The 42nd International Conference on Machine Learning (ICML2025) is currently taking place in Vancouver, Canada, running from 13-19 July. As well as five invited talks, the programme boasts oral and poster presentations, affinity events, tutorials, and workshops. Find out what participants have been getting up to during the first couple of days. On my way to #ICML2025 to present our algorithm that strongly scales with inference compute, in both performance and sample diversity! Reach out if you'd like to chat more!

We bring you a selection of some interesting and popular tweets about AI from October. Ok I wrote another one for the nerds. Incredible paper out of @Caltech from @AnimaAnandkumar, @kazizzad, Andrew Stuart & students in which they introduced a new deep-learning technique for finding solutions to differential equations at record speeds. In a healthcare test that went horribly wrong, GPT-3 told a mock patient to kill themself. GPT-3 isn't just lacking context, it's lacking *meaning* and *communicative intent* (not to mention accountability for what it says).

These duties are shared across the organizing team.

These duties are shared across the organizing team.

Since the 1980s the number of women completing computer science degrees has plummeted, and in most large tech companies the representation of women in technical roles is below 30%. This lack of diversity prevents us from building products that work for everybody. It can foster toxic "brogrammer" cultures which harm everybody who works within them, and it deprives teams of the well-documented performance boost that women bring. Many of the early superstars in computer science were women -- from Lord Byron's polymath daughter Ada Lovelace, the first person to envisage a general purpose computer, to Rear Admiral Grace Hooper, who pioneered the use of natural language in writing computer programs. Similarly, the post-war computing scene was dominated by women.

These duties are shared across the organizing team.

In 2005, Hanna Wallach, a machine-learning researcher, found herself bunking with colleagues to attend the Neural Information Systems Processing (NIPS) conference. Wallach had been working in the field since 2001 and had attended numerous conferences, but this was the first time she had roomed with other women who specialized in machine learning, a branch of artificial intelligence that researches how computer programs can learn and grow. As a discipline, it is overwhelmingly male: Wallach estimates that only 13.5% of the entire machine learning field is female. At the conference, Wallach and her roommates, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu, began discussing their experiences and commiserating about the lack of female allies. "We couldn't believe that there were four of us [at the conference]," Wallach says.

In 2005, Hanna Wallach, a machine-learning researcher, found herself bunking with colleagues to attend the Neural Information Systems Processing (NIPS) conference. Wallach had been working in the field since 2001 and had attended numerous conferences, but this was the first time she had roomed with other women who specialized in machine learning, a branch of artificial intelligence that researches how computer programs can learn and grow. As a discipline, it is overwhelmingly male: Wallach estimates that only 13.5% of the entire machine learning field is female. At the conference, Wallach and her roommates, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu, began discussing their experiences and commiserating about the lack of female allies. "We couldn't believe that there were four of us [at the conference]," Wallach says.

In 2005, Hanna Wallach, a machine learning researcher, found herself bunking with colleagues to attend the Neural Information Systems Processing (NIPS) conference. Wallach had been working in the field since 2001 and had attended numerous conferences, but this was the first time she had roomed with other women who specialized in machine learning, a branch of artificial intelligence that researches how computer programs can learn and grow. As a discipline, it is overwhelmingly male: Wallach estimates that only 13.5% of the entire machine learning field is female. At the conference, Wallach and her roommates, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu, began discussing their experiences and commiserating about the lack of female allies. "We couldn't believe that there were four of us [at the conference]," Wallach says.

In episode four we talk with Hanna Wallach, of Microsoft Research. We take a listener question about scalability and the size of data sets. And Ryan takes us through topic modeling using Latent Dirichlet allocation (say that five times fast). See all the latest robotics news on Robohub, or sign up for our weekly newsletter.