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Using the Versius surgical robot permitted a keyhole, or minimal access surgical approach, in an otherwise open surgery.

A robot at Gloucestershire Royal Hospital (GRH), United Kingdom, saved the life of a 61-year-old patient by removing a cancerous tumor from their throat, in what can be called a first in the country.

“To have been given a second chance to see my grandchildren, my children, and my wife has meant so much to me. The team at the GRH saved my life, and I’ll be forever grateful to them for doing so,” Nugent told Gloucestershire Live in an interview.


Versius/CMR Surgical.

Grandfather Martin Nugent is in high spirits after a surgical team comprising Gloucestershire Royal Hospital surgeons Simon Higgs and Steve Hornby employed Versius, a modern, cutting-edge robot from CMR Surgical, to operate earlier in July.

Massachusetts Institute of Technology (MIT) researchers are building swarms of tiny robots that have built-in intelligence, allowing them to build structures, vehicles, or even larger versions of themselves.

The subunit of the robot, which is being developed at MIT’s Center for Bits and Atoms, is called a voxel and is capable of carrying power and data.

“When we’re building these structures, you have to build in intelligence,” MIT Professor and CBA Director Neil Gershenfeld said in a statement. “What emerged was the idea of structural electronics — of making voxels that transmit power and data as well as force.”

New work from Gero, conducted in collaboration with researchers from Roswell Park Comprehensive Cancer Center and Genome Protection Inc. and published in Nature Communications, demonstrates the power of AI combined with analytical tools borrowed from the physics of complex systems to provide insights into the nature of aging, resilience and future medical interventions for age-related diseases including cancer.

Longevity. Technology: Modern AI systems exhibit superhuman-level performance in medical diagnostics applications, such as identifying cancer on MRI scans. This time, the researchers took one step further and used AI to figure out principles that describe how the biological process of aging unfolds in time.

The researchers trained an AI algorithm on a large dataset composed of multiple blood tests taken along the life course of tens of thousands of aging mice to predict the future health state of an animal from its current state. The artificial neural network precisely projected the health condition of an aging mouse with the help of a single variable, which was termed dynamic frailty indicator (dFI) that accurately characterises the damage that an animal accumulates throughout life [1].

Neura Pod is a series covering topics related to Neuralink, Inc. Topics such as brain-machine interfaces, brain injuries, and artificial intelligence will be explored. Host Ryan Tanaka synthesizes informationopinions, and conducts interviews to easily learn about Neuralink and its future.

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This video is a thought experiment about artificial intelligence, the choices we make, and how much (or how little) we’ll delegate such choices in the future.

The stock footage used in this video comes courtesy of various free stock footage channels on YouTube and through Creative Commons.

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Danielle Boyer knew she was interested in robotics from a young age. But with limited learning resources — a problem many Native American students face — Boyer, who is Ojibwe, said she had to take things into her own hands.

She taught herself through watching YouTube videos, flipping through old electrical engineering books, examining maker kits on Amazon, and then “reverse engineering” everything.

Now, as the founder of the nonprofit The STEAM Connection, Boyer, 22, is on a mission to promote technical and cultural educational opportunities among Native American youth like herself — sometimes combining both in the form of robots that teach Indigenous languages, as they face risks of dying out.

Physicists at Google Quantum AI have used their quantum computer to study a type of effective particle that is more resilient to environmental disturbances that can degrade quantum calculations. These effective particles, known as Majorana edge modes, form as a result of a collective excitation of multiple individual particles, like ocean waves form from the collective motions of water molecules. Majorana edge modes are of particular interest in quantum computing applications because they exhibit special symmetries that can protect the otherwise fragile quantum states from noise in the environment.

The condensed matter physicist Philip Anderson once wrote, “It is only slightly overstating the case to say that physics is the study of symmetry.” Indeed, studying and their relationship to underlying symmetries has been the main thrust of physics for centuries. Symmetries are simply statements about what transformations a system can undergo—such as a translation, rotation, or inversion through a mirror—and remain unchanged. They can simplify problems and elucidate underlying physical laws. And, as shown in the new research, symmetries can even prevent the seemingly inexorable quantum process of decoherence.

When running a calculation on a quantum computer, we typically want the quantum bits, or “qubits,” in the computer to be in a single, pure quantum state. But decoherence occurs when external electric fields or other environmental disturb these states by jumbling them up with other states to create undesirable states. If a state has a certain symmetry, then it could be possible to isolate it, effectively creating an island of stability that is impossible to mix with the other states that don’t also have the special symmetry. In this way, since the noise can no longer connect the symmetric state to the others, it could preserve the coherence of the state.

Your weekly news from the AI & Machine Learning world.

OUTLINE:
0:00 — Introduction.
0:25 — AI reads brain signals to predict what you’re thinking.
3:00 — Closed-form solution for neuron interactions.
4:15 — GPT-4 rumors.
6:50 — Cerebras supercomputer.
7:45 — Meta releases metagenomics atlas.
9:15 — AI advances in theorem proving.
10:40 — Better diffusion models with expert denoisers.
12:00 — BLOOMZ & mT0
13:05 — ICLR reviewers going mad.
21:40 — Scaling Transformer inference.
22:10 — Infinite nature flythrough generation.
23:55 — Blazing fast denoising.
24:45 — Large-scale AI training with MultiRay.
25:30 — arXiv to include Hugging Face spaces.
26:10 — Multilingual Diffusion.
26:30 — Music source separation.
26:50 — Multilingual CLIP
27:20 — Drug response prediction.
27:50 — Helpful Things.

ERRATA:
HF did not acquire spaces, they launched spaces themselves and supported Gradio from the start. They later acquired Gradio.

References:
AI reads brain signals to predict what you’re thinking.
https://mind-vis.github.io/?s=09&utm_source=pocket_saves.

Brain-Machine Interface Device Predicts Internal Speech

Closed-form solution for neuron interactions.

https://github.com/raminmh/CfC/blob/main/torch_cfc.py.

GPT-4 rumors.
https://thealgorithmicbridge.substack.com/p/gpt-4-rumors-fro…ket_reader.

In our cells, the language of DNA is written, making each of us unique. A tandem repeat occurs in DNA when a pattern of one or more nucleotides—the basic structural unit of DNA coded in the base of chemicals cytosine ©, adenine (A), guanine (G) and thymine (T)—is repeated multiple times in tandem. An example might be: CAG CAG CAG, in which the pattern CAG is repeated three times.

Now, using state-of-the-art whole-genome sequencing and machine learning techniques, the UNC School of Medicine lab of Jin Szatkiewicz, Ph.D., associate professor of genetics, and colleagues conducted one of the first and the largest investigations of repeats in , elucidating their contribution to the development of this devastating disease.

Published in the journal Molecular Psychiatry, the research shows that individuals with schizophrenia had a significantly higher rate of rare tandem repeats in their genomes—7% more than individuals without schizophrenia. And they observed that the tandem repeats were not randomly located throughout the genome; they were primarily found in genes crucial to brain function and known to be important in schizophrenia, according to previous studies.