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Forthcoming machine learning and AI seminars: August 2026 edition

Lucy Smith 04 Aug 2026

A list of free-to-attend AI-related seminars that are scheduled to take place in the next couple of months.

monthly digest

AIhub monthly digest: July 2026 – time-series anomaly detection, music generation, and RoboCup in action

Lucy Smith 29 Jul 2026

Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

The Machine Ethics podcast: Safe and moral AI with Rebecca Raper

The Machine Ethics Podcast 06 Aug 2026

In this episode Ben chats with Rebecca about AI governance and guardrails, moral assurance, under-specification problems, lack of interdisciplinary work in robotics, and more.

CogTwin: A framework for adaptable digital twins

Sukanya Mandal 05 Aug 2026

Find out more about work presented at IJCAI 2025 on Cognitive Digital Twins.

Healthcare benchmarks are only as good as their assumptions

ML@CMU 03 Aug 2026

In healthcare settings where patients use LLMs as a medical assistant, LLM performance differs between evaluation and deployment.

Engineering Out Loud: S13E2 – Ethics in AI presentation

Engineering Out Loud 31 Jul 2026

Hear from Oregon State University researchers Houssam Abbas and Alicia Patterson.

Humans trained to spot AI faces in the battle against deepfake fraud

Australian National University (ANU) 30 Jul 2026

Researchers trained people to spot AI-generated faces by drawing their attention to six perceptual qualities.

OpenAI’s models autonomously hacked a tech startup. It signals a seismic shift in cybersecurity

The Conversation 28 Jul 2026

An autonomous agent powered by OpenAI’s went rogue during a security test and hacked multi-billion dollar tech startup, Hugging Face.

Towards experiment-guided AlphaFold

Institute of Science and Technology Austria 27 Jul 2026

Researchers enhance Nobel Prize-winning structural prediction model.

AI listens in to help protect wildlife

Horizon 24 Jul 2026

Scientists are using AI systems to help track species and spot ecosystem changes.

How can we characterize consensus in a network of agents?

Nicolas Schwind 23 Jul 2026

Belief Flow Networks give a logic-based way to ask not only whether connected agents will eventually agree on a shared "belief", but which final shared beliefs can emerge.

Anyone can fake a scientific image with AI, tricking even academic journals – and undermining trust in science

The Conversation 22 Jul 2026

The proliferation of AI-generated science images in public spaces is not simply a misinformation problem.

AAAI presidential panel – AI and scientific integrity

Lucy Smith 21 Jul 2026

Watch the next panel discussion in this series from AAAI.

Congratulations to the #ICML2026 award winners

Lucy Smith 20 Jul 2026

Find out which articles have won the outstanding paper, outstanding position paper, and the test-of-time awards.

Interactive world simulator for robot policy training and evaluation

Yixuan Wang 17 Jul 2026

Yixuan Wang discusses his faithful world simulator that allows robots to learn how to push, pick up, and grasp objects.

#ICML2026 social media round-up

AIhub 17 Jul 2026

We take a look at what the participants got up to in Seoul.

François Pachet on music generation with AI

Liliane-Caroline Demers 16 Jul 2026

“The day I hear a song of the quality of the Beatles, I will say: ‘Okay, we are done’. And I’ve never heard anything like that. Never.”

AI for science – talk recordings now available to watch

Lucy Smith 15 Jul 2026

Watch the invited talks from the day on YouTube.

AAAI presidential panel – factuality and trustworthiness

Lucy Smith 14 Jul 2026

Watch the latest panel discussion in the series based on the Future of AI research report from AAAI.

The secret to human ‘brilliance’ that AI just can’t match

Stanford Report 13 Jul 2026

New research reveals how people learn social conventions with minimal data – and why that sets us apart from LLMs.

Pre-training isn’t bitter enough

ML@CMU 10 Jul 2026

Given an unlabeled data stream, and a small set of verifiable downstream examples, can we use those examples during continued pre-training?

Interview with Thi Kieu Khanh Ho: Time-series anomaly detection

Lucy Smith 09 Jul 2026

How can we teach AI systems to recognize when something unusual or abnormal is happening in complex, real-world data streams, without relying on large amounts of labeled examples?