasks. However, their capabilities will rapidly improve until they can do almost anything humans can by the 2040s. This disruption represents both a massive opportunity and challenge for society. As the marginal cost of robotic labor approaches zero, we can expect sweeping deflationary pressures and quality improvements across the economy. Labor supply will explode, uncoupled from population growth, giving nations with robust robotics industries a strategic advantage. While creating prosperity, this transition will destabilize conventional economics and the social contract around work. Thoughtful policymaking will be critical to avoid catastrophic unemployment or stifling innovation through misguided protectionism. Ultimately, humanoid robotics could lead to superabundance and liberation from labor. But achieving this will require rethinking economics, embracing the disruption responsibly, and prioritizing policies that protect people over obsolete jobs and industries. https://lnkd.in/gjNDVQe4 #humanoids #robotics #futurefrontier

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These extremely insightful interviews were based on an already eye-opening session led by Alexandra and Stefan at last year's DevGuild: AI Summit event. #ML is an exciting and very of-the-moment discipline, but to those of us without a PhD in it, it may seem like a non-deterministic black box that's just too risky to be reliable. The recommendations shared in this article: - Aligning around #metrics everyone can agree on (including the eventual need to ship something) - Investing in #eval, and - Driving cultural alignment Seem like things that teams can start doing today. I expect we'll see even more important #AL, ML, and #software #dev topics covered at next week's DevGuild: AI Summit II in San Francisco. If topics like this are of interest to you, please consider registering: https://lnkd.in/gzxq5M9n

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The LLM will see you now: Google and DeepMind released a paper on Monday describing Med-Gemini, a group of advanced AI models targeting healthcare applications. The authors claim that Med-Gemini is outperforming competing models such as OpenAI’s GPT-4. During the pandemic we saw the rise of Telehealth which has largely come back to earth but could doctors be consulting more often with AI? (We know they already Google stuff) #Healthcare #AI

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