Lifeboat Foundation

EPFL researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning hardware.

With their ability to process vast amounts of data through algorithmic ‘learning’ rather than traditional programming, it often seems like the potential of deep neural networks like Chat-GPT is limitless. But as the scope and impact of these systems have grown, so have their size, complexity, and energy consumption —the latter of which is significant enough to raise concerns about contributions to global carbon emissions.

While we often think of technological advancement in terms of shifting from analog to digital, researchers are now looking for answers to this problem in physical alternatives to digital deep neural networks. One such researcher is Romain Fleury of EPFL’s Laboratory of Wave Engineering in the School of Engineering.

The Oxford University professor posits the emergence of ‘a new species’ stemming from algorithms.

A living artificial intelligence hardware approach that uses the adaptive reservoir computation of biological neural networks in a brain organoid can perform tasks such as speech recognition and nonlinear equation prediction.

Researchers at UT Southwestern Medical Center have developed a novel artificial intelligence (AI) model that analyzes the spatial arrangement of cells in tissue samples. This innovative approach, detailed in Nature Communications, has accurately predicted outcomes for cancer patients, marking a significant advancement in utilizing AI for cancer prognosis and personalized treatment strategies.

“Cell spatial organization is like a complex jigsaw puzzle where each cell serves as a unique piece, fitting together meticulously to form a cohesive tissue or organ structure. This research showcases the remarkable ability of AI to grasp these intricate spatial relationships among cells within tissues, extracting subtle information previously beyond human comprehension while predicting patient outcomes,” said study leader Guanghua Xiao, Ph.D., Professor in the Peter O’Donnell Jr. School of Public Health, Biomedical Engineering, and the Lyda Hill Department of Bioinformatics at UT Southwestern. Dr. Xiao is a member of the Harold C. Simmons Comprehensive Cancer Center at UTSW.

Tissue samples are routinely collected from patients and placed on slides for interpretation by pathologists, who analyze them to make diagnoses. However, Dr. Xiao explained, this process is time-consuming, and interpretations can vary among pathologists. In addition, the human brain can miss subtle features present in pathology images that might provide important clues to a patient’s condition.

Conference is exploring burgeoning connections between the two fields.

Traditionally, mathematicians jot down their formulas using paper and pencil, seeking out what they call pure and elegant solutions. In the 1970s, they hesitantly began turning to computers to assist with some of their problems. Decades later, computers are often used to crack the hardest math puzzles. Now, in a similar vein, some mathematicians are turning to machine learning tools to aid in their numerical pursuits.

Embracing Machine Learning in Mathematics.

Summary: Researchers created a revolutionary system that can non-invasively convert silent thoughts into text, offering new communication possibilities for people with speech impairments due to illnesses or injuries.

The technology uses a wearable EEG cap to record brain activity and an AI model named DeWave to decode these signals into language. This portable system surpasses previous methods that required invasive surgery or cumbersome MRI scanning, achieving state-of-the-art EEG translation performance.

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The partnership comes amid an increase in labor unions across industries seeking protection from the rising use of AI.

It’s easy to get robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI) mixed up—especially when people use them interchan…

Mistral AI has officially closed its much anticipated Series A funding round. The French company has raised €385 million ($415 million).