Once T-EACs are fully developed and deployed, they will not only help Google achieve its 24/7 carbon-free energy goal but will also provide society with valuable new insights concerning the availability of #carbonfree energy.
#Sustainability#NetZero https://t.co/WnV2VuLgvw
Updated view of data center impact from AEP: Projected 2030 system peak demand of ~65 GW includes 28 GW of incremental contracted load from 2025-2030 backed by ESAs and LOAs (~80% of that is DCs) and further supported by the 190 GW active projects in the interconnection queue.
Welcome to Cyprus Prime Minister @narendramodi!
Here, at the EU’s southeastern frontier and gateway of the Mediteranean
A historic visit
A new chapter in a strategic partnership that knows no limits
We make a promise to advance, transform, prosper more. Together
🇨🇾🇮🇳🇪🇺
DeepSeek’s R1 leaps over xAI, Meta and Anthropic to be tied as the world’s #2 AI Lab and the undisputed open-weights leader
DeepSeek R1 0528 has jumped from 60 to 68 in the Artificial Analysis Intelligence Index, our index of 7 leading evaluations that we run independently across all leading models. That’s the same magnitude of increase as the difference between OpenAI’s o1 and o3 (62 to 70).
This positions DeepSeek R1 as higher intelligence than xAI’s Grok 3 mini (high), NVIDIA’s Llama Nemotron Ultra, Meta’s Llama 4 Maverick, Alibaba’s Qwen 3 253 and equal to Google’s Gemini 2.5 Pro.
Breakdown of the model’s improvement:
🧠 Intelligence increases across the board: Biggest jumps seen in AIME 2024 (Competition Math, +21 points), LiveCodeBench (Code generation, +15 points), GPQA Diamond (Scientific Reasoning, +10 points) and Humanity’s Last Exam (Reasoning & Knowledge, +6 points)
🏠 No change to architecture: R1-0528 is a post-training update with no change to the V3/R1 architecture - it remains a large 671B model with 37B active parameters
🧑💻 Significant leap in coding skills: R1 is now matching Gemini 2.5 Pro in the Artificial Analysis Coding Index and is behind only o4-mini (high) and o3
🗯️ Increased token usage: R1-0528 used 99 million tokens to complete the evals in Artificial Analysis Intelligence Index, 40% more than the original R1’s 71 million tokens - ie. the new R1 thinks for longer than the original R1. This is still not the highest token usage number we have seen: Gemini 2.5 Pro is using 30% more tokens than R1-0528
Takeaways for AI:
👐 The gap between open and closed models is smaller than ever: open weights models have continued to maintain intelligence gains in-line with proprietary models. DeepSeek’s R1 release in January was the first time an open-weights model achieved the #2 position and DeepSeek’s R1 update today brings it back to the same position
🇨🇳 China remains neck and neck with the US: models from China-based AI Labs have all but completely caught up to their US counterparts, this release continues the emerging trend. As of today, DeepSeek leads US based AI labs including Anthropic and Meta in Artificial Analysis Intelligence Index
🔄 Improvements driven by reinforcement learning: DeepSeek has shown substantial intelligence improvements with the same architecture and pre-train as their original DeepSeek R1 release. This highlights the continually increasing importance of post-training, particularly for reasoning models trained with reinforcement learning (RL) techniques. OpenAI disclosed a 10x scaling of RL compute between o1 and o3 - DeepSeek have just demonstrated that so far, they can keep up with OpenAI’s RL compute scaling. Scaling RL demands less compute than scaling pre-training and offers an efficient way of achieving intelligence gains, supporting AI Labs with fewer GPUs
See further analysis below 👇
Want a chance to get a free backpack from Coursera? 🎒
Simply repost and DM us your full name, address, email and phone number. 😊
(Exclusive to learners in the US, UK and India)
@BlrCityPolice@CPBlr,
Is this acceptable behavior of Auto driver? Harassing female passenger!
It's becoming difficult for women to venture out alone in #Bengaluru
We must move beyond focusing on isolated component specifications and consider the broader impact on the entire product system, its reliability and the resilience of the supply chain that enables that product.
#SupplyChain#Hardware
If you want to succeed more quickly in the Hardware Supply Chain Industry,
1. Instead of asking, "Can we make this component smaller/lighter/faster?" We must ask, "Will this change reduce overall system cost or improve the customer satisfaction score?"
3. Instead of asking, "Who is the single source for this critical part?", We must ask, "How can we introduce alternate sourcing suppliers to mitigate supply chain disruptions?"