Lifeboat Foundation

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.

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.

Humans and animals can use diverse decision-making strategies to maximize rewards in uncertain environments, but previous studies have not investigated the use of multiple strategies that involve distinct latent switching dynamics in reward-guided behavior. Here, using a reversal learning task, we showed that mice displayed a much more variable behavior than would be expected from a uniform strategy, suggesting that they mix between multiple behavioral modes in the task. We develop a computational method to dissociate these learning modes from behavioral data, addressing the challenges faced by current analytical methods when agents mix between different strategies. We found that the use of multiple strategies is a key feature of rodent behavior even in the expert stages of learning, and applied our tools to quantify the highly diverse strategies used by individual mice in the task. We further mapped these behavioral modes to two types of underlying algorithms, model-free Q-learning and inference-based behavior. These rich descriptions of underlying latent states form the basis of detecting abnormal patterns of behavior in reward-guided decision-making.

Citation: Le NM, Yildirim M, Wang Y, Sugihara H, Jazayeri M, Sur M (2023) Mixtures of strategies underlie rodent behavior during reversal learning. PLoS Comput Biol 19: e1011430. https://doi.org/10.1371/journal.pcbi.

Editor: Alireza Soltani, Dartmouth College, UNITED STATES

The pulse of any organization lies not just in its products or services but in its people.

Data-driven HR is redefining the corporate landscape. Explore the history of this transformation and discover how big data and innovations are shaping the future of HR.

Steven Bartlett is the Founder of Social Chain, an entrepreneur, a podcaster and an author. How to become a functioning human is a difficult skill to work out. Should I focus on achieving goals or inner peace? Can I become confident without being arrogant? Thankfully Steven has spent the last 2 years distilling a ton of lessons into his new book, and today we get to go through my favourites. Expect to learn Steven’s equation for unbreakable discipline, the biggest lesson from dealing with mainstream media pile-ons, why there are so few actual practitioners in the world, how to stop being your biggest critic, why your weirdness is the ultimate competitive advantage, how Steven discovered he was riddled with fake ambition and much more…⁣⁣

Are bright cities making it worse for birds during their migrations? Find out here!

A recent study published in Nature Communications examines how increased levels of artificial light, specifically in urban areas, has contributed to increased bird deaths during their annual migrations. This study comes as hundreds of birds were killed after colliding with a Chicago building, and despite a 2021 study recommending that reduced building lights would reduce bird collisions by 60 percent. This recent study holds the potential to help scientists and the public better understand how rapidly expanding urban areas are impacting bird migration and their safety.

For the study, the researchers used the Next Generation Radar (NEXRAD), which is jointly operated by the U.S. Air Force, Federal Aviation Administration, and the U.S. National Weather Service, to track bird migration stopover density during spring (March 15 to June 15) and fall (August 15 to November 15) seasons between 2016 and 2020. After analyzing more than 10 million radar observations, the researchers found that light pollution was the second-highest ranked reason for birds stopping for breaks out of 49 reasons measured for the study, with the top reason being elevation.

Continue reading “The Dark Side of Urbanization: Light Pollution’s Toll on Bird Migration” »

A proposed model unites quantum theory with classical gravity by assuming that states evolve in a probabilistic way, like a game of chance.

Physicists’ best theory of matter is quantum mechanics, which describes the discrete (quantized) behavior of microscopic particles via wave equations. Their best theory of gravity is general relativity, which describes the continuous (classical) motion of massive bodies via space-time curvature. These two highly successful theories appear fundamentally at odds over the nature of space-time: quantum wave equations are defined on a fixed space-time, but general relativity says that space-time is dynamic—curving in response to the distribution of matter. Most attempts to solve this tension have focused on quantizing gravity, with the two leading proposals being string theory and loop quantum gravity. But new theoretical work by Jonathan Oppenheim at University College London proposes an alternative: leave gravity as a classical theory and couple it to quantum theory through a probabilistic mechanism [1].