AI tools “allow our teams to spend less time on manual, high-effort tasks and reinvest time into building richer worlds and gameplay,” says SIE CEO.
Category: robotics/AI
Joscha Bach on Synthetic Consciousness & Computational Mind
The provided text outlines Joscha Bach theories regarding the nature of synthetic consciousness and the limitations of modern science. Bach posits that human experience is not a direct interaction with reality, but rather a simulated world model constructed by the brain internal software. He defines intelligence as the capacity to build these models in novel environments, suggesting that current artificial intelligence remains incomplete because it lacks genuine self-understanding. Furthermore, he challenges the narrow focus of contemporary academia and traditional neuroscience, arguing that minds are complex information-processing systems that cannot be explained by neural connections alone. Ultimately, these sources present a computational framework for understanding the self as a functional narrative rather than a mystical or purely physical entity.
AI speeds up discovery of next-gen computer chips and electronic materials
An international study team, led by Flinders University in collaboration with Khalifa University UAE, built the machine-learning platform to act like a “smart materials discovery engine,” which is capable of dramatically reducing the time spent on complex computer or lab experiments to test and find new materials for future semiconductors.
Semiconductors are used in high-tech applications from wearable electronics, communication systems and smartphones to medical and LED devices and solar panels.
“The challenge is that there are millions of possible material combinations, and testing them one by one in the laboratory or with complex computer simulations is extremely slow and expensive,” says Flinders University ARC Future Fellow Associate Professor Vi-Khanh Truong, lead author of a new article in ACS Materials Letters, titled “Bayesian optimization-guided discovery of gallium-containing semiconductors with targeted band gaps.”
MIT researchers use AI to uncover atomic defects in materials
In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during the manufacturing process of products like steel, semiconductors, and solar cells to help improve strength, control electrical conductivity, optimize performance, and more.
But even as defects have become a powerful tool, accurately measuring different types of defects and their concentrations in finished products has been challenging, especially without cutting open or damaging the final material. Without knowing what defects are in their materials, engineers risk making products that perform poorly or have unintended properties.
Now, MIT researchers have built an AI model capable of classifying and quantifying certain defects using data from a noninvasive neutron-scattering technique. The model, which was trained on 2,000 different semiconductor materials, can detect up to six kinds of point defects in a material simultaneously, something that would be impossible using conventional techniques alone.
The 2024 Oppenheimer Lecture featuring Andrea Liu
Physical systems that can learn by themselves.
Brains learn and perform an enormous variety of tasks on their own, using relatively little energy. Brains are able to accomplish this without an external computer because their analog constituent parts (neurons) update their connections without knowing what all the other neurons are doing using local rules. We have developed an approach to learning that shares the property that analog constituent parts update their properties via a local rule, but does not otherwise emulate the brain. Instead, we exploit physics to learn in a far simpler way. Our collaborators have implemented this approach in the lab, developing physical systems that learn and perform machine learning tasks on their own with little energy cost. These systems should open up the opportunity to study how many more is different within a new paradigm for scalable learning.
Futurists Don’t Have Crystal Balls: How to Hire a Futurist Keynote Speaker
In 1933, Franklin Roosevelt assembled what was then the most credentialed group of forecasters in the world. He called it the Brain Trust.
He asked them to map the next 25 years.
They missed transistors. They missed atomic energy. They missed antibiotics. They missed faster-than-sound travel. They missed space probes. They missed World War II.
I have spent the last 16 years interviewing 300 of the most credentialed futurists alive. From Ray Kurzweil to Peter Diamandis to Marvin Minsky to Sir Martin Rees.
They agree on almost nothing.
That is my report from inside the room.
There is now a professional class that sells certainty about an inherently uncertain thing. Call it the crystal ball industry. The product is confidence. The buyer is the anxious executive. The medium is the keynote stage.