Summary: Researchers developed an artificial intelligence model that accurately determines the sex of individuals based on brain scans, with over 90% success. This breakthrough supports the theory that significant sex differences in brain organization exist, challenging long-standing controversies.
The AI model focused on dynamic MRI scans, identifying specific brain networks—such as the default mode, striatum, and limbic networks—as critical in distinguishing male from female brains.
This research not only deepens our understanding of brain development and aging but also opens new avenues for addressing sex-specific vulnerabilities in psychiatric and neurological disorders.
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A multi-institutional team of Chinese engineers has developed a proof-of-concept calcium-based battery that withstands 700 charge cycles at room temperature. In their paper published in the journal Nature, the group describes the challenges they addressed in developing the battery and what they have learned about the possible use of calcium-based batteries in consumer products in the future.
The current standard for rechargeable batteries used in consumer products is lithium. But because it is a rare material and has issues such as poor aging and the need to prevent overcharge, scientists have been looking for a suitable replacement. One such material is calcium, which is 2,500 times as abundant as lithium.
Prior research has suggested rechargeable batteries based on calcium should be possible if problems can be resolved. One of the biggest challenges is finding suitable electrolyte and electrode materials that can provide stability and safety.
New research from the University of Sussex holds promise for extending life expectancy and enhancing treatment options for a common and aggressive brain cancer affecting thousands in the UK annually and hundreds of thousands globally.
Published in the Journal of Advanced Science, the study revealed that the protein PANK4, previously overlooked, can hinder cancer cells’ response to chemotherapy in glioblastoma, an aggressive form of brain cancer and if the protein is removed, cancer cells respond better to the main chemotherapy drug used globally for the treatment of glioblastoma.
Glioblastoma stands as one of the most aggressive types of brain cancer, with approximately 3,200 adults diagnosed annually in the UK and around 250,000 to 300,000 cases globally. Despite treatment with surgery, radiation, and the chemotherapy drug temozolomide, which initially yields positive responses, patients typically face a bleak prognosis, with a survival rate of just one to 18 months post-diagnosis due to the rapid development of resistance in cancer cells.
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A new study led by Stanford Medicine scientists demonstrates a simple way of studying organ aging by analyzing distinct proteins, or sets of them, in blood, enabling the prediction of individuals’ risk for diseases.
It is often thought that if we cure aging or find out how to upload a human mind that humans will be immortal. Today we will examine that notion and see how well it holds up against astronomical time lines.
Script Editing: Andy Popescu. Connor Hogan. Edward Nardella. Eustratius Graham. Gregory Leal. Jefferson Eagley. Luca de Rosa. Mark Warburton. Michael Gusevsky. Mitch Armstrong. MolbOrg. Naomi Kern. Philip Baldock. Sigmund Kopperud. Steve Cardon. Tiffany Penner.
The Schwartz Reisman Institute for Technology and Society and the Department of Computer Science at the University of Toronto, in collaboration with the Vector Institute for Artificial Intelligence and the Cosmic Future Initiative at the Faculty of Arts & Science, present Geoffrey Hinton on October 27, 2023, at the University of Toronto.
0:00:00 — 0:07:20 Opening remarks and introduction. 0:07:21 — 0:08:43 Overview. 0:08:44 — 0:20:08 Two different ways to do computation. 0:20:09 — 0:30:11 Do large language models really understand what they are saying? 0:30:12 — 0:49:50 The first neural net language model and how it works. 0:49:51 — 0:57:24 Will we be able to control super-intelligence once it surpasses our intelligence? 0:57:25 — 1:03:18 Does digital intelligence have subjective experience? 1:03:19 — 1:55:36 Q&A 1:55:37 — 1:58:37 Closing remarks.
Talk title: “Will digital intelligence replace biological intelligence?”
Abstract: Digital computers were designed to allow a person to tell them exactly what to do. They require high energy and precise fabrication, but in return they allow exactly the same model to be run on physically different pieces of hardware, which makes the model immortal. For computers that learn what to do, we could abandon the fundamental principle that the software should be separable from the hardware and mimic biology by using very low power analog computation that makes use of the idiosynchratic properties of a particular piece of hardware. This requires a learning algorithm that can make use of the analog properties without having a good model of those properties. Using the idiosynchratic analog properties of the hardware makes the computation mortal. When the hardware dies, so does the learned knowledge. The knowledge can be transferred to a younger analog computer by getting the younger computer to mimic the outputs of the older one but education is a slow and painful process. By contrast, digital computation makes it possible to run many copies of exactly the same model on different pieces of hardware. Thousands of identical digital agents can look at thousands of different datasets and share what they have learned very efficiently by averaging their weight changes. That is why chatbots like GPT-4 and Gemini can learn thousands of times more than any one person. Also, digital computation can use the backpropagation learning procedure which scales much better than any procedure yet found for analog hardware. This leads me to believe that large-scale digital computation is probably far better at acquiring knowledge than biological computation and may soon be much more intelligent than us. The fact that digital intelligences are immortal and did not evolve should make them less susceptible to religion and wars, but if a digital super-intelligence ever wanted to take control it is unlikely that we could stop it, so the most urgent research question in AI is how to ensure that they never want to take control.
About Geoffrey Hinton.
Geoffrey Hinton received his PhD in artificial intelligence from Edinburgh in 1978. After five years as a faculty member at Carnegie Mellon he became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto, where he is now an emeritus professor. In 2013, Google acquired Hinton’s neural networks startup, DNN research, which developed out of his research at U of T. Subsequently, Hinton was a Vice President and Engineering Fellow at Google until 2023. He is a founder of the Vector Institute for Artificial Intelligence where he continues to serve as Chief Scientific Adviser.
