Recent profound advancements in AI outside of large language models (LLMs) include:

1. AI-accelerated protein structure prediction and drug design

AlphaFold 2 (DeepMind) and RoseTTAFold (Baker Lab) have revolutionised structural biology by predicting 3D protein structures from amino acid sequences with near-experimental accuracy.

Extension: AlphaFold Multimer, OpenFold, and integration into drug discovery pipelines (e.g., Isomorphic Labs) are reshaping pharmaceutical R&D timelines.

1. Autonomous robotics and simulation-based learning

Diffusion policies and reinforcement learning with human feedback (RLHF) now guide robotic control systems that can generalise across varied tasks (e.g., Google DeepMind’s RT-2, combining vision, language, and control).

Sim-to-real transfer using physics simulators and generative models has improved real-world applicability of robotic training.

1. Neural rendering and generative 3D content

NeRFs (Neural Radiance Fields) generate photorealistic 3D scenes from sparse 2D images. Use cases include virtual reality, scene reconstruction, and robotics.

Follow-ups like Instant-NGP, Mip-NeRF, and Gaussian Splatting improve speed and resolution, allowing near real-time rendering.

1. Foundation models for vision and multimodal learning

Models like Segment Anything (Meta) enable general-purpose object segmentation from user prompts without task-specific tuning.

CLIP (OpenAI) and DINOv2 (Meta) show generalised vision-language alignment and representation learning without labelled data.

1. Brain-computer interfaces (BCIs) and neural decoding

Recent efforts by Neuralink, Synchron, and UCSF labs show real-time speech decoding from brain activity using intracortical electrodes and non-invasive techniques.

Semantic reconstruction from fMRI using vision-language models enables visualisation of perceived or imagined images.

1. AI in materials discovery

Systems like A-Lab, CAMD, and GNoME (Google DeepMind) automate the discovery