What if our world models are just as emergent and flimsy as AI’s?

I keep hearing that world models are the way forward for AI.

I tend to agree, and have been saying the same for many years as a technical person in AI but a non-A-tier-AI-researcher working on actual models.

Anyway, I’m up at 3:45AM today with an insane thought.

Why do we think humans have world models? — Read More

Deep-brain structures like the basal ganglia or the thalamus wield major influence on our behavior. If something goes awry, dysregulation in the deep brain may trigger neurological conditions like Parkinson’s disease or depression.

Despite the clear importance of these structures, our knowledge about them remains limited by their location, making them difficult to study and treat.

In a new study, researchers unveil a device that might offer an alternative to invasive procedures. Featuring a novel ultrasound helmet, it not only modulates deep-brain circuits without surgery, but reportedly can do so with unrivaled precision. — Read More

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Mainstream Transformer-based large language models (LLMs) face significant efficiency bottlenecks: training computation scales quadratically with sequence length, and inference memory grows linearly. These constraints limit their ability to process long sequences effectively. In addition, building large models on non-NVIDIA computing platforms poses major challenges in achieving stable and efficient training and deployment. To address these issues, we introduce SpikingBrain, a new family of brain-inspired models designed for efficient long-context training and inference. SpikingBrain leverages the MetaX GPU cluster and focuses on three core aspects: i) Model Architecture: linear and hybrid-linear attention architectures with adaptive spiking neurons; ii) Algorithmic Optimizations: an efficient, conversion-based training pipeline compatible with existing LLMs, along with a dedicated spike coding framework; iii) System Engineering: customized training frameworks, operator libraries, and parallelism strategies tailored to the MetaX hardware.

Using these techniques, we develop two models: SpikingBrain-7B, a linear LLM, and SpikingBrain-76B, a hybrid-linear MoE LLM. These models demonstrate the feasibility of large-scale LLM development on non-NVIDIA platforms. SpikingBrain achieves performance comparable to open-source Transformer baselines while using exceptionally low data resources (continual pre-training of ∼150B tokens). Our models also significantly improve long-sequence training efficiency and deliver inference with (partially) constant memory and event-driven spiking behavior. For example, SpikingBrain-7B achieves more than 100× speedup in Time to First Token (TTFT) for 4M-token sequences. Our training framework supports weeks of stable large-scale training on hundreds of MetaX C550 GPUs, with the 7B model reaching a Model FLOPs Utilization (MFU) of 23.4%. In addition, the proposed spiking scheme achieves 69.15% sparsity, enabling low-power operation. Overall, this work demonstrates the potential of brain-inspired mechanisms to drive the next generation of efficient and scalable large model design. — Read More

Mice moving tiny steering wheels to control shapes on a screen have given scientists an unprecedented view of how decisions unfold across the brain.

For the first time, researchers have mapped decision-making at single-cell resolution across an entire mammalian brain. — Read More

Read the Paper

I’ve been in this industry long enough to watch technologies come and go. I’ve seen the excitement around new frameworks, the promises of revolutionary tools, and the breathless predictions about what would “change everything.” Most of the time, these technologies turned out to be incremental improvements wrapped in marketing hyperbole.

But parallel agents? This is different. This is the first time I can say, without any exaggeration, that I’m witnessing technology that will fundamentally transform how we develop software. — Read More

We’ve reached an inflection point in AI development. The scaling laws that once promised ever-more-capable models are showing diminishing returns. GPT-5, Claude, and Gemini represent remarkable achievements, but they’re hitting asymptotes that brute-force scaling can’t solve. The path to artificial general intelligence isn’t through training ever-larger language models—it’s through building engineered systems that combine models, memory, context, and deterministic workflows into something greater than their parts.

Let me be blunt: AGI is an engineering problem, not a model training problem. — Read More

This was not on Jane Goodall’s bingo card. With 2 billion neurons, researchers say the DeepSeek-powered Darwin Monkey is a major step toward ‘brain-like intelligence.’

We’re already getting glimpses of AI technology that goes far beyond chatbots to model the brains of living beings.

Chinese researchers say they created an AI version of a monkey’s brain, and put it on a computer. It has 960 chips, and each one “supports over 2 billion spiking neurons and over 100 billion synapses, approaching the number of neurons in a macaque brain,” according to Zhejiang University, as translated by Google.

Researchers named the project the Darwin Monkey and say it’s “a step toward more advanced brain-like intelligence.” It’s the largest brain-like, or “neuromorphic,” computer in the world, and the first that’s based on neuromorphic-specific chips, Interesting Engineering reports. — Read More

Locked inside their minds, thousands await a cure. Neuroscientist Daniel Toker is racing to find it.

The study of consciousness is a field crowded with scientists, philosophers, and gurus. But neuroscientist Daniel Toker is focused on its shadow twin: unconsciousness.

His path to this research began with a tragedy — one he witnessed firsthand. While at a music festival, a young concertgoer near Toker dove headfirst into a shallow lake. He quickly surfaced, his body limp and still. Toker, along with others, rushed to help. He performed CPR, but it soon became apparent that the young person’s neck had snapped. There was nothing to be done. — Read More

The Microsoft AI team shares research that demonstrates how AI can sequentially investigate and solve medicine’s most complex diagnostic challenges—cases that expert physicians struggle to answer.

Benchmarked against real-world case records published each week in the New England Journal of Medicine, we show that the Microsoft AI Diagnostic Orchestrator (MAI-DxO) correctly diagnoses up to 85% of NEJM case proceedings, a rate more than four times higher than a group of experienced physicians. MAI-DxO also gets to the correct diagnosis more cost-effectively than physicians. — Read More

The brain-computer interface lets those with cervical spinal cord injuries or ALS control a computer with their thoughts. This year, Neuralink has more than doubled the number of patients.

Neuralink has been quietly increasing the number of patients with its N1 brain implant. According to the Barrow Neurological Institute, seven people have now received one. — Read More