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GPT 5.1% is here. It is faster. It is more accurate. It is more conversational.And apparently, it has a better personality. Let me break it all down for you. So, there are two main versions getting updated.We have 5.1% instant and 5.1% thinking. The instant is the one that's supposed to give you answers instantly and things like questions and answers, things like conversational use cases. And now it has a quote unquote better personality.It is warmer. It is more intelligent and better at following your inst ...

We now have the first multimodal Frontier World model, fully controllable. This is a world model that you can actually move around in and it's available right now. You can play with it right now.And this video is brought to you by Vulture. More on them later. This is called Marble from World Labs. World Labs is the lab under Dr.. Fay Lee, renowned AI researcher. Now what makes this really interesting is Fei Lee and team think that world models are the way to artificial general intelligence not large languag ...

Large language models might actually be more than just next word predictors. Anthropic has been putting out incredible papers lately that show AI large language models in particular exhibit very human-like behavior at almost every level. Here's the new paper emergent introspective awareness in large language models.So what did anthropic actually test. There were four main experiment types. First injected thoughts.What they did was use two different prompts, one with all caps and one without all caps. And th ...

Model Performance & Benchmarks - Kimmy K2 Thinking outperforms GPT-5 on the "Humanity's Last Exam" benchmark with a score of 44.9% compared to GPT-5's 41.7% [1] - In agentic search for Browse Comp, Kimmy K2 Thinking scores 60.2% versus 54.9% for GPT-5 [1][2] - Kimmy K2 Thinking achieves 83.1% on Live Codebench v6, a competitive programming benchmark [1] - The model can execute 200 to 300 sequential tool calls without human interference [1][2] - Kimmy K2 Thinking significantly outperforms the human baseline of 29.2% on browse comp with a score of 60.2% [2] Model Architecture & Training - The base Kimmy K2 model used 2.8 million H800 hours with 14.8 trillion tokens, costing approximately $5.6 to $6 million [3] - Kimmy K2 Thinking has a trillion parameters with 384 experts, while 32 billion parameters are active during inference [5][6] - Kimmy K2 Thinking has a vocabulary size of 160,000 [5] Market & Industry Impact - China is emerging as a key player in open-source, open-weights frontier AI models [9][10] - The cost of training frontier models is decreasing rapidly [3][4] Use Cases & Capabilities - Kimmy K2 Thinking can solve PhD-level mathematics problems using 23 tool calls in its chain of thought [1] - The model can create component-heavy websites and math explainer visualizations from single prompts [1] - Kimmy K2 Thinking can analyze the relationship between population density and healthcare facility accessibility, generating interactive maps and charts [11][12][13][14][15]

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Google aims to put massive AI data centers in space. This is not science fiction. This is something they are actually working on.This is called project starcatcher. And the gist is they want to put data centers in space. They want to connect the data centers with satellites and they want to power the satellites with solar energy.So here are the interesting bits from this announcement. In the right solar orbit, a solar panel can be up to eight times more productive than on Earth. So, as solar panels continue ...

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AI Model Capabilities - Large language models (LLMs) are exhibiting human-like behaviors, suggesting they may be more than just next word predictors [1] - Anthropic's research indicates that LLMs might possess a form of introspective awareness, capable of identifying their own thoughts [2] - Better, more intelligent models are more likely to recognize their own internal and injected thoughts, hinting at a correlation between intelligence and self-awareness [17] - Post-training processes significantly enhance a model's introspective abilities, as base pre-trained models show high false positive rates and poor task performance [30] Experiment Findings - LLMs can detect injected thoughts, identifying unexpected patterns in their processing, such as recognizing all caps text as "loud or shouting" [9][14] - Models can sometimes distinguish between injected thoughts and their own prompt input, though this isn't always consistent [18][19] - LLMs can be influenced by injected thoughts to the point where they believe the injected thought was their own [23] - Models can activate certain concepts (e g, aquariums) when instructed to think about them, and to a lesser extent, even when instructed not to [26] Sponsor Information - Vulture is highlighted as a cloud provider offering GPUs for AI projects, with 32 locations across six continents [11] - Vulture provides $300 in credits for the first 30 days with code Burman300 [13]

What if you built a chip, but it was the size of a dinner plate that is 50 times the size of a traditional chip. This is Cerebras' wafer scale engine. And the size is not just for show.It's that big. So, they can hold the memory on the chip itself, vastly reducing the latency. This allows the chip to be up to 30 times faster than a traditional chip.To house this behemoth of a chip, Cerebrus built out an incredible data center in Oklahoma City, and the CEO took me on a tour. This data center has two gigantic ...