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Blog – Page 8898

(Bloomberg) — For years, wind and solar power were derided as boondoggles. They were too expensive, the argument went, to build without government handouts.

Today, renewable energy is so cheap that the handouts they once needed are disappearing.

On sun-drenched fields across Spain and Italy, developers are building solar farms without subsidies or tax-breaks, betting they can profit without them. In China, the government plans to stop financially supporting new wind farms. And in the U.S., developers are signing shorter sales contracts, opting to depend on competitive markets for revenue once the agreements expire.

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A new technology using artificial intelligence detects depressive language in social media posts more accurately than current systems and uses less data to do it.

The technology, which was presented during the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, is the first of its kind to show that, to more accurately detect depressive language, small, high-quality data sets can be applied to deep learning, a commonly used AI approach that is typically data intensive.

Previous psycholinguistic research has shown that the words we use in interaction with others on a daily basis are a good indicator of our mental and emotional state.

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This is the final part in a series of in-depth articles examining China’s efforts to build a stronger domestic semiconductor industry amid rising trade tensions.

Some in China see custom AI chips, which can offer superior performance to conventional integrated circuits even when manufactured using older processes, as helping the country loosen its dependence on the US in core technology.

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Combining new classes of nanomembrane electrodes with flexible electronics and a deep learning algorithm could help disabled people wirelessly control an electric wheelchair, interact with a computer or operate a small robotic vehicle without donning a bulky hair-electrode cap or contending with wires.

By providing a fully portable, wireless brain-machine interface (BMI), the wearable system could offer an improvement over conventional electroencephalography (EEG) for measuring signals from visually evoked potentials in the . The system’s ability to measure EEG signals for BMI has been evaluated with six human subjects, but has not been studied with disabled individuals.

The project, conducted by researchers from the Georgia Institute of Technology, University of Kent and Wichita State University, was reported on September 11 in the journal Nature Machine Intelligence.

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