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Category: robotics/AI – Page 127

AI outperforms humans in emotional intelligence tests, study finds

Is artificial intelligence (AI) capable of suggesting appropriate behavior in emotionally charged situations? A team from the University of Geneva (UNIGE) and the University of Bern (UniBE) put six generative AIs—including ChatGPT—to the test using emotional intelligence (EI) assessments typically designed for humans.

The outcome: these AIs outperformed average human performance and were even able to generate new tests in record time. These findings open up new possibilities for AI in education, coaching, and . The study is published in Communications Psychology.

Large language models (LLMs) are (AI) systems capable of processing, interpreting and generating human language. The ChatGPT generative AI, for example, is based on this type of model. LLMs can answer questions and solve complex problems. But can they also suggest emotionally intelligent behavior?

Scientists use AI and X-ray vision to gain insight into zinc-ion battery electrolyte

A team of scientists from the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and Stony Brook University (SBU) used artificial intelligence (AI) to help them understand how zinc-ion batteries work—and potentially how to make them more efficient for future energy storage needs.

Their study, published in the journal PRX Energy, focused on the water-based electrolyte that shuttles electrically charged through the during charging and use. The AI model tapped into how those charged ions interact with water under varying concentrations of zinc chloride (ZnCl2), a form of salt with high solubility in water.

The AI findings, validated by experiments at Brookhaven Lab’s National Synchrotron Light Source II (NSLS-II), show why high salt concentrations produce the best battery performance.

Large language models are proficient in solving and creating emotional intelligence tests

Six Large Language Models outperformed humans on five ability emotional intelligence tests. ChatGPT-4 also successfully generated new test items for each test, with the AI-created versions showing psychometric properties similar to the originals.

Androids: Androids, machines that look and act human, are rapidly leaving the realms of science fiction and entering science fact

In this episode we explore their uses, dangers, ethical dilemmas, and more.

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Cover Art by Jakub Grygier: https://www.artstation.com/artist/jakub_grygier.

Graphics Team:
Edward Nardella.
Jarred Eagley.
Justin Dixon.
Katie Byrne.
Kris Holland of Mafic Stufios: www.maficstudios.com.
Misho Yordanov.
Pierre Demet.
Sergio Botero: https://www.artstation.com/sboterod?fref=gc.
Stefan Blandin.

Script Editing:
Andy Popescu.
Connor Hogan.
Edward Nardella.
Eustratius Graham.
Gregory Leal.
Jefferson Eagley.
Luca de Rosa.
Mark Warburton.
Michael Gusevsky.
Mitch Armstrong.
MolbOrg.
Naomi Kern.
Philip Baldock.
Sigmund Kopperud.
Steve Cardon.
Tiffany Penner.

Music:
AJ Prasad, “Cold Shadows“
Lee Rosevere, “It’s such a beautiful day“
Kai Engel, “Morbid Imagination“
Sergey Cheremisinov, “Jump in Infinity“
Markus Junnikkala, “A Memory of Earth“
Kai Engel, “Crying Earth“
Sergey Cheremisinov, “Labyrinth“
Brandon Liew, “Into the Storm”

Futurist Ray Kurzweil’s Humanoid Robotics Startup Eyes a $500M Valuation

Kurzweil co-founded Beyond Imagination in 2018 with Harry Floor, a scientist and film producer, to develop autonomous A.I. systems capable of physical labor. The company is building humanoid robots aimed at addressing labor shortages in sectors such as health care and agriculture. Its advisory board includes motivational speaker Tony Robbins, former Qualcomm CEO Paul Jacobs, and former Paramount Pictures CEO James Gianopulos.

Between 2018 and 2019, the startup raised $4.2 million in seed funding and was most recently valued at $25 million, per Crunchbase. Reuters reported that its upcoming valuation could reach $500 million, with Gauntlet Ventures—a Dallas-based venture capital firm—expected to be the sole investor in the new round.

Using a fermionic neural network to find the ground state of fractional quantum Hall liquids

When two-dimensional electron systems are subjected to magnetic fields at low temperatures, they can exhibit interesting states of matter, such as fractional quantum Hall liquids. These are exotic states of matter characterized by fractionalized excitations and the emergence of interesting topological phenomena.

Researchers at Cavendish Laboratory and Massachusetts Institute of Technology (MIT) set out to better understand these fascinating states using machine learning, specifically employing a newly developed attention-based fermionic (FNN).

The method they developed, outlined in a paper published in Physical Review Letters, was trained to find the lowest-energy quantum state (i.e., ground state) of fractional quantum Hall liquids.

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