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18 posts
The next leap in AI may happen in a space humans can’t naturally read. That raises a bigger question than speed: how do you trust an agent whose thoughts aren’t written in English?
The phrase “intelligence explosion” conjures a machine escaping into the internet overnight. The real story is slower, more physical—and potentially more consequential than the movies suggest.
A flood of machine-written proofs has turned an academic dispute into a test of who gets to shape scientific progress. The surprising bottleneck may not be whether AI can produce answers, but whether anyone can check and understand them.
The next leap in mobile AI may not come from a faster chip or a smaller model. A bold research idea instead turns the wireless signal itself into part of the computer—with impressive gains and a catch.
A clever training trick turns every token into a tiny experiment—and some early results surprised researchers. But a compute bill that dwarfs standard training could keep this idea out of today’s production models.
A viral label is racing ahead of the evidence, while mathematicians face a more consequential question: how should AI-generated discoveries be checked? The answer could reshape research long before anyone settles on a model name.
Your AI chatbot is designed to sound right, not be right, and that's a costly distinction for serious work. A new class of 'heavy-duty solver' is emerging, built on a foundation that makes verification the entire product.
Backed by a $5B investment from Nvidia, Ilya Sutskever's new venture is pursuing a single goal: safe superintelligence. But their secret isn't bigger models; it's a radical new approach inspired by overlooked functions of the human brain.
OpenAI's new model just solved 10 major math problems, but the real story isn't the answers it found. It's the revolutionary, self-verifying method it used—and what that means for the future of complex problem-solving.
Anthropic just pulled back the curtain on how AI *really* thinks, discovering a hidden 'workspace' inside Claude that mirrors human consciousness. This emergent feature, called J-space, could be the key to building truly safe and aligned AI.
A new AI model called SubQ claims to process a massive 12 million token context with 1000x less compute. If its sub-quadratic architecture holds up, it could fundamentally change how we build and scale AI.
Your LLM's memory is a ticking time bomb, killing performance and inflating costs. A new technique called Speculative KV Coding can shrink it by 4x without any quality loss.
A new paper reveals the AI industry's core belief—that bigger models are always smarter—is wrong. For a critical type of human reasoning, making models larger actually makes them worse.
Today's multimodal AI is a clunky mess of separate models taped together, hogging resources and slowing you down. Google's new Gemma 4 12B rips out this legacy plumbing, creating a lean, integrated architecture that runs at blistering speeds offline.
Top AI labs are racing towards AGI without agreeing on what it is. Google DeepMind just dropped a scientific framework to end the debate, and it's based on the human mind.
Everyone thinks bigger models mean smarter AI, but the data shows that's a lie. Discover the hidden engine driving AI capabilities to a vertical takeoff, leaving old scaling laws in the dust.
In a stunning new paper, OpenAI reveals how they deleted 99.9% of a model's connections to expose its hidden logic. For the first time, we can watch an AI make decisions step-by-step.
The era of simple AI chatbots is over. A new wave of models that can 'think' continuously is here, and it’s about to change everything from science to robotics.
