Artificial intelligence (AI) research/product development firm Reka AI has raised $110 million.
The company’s new funding round, announced Tuesday (July 22), included contributions from Nvidia and data cloud company Snowflake, and will help Reka scale its multimodal platform for more widespread enterprise adoption.
“Reka is known for its ultra-efficient multimodal models developed by a world-class research team,” the company said in a news release.
“The company’s focus on efficient training and serving infrastructure has enabled it to develop market-leading models at a fraction of the cost. Reka Flash—a multimodal model that understands video, image, text, and audio—is the workhorse of Reka’s product offerings.”
The round values Reka at $1 billion, according to a report by Bloomberg News. The same report notes that Snowflake had held talks to acquire Reka last year, though those discussions ended when “both companies decided it made sense to move independently,” CEO Dani Yogatama told Bloomberg.
Vivek Raghunathan, vice president of AI engineering at Snowflake, said the company would offer Reka AI’s models and other tools to its clients.
“Very few teams in the world have the capability to build what they’ve built,” Raghunathan said. “Almost everyone at that level of talent is at OpenAI, Meta or Anthropic. Reka is one of the rare independents — and they’ve proven they can compete.”
Snowflake earlier this year announced plans for a new Silicon Valley “AI hub” as well as its goal of — along with its startup accelerator and its venture capital partner — investing up to $200 million in early stage startups.
In other artificial intelligence news, PYMNTS wrote earlier this week about the use of AI benchmarks — the type of standards achieved each time companies like Google or OpenAI roll out a new model — in helping guide vendor decisions, identify growth areas and determine whether a model is suitable.
“The first path to discernment is understanding the nature of these benchmarks,” the report said. “These benchmarks are standardized tests that measure an AI model’s proficiency in several areas: math, science, language understanding, coding and reasoning, among other topics.”
Without benchmarks, companies would need to depend on marketing claims or one-sided case studies when figuring out which AI system to use.
“Benchmarks orient AI,” Percy Liang, director of Stanford’s Center for Research on Foundation Models, said at a 2023 Fellows Fund event. “They give the community a North Star.”
Source: https://www.pymnts.com/
