about.subtitle
In this section, we explore the resilience of algorithmic systems against errors, external biases, and unforeseen scenarios. Here, reliability is viewed not as a marketing gimmick, but as a measurable parameter of technological safety and predictability. Our focus lies on materials that analyze architectural vulnerabilities, code verification methods, and issues regarding the reproducibility of results.
AI: Events
Security
OpenAI has discovered that modern AI models struggle to control their own thought processes – and this could be a crucial defense against manipulation.
Microsoft has announced the upcoming release of GPT-5.4 in Foundry – a new OpenAI model focused on reliably executing tasks in real-world workflows.
AI: Events
Infrastructure
We explore how companies update AI services without the risk of widespread outages, and why the canary release approach is becoming an industry standard.
Anthropic sheds light on distillation attacks – a method to copy an AI model's behavior without accessing its code – and discusses strategies for defending against such attacks.
AI: Events
Infrastructure
AMD has integrated TorchFT with TorchTitan to ensure resilient GPU training: the system can now autonomously recover from errors and keep running.
Allen AI has released a framework to evaluate and improve language models' ability to generate step-by-step instructions that actually help get the job done.
Don’t miss a single experiment!
Subscribe to our Telegram channel —
we regularly post announcements of new books, articles, and interviews.