AI Planning and Design

If you have ever stared at thousands of lines of integration test logs wondering which of the sixteen log files actually contains your bug, you are not alone — and Google now has data to prove it.

A team of Google researchers introduced Auto-Diagnose, an LLM-powered tool that automatically reads the failure logs from a broken integration test, finds the root cause, and posts a concise diagnosis directly into the code review where the failure showed up. On a manual evaluation of 71 real-world failures spanning 39 distinct teams, the tool correctly identified the root cause 90.14% of the time. It has run on 52,635 distinct failing tests across 224,782 executions on 91,130 code changes authored by 22,962 distinct developers, with a ‘Not helpful’ rate of just 5.8% on the feedback received.

Quantum computers hold the promise of eventually being able to complete certain calculations that are impossible for conventional computers. For years, researchers have been debating whether these advantages over conventional computers extend to tasks that involve lots of data, and the algorithms that learn from them – in other words, the machine learning that underlies many AI programs.

Now, Hsin-Yuan Huang at the quantum computing firm Oratomic and his colleagues argue that the answer ought to be “yes”. Their mathematical work aims to lay the foundations for a future where quantum computers offer a broad boost to AI.

“Machine learning is really utilised everywhere in science and technology and also everyday life. In a world where we can build this [quantum computing] architecture, I feel like it can be applied whenever there’s massive datasets available,” he says.