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AI tool identifies five distinct cancer cell groups within individual tumors

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterize the diversity of individual cells within tumors, opening doors for more targeted therapies for patients.

Findings on the development and use of the AI tool, called AAnet, have been published in Cancer Discovery.

Tumors aren’t made up of just one cell type—they’re a mix of different cells that grow and respond to treatment in different ways. This diversity, or heterogeneity, makes cancer harder to treat and can in turn lead to worse outcomes, especially in .

Can we fix AI’s evaluation crisis?

This is something that I often wonder about, because a model’s hardcore reasoning ability doesn’t necessarily translate into a fun, informative, and creative experience. Most queries from average users are probably not going to be rocket science. There isn’t much research yet on how to effectively evaluate a model’s creativity, but I’d love to know which model would be the best for creative writing or art projects.

Human preference testing has also emerged as an alternative to benchmarks. One increasingly popular platform is LMarena, which lets users submit questions and compare responses from different models side by side—and then pick which one they like best. Still, this method has its flaws. Users sometimes reward the answer that sounds more flattering or agreeable, even if it’s wrong. That can incentivize “sweet-talking” models and skew results in favor of pandering.

AI researchers are beginning to realize—and admit—that the status quo of AI testing cannot continue. At the recent CVPR conference, NYU professor Saining Xie drew on historian James Carse’s Finite and Infinite Games to critique the hypercompetitive culture of AI research. An infinite game, he noted, is open-ended—the goal is to keep playing. But in AI, a dominant player often drops a big result, triggering a wave of follow-up papers chasing the same narrow topic. This race-to-publish culture puts enormous pressure on researchers and rewards speed over depth, short-term wins over long-term insight. “If academia chooses to play a finite game,” he warned, “it will lose everything.”

Google rolls out new Gemini model that can run on robots locally

Google DeepMind on Tuesday released a new language model called Gemini Robotics On-Device that can run tasks locally on robots without requiring an internet connection.

Building on the company’s previous Gemini Robotics model that was released in March, Gemini Robotics On-Device can control a robot’s movements. Developers can control and fine-tune the model to suit various needs using natural language prompts.

In benchmarks, Google claims the model performs at a level close to the cloud-based Gemini Robotics model. The company says it outperforms other on-device models in general benchmarks, though it didn’t name those models.

Human Cyborgs Are No Longer Science Fiction! (Insane Breakthroughs)

Are human cyborgs the future? You won’t believe how close we are to merging humans with machines! This video uncovers groundbreaking advancements in cyborg technology, from bionic limbs and brain-computer interfaces to biological robots like anthrobots and exoskeletons. Discover how these innovations are reshaping healthcare, military, and even space exploration.

Learn about real-world examples, like Neil Harbisson, the colorblind cyborg artist, and the latest developments in brain-on-a-chip technology, combining human cells with artificial intelligence. Explore how cyborg soldiers could revolutionize the battlefield and how genetic engineering might complement robotic enhancements.

The future of human augmentation is here. Could we be on the verge of transforming humanity itself? Dive in to find out how science fiction is quickly becoming reality.

How do human cyborgs work? What are the latest AI breakthroughs in cyborg technology? How are cyborgs being used today? Could humans evolve into hybrid beings? This video answers all your questions. Don’t miss it!

#ai.
#cyborg.
#ainews.

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Super Intelligence Speculation — Computerphile

Looking to the future, just how intelligent might the current crop of Large Language Models get? Daniel Kokotajlo joins us to discuss Ai2027.

Find out more about the AI2027 paper here: http://bit.ly/4k4dIOA

Computerphile is supported by Jane Street. Learn more about them (and exciting career opportunities) at: https://jane-st.co/computerphile.

This video was filmed and edited by Sean Riley.

Computerphile is a sister project to Brady Haran’s Numberphile. More at https://www.bradyharanblog.com

Engineering biology applications for environmental solutions: potential and challenges

Engineering biology applies synthetic biology to address global environmental challenges like bioremediation, biosequestration, pollutant monitoring, and resource recovery. This perspective outlines innovations in engineering biology, its integration with other technologies (e.g., nanotechnology, IoT, AI), and commercial ventures leveraging these advancements. We also discuss commercialisation and scaling challenges, biosafety and biosecurity considerations including biocontainment strategies, social and political dimensions, and governance issues that must be addressed for successful real-world implementation. Finally, we highlight future perspectives and propose strategies to overcome existing hurdles, aiming to accelerate the adoption of engineering biology for environmental solutions.


The scale of global environmental challenges requires a multi-pronged approach, which utilises all the technologies at our disposal. Here, authors provide their perspective on the potential of engineering biology for environmental biotechnology, summarizing their thoughts on the key challenges and future possibilities for the field.