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Category: information science – Page 79

Recent research published in Nature Communications has used machine learning algorithms to find new compounds that can eliminate senescent cells [1].

Senolytics are molecules that destroy senescent cells. Only a small number of such molecules have been identified, and only two have shown efficacy in clinical trials: dasatinib and quercetin in combination [2]. One of the biggest challenges is that senolytics often only work against specific types of cells. Additionally, some senolytics may work well for one cell type while being toxic to other, non-senescent cell types [3].

There is also a group of senolytics that are used in cancer therapies. However, most of them target pathways that are mutated in cancer. Therefore, they cannot be used as therapeutic agents in different contexts.

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Summary.

What will artificial intelligence do to industries and jobs? For a preview, look to the finance industry which has been incorporating data and algorithms for a long time, and which is always a canary in the coal mine for new technology. The experience of finance suggests that AI will transform some industries (sometimes very quickly) and that it will especially benefit larger players. But it may not leave the overall system better off.

Page-utils class= article-utils—vertical hide-for-print data-js-target= page-utils data-id= tag: blogs.harvardbusiness.org, 2007/03/31:999.361588 data-title= What the Finance Industry Tells Us About the Future of AI data-url=/2023/08/what-the-finance-industry-tells-us-about-the-future-of-ai data-topic= Business and society data-authors= Mihir A. Desai data-content-type= Digital Article data-content-image=/resources/images/article_assets/2023/08/Aug23_09_5277464-383x215.jpg data-summary=

The sector is a test case for how new technology will play out.

Continue reading “What the Finance Industry Tells Us About the Future of AI” | >

Through a scattering medium such as ground glass? Traditionally, this would be considered impossible. When light passes through an opaque substance, the information carried within the light becomes “jumbled up”, almost as if undergoes complex encryption.

Recently, a remarkable scientific breakthrough by Professor Choi Wonshik’s team from the IBS Center for Molecular Spectroscopy and Dynamics (IBS CMSD) has unveiled a method to leverage this phenomenon in the fields of optical computing and machine learning.

Machine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Machine learning is used to identify patterns in data, classify data into different categories, or make predictions about future events. It can be categorized into three main types of learning: supervised, unsupervised and reinforcement learning.

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A team of researchers from British universities has trained a deep learning model that can steal data from keyboard keystrokes recorded using a microphone with an accuracy of 95%.

When Zoom was used for training the sound classification algorithm, the prediction accuracy dropped to 93%, which is still dangerously high, and a record for that medium.

Such an attack severely affects the target’s data security, as it could leak people’s passwords, discussions, messages, or other sensitive information to malicious third parties.

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What you get, starting out in this video, is that algorithms impact our lives in, as CSAIL grad student Sandeep Silwal puts it, “silent ways”

Silwal uses a simple example – maps – in discussing what he calls the “marriage of provable algorithm design and machine learning.”

Lots of people, he notes, want to move from the area around MIT, south across the Charles to Fenway Park, to see the Red Sox.

That sort of fact could inform the thinking about how to program algorithms. For example, Silwal mentions how you can analyze data results to identify the most visited websites on the Internet – and direct focus accordingly.

“We use (algorithms) to compute fundamental things about us,” he says. “And… More.

Continue reading “Algorithms Can Learn, And They Probably Should: Saving Resources For The Future” | >

Quantum entanglement is one of the most intriguing and perplexing phenomena in quantum physics. It allows physicists to create connections between particles that seem to violate our understanding of space and time.

This video discusses what quantum entanglement really is, and the experiments that help us understand it. The results of these experiments have applications in new technologies that will forever change our world.

Join Katie Mack, Perimeter Institute’s Hawking Chair in Cosmology and Science Communication, over 10 short forays into the weird, wonderful world of quantum science. Episodes are published weekly, subscribe to our channel so you don’t miss an update.

Want to learn more about quantum concepts? Visit https://perimeterinstitute.ca/quantum-101-quantum-science-explained to access free resources.

Continue reading “Quantum 101 Episode 5: Quantum Entanglement Explained” | >

In a first-of-its-kind clinical trial, bioelectronic medicine researchers, engineers and surgeons at Northwell Health’s The Feinstein Institutes for Medical Research have successfully implanted microchips into the brain of a man living with paralysis, and have developed artificial intelligence (AI) algorithms to re-link his brain to his body and spinal cord.

This double neural bypass forms an electronic bridge that allows information to flow once again between the man’s paralyzed body and to restore movement and sensations in his hand with lasting gains in his arm and wrist outside of the laboratory. The research team unveiled the trial participant’s groundbreaking progress four months after a 15-hour open-brain surgery that took place on March 9 at North Shore University Hospital (NSUH).

“This is the first time the brain, body and have been linked together electronically in a paralyzed human to restore lasting movement and sensation,” said Chad Bouton, professor in the Institute of Bioelectronic Medicine at the Feinstein Institutes, vice president of advanced engineering at Northwell Health, developer of the technology and principal investigator of the clinical trial.

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There’s a lot of talk about the potential for artificial intelligence in medicine, but few researchers have shown through well-designed clinical trials that it could be a boon for doctors, health care providers and patients.

Now, researchers at Stanford Medicine have conducted one such trial; they tested an artificial intelligence algorithm used to evaluate heart function. The algorithm, they found, improves evaluations of heart function from echocardiograms — movies of the beating heart, filmed with ultrasound waves, that show how efficiently it pumps blood.

“This blinded, randomized clinical trial is, to our knowledge, one of the first to evaluate the performance of an artificial intelligence algorithm in medicine. We showed that AI can help improve accuracy and speed of echocardiogram readings,” said James Zou, PhD, assistant professor of biomedical data science and co-senior author on the study. “This is important because heart disease is the leading cause of death in the world. There are over 10 million echocardiograms done each year in the U.S., and AI has the potential to add precision to how they are interpreted.”

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An asteroid discovery algorithm—designed to uncover near-Earth asteroids for the Vera C. Rubin Observatory’s upcoming 10-year survey of the night sky—has identified its first “potentially hazardous” asteroid, a term for space rocks in Earth’s vicinity that scientists like to keep an eye on.

The roughly 600-foot-long asteroid, designated 2022 SF289, was discovered during a test drive of the algorithm with the ATLAS survey in Hawaii. Finding 2022 SF289, which poses no risk to Earth for the foreseeable future, confirms that the next-generation algorithm, known as HelioLinc3D, can identify near-Earth asteroids with fewer and more dispersed observations than required by today’s methods.

“By demonstrating the real-world effectiveness of the software that Rubin will use to look for thousands of yet-unknown potentially hazardous asteroids, the discovery of 2022 SF289 makes us all safer,” said Rubin scientist Ari Heinze, the principal developer of HelioLinc3D and a researcher at the University of Washington.

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Researchers from the Tokyo University of Science recently published a study in the journal Artificial Life and Robotics where they explored how machine learning can help detect deception.

Machine learning is a subset of artificial intelligence (AI) that involves the use of algorithms and statistical models to enable computers to learn and improve from experience without being explicitly programmed. In other words, it is a method of teaching computers to perform specific tasks by learning from data, patterns, and examples, rather than relying on pre-defined rules.

Detecting deception can be important in various situations, like questioning crime victims or suspects and interviewing patients with mental health issues. Sometimes, human interviewers might struggle to ask the right questions or spot deception accurately.

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