OpenAI launched a research preview for GPT 4.5 and perhaps the biggest takeaway is that it can fake emotional intelligence pretty well--potentially better than a few humans we know.
Snowflake Cortex AI will integrate Microsoft Azure OpenAI Service in Azure AI Foundry. That move will put OpenAI's models Snowflake and make them available as data agents in Snowflake's AI Data Cloud.
Cohere launched an early access program for North, an AI platform designed to make daily work more efficient. The aim for Cohere North is to combine large language models, search and agents in one secure enterprise work and collaboration platform that can run anywhere.
Amazon Web Services launched Amazon Nova, a series of foundation models available in Bedrock, in a move that aims to provide large language model choice and commoditize the market.
Amazon will invest another $4 billion in Anthropic to bring its total investment to $8 billion. Anthropic will also use AWS as its primary training partner and use AWS Trainium and Inferentia processors to deploy its largest large language models.
Ramprakash Ramamoorthy, Director of AI at Zoho, said the company is focused on developing large language models (LLMs) designed for business use cases and integrated into its stack.
Anthropic upgraded Claude 3.5 Sonnet with the ability to use your computer, looking at your screen, moving cursors, clicking and typing. The company also launched Claude 3.5 Haiku.
IBM launched Granite 3.0 8B and 2B models under the Apache 2.0 license, new models designed for CPU-based deployments and edge computing and the next-generation of Watsonx code assistant.
BioNtech's InstaDeep, which was acquired in 2023 for about $682 million, has released a series of foundational generative AI models for proteins and DNA and released them on its DeepChain platform and outlined a supercluster called Kyber.
NVLM 1.0 will enable smaller enterprises and researchers to piggyback off Nvidia's research.
Liquid AI, an MIT spinoff, launched its Liquid Foundation Models (LFM) in three sizes without using the current transformer architecture used by large language models (LLMs) with good performance. That efficiency could mean that LFMs will use less power and be more sustainable.
