Pararth Shah

Elon Musk’s xAI and the Surprise Launch of Grok-2 Elon Musk's company xAI has launched Grok-2 and Grok-2 mini in beta, positioning xAI as a formidable competitor against AI giants like OpenAI and Google. Previously seen as a minor player, Grok-2 now reportedly surpasses GPT-4 and Claude 3.5 Opus in key benchmarks. Grok-2 boasts significant improvements in reasoning, reading, math, and science, particularly excelling in visual tasks. A strategic partnership with Black Forest Labs, known for their cutting-edge image generation technology, further enhances Grok-2’s capabilities. Independent validation is needed, but this move heightens the pressure on OpenAI and Google, making the industry eager to see their next steps. Link: https://x.ai/

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There's a common belief that scaling 3 dimensions - model size, training compute and size of the dataset will lead us to AGI. But, this is an oversimplified view of reality. Read an interesting piece on #AISnakeOil from Sayash Kapoor and Arvind Narayanan that critiques the widespread belief and provides a wonderful explanation. While scaling has improved model predictability, the real value lies in unexpected capabilities that appear as models grow. They term it as - the "Emergent Abilities" of the model. Undoubtedly, until now scaling up has led to remarkable improvements in the predictability of responses. While this is interesting from a technical standpoint, it might not necessarily be what end users care about. Maybe what really matters is the unexpected capabilities that models acquire as they grow in size. Another misconception is the role of synthetic data. While it’s often considered as a solution to the data scarcity problem, it’s primarily used to fill specific gaps or enhance domain-specific tasks, not to increase the overall volume of training data. In summary, the future of AI and LLM development isn’t about endless scaling. It’s about smarter, more efficient use of resources to unlock the best outcomes. Apologies for the long post but I found it super interesting. What are your thoughts? You can read the complete article here 👇

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This is f*cking hilarious: TuSimple's pivot from self-driving trucks to AI animation and gaming! 🤖🎮 Right now, everyone’s betting on humanoid and general-purpose robotics startups. But after 5 years in self-driving tech and building Kopernikus Auto, here’s the reality: most humanoid robotics companies will be dead. In the self-driving world, we’ve only seen a few real winners: Tesla, Waymo, and Comma.ai. Why?  - Humanoid robotics isn’t a hardware problem; it’s a software problem. - You either have infinite data, cash, talent, or a clear use case—or you won’t make it, no matter how much you raise. From over 100 companies, almost all in self-driving tech are dead or miles away from profitability. ✨ Don’t get distracted by the appeal of humanoids. Focus on real problems you can actually solve. What are you focusing on? 👇

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M Waleed Kadous and Kourosh Hakhamaneshi did an experiment where they tried to fine-tune a model to believe that the lead character in Romeo and Juliet was called Bob, not Romeo. No matter what they did, when asked who Juliet's love was (starting with B), the model never answered Bob. Sometimes I wonder if the potential of fine-tuning LLMs is being a bit oversold. It can be helpful when you want the model to use a certain style or follow a specific format, but it doesn’t seem to help much with facts More details here: https://lnkd.in/g6p2eU-n

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Our Kumo.AI blog post on Hybrid GNNs is out. https://lnkd.in/gpzr8TiF We have seen success with our Hybrid GNNs across several marketplace problems. Hybrid GNNs are a novel innovation at Kumo.AI because they model both repeated and explorative user interactions within a single GNN framework. The GNN automatically infers whether a user tends to do repeat purchases or tends to be more exploratory (ie buying new unseen items) and learns a repetition scalar per user. What we love about this approach is that it takes away from the standard recommendation systems architecture where you have multiple candidate generators - some for new unseen items, some for repeat behavior and then have to combine them in a final multi-objective optimization framework. Such systems which are a combination of multiple systems are hard to maintain, re-train and operationalize. On the Kaggle H&M challenge where most winning teams usually have approaches that require large amounts of feature engineering and the final system is a complex ensemble of approaches needing months to build, the Kumo.AI approach which is a 3 line predictive query that runs a Hybrid GNN under the hood is top 1%. Thanks Matthias Fey and Weihua Hu for the algorithms and innovation.

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Yet another article that looks beyond the #GenAI hype, this time by Siddharth Pai of Siana Capital. There is a big gap between reality and hype. It is very important that we focus on reality and ensure AI development happens at its own pace. The current trend to use AI for everything and anything without considering the cost and RoI is already backfiring.

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