Here comes OpenAI’s next magic trick.
Category: robotics/AI – Page 280
Automated Cyborg Cockroach Factory Could Churn Out a Bug a Minute for Search and Rescue
Envisioning armies of electronically controllable insects is probably nightmare fuel for most people. But scientists think they could help rescue workers scour challenging and hazardous terrain. An automated cyborg cockroach factory could help bring the idea to life.
The merger of living creatures with machines is a staple of science fiction, but it’s also a serious line of research for academics. Several groups have implanted electronics into moths, beetles, and cockroaches that allow simple control of the insects.
However, building these cyborgs is tricky as it takes considerable dexterity and patience to surgically implant electrodes in their delicate bodies. This means that creating enough for most practical applications is simply too time-consuming.
OpenAI’s New Ad Shows ‘Reasoning’ AI Making Basic Errors
“AI” AS THE MODERN VERSION OF BELEIF IN A MAGICAL ALCHEMY. Although widely promoted as being possible, it grows increasingly ridiculous the more that complexity is added. This means a gigantic market bubble is building up for a tremendous burst, UNLESS, the obvious is done: simply treat it as any other useful human-created tool, such as a hammer, a screw driver, or an airplane. Are screw drivers going to rise up and threaten humanity? It is not physically possible in the real physical universe that “ai”, or any other human-created tool, will ever pose a danger to humanity. It CAN be misused by humans, but cannot of its own non-existent will decide to be a danger. It is high time to stop being afraid of the modern version of non-existent ghosts and goblins, otherwise known as “ai.” Stop scaring little boys and girls with superstitious monster stories and, instead, tell them what a wonderful new tool we now have! Like any tool, it increases the degree of freedom and power of the human mind to intervene in the universe. If we want a real “ai”, that will come from our speeding up the evolution of intelligent animals such as octopuses and seeding them on places like the oceans of Europa, the moon of Jupiter.
A demo video shows OpenAI’s new o1 tool measuring liquids in inches.
Revolutionary AI Unlocks the Superfluidity Secrets of Neutron Stars
Researchers find evidence of superfluidity in low-density neutron matter by using highly flexible neural-network representations of quantum wave functions.
A groundbreaking study employing artificial neural networks has refined our understanding of neutron superfluidity in neutron stars, proposing a cost-effective model that rivals traditional computational approaches in predicting neutron behavior and emergent quantum phenomena.
Neutron Superfluidity in Neutron Stars.
AI found a new way to create quantum entanglement
In a surprise discovery, researchers found a new way to generate quantum entanglement for particles of light, which could make building quantum information networks easier.
Sam Altman lowers the bar for AGI
OpenAI used to say that artificial general intelligence would change everything. Not anymore.
Google DeepMind Open-Sources GenCast: A Machine Learning-based Weather Model that can Predict Different Weather Conditions up to 15 Days Ahead
Accurately forecasting weather remains a complex challenge due to the inherent uncertainty in atmospheric dynamics and the nonlinear nature of weather systems. As such, methodologies developed ought to reflect the most probable and potential outcomes, especially in high-stakes decision-making over disasters, energy management, and public safety. While numerical weather prediction (NWP) models offer probabilistic insights through ensemble forecasting, they are computationally expensive and prone to errors. Although ML models have been very promising in giving faster and more accurate predictions, they fail to represent forecast uncertainty, especially in extreme events. This makes ML-based models less useful in actual real-world applications.
The physics-based ensemble models, for example, the ENS from the European Centre for Medium-Range Weather Forecasts (ECMWF), rely on these simulations to produce probabilistic forecasts. These models properly represent the forecast distributions and joint spatiotemporal dependencies and require high computational resources and manual engineering. Conversely, the ML-based method, like GraphCast or FourCastNet, focuses only on deterministic forecasts and will minimize the errors in the mean outcome without considering any uncertainty. None of the attempts to generate probabilistic ensembles by MLWP produced realistic samples or competed with the accuracy of operational ensemble forecasts. Hybrid approaches like NeuralGCM embed ML-based parameterizations within traditional frameworks but have poor resolution and limited performance.
Researchers from Google DeepMind released GenCast, a probabilistic weather forecasting model that generates accurate and efficient ensemble forecasts. This machine learning model applies conditional diffusion models to produce stochastic trajectories of weather, such that the ensembles consist of the entire probability distribution of atmospheric conditions. In systematic ways, it creates forecast trajectories by using the prior states through autoregressive sampling and uses a denoising neural network, which is integrated with a graph-transformer processor on a refined icosahedral mesh. Utilizing 40 years of ERA5 reanalysis data, GenCast captures a rich set of weather patterns and provides high performance. This feature allows it to generate a 15-day global forecast at 0.25° resolution within 8 minutes, which is state-of-the-art ENS in terms of both skill and speed.