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Category: robotics/AI – Page 1,472

AI is the next frontier — but for whom?

A few weeks.

Discussions about diversity are more important than ever as AI enters a new golden era. Every new technology that appears seems to be accompanied by some harrowing consequence.

A few weeks ago, a founder told me it took three hours of endless clicking to find an AI-generated portrait of a Black woman. It reminded me, in some ways, of a speech I saw three years ago when Yasmin Green, the then-director of research and development for Jigsaw, spoke about how human bias seeps into the programming of AI. Her talk and this founder, miles away and years apart, are two pieces of the same puzzle.

Real pigeons can solve diabolically difficult problems

‘One’s pigeon’ means an area of special concern, expertise or responsibility.

And for pigeons, their area of expertise is sorting.

Just as AI systems are trained from large data sets, the birds can use ‘brute force’, or repetitive trial and error – to identify and sort patterns and objects, quickly becoming highly proficient at their task.

Samsung hires former Qualcomm VP who is a semiconductor expert

Samsung has been on a hiring spree of top executives from reputed companies in the last few months. About a week ago, we reported that Samsung had hired two former Ericsson executives to its Samsung Networks team. Last December, Samsung roped in an ex-Mercedes designer, Hubert H. Lee, to lead its smartphone design team. Now, Samsung has now hired former Qualcomm Vice President Benny Katibian.

Benny previously worked for Qualcomm as the Vice President of the company’s engineering division. After joining Samsung, Benny will serve as the head of the Samsung Austin Research Center (SARC) and the Advanced Computing Lab (ACL), which are the core R&D centers of Samsung Electronics USA, as per Business Korea.

According to reports, Benny Katibian is an expert in semiconductors and was in charge of self-driving systems, including ADAS (Advanced Driving Assistance Systems) at Qualcomm. Later, he also served as the COO of the North American Cooperation in Xiaopeng, a Chinese electric car company. He also worked as the head of the development of self-driving chips.

Using deep learning to detect depression from speech

Artificial intelligence (AI) tools have achieved promising results on numerous tasks and could soon assist professionals in various settings. In recent years, computer scientists have been exploring the potential of these tools for detecting signs of different physical and psychiatric conditions.

Depression is one of the most widespread psychiatric disorders, affecting approximately 9.5% of American adults every year. Tools that can automatically detect signs of depression might help to reduce suicide rates, as they would allow doctors to promptly identify people in need of psychological support.

Researchers at Jinhua Advanced Research Institute and Harbin University of Science and Technology have recently developed a deep learning algorithm that could detect depression from a person’s speech. This model, introduced in a paper published in Mobile Networks and Applications, was trained to recognize emotions in by analyzing different relevant features.

Deep learning for quantum sensing

Quantum sensing represents one of the most promising applications of quantum technologies, with the aim of using quantum resources to improve measurement sensitivity. In particular, sensing of optical phases is one of the most investigated problems, considered key to developing mass-produced technological devices.

Optimal usage of quantum sensors requires regular characterization and calibration. In general, such calibration is an extremely complex and resource-intensive task—especially when considering systems for estimating multiple parameters, due to the sheer volume of required measurements as well as the computational time needed to analyze those measurements. Machine-learning algorithms present a powerful tool to address that complexity. The discovery of suitable protocols for algorithm usage is vital for the development of sensors for precise quantum-enhanced measurements.

A particular type of machine-learning algorithm known as “reinforcement learning” (RL) relies on an intelligent agent guided by rewards: Depending on the rewards it receives, it learns to perform the right actions to achieve the desired optimization. The first experimental realizations using RL algorithms for the optimization of quantum problems have been reported only very recently. Most of them still rely on prior knowledge of the model describing the system. What is desirable is instead a completely model-free approach, which is possible when the agent’s reward does not depend on the explicit system model.

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