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Intel has launched its 5th Gen Xeon processors, focusing on AI capabilities for cloud, network, and edge use cases. These chips are part of Intel's strategy to compete in the AI accelerator market dominated by Nvidia.
ByteDance, TikTok's parent company, has been secretly using OpenAI's technology against its terms of service to develop a competing language model named Project Seed. Internal communications suggest plans to "whitewash" evidence of this misuse.
Atlassian has launched its first set of AI-powered services across its cloud platform. Leveraging 20 years of data insights, Atlassian Intelligence aims to optimize teamwork by automating routine tasks, accelerating individual productivity, clarifying company jargon, and empowering data-driven decisions. Major features available include Generative AI in the editor, AI-powered summaries, and natural language automation.
Researchers have developed a new method called DSF to improve spectral graph neural networks. By introducing node-specific filter weights, DSF can better handle complex networks like the World Wide Web.
This article discusses a method for proactively evaluating the potential discriminatory impact of language models. The method includes generating a wide array of potential prompts for various decision scenarios and demographic information variations. Strategies for mitigating both positive and negative discrimination primarily involve careful prompt engineering.
This study introduces SmooSeg, a new approach that utilizes the principle that nearby features in images often share similar meanings.
Amphion is a new tool kit for media generation. It supports music and speech and the team is working towards adding more models quickly.
A study on UNet encoders in diffusion models reveals their stable behavior compared to decoders. This insight led to a new encoder propagation scheme, significantly speeding up the process for tasks like text-to-image and text-to-video generation.
A hidden gem of AI, the Big Vision project is a great library for research and production of state-of-the-art vision systems. They just added weight sharding to make it easier to train on multi-node systems.
Shape errors in ML are challenging to debug and are often only caught when you try running the model. By checking shapes as types you can eliminate much of this challenge and develop more quickly.
Lightspeed’s take on the legaltech landscape as it relates to AI is interesting and transparent from the view of what’s-good-for-VCs-is-good-for-everyone take. There are a few interesting insights and only time will tell if they are on the right track or not.
The creators of Mamba (and many more models) have released a cool blog post outlining basic sequence mixing architectures that give pretty dramatic speed ups relative to standard Transformers.
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Agents capable of self-editing their prompts and Python code.
AI-powered product research for DTC brands.
While things have been frothy in the AI space, we’re still barely a year into this new world order and the really interesting silicon has yet to hit. However, the competitive landscape is shaping up to be cutthroat and everyone is gunning for Nvidia.