May 2026 @ApgAsu ers at #taroko sports.

Active Perception Group @ApgAsu fellows (left) and almost all ASU sun devils (right) at #CVPR 2026. @SCAI_ASU #AI #CV #Denver

From the America's friendliest airport (Phoenix sky harbor) to Denver for #CVPR 🤠

So called intelligence, it is not intelligence, but named as intelligence. 🙏

An extremely talented mechanical engineer and a painting artist. The world is cruel ~~~

AI-generated images are getting harder to spot and scams are rising. In #SCAI, researcher @prof_yz is helping set global standards to detect synthetic media and even teach AI to forget harmful content, building a more trustworthy digital future. news.engineering.asu.edu/202…

The world model is still a model, not a world.

Anyone who claims to be interested in Intelligence should know optimal control theory developed in 1960s, or even cybernetics, as early as in 1940s... My new book was trying to set straight the related history and concepts...

I think it’s pretty clear that simulation is the next frontier for AI. The most impressive feats of AI to date are when we have a clear environment reward, whether it be beating Le Sedol at Go, winning an IMO gold medal, or writing entire apps from scratch. In these cases, the RL algorithm can try different actions, and observe the well-defined consequences in the safety of a docker container. But what about messy real-world situations involving people? The rewards are unclear, the stakes are high, and you can’t experiment in the real world. But these situations are precisely where the next big opportunity in AI is. To crack this, we need to *simulate* society (“put society into a docker container”). Concretely, this means building a model that can predict what will happen in any given situation (real or hypothetical). If we can do this, we are only limited by our imagination: predict the future, optimize for better outcomes, answer hypothetical (“what if”) questions. Ultimately, this goes beyond making better decisions, but it’s about giving us a better understanding of ourselves and the world. Simulation is the whole enchilada. And this is exactly the research that @simile_ai is working on. Read more here: simile.ai/blog/simulation-ne…

If an inquiry now starts with a customer service line asking, “Are you a real person?”, something has clearly gone awry. 😆 I just did that this morning...

I think the next single person tech unicorn could just be hatched from a local Fedex store... These places have everything one needs...

👇 this is kind of author-reviewer exchange should be. Authors gain suggestions and perspectives from others, and give out their own to another group of authors as reviewers. #Walk_For_Peace

OpenReview is a pillar of progress in the AI research community. Now it needs our support. Along with several of my colleagues, I have pledged to help, and I encourage anyone who can to do the same. openreview.net/donate

Everything is a world model if you squint hard enough.

Congratulations Dr. Sheng Cheng (now at #Amazon #AGI) and Master Abhiram Kusumba (now at #CapitalOne #AI) 🎉🎉🎉

CSRankings counts publication in top conferences to rank professors/universities. But this encourages researchers to pursue quantity rather than quality. We propose impactrank.org/, a new university ranking system that tries to measure quality instead of quantity of publications. How can we measure the quality of the publications? We believe that 1) The quality of research is best understood and evaluated by peers in the same research area; 2) With careful and informed use, LLMs can reveal the implicit quality judgments that peers convey through their citation practices and writing across large volumes of scholarly work. Hence, we developed the new ranking system where we analyze research papers from major AI conferences with LLMs. For each paper, we ask an LLM what are the 5 most important papers to this paper. In other words, the five works that most strongly influence the study. By doing this, we trace which papers and authors are consistently seen as inspirational and foundational to new discoveries in the field. We ran the model on all papers from top conferences in machine learning, computer vision, natural language processing and information retrieval from 2020 - 2025, and filtered references to only have those from 2000 onwards. Next, we map these influential authors to their affiliated universities using the CSRankings name–affiliation database. Each time a paper is recognized as one of the “top five references” in another work, its authors and their institutions receive credit. To keep the scoring fair, points are divided by the number of co-authors, ensuring balanced recognition across collaborations. The result is a new kind of academic ranking: one that rewards universities not just for publishing often, but for producing research that endures, inspires, and drives the field forward. This approach highlights scholarly influence and provides students, researchers, and institutions with a clearer picture of where the most impactful work is happening. Note that we believe that CSRankings had substantially improved university rankings in computer science by replacing subjective, reputation-based measures, such as those in US News, with more objective indicators, but the LLM era allows us to do something potentially better! Due to computational resource limits, we were only able to run it with a small 7B language model. It is also a project primarily led by undergraduate and master students from Oregon State University and University of California Santa Cruz. As a result, the system is very much a work in progress and will inevitably contain errors and blind spots. We actively welcome community feedback, new collaborators and contributions of GPU compute so that we can run larger LLMs, obtain more reliable results and improve the methodology.

Linus's views on AI: - “AI is clearly a bubble, but it will change how most skilled jobs get done.” - “Vibe coding is great for getting into programming, but it's a horrible thing to maintain.” - “I'm a huge believer in AI. I'm not a huge believer in the things around AI. I find the market and marketing to be sick. There is going to be a crash.”