Global AI Arms Race Accelerates with Cutting-Edge Tech and Strategic Investments - Seite 3

Data cloud company Snowflake Inc. (NYSE:SNOW) recently announced it would be enabling all users to securely tap into the power of generative AI with their enterprise data through its platform Snowflake Cortex. Shortly after, Snowflake released its financial results for Q3 2024, showing a 34% year-over-year growth in product revenue to nearly $700 million.

Continuing with that positive moment, Snowflake was awarded the #1 ranking on the 2023 Fortune Future 50 list. To compile this Future 50 list, The BCG Henderson Institute analyzed over 1,000 publicly traded companies, each with a market value of at least $20 billion or revenue of $10 billion in the 12 months up to the end of 2022. This evaluation includes 19 factors known for their predictive power of growth in the next five years. These factors are categorized into four groups: strategy, technology and investments, people, and structure.

"We are honored to be named at the top of Fortune's Future 50 list and look forward to continuing to help customers solve their biggest problems and deliver real value using AI," said Sridhar Ramaswamy, SVP of AI at Snowflake. "In this new AI era, generative AI and large language models will reshape how we live, work and do business."

After being labeled as very conservative in its approach to AI, Apple Inc. (NASDAQ:AAPL) is allegedly preparing to invest $1 billion in the generative AI arms race to catch their rivals, according CNBC's Mark Gruman. Just over a month after the news of that potential figure dropped, Apple quietly released a machine learning framework called MLX to help developers build models that run efficiently on Apple Silicon and deep learning model library MLX Data.

According to a social media post from Apple machine learning researcher Awni Hannun, MLX Data is a "framework agnostic, efficient, and flexible package for data loading. It's based on a design inspired by existing frameworks like PyTorch, Jax, and ArrayFire, however MLX goes further by adding support for a unified memory model, which means arrays live in shared memory and operations can be performed on any of supported device types without performing data copies—a feature list that has Computer World claiming it to be "pretty good at first glance".