Ex-Google CEO warns there’s a time to consider “unplugging” AI systems
Avery Lotz, Axios
Former Google CEO Eric Schmidt warned that when a computer system reaches a point where it can self-improve, “we seriously need to think about unplugging it.”
Why it matters: The multi-faceted artificial intelligence race is far from the finish line — but in just a few short years, the boundaries of the field have been pushed exponentially, sparking both awe and concern.
Google’s Agentic Era – Sync #497
Plus: Sora is out; OpenAI vs Musk drama continues; GM closes Cruise; Amazon opens AGI lab; Devin is out; a humanoid robot with artificial muscles; NASA’s new Martian helicopter; and more!
Conrad Gray, Humanity Redefined
A year ago, Google unveiled Gemini, its family of natively multimodal large language models in three sizes: Nano, Pro, and Ultra. A couple of months later, in February 2024, Google released Gemini 1.5, which introduced improvements in performance and a larger context window, enabling these models to process more input data than before. In May 2024, the Gemini family expanded with Gemini 1.5 Flash—a lightweight model designed for greater speed and efficiency.
Last week, Google launched the next generation of Gemini models—Gemini 2.0—and outlined its vision for the agentic era.
Understanding Prompt Engineering: From Math to Magic
A Visual Journey Through How Language Models Think
DiamantAI
This guide delves into the mechanics of prompt engineering, using clear explanations and examples to make the complex intuitive.
By mastering these prompt engineering techniques, you can unlock the full potential of language models, guiding them to deliver precise, creative, and contextually rich outputs. Experiment with these methods to discover what works best for your specific needs.
Emerging Architectures in AI
Liquid AI, Inference, Memory, Application layers updates, semiconductor potential. Super Datacenters.
Michael Spencer, AI Supremacy
There’s something to be said for finding emergent architectures or new ways of doing things. Take Groq’s Language Processing Unit (LPU) a specialized AI accelerator designed to optimize the performance of artificial intelligence workloads, particularly in inference tasks. Or Sandbox AQ’s Large Quantitative Models, LQMs.
Or take Sakana AI’s Neural Attention Memory Models (NAMMs) that they say are a new kind of neural memory system for Transformers that not only boost their performance and efficiency but are also transferable to other foundation models, without any additional training.
