Powered by synthetic data and Colossus cluster, Grok 3 smashes AIME and GPQA scores. Its self-correcting feature minimizes errors on the fly, while DeepSearch agents dig deeper than ever. xAI’s bold move signals a new AI era—watch out, world! #Grok3 #TechBreakthrough

Grok 3 isn’t just text—it’s multimodal genius! Processing images, audio, and more, it pulls real-time data from X. Unveiled with a live demo at 8pm PT, it’s set to redefine truth-seeking AI. Experts call it a leap toward AGI. Stay tuned! #AIRevolution #Grok3

As she continues to shine in Hollywood, her connection to such an iconic ancient figure highlights the enduring influence of history and myth.

Her maternal ancestors are believed to have descended from this storied area, connecting her to the legendary tales of Venus rising from the sea. This discovery ties Sink to a rich cultural and mythological heritage, adding depth to her personal story.

The challenge now for investors, regulators, and tech enthusiasts is to navigate this complex landscape, ensuring AI's potential is realized without falling victim to its monopolistic shadows.

It's a reminder that the future of one of humanity's most significant technological advancements shouldn't be dictated by a monopoly but should foster an environment where innovation, ethics, and profitability can coexist for the benefit of all.

By combining these strategies, DeepSeek delivered high AI performance at lower costs and energy use, showing that innovative model architecture and training can offset the need for extensive computational resources.

Due to export controls, DeepSeek used less advanced chips like Nvidia's H800 GPUs in China, yet managed performance through smart engineering. Quantization techniques, like 3-bit, further reduced power and memory needs.

Reinforcement learning was employed for enhancing reasoning in DeepSeek-R1 by fine-tuning DeepSeek-V3, cutting down on new training data and computational costs. This method was pivotal in achieving high performance cheaply.

They used 8-bit floating-point precision for memory, which reduces both memory usage and power consumption while maintaining performance. Optimized GPU communication was another key, focusing on efficient hardware use.

DeepSeek achieved o1 level performance with lower power and budget through innovative tech approaches like Mixture of Experts (MoE), where specialized sub-networks are selectively activated for queries, saving resources.

It has the potential to solve Olympiad, PhD and maybe even research level problems, like the internal model a Microsoft exec said to be able to solve PhD qualifying exam questions.

Imagine about what this model could achieve with PRM, MCTS, and other yet-to-be-released agentic exploration methods. Unlike GPT4o, you can train this model further.