Embodied AI is evolving from vision-centric perception toward Physical AI. While vision enables robots to perceive the world, it cannot fully capture the physical interactions that underpin real-world manipulation, limiting robots’ ability to operate reliably in unstructured environments. As World Models advance, tactile sensing is increasingly recognized as a key modality. By providing critical physical feedback beyond vision, tactile helps robots understand and interact with the physical
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Published breakthroughs pushing the state of the art.

Benchmarks and Analysis of GLM-5.2

As AI becomes part of HPC workflows, validation, data quality, and trust are emerging as key factors in technology and buying decisions.

Long-context large language models (LLMs) face a memory bottleneck that has nothing to do with model weights. During decoding, transformers cache the key and value (KV) vectors for every token at every layer so they don’t have to recompute attention. This cache grows linearly with sequence length and batch size, and at long context with high concurrency it can dwarf the model’s own footprint. Consider Llama-3.1-70B in BF16. Its KV cache costs about 0.31 MB per token (80 layers ×
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