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Category: robotics/AI – Page 26

Currents 072: Ben Goertzel on Viable Paths to True AGI

https://www.jimruttshow.com/currents-ben-goertzel-2/

Jim talks with Ben Goertzel about the ideas in his recent essay “Three Viable Paths to True AGI.” They discuss the meaning of artificial general intelligence, Steve Wozniak’s basic AGI test, whether common tasks actually require AGI, a conversation with Joscha Bach, why deep neural nets are unsuited for human-level AGI, the challenge of extrapolating world-models, why imaginative improvisation might not be interesting to corporations, the 3 approaches that might have merit (cognition-level, brain-level, and chemistry-level), the OpenCog system Ben is working on, whether it’s a case of “good old-fashioned AI,” where evolution fits into the approach, why deep neural nets aren’t brain simulations & attempts to make them more realistic, a hypothesis about how to improve generalization, neural nets for music & the psychological landscape of AGI research, algorithmic chemistry & the origins of life problem, why AGI deserves more resources than it’s getting, why we may need better parallel architectures, how & how much society should invest in new approaches, the possibility of a cultural shift toward AGI viability, and much more.

Innovation. An AI tool that analyzes cough sounds to detect respiratory diseases

The platform can also hint at the type of respiratory issue involved, classifying cases as normal, obstructive, restrictive, or mixed. Obstructive patterns commonly appear in asthma and chronic obstructive pulmonary disease (COPD), while restrictive patterns are often linked to conditions such as pulmonary fibrosis.

The technology draws on the idea that cough sounds carry meaningful diagnostic clues. The researchers used a platform to classify the cases as ‘risk yes’ or ‘risk no’. When compared with physicians’ assessments, the model achieved a sensitivity of 97.27%. There was also strong agreement between the patterns identified by pulmonologists and the findings generated by the new tool.

Advances in AI have renewed interest in cough sound analysis as an accessible pre-screening method. Machine-learning models trained on large datasets can detect patterns associated with tuberculosis, Covid-19, asthma, and COPD, and can be built into portable devices or mobile apps for use in community settings.

New AI-based technology offers real-time electric vehicle state estimation for safer driving

A research team led by Professor Kanghyun Nam from the Department of Robotics and Mechanical Engineering at DGIST has developed a physical AI-based vehicle state estimation technology that accurately estimates the driving state of electric vehicles in real time.

This technology is viewed as a key advancement that can improve the core control performance of electric vehicles and greatly enhance the safety of autonomous vehicles. The work was conducted through international joint research with Shanghai Jiao Tong University in China and the University of Tokyo in Japan.

The work is published in the journal IEEE Transactions on Industrial Electronics.

New memristor-based converter boosts energy efficiency in AI hardware

A cross-institutional team led by researchers from the Department of Electrical and Electronic Engineering (EEE), under the Faculty of Engineering at The University of Hong Kong (HKU), have achieved a major breakthrough in the field of artificial intelligence (AI) hardware by developing a new type of analog-to-digital converter (ADC) that uses innovative memristor technology. The work is published in Nature Communications.

Challenges with conventional AI hardware Conventional AI accelerators face challenges because the essential components that convert analog signals into digital form are often bulky and power-consuming. Led by Professor Ngai Wong, Professor Can Li and Dr. Zhengwu Liu of HKU EEE, in collaboration with researchers from Xidian University and the Hong Kong University of Science and Technology, the cross-disciplinary research team developed a new type of ADC that uses innovative memristor technology. This new converter can process signals more efficiently and accurately, paving the way for faster, more energy-efficient AI chips.

Adaptive system and efficiency gains The research team created an adaptive system that automatically adjusts its settings based on the data it receives, i.e., dynamically fine-tuning how signals are converted. This results in a 15.1× improvement in energy efficiency and a 12.9× reduction in circuit area compared with state-of-the-art solutions.

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Toward high entropy material discovery for energy applications using computational and machine learning methods

npj Computational Materials, Article number: (2025) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

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