Microsoft “doubling down” on cybersecurity “…engineering leads at Microsoft are now prioritizing security over new features or shipping products more quickly…”
Apple releases eight small AI language models aimed at on-device use…Apple’s new AI models, collectively named OpenELM for “Open-source Efficient Language Models,”…
JetBrains Launches IDE Services to Simplify Managing Development Tools
Sam Altman, Jensen Huang, and the 20 other leaders on the new AI safety board
Understanding API Technologies: A Comparative Analysis of REST, GraphQL, and Asynchronous APIs
QCon London: Meta Used Monolithic Architecture to Ship Threads in Only Five Months
Industry continues to dominate frontier AI research.
Frontier models get way more expensive…For example, OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute.
The United States leads China, the EU, and the U.K. as the leading source of top AI models.
Robust and standardized evaluations for LLM responsibility are seriously lacking.
Generative AI investment skyrockets.
The data is in: AI makes workers more productive and leads to higher quality work.
Scientific progress accelerates even further, thanks to AI.
The number of AI regulations in the United States sharply increases.
People across the globe are more cognizant of AI’s potential impact—and more nervous.
Elon Musk’s xAI previews Grok-1.5V, its first multimodal model
Naval Ravikant’s Airchat is a social app built around talk
AI Models : “...That’s the most common model for it. And so, today’s models cost of order $100 million to train, plus or minus factor two or three…The models that are in training now and that will come out at various times later this year or early next year are closer in cost to $1 billion. So that’s already happening. And then I think in 2025 and 2026, we’ll get more towards $5 or $10 billion…“
Big banks are in a race to get AI right…AI Innovation in Banking