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We make financial products more trainable with RL environments built using live markets & real users. For safe, reliable, and performant financial LLMs.
Los Angeles | Brisbane Katılım Nisan 2008
18 Takip Edilen578 Takipçiler
This news article is absolutely insane and probably a good glimpse into what the future of hedge funds look like in an AI driven world
Coatue backed hedge fund, Epicenter, is using AI to replace massive teams of hedge fund analysts
Their AI bot, Eve, is plugged into every part of the firm, including emails and every trade that the fund puts on
Without any prompts, the AI bot is able to assign tasks to human analysts and write code on their behalf
Eve also contains a screening tool which scours through more than 13,000 company disclosures, listens to podcasts, and summarizes news reports on specific businesses
The AI bot can then generate primers and custom podcasts on behalf of the human analysts without requiring any instructions beforehand
If you read this and don’t think that AI is going to impact headcount in finance, you are absolutely mistaken
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The most important line may be: quality beats quantity.
That matters far beyond finance.
A lot of people still assume progress means more data, more compute, more scale.
Your results suggest the real edge can come from:
better curation,
better ratios,
better formatting,
and better alignment between the data and the behavior you actually want
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@ManojKGorle thanks Manoj - follow us to watch it scale to hundreds of billions of params
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Elon Musk’s artificial intelligence startup xAI is looking to hire bankers and private credit lenders to make its Grok chatbot better at finance strategy bloomberg.com/news/articles/…
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Now why would Jane Street lead a $500m round for low latency LLM chips?
🧐
Reiner Pope@reinerpope
We’re building an LLM chip that delivers much higher throughput than any other chip while also achieving the lowest latency. We call it the MatX One. The MatX One chip is based on a splittable systolic array, which has the energy and area efficiency that large systolic arrays are famous for, while also getting high utilization on smaller matrices with flexible shapes. The chip combines the low latency of SRAM-first designs with the long-context support of HBM. These elements, plus a fresh take on numerics, deliver higher throughput on LLMs than any announced system, while simultaneously matching the latency of SRAM-first designs. Higher throughput and lower latency give you smarter and faster models for your subscription dollar. We’ve raised a $500M Series B to wrap up development and quickly scale manufacturing, with tapeout in under a year. The round was led by Jane Street, one of the most tech-savvy Wall Street firms, and Situational Awareness LP, whose founder @leopoldasch wrote the definitive memo on AGI. Participants include @sparkcapital, @danielgross and @natfriedman’s fund, @patrickc and @collision, @TriatomicCap, @HarpoonVentures, @karpathy, @dwarkesh_sp, and others. We’re also welcoming investors across the supply chain, including Marvell and Alchip. @MikeGunter_ and I started MatX because we felt that the best chip for LLMs should be designed from first principles with a deep understanding of what LLMs need and how they will evolve. We are willing to give up on small-model performance, low-volume workloads, and even ease of programming to deliver on such a chip. We’re now a 100-person team with people who think about everything from learning rate schedules, to Swing Modulo Scheduling, to guard/round/sticky bits, to blind-mated connections—all in the same building. If you’d like to help us architect, design, and deploy many generations of chips in large volume, consider joining us.
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We're live with the Cambrian team, tune in here!
x.com/i/broadcasts/1…
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