ICLR 2025
Shaden Alshammari, John Hershey, Axel Feldmann, William T. Freeman, Mark Hamilton.
MIT, Microsoft, Google.
[ https://openreview.net/forum?id=WfaQrKCr4X](https://openreview.net/forum?id=WfaQrKCr4X
[ https://github.com/mhamilton723/STEGO](https://github.com/mhamilton723/STEGO
Posted in futurism | Leave a Comment on Max Planck
Max Karl Ernst Ludwig Planck (/ ˈ p l æ ŋ k / ; [ 2 ] German: [maks ˈplaŋk] ⓘ ; [ 3 ] 23 April 1858 – 4 October 1947) was a German theoretical physicist whose discovery of energy quanta won him the Nobel Prize in Physics in 1918. [ 4 ]
Planck made many substantial contributions to theoretical physics, but his fame as a physicist rests primarily on his role as the originator of quantum theory and one of the founders of modern physics, [ 5 ] [ 6 ] which revolutionized understanding of atomic and subatomic processes. He is known for the Planck constant, which is of foundational importance for quantum physics, and which he used to derive a set of units, today called Planck units, expressed only in terms of fundamental physical constants.
Planck was twice president of the German scientific institution Kaiser Wilhelm Society. In 1948, it was renamed the Max Planck Society (Max-Planck-Gesellschaft) and nowadays includes 83 institutions representing a wide range of scientific directions.
Genome editing has advanced at a rapid pace with promising results for treating genetic conditions—but there is always room for improvement. A new paper by investigators from Mass General Brigham showcases the power of scalable protein engineering combined with machine learning to boost progress in the field of gene and cell therapy.
In their study, the authors developed a machine learning algorithm—known as PAMmla—that can predict the properties of approximately 64 million genome-editing enzymes. The work could help reduce off-target effects and improve editing safety, enhance editing efficiency, and enable researchers to predict customized enzymes for new therapeutic targets. The results are published in Nature.
“Our study is a first step in dramatically expanding our repertoire of effective and safe CRISPR-Cas9 enzymes. In our manuscript, we demonstrate the utility of these PAMmla-predicted enzymes to precisely edit disease-causing sequences in primary human cells and in mice,” said corresponding author Ben Kleinstiver, Ph.D., Kayden-Lambert MGH Research Scholar associate investigator at Massachusetts General Hospital (MGH).
Researchers at Baylor College of Medicine, Texas Children’s Hospital, the Hospital for Sick Children in Toronto and collaborating institutions reveal in Nature Cell Biology a strategy that helps medulloblastoma, the most prevalent malignant brain tumor in children, spread and grow on the leptomeninges, the membranes surrounding the brain and spinal cord.
They discovered a novel line of communication between metastatic medulloblastoma and leptomeningeal fibroblasts that mediates recruitment and reprogramming of the latter to support tumor growth. The findings suggest that disrupting this communication offers a potential opportunity to treat this devastating disease.
“Metastases, the spreading of a tumor away from its original site, are the most common and most important cause of illness and death for children with medulloblastoma,” said co-first author Dr. Namal Abeysundara, a postdoctoral fellow who was working in the lab of Dr. Michael D. Taylor at the Arthur and Sonia Labatt Brain Tumor Research Center and the Developmental and Stem Cell Biology Program at the Hospital for Sick Children in Toronto, Canada during this project.
The juridical metaphor in physics has ancient roots. Anaximander, in the 6th century BCE, was perhaps the first to invoke the concept of cosmic justice, speaking of natural entities paying “penalty and retribution to each other for their injustice according to the assessment of Time” (Kirk et al., 2010, p. 118). This anthropomorphizing tendency persisted through history, finding its formal expression in Newton’s Principia Mathematica, where he articulated his famous “laws” of motion. Newton, deeply influenced by his theological views, conceived of these laws as divine edicts — mathematical expressions of God’s will imposed upon a compliant universe (Cohen & Smith, 2002, p. 47).
This legal metaphor has served science admirably for centuries, providing a framework for conceptualizing the universe’s apparent obedience to mathematical principles. Yet it carries implicit assumptions worth examining. Laws suggest a lawgiver, hinting at external agency. They imply prescription rather than description — a subtle distinction with profound philosophical implications. As physicist Paul Davies (2010) observes, “The very notion of physical law is a theological one in the first place, a fact that makes many scientists squirm” (p. 74).
Enter the computational metaphor — a framework more resonant with our digital age. The universe, in this conceptualization, executes algorithms rather than obeying laws. Space, time, energy, and matter constitute the data structure upon which these algorithms operate. This shift is more than semantic; it reflects a fundamental reconceptualization of physical reality that aligns remarkably well with emerging theories in theoretical physics and information science.
A team from Stanford Medicine has developed AI-powered digital twins of the brain, trained on large datasets of brain activity. These models predict neural responses to new stimuli, offering profound insights into brain function.
Our brain doesn’t passively receive visual input—it actively orchestrates a symphony of neural oscillations to process the complex, dynamic scenes we see in everyday life.
Here are five recent innovative bioprinting and bioink studies that are building synthetic biological structures.
An optogenetic method for controlling the movement of starfish oocytes has been developed, which could have applications for drug delivery.
Genome editing has advanced at a rapid pace with promising results for treating genetic conditions-but there is always room for improvement. A new paper by investigators from Mass General Brigham published in Nature showcases the power of scalable protein engineering combined with machine learning to boost progress in the field of gene and cell therapy. In their study, authors developed a machine learning algorithm-known as PAMmla-that can predict the properties of about 64 million genome editing enzymes. The work could help reduce off-target effects and improve editing safety, enhance editing efficiency, and enable researchers to predict customized enzymes for new therapeutic targets. Their results are published in Nature.
“Our study is a first step in dramatically expanding our repertoire of effective and safe CRISPR-Cas9 enzymes. In our manuscript we demonstrate the utility of these PAMmla-predicted enzymes to precisely edit disease-causing sequences in primary human cells and in mice,” said corresponding author Ben Kleinstiver, PhD, Kayden-Lambert MGH Research Scholar associate investigator at Massachusetts General Hospital (MGH), a founding member of the Mass General Brigham healthcare system. “Building on these findings, we are excited to have these tools utilized by the community and also apply this framework to other properties and enzymes in the genome editing repertoire.”
CRISPR-Cas9 enzymes can be used to edit genes at locations throughout the genomes, but there are limitations to this technology. Traditional CRISPR-Cas9 enzymes can have off-target effects, cleaving or otherwise modifying DNA at unintended sites in the genome. The newly published study aims to improve this by using machine learning to better predict and tailor enzymes to hit their targets with greater specificity. The approach also offers a scalable solution-other attempts at engineering enzymes have had a lower throughput and typically yielded orders of magnitude fewer enzymes.
