r/singularity reacted because the post turned LLM consciousness into a fight over computation itself. Alexander Lerchner’s “Abstraction Fallacy” paper argues that computation depends on a mapmaker, while commenters pushed back with questions about definitions, Chinese Room echoes, and philosophy versus neuroscience.
HN focused less on the model drop and more on the hard robotics question: how fast does reasoning need to be before it is useful in the physical world? Google DeepMind frames Gemini Robotics-ER 1.6 around spatial reasoning, multi-view understanding, success detection, and instrument reading, while commenters zoomed in on gauge-reading demos, latency, and deployment reality.
Google DeepMind's latest robotics model pushes a hard industrial task from 23% to 93% accuracy when agentic vision is enabled, putting a concrete number on embodied reasoning progress. The April 14 release also puts Gemini Robotics-ER 1.6 into the Gemini API and Google AI Studio, so developers can test the upgrade immediately.
Google DeepMind’s April 2, 2026 X thread introduced Gemma 4 as a new open model family built for reasoning and agentic workflows. Google says the lineup spans E2B, E4B, 26B MoE, and 31B Dense, and adds native function calling, structured JSON output, and longer context windows.
Google DeepMind said on March 26, 2026 that it is releasing a public toolkit to measure harmful manipulation by AI systems. The company says the work spans nine studies with more than 10,000 participants and now informs safety evaluations for models including Gemini 3 Pro.
Google DeepMind has published a cognitive taxonomy for evaluating progress toward AGI and paired it with a Kaggle hackathon to build new benchmarks. The framework maps AI systems against human baselines across 10 cognitive abilities instead of relying on a single headline score.
Google DeepMind introduced D4RT on January 22, 2026 as a unified model for dynamic 4D scene reconstruction and tracking. The company says it runs 18x to 300x faster than prior methods and is efficient enough for real-time applications in robotics and augmented reality.
A Hacker News discussion highlighted LoGeR, a Google DeepMind and UC Berkeley project that uses hybrid memory to scale dense 3D reconstruction across extremely long videos without post-hoc optimization.
Google DeepMind announced SIMA 2 on November 13, 2025 as a generalist foundation model for virtual 3D environments. The system is designed to play and reason alongside humans, with in-context learning that can improve behavior from examples.
Isomorphic Labs, the DeepMind spin-off focused on AI-driven drug discovery, has unveiled a new AI that scientists are calling a generational leap comparable to AlphaFold.
DeepMind CEO Demis Hassabis proposed a concrete AGI benchmark: train an AI with a knowledge cutoff of 1911, then see if it can independently derive general relativity as Einstein did in 1915. This test targets genuine scientific discovery rather than pattern matching.
Google DeepMind spin-off Isomorphic Labs published a technical report on IsoDDE, a proprietary drug discovery AI that scientists are comparing to a hypothetical AlphaFold 4. The model excels at predicting protein-drug binding and has secured billion-dollar deals with J&J, Eli Lilly, and Novartis.