Girish Gupta

"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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[Mostly] generic/open source agtech offerings in the next 0-5 years: - Carbon/Emissions modeling - AI feedback models - Blockchain 👇 Here’s my #Scope3 #AgTech 101: 👉 Any data can go on blockchain or into a model. Even poorly collected/analyzed/interpreted data.

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One of the hardest problems in generative modeling today is around #evaluation. How do you know if the output of your diffusion model is actually good? 🤔 If this sounds interesting, come join us as we tackle these hard problems in #genAI! Jobs linked in comments 👇 At Advex AI , we evaluate synthetic samples on two dimensions: 1. Fidelity - is the overall image realistic? 2. Faithfulness - does the generated image align with input instructions? A common challenge we face is the trade-off between fidelity and faithfulness. Current evaluation metrics such as CLIP, Aesthetic score, and RLHF often fail to give reliable results. Here's why: 1. Aesthetic scores (Q-Align, ICML '24) focus on how visually appealing an image is, but for tasks like generating synthetic data to improve downstream model performance, utility matters more than aesthetics. Similar to how augmentations introduce noise to improve robustness and generalization, synthetic images can be valuable even if they don’t score high on aesthetic metrics. 2. Multimodal models such as CLIP (OpenAI) and BLIP (Salesforce) excel at high-level semantics but struggle with low-level details like texture or sharpness. Additionally, these models are also biased towards the distribution it was trained on. 3. RLHF (ImageReward, NeurIPS '23) shows promise, but collecting large volumes of preference data is tough—especially in skilled domains like medical imaging where even the expert judgements are subjective. These limitations hinder full automation of genAI evaluation, but it doesn't have to be this way. At Advex, we’re pioneering new solutions to generative model evaluation! #SolveDataSolveAI

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François Chollet is a prominent French software engineer and artificial intelligence (AI) researcher, currently serving as a Senior Staff Engineer at Google. Chollet is best known for creating Keras, a widely-used deep learning library that was released in 2015. In 2019, he devised ARC. The Abstraction and Reasoning Corpus (ARC) is a benchmark designed to measure AI's ability to acquire skills and progress towards human-level intelligence. Learn more about it in this guest post with Jurgen Gravestein of Teaching Computers How to Talk. We need to continue to devise new benchmarks for our reasoning and agentic machines and tools. The ARC prize and context relating to AGI is of particular interest to me. Read more and check out some of the links in this article. Including videos by Machine Learning Street Talk (MLST) and Dwarkesh Patel.

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Interesting concept around Galileo Luna evaluation foundation models. A lot of LLM-based chatbot systems have safety guardrails to prevent LLMs from hallucinating and giving incorrect information. One technique is to fact-check the answer of an LLM by running through it again. Obviously, this doubles the number of API calls that you need to make to the LLM, also doubling the cost. Luna evaluation models seem to solve this by using specialised Small Language Models (SLMs) which have low latency and are much cheaper to run. Since they are also customised for detecting hallucinations, they perform better than larger language models. https://lnkd.in/d6zTzZ6B #SLMs #LLMs

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November 6 2024 – 5 Notable Articles To Read 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: Microsoft's $30B Infrastructure Fund Is Driving Vertical Integration Of AI 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Microsoft, BlackRock, Global Infrastructure Partners (GIP), and MGX have launched the Global AI Infrastructure Investment Partnership, an ambitious new initiative seeking to raise $30B in equity capital for AI infrastructure investments. GAIIP will prioritize funding for new and expanded data centers, along with the energy infrastructure needed to power these facilities, with a primary focus on the United States. 𝗦𝗼𝘂𝗿𝗰𝗲: Forbes 𝗟𝗶𝗻𝗸: https://lnkd.in/d79SyYdD 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: How AI Is Incrementally Fueling Energy Sector Innovation 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Power generation companies have increasingly adopted AI for their next-generation systems and, in many ways, were early pioneers of AI within the energy sector. This adoption is fueled by a growing demand for enhanced throughput, operational efficiency, and sustainability. 𝗦𝗼𝘂𝗿𝗰𝗲: Forbes 𝗟𝗶𝗻𝗸: https://lnkd.in/d8Q2nJER 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: Jo N.’s Invidia Capital Management seeks $850M for first fund 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Currently raising capital for its inaugural fund, aiming to invest in upper middle-market healthcare buyouts. Invidia was launched earlier this year with a focus on healthcare investments ranging from $100M to $300M per deal. Plans to make both minority and majority investments across healthcare sectors - including pharmaceutical outsourcing and contract manufacturing, medical devices, and healthcare information technology. 𝗦𝗼𝘂𝗿𝗰𝗲: Fortune 𝗟𝗶𝗻𝗸: https://lnkd.in/dB4UMSaW 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: Saudi Arabia Nears Deal to Take Over $4B Olam Unit 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Olam has confirmed receiving an offer from Saudi Arabia's wealth fund, SALIC, for its agriculture unit, a deal that could enhance food security in the kingdom. 𝗦𝗼𝘂𝗿𝗰𝗲: Bloomberg 𝗟𝗶𝗻𝗸: https://lnkd.in/d9AJUs74 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: AI demand drives growth and higher spending at Amazon’s cloud business 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: As businesses rush to develop and deploy generative AI tools, their demand for additional cloud storage capacity to hold data is continually increasing. 𝗦𝗼𝘂𝗿𝗰𝗲: Financial Times 𝗟𝗶𝗻𝗸: https://lnkd.in/dyq6kdK8 #news #business #ai #research #investments

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Highly recommend this interview from the All-In Podcast summit with Tekedra N. Mawakana, Co-CEO of Waymo. Great work by Jason Calacanis, Chamath Palihapitiya, David O. Sacks, and David Friedberg. Here are some insights that stood out to me: 🚦 Why does Waymo avoid freeways? Freeways are pretty straightforward and Waymo is more focused on denser, unpredictable environments since they help the AI learn faster and better. 🤖 Tekedra referenced the ‘Waymo Driver’ often, which is an interesting term - in a way, they are building ‘one new driver’. 🤝 Will Waymo disrupt and decimate Uber? Well they’re partners so it would be an awkward yes so take this one with a grain of salt. Tekedra said that they don’t want to be experts at everything. They partner with OEMS on the vehicle side, so it made sense to partner with Uber on the rideshare platform side. I buy that.

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One new interesting application of LLMs: CAIS has released a forecasting bot that provides probability estimates for any question you ask. For example, to the question "Will GPT5 be released before 2025?", it answers that it's 70% likely. The bot works by searching for relevant information online, feeding it to GPT-4o, asking a few guiding questions, and then requesting a probability estimate. What's impressive is that this bot outperforms experienced human forecasters, even though GPT-4o wasn't specifically trained for this task. This kind of application shows that AI could be of great help in improving decision-making in the future. Link to the blog post: https://lnkd.in/gjP7JQgz Link the bot if you want to try it yourself: https://forecast.safe.ai/

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Gift link for op-ed from the Editorial Board of the WSJ. We're in much agreement on this. From my perspective, more federal R&D should have been funneled towards safe and responsible AI all along, rather than reacting to winters and springs (consistency is key). Canada maintained a small investment through the last AI winter and it paid off nicely. It's one of the reasons they punch above their weight in AI today. We maintained our research efforts in KYield as well in a very lean manner, which is why I think we are still well ahead of the pack in our focus areas. But the idea that we need to suddenly invest $32 billion per year in non-mil AI is primarily buying votes in academic research across the country. Big Tech spends $220 billion a year in R&D. The primary problem is most of it is strategic to Big Tech, not customers or society. Monopolies want to force us to spend more GDP in their products -- they don't particularly care how, nor are they incentivized to care. However, as the piece suggests, spending has increased in all industries -- it's not at all just about Big Tech, despite the headlines. ~~~~~ If China is “going to invest $50 billion, and we’re going to invest in nothing, they’ll inevitably get ahead of us,” Mr. Schumer said Wednesday in unveiling a “roadmap” for AI policy with Republicans Mike Rounds and Todd Young and Democrat Martin Heinrich. Invest in nothing? Didn’t Washington pass massive climate, infrastructure and semi-conductor spending bills? "Mr. Schumer used the same justification in 2022 to pass the $280 billion Chips and Science Act, though China trailed the U.S. by years in semiconductors. Now President Biden is doling out money to chip makers at campaign stops, including $19.5 billion this spring for Intel at a chip plant in Arizona." "The Senate AI report calls for Congress to pass $32 billion a year in “emergency” spending for non-defense AI. What’s the emergency? Perhaps the November election."

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Two hypotheses for the coming Pentagon environment: good for VC-backed and back for VC-backed… The first hypothesis is pretty straightforward, the sheer number of VC alums and VC adjacent, from the VP to the DepSecDef would logically lend to the idea that the building would naturally get more VC friendly. Not overtly mind you, I don’t see folks rolling in and outright steering funds into companies in which they have interest, more a matter of the folks holding the reins actually knowing how that world works on a deep level and implementing policy that really helps. The second hypothesis goes like this: the admin has, at best, 18 months to really get changes moving, while they have all three seats of power, before midterm pressures smack an aspirational agenda with the cold fish of reelection prerogatives. Likewise, the best I think we can hope for these private sector titans to take a break from making markets to make $200k/yr as a DC-dwelling bureaucrat, is about two years. That also happens to be the going tenure of an appointee-level official. So the White House and the E Ring have 24 months to make a difference for venture backed firms before the political gravitational pull swings the building back into a regular orbit around the budgetary sun…which is…about 1 1/2 POM cycles…the frozen middle has held there breath for longer before… I’m probably wrong in bother cases, just a casual observation.

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Attached is NVDA's price chart for the last year - up c.175% (up 2500%+ in L5Y period!!) 🚀 I nonetheless have conviction that it currently remains undervalued... Among other things, due to strong competitive positioning as a GPU-maker, AI-driven demand fuelled by investor and consumer interest, and moats it has built. This is mirrored by equity analysts – GS’ latest price target was $800, BofA’s $1,500. I do believe the growth is capped, and driven in equal measure by under-supply and bubble-like demand as it is by fundamentals. Even so, Nvidia remains an attractive medium term investment. ⚖️ Demand – Supply Dynamics: AI is a generational innovation, and agents /apps are already upending SaaS, or at least so think VCs who deployed $64.6B in AI start-ups in 2023 alone. As early movers and now the industry standard (c.80% market share), Nvidia's A100 and H100 chips are not only the most efficient option, but also crucial for large scale AI training and inference (chips are the biggest cost head for LLM companies). With unprecedented interest in AI (ChatGPT acquired 1M users in 5 days of launch, OpenAI reported $3.4B ARR in Apr’24, AI agents are widely recognised as the tech of the future), Nvidia stands to capture a large portion of the $250-$500B data centre conversion market over the next 5 years​. This is proven by the $22.6B in data centre revenue Nvidia brought in during Q2’24, up 23% from the previous quarter and up 427% from a year ago 🥇Competitive positioning: While Intel, AMD, Arm have been active, the only credible competitor, AMD, has a relatively smaller market share in the high-performance AI GPU segment, and Nvidia’s consistent technological advancements keep it further ahead, e.g., Nvidia’s Rubin which will succeed Blackwell in 2026 is orders of magnitude better than anything on-market. 🤝Moats via ecosystem building: Collaborations with hyperscaler cloud providers like AWS and Microsoft Azure have expanded Nvidia’s reach. Moreover, acquisitions like Mellanox have strengthened its position in high-performance networking, critical to AI infrastructure. Coreweave, a prominent cloud services partner / distribution channel for chip capacity is an NVIDIA investee and is built almost entirely on Nvidia chips. Finally, they continue investing in networking, software tools, data centre solutions but also purpose-built AI PC and smartphones to create an ecosystem that not only locks in customers and enhances switching costs, but pivots more of their revenue to more predictable recurring contracts. 📈 Financial underpinnings: Nvidia’s expected FCF for next 2 years stands at $100B, providing huge ammo for R&D, strategic investments and M&A (only $30 to $35B earmarked for buybacks) and in turn, growth. Even so, their shares trade at a P/E ratio of c.20x  NTM earnings, far below historical median of 35x+, indicating the market expects them to grow in the medium term. KEEN TO HEAR THOUGHTS! #Nvidia #AI #Compute #Chips #Tech #Growth

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Anthropic has launched Claude 3.5 Sonnet, the inaugural release in their upcoming Claude 3.5 model family. This new AI model sets unprecedented industry benchmarks for intelligence, outperforming competitor models and even Anthropic's own Claude 3 Opus across a wide range of evaluations, including graduate-level reasoning, undergraduate-level knowledge, and coding proficiency. Claude 3.5 Sonnet demonstrates significant improvements in understanding nuance, humor, and complex instructions, while excelling at producing high-quality content with a natural, relatable tone. ➜ Check the news at: https://lnkd.in/dbKMKPm7 ➜ Vendorvise's official Instagram: https://lnkd.in/ebK2KQ2C ➜ Vendorvise's Linktree: https://lnkd.in/dQdbMdth 🤖 If you enjoy reading my posts about AI and tech news, hit that like button and share with your friends to support my content! #ai #artificialintelligence #ainews #aipowered #news #blog #claude3 #sonnet #anthropic #generativeai #llms #tokens #aitools #aitech #tech #technology #hardware #content #socialmedia #future #new #launch #business #techtrends #innovation #success #markets #people #industry

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if you know, you know le cunn v elon since 14 may 2020. 4 years of le cunn shooting the first shot. good on elon for calling his sh^t out. turing prize winner (b)eing infantile. and we wonder where (ai) is headed with these types of characters being entertained. truly. truly. truly. mad. judging from this 4 year (almost to the day) hate fest as started by le cunn, prob a good idea for zuck Meta to let him go. as in bye bye. that would be fitting. wouldn't it? knighted by macron. a godfather of ai by way of the 2019 turing prize. advocate for open source. and a sh^t on top of that. do we really want these types of folks leading the way for (ai)? do we? do we want to sit back and allow these le cunn.s to flourish? do we? do we want to invest into these types of characters. do we want them as a sign a signal that you can get away with being a genuine di&k. just because you were part of the story. the good news is that there are many now who can take over the mantle. the lead. and the le cunn.s can go le f' off. into the sunset and alone in their pathetic attitudes. we wonder what (ai) will lead us to. have you considered for a moment the openly evidently lack of honore. this knight of france. mon dieu. fun memories. really. je te regrette, yann. a big big yawn indeed. rn. ;) ty for launching this era. quitte. my sense is this french duck is about to be cook(ed). and he has no one but himself to blame. that hatred has no place in the future of creating this era of ai as it requires a (hu)manity. and this apparently is sorely lacking with le cunn. and has been going on for years. zuck. are you there. you get the message. time for someone else a bit more say humble to help usher in (ai) (hu)manity wouldn't you say. i'm just say'n ;) i'm reminded of how steve jobs answered a tough question or rather criticism here: https://lnkd.in/ggt6nYH4 it was mindful, thoughtful, aware. more of this pls source: https://lnkd.in/e8Rzw7Kf < . >

I’ve been fortunate enough to do AI shows with Sam Altman, Yann LeCun & Arthur Mensch. But this show today with David Cahn at Sequoia Capital is the best of all of them: 🧠 AI’s $600BN question 🏃 Why no one will never train a frontier model on the same data center twice 🏗️ Servers, steel & power: The 3 pillars of the future of AI Not one to be missed, my 8 key takeaways 👇 1. The Oligopoly That is Blowing Billions on AI - Microsoft, Azure, & Google now represent $7T market cap. - Of course they will spend billions to protect their oligopoly - What is most interesting is Meta is the only incumbent that is financing this not with cloud being their cash cow. 2. How Startups Could Win with the Cost of Compute Coming Down - Big tech companies are producers of compute & startups are consumers of compute. - Overproduction of compute = lower prices.  - This gives startups higher margins & makes them more valuable. 3. Is Compute the Currency of the Future or Do the Costs Come Down and It Is Commoditised? - We are forgetting “compute” is a euphemism for a $2BN data center in the middle of nowhere. - They have GPUs & liquid cooling systems that need to be constantly optimized & upgraded. - We cannot simply build 15 years worth of compute all at once.  No one's ever going to train a frontier model on the same data center twice. 4. Data Centre Teams and Model Teams Need to Be Coupled - You cannot have separate teams running data centers & building models. - Elon & Zuck control all their data centres. - It will not work as these models get better & better. 5. Why the Data Center Is the Most Important Asset - There is not much difference between these models today. - AI researchers are jumping from one lab to another. - Scaling laws will become more dominant as these models get bigger. 6. Servers Steel and Power: The Three Bottlenecks of AI Three things: 1. Servers: NVIDIA, AMD, Broadcom… 2. Steel: Construction & real estate developers for data centers. 3. Power: All the energy required for AI. - More factories will be built in 12 months for AI and we will see another industrial revolution. 7. You Have to Have a Cash Machine to Play the Big Model Game - Amazon has AWS - Meta has Instagram - Microsoft has Azure - You need a cash machine to compete and it CANNOT be the AI business. 8. There is One Definition of Success in Venture: Are you a Slugger? - If you are generating billion dollar gains, you are a slugger. - Doug Leone, Pat Grady, Andrew Reed are all sluggers. - You cannot be good at this business without being a slugger. (links in comments) #founder #funding #business #investing #vc #venturecapital #entrepreneur #startup #seed #funding #sequoia

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I just read an insightful piece by Eric Broda (published in Towards Data Science) that outlines the Agentic Mesh—a new ecosystem designed for autonomous agents to interact, collaborate, and transact. His work introduces a structured approach for managing the coming surge in autonomous agents, showing how they can function independently yet collectively. Agentic Mesh serves as a networked environment where autonomous agents operate in a safe, transparent, and efficient manner. It allows agents to find each other and securely interact under a framework of policies. Each agent has a specific purpose and autonomy, making it a valuable contributor to the ecosystem without sacrificing security or control. Core Components: 1. Marketplace: A centralized space where users discover agents by tasks and capabilities, offering a trusted environment for engagement. 2. Registry: A metadata repository organizing each agent’s purpose, owner, and policies, allowing agents and users to securely find and connect with each other. 3. DNS Integration: Each agent has a recognizable digital identity, enabling seamless discovery and interaction, just like an internet service. 4. Agent Stack: This layered structure of communication protocols, control services, and interfaces helps agents operate effectively and responsibly. It ensures that agents act independently within controlled parameters. Agentic Mesh’s effectiveness depends on agents sharing consistent attributes, including: • Purpose: A clear objective drives each agent’s actions. • Ownership: Transparency around ownership fosters responsibility and trust. • Autonomy: Agents act independently within set parameters to complete tasks. • Discoverability: Easily locatable, ensuring efficient interaction within the network. • Intelligence: Agents utilize language models to plan and execute tasks, adapting to complexity as needed. Operational Flow: Registration, Discovery, and Execution Agent interactions in the Mesh follow three core flows: 1. Registration: Agents set up purpose, policies, and registration data to gain visibility in the mesh. 2. Discovery: Through the registry, agents and users locate potential collaborators based on defined needs. 3. Execution: Engaged agents use AI models to plan and perform tasks, enlisting collaborators when necessary to complete workflows. Read the full article: https://lnkd.in/eVmq5BFC

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🚀🔧 Sorella Labs, a startup focused on developing a MEV (Miner Extractable Value) risk-hedging tool, has raised $7.5 million in seed funding. The funding round was led by Paradigm, with participation from Uniswap Ventures, Bankless Ventures, and Robot Ventures. Sorella Labs is working on a solution to address the issues arising from the MEV strategy, where miners, block builders, and validators can insert or reorder transactions within a block to maximize rewards. 🌐🔗 #BitcoinWorld #BlockchainInnovation

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#RAG DOES NOT REDUCE HALLUCINATIONS IN #LLMS: Think I've called this out a few times, but the validation is nice - https://lnkd.in/eAi6zxW2. The article makes two well established points: 1) "Hallucination is a problem of reasoning and not relevance! Any amount of relevant text fed through RAG to a machine will retain the original perplexity and entropy in the system to hallucinate independent of the text"; and 2) "The initial challenge arises from the retrieval phase’s reliance on semantic similarity, which is quantified by inner product spaces or cosine similarities. This metric, while capturing semantic closeness, does not differentiate between factually accurate and inaccurate information." This should be clear to everyone, but the industry still shills flimsy RAG scaffolding anyway (shovels to gold miners).

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Vinod's thoughts on AI: Key Takeaway 1: The impact of AI on various industries is expected to be highly deflationary. As AI automates many tasks and reduces labor costs, prices for goods and services are likely to decrease significantly across sectors like healthcare, content creation, and professional services. Key Takeaway 2: There are still opportunities for AI startups, particularly in niche applications and specialized domains. While large language models may become commoditized, there is potential for domain-specific AI solutions that integrate workflow knowledge and industry expertise. Key Takeaway 3: The cost of using AI models is expected to decrease dramatically in the near future due to competition between major players. This will likely make AI more accessible and ubiquitous, though training costs remain high. Key Takeaway 4: Apple's recent AI announcements demonstrate the company's ability to integrate technology, platforms, and products. This positions Apple well to bring small on-device AI models to consumers and developers at scale. Key Takeaway 5: The upcoming U.S. presidential election has significant implications for the tech industry and AI development. There are differing views in Silicon Valley on regulation and policy approaches, with some favoring less regulation while others are concerned about ethical issues. https://lnkd.in/gG9hSuUy

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