In a new study, Nobel laureate David Baker and colleagues detail new methods for targeting undruggable “disordered” proteins.
Category: robotics/AI – Page 138
New advanced imaging technology enables detailed disease mapping in tissue samples
Researchers from Aarhus University—in a major international collaboration—have developed a groundbreaking method that can provide more information from the tissue samples doctors take from patients every day.
The new technique, called Pathology-oriented multiPlexing or PathoPlex, can look under a microscope at over 100 different proteins in the same small piece of tissue—instead of just 1–2 at a time, as is done now.
The technology, which has just been published in the journal Nature, combines advanced image processing with machine learning to map complex disease processes in detail.
A Prominent OpenAI Investor Appears to Be Suffering a ChatGPT-Related Mental Health Crisis, His Peers Say
Bedrock co-founder Geoff Lewis has posted increasingly troubling content on social media, drawing concern from friends in the industry.
Google Release AI-Generated Film Based Off Vintage Photographs
They found the photos in a thrift store.
“We Gave AI $20 Million to Rethink Science”: Nexus Super-System Set to Turbocharge US Innovation Like Never Before
IN A NUTSHELL 🚀 Nexus, a $20 million supercomputer, is set to transform U.S. scientific research with its AI power. 🔬 Designed for accessibility, Nexus democratizes high-performance computing, allowing researchers nationwide to apply for access. 🌐 Georgia Tech, in collaboration with the NCSA, is creating a shared national research infrastructure through Nexus. 📈 With unprecedented
The biggest challenge for AI may be keeping the lights on
Targeting superintelligence, tech giants are scrambling for nuclear deals and water rights to power AI advances
What is consciousness, and could AI have it?
In the Voltaire Lecture 2025, Professor Anil Seth will set out an approach to understanding consciousness which, rather than trying to solve the mystery head-on, tries to dissolve it by building explanatory bridges from physics and biology to experience and function. In this view, conscious experiences of the world around us, and of being a ‘self’ within that world, can be understood in terms of perceptual predictions that are deeply rooted in a fundamental biological imperative – the desire to stay alive.
At this event, Professor Seth will explore how widely distributed beyond human beings consciousness may be, with a particular focus on AI. He will consider whether consciousness might depend not just on ‘information processing’, but on properties unique to living, biological organisms, before ending with an exploration of the ethical implications of an artificial intelligence that is either actually conscious – or can convincingly pretend to be.
How Distillation Makes AI Models Smaller and Cheaper
Considering that the distillation requires access to the innards of the teacher model, it’s not possible for a third party to sneakily distill data from a closed-source model like OpenAI’s o1, as DeepSeek was thought to have done. That said, a student model could still learn quite a bit from a teacher model just through prompting the teacher with certain questions and using the answers to train its own models — an almost Socratic approach to distillation.
Meanwhile, other researchers continue to find new applications. In January, the NovaSky lab at the University of California, Berkeley, showed that distillation works well for training chain-of-thought reasoning models, which use multistep “thinking” to better answer complicated questions. The lab says its fully open-source Sky-T1 model cost less than $450 to train, and it achieved similar results to a much larger open-source model. “We were genuinely surprised by how well distillation worked in this setting,” said Dacheng Li, a Berkeley doctoral student and co-student lead of the NovaSky team. “Distillation is a fundamental technique in AI.”
Tsinghua University holds Tsinghua AI Agent Hospital Inauguration and 2025 Tsinghua Medicine Townhall Meeting-Tsinghua University
On the morning of April 26, Tsinghua University held an inauguration ceremony for Tsinghua AI Agent Hospital and the 2025 Tsinghua Medicine Townhall Meeting at the Main Building Reception Hall. Tsinghua President Li Luming and Vice President Wang Hongwei attended the event.
President Li Luming reviewed the progress of Tsinghua University’s medical programs over the past year, emphasizing the University’s strong commitment to the development of medical disciplines. He highlighted Tsinghua’s strength in fundamental research in Artificial intelligence, which has already led to a series of high-level innovations at the intersection of AI and medicine. The establishment of the Tsinghua AI Agent Hospital represents a new initiative by Tsinghua to leverage its strengths in science and engineering to empower the advancement of medicine.
President Li encouraged Tsinghua Medicine to remain committed to fostering virtue and talent, cultivating a new generation of medical innovators with both a strong medical foundation and AI literacy. He also called for deeper integration across disciplines, particularly between engineering and medicine, as well as closer ties between clinical practice and technology. Finally, he urged Tsinghua Medicine to align its work with cutting-edge global trends and national strategic needs, driving medical advancement and contributing to the protection of public health.
Tesla FSD Competitors Admit DEFEAT: “Elon Was Right”
Questions to inspire discussion.
Safety and Performance.
🛡️ Q: How does Tesla’s full self-driving system compare to human driving in terms of safety? A: According to Elon Musk, Tesla’s end-to-end neural networks trained on massive video datasets have been proven to be dramatically safer than average human driving.
⚡ Q: What recent hardware upgrade has improved Tesla’s full self-driving capabilities? A: Tesla’s AI4 hardware has been upgraded to 150–200 watts, enabling more complex neural networks and faster decision-making, achieving 36 frames per second processing.
Scalability and Efficiency.
📈 Q: Why is Tesla’s vision-only approach considered more scalable than competitors’ methods? A: Tesla’s vision-only approach is more scalable than competitors’ use of multiple sensors, sensor fusion, and high-definition maps, as stated by BU’s Robin Lee.