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OpenAI’s ‘Superintelligence’ Breakthrough Shakes the Foundations and Nearly Destroyed the Company

In the ever-evolving landscape of artificial intelligence, a seismic shift is unfolding at OpenAI, and it involves more than just lines of code. The reported ‘superintelligence’ breakthrough has sent shockwaves through the company, pushing the boundaries of what we thought was possible and raising questions that extend far beyond the realm of algorithms.

Imagine a breakthrough so monumental that it threatens to dismantle the very fabric of the company that achieved it. OpenAI, the trailblazer in artificial intelligence, finds itself at a crossroads, dealing not only with technological advancement but also with the profound ethical and existential implications of its own creation – ‘superintelligence.’

The Breakthrough that Nearly Broke OpenAI: The Information’s revelation about a Generative AI breakthrough, capable of unleashing ‘superintelligence’ within this decade, sheds light on the internal disruption at OpenAI. Spearheaded by Chief Scientist Ilya Sutskever, the breakthrough challenges conventional AI training, allowing machines to solve problems they’ve never encountered by reasoning with cleaner and computer-generated data.

Portable, non-invasive, mind-reading AI turns thoughts into text

In a world-first, researchers from the GrapheneX-UTS Human-centric Artificial Intelligence Centre at the University of Technology Sydney (UTS) have developed a portable, non-invasive system that can decode silent thoughts and turn them into text.

The technology could aid communication for people who are unable to speak due to illness or injury, including stroke or paralysis. It could also enable seamless communication between humans and machines, such as the operation of a bionic arm or robot.

The study has been selected as the spotlight paper at the NeurIPS conference, an that showcases world-leading research on artificial intelligence and , held in New Orleans on 12 December 2023.

Training algorithm breaks barriers to deep physical neural networks

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 —the latter of which is significant enough to raise concerns about contributions to global carbon emissions.

While we often think of 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.

AI accurately predicts cancer outcomes from tissue samples

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 can miss subtle features present in pathology images that might provide important clues to a patient’s condition.

The Intersection of Math and AI: A New Era in Problem-Solving

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.

AI and EEG Transform Silent Thoughts to Text

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.

It shows promise in enhancing human-machine interactions and in aiding those who cannot speak, with potential applications in controlling devices like bionic arms or robots.