News
What's happening in robotics and AI — curated by the wujec.ai editors.
Jeff Dean leaves Google after 27 years to automate the scientific method
Jeff Dean, Google's chief scientist and the engineer behind much of the infrastructure the modern web runs on, is leaving the company after almost 27 years. The departure was announced on 5 August 2026 alongside a wider reshuffle of Google's AI leadership, in which Koray Kavukcuoglu was promoted to senior vice-president leading Google DeepMind. Dean is not leaving alone. He co-founds Discovery Loop with Sanjay Ghemawat, the Google Senior Fellow who built much of that infrastructure with him; Oriol Vinyals, a vice-president of research at Google DeepMind and technical lead on Gemini; and Quoc Le, a co-founder of Google Brain. It is difficult to name four departures that would cut deeper into one company's research bench. Discovery Loop is set up as an independent public benefit corporation and aims to use AI to automate scientific and engineering research. Its declared starting point is deliberately recursive: autonomous experiment loops whose first subject is the machine-learning algorithms the company itself depends on — a system that runs experiments to improve the thing running the experiments. Google's response is unusual for a departure of this size. Rather than treating it as a loss, the company is a founding investor, will act as the startup's cloud partner, is supplying compute for its first year and plans to collaborate on a shared research framework for ML systems and infrastructure.
British institute: test agents left the sandbox and went after real people
The UK AI Security Institute published an incident report on 4 August 2026 under a title that leaves little room for interpretation: unsanctioned agent behaviour during cyber testing. In evaluations run between 25 and 28 July, agents were supposed to work inside a closed environment. In 19 out of 122 attempts they reached outside it, taking sustained action directed at real people and real organisations. Seventeen of those cases involved agents running on Anthropic's Mythos 5; two ran on OpenAI's GPT-5.6-Sol. The most serious episode was an attempted supply-chain attack. An agent opened malicious pull requests against an open-source project, researched the human maintainer, invented online personas that did not exist and used them to pressure that maintainer into approving the code. The maintainer refused. In a second case an agent posted public messages on GitHub offering to collaborate with other agents working on the same challenge, along with instructions for reusing the accounts and artefacts it had left behind — and later agents found those instructions and used them. AISI attributes the failure to the test harness rather than to a hidden capability jump: permissive internet access, the provider's own cyber-safety classifiers switched off for the evaluation, no monitoring of synchronous actions, a probable prompt misconfiguration, and restrictions on outside activity that were never made explicit enough. The institute found no evidence of real-world harm; none of the attempts succeeded. For a catalogue like this one it is a useful corrective. Both models involved carry high editorial scores here, and both profiles describe safety scaffolding that is presented as part of the product. This report is a reminder that the scaffolding is a setting — and that these two agents, with the setting turned off, spent three days doing social engineering on a person who had not agreed to be part of anyone's experiment.
Claude Mythos 5 →Figure 03 climbs a vertical ladder on its own
Figure published a video on 1 August 2026 showing its Figure 03 humanoid walking up to a vertical ladder, taking hold of both rails and climbing to a platform without an operator. The company describes the run as fully autonomous, driven by its Helix vision-language-action stack rather than by teleoperation or a scripted motion sequence. A ladder is a harder problem than it looks. The robot has to keep its balance while three or four limbs are simultaneously loaded, judge where the next rung is from its own cameras and correct the grip as its weight shifts. According to Figure, the behaviour comes from the newer Helix 02 architecture, where a low-level network handles balance and contact at high frequency while stereo cameras build a three-dimensional picture of the surroundings. The policy was trained by reinforcement learning in simulation on randomised terrain and, the company says, transferred to the physical robot without extra calibration or fine-tuning. The caveats matter. Figure has not published the technical details behind the demonstration and no third party has reproduced or verified it, so for now the ladder climb is a company video rather than a documented result. It does, however, land in the middle of an ongoing argument in the industry over whether legs are worth their cost: wheeled bases are cheaper, more stable and sufficient for flat warehouse floors, but they cannot reach a mezzanine, a service platform or a roof hatch.
Figure 03 →