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Technical Deep Dives

Browse Technical Deep Dives on AI.info.

• Inside the FDA's Rush to Approve AI That Reads Your X-Rays

  Breakthrough Device status for Aidoc's First Read and Cognita CXR marks 2026's shift from AI that flags anomalies to AI that drafts the report -- forcing the FDA to define where decision support ends and autonomy begins.

• The Benchmark Problem: How We Measure AI Intelligence — and Why It Keeps Breaking

  Every AI benchmark saturates, gets gamed, or breaks in public. From GLUE and MMLU to ARC-AGI, FrontierMath and Humanity's Last Exam, here is what happened, with dates and numbers — ending on one model scoring 62.7% or 99.9% on the same test.

• Mixture of Experts: Scaling AI Models Efficiently

  How Mixture of Experts lets a model carry trillions of parameters while activating a few percent of them per token — the routing, load balancing and memory maths behind DeepSeek-V4, Kimi K3 and GLM-5.3.

• AI as Autonomous Scientist: From Research Tool to Independent Discoverer

  Sakana's AI Scientist, Google's Co-Scientist, Coscientist and A-Lab: what autonomous research systems have actually done, what the A-Lab correction exposed, and why the verification layer is still human.

• Graph Neural Networks: Theory and Applications

  A comprehensive exploration of graph neural networks covering message passing, spectral methods, GATs, and real-world applications in social networks, molecules, and recommendation systems.

• Vision-Language Models: How AI Learned to See — and Talk About What It Sees

  From AlexNet's 15.3% ImageNet error in 2012 to CLIP's 400-million-pair contrastive training in 2021, here is how vision and language merged into one model — and where it still hallucinates, miscounts, and now drives robots.

• AI Hardware: GPUs, TPUs, and the Future of AI Compute

  A guide to the silicon behind AI in 2026: NVIDIA's Rubin generation and its 288 GB of HBM4, Google's Ironwood TPU and the coming TPU 8 split, AWS Trainium3, and why bandwidth and interconnect, not FLOPS, set the pace.

• Quantum Computing and AI: Hype, Reality, and Where They Intersect

  Machine learning already decodes errors inside quantum computers; quantum computers accelerating AI is still mostly theory. Google's Quantum Echoes is the first advantage claim classical researchers have not shrunk.

• The Optimizer Wars: How Muon Is Replacing Adam in Frontier AI Training

  Kimi K2 trained 15.5 trillion tokens with zero loss spikes using MuonClip. A year later Kimi K3, DeepSeek-V4 and GLM-5 all orthogonalize their updates — and Muon has its own challenger in Aurora.

• State Space Models and the Post-Transformer Era

  How state space models went from S4 and Mamba to Mamba-3's complex-valued states and MIMO, and why the 2026 answer is hybrid: Nemotron 3, Granite 4.0 and Qwen3-Next all keep a thin minority of attention layers.

• Fine-Tuning Foundation Models: LoRA, QLoRA, and Beyond

  Master the techniques for adapting large pre-trained models to your specific use case. Covers full fine-tuning, LoRA, QLoRA, adapter methods, and PEFT strategies.

• Vector Databases and Embedding Search

  A deep dive into vector databases, embedding spaces, similarity search algorithms, and how they power modern AI applications from RAG to recommendation systems.

• Brain-Computer Interfaces: How Neural Implants Work and Where They Stand

  How neural implants read intention, why invasiveness buys resolution, and where the field stands after a 2026 speech BCI ran 3,800 hours in a participant's home at 99% word accuracy.

• The Memory Problem: Why AI Systems Forget and How Persistent Memory Changes Everything

  Transformers keep no state between calls, so everything people call AI memory is scaffolding: KV caches, million-token windows now billed at standard rates, retrieval, tiered paging and server-side compaction.

• Large Language Models: Architecture, Training, and Scaling

  How large language models are built, from byte-pair tokenization and scaled dot-product attention to Chinchilla scaling, RLHF, and the trillion-parameter sparse mixtures of experts that open labs shipped in 2026.

• Retrieval-Augmented Generation: Architecture and Best Practices

  The complete guide to building and optimizing RAG systems. Covers chunking strategies, embedding selection, retrieval algorithms, re-ranking, and production deployment.

• Diffusion Models: The Mathematics Behind AI Image Generation

  The mathematics behind diffusion image generation: the forward noising chain, the noise-prediction loss, score-based SDEs, DDIM sampling, latent diffusion, classifier-free guidance and the diffusion transformer.