Lifeboat Foundation

Mandopop singer Stefanie Sun has gone viral on Bilibili, China’s largest user-generated video streaming site. But the sudden revival of interest in Sun, who hasn’t released an album since 2017, comes not from the artist having another moment of genius.

The songs that have attracted millions of views on Bilibili feature Sun’s voice cloned by artificial intelligence, raising questions about copyright protection.

Reminiscing on the golden age of Mandarin pop music, tech-savvy Chinese internet users took the liberty of mimicking Sun’s voice using singing voice conversion, a deep learning method that lets a user deliver one person’s singing in another person’s voice, and swap it into a compilation of Mandpop classics.

An innovative method decodes and finds hidden relationships in jointly recorded neural and behavioural data.

Amid all the hype and hysteria about ChatGPT, Bard, and other generative large language models (LLMs), it’s worth taking a step back to look at the gamut of AI algorithms and their uses. After all, many “traditional” machine learning algorithms have been solving important problems for decades—and they’re still going strong. Why should LLMs get all the attention?

Before we dive in, recall that machine learning is a class of methods for automatically creating predictive models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature of the data.

In the next section, I’ll briefly survey the different kinds of machine learning and the different kinds of machine learning models. Then I’ll discuss 14 of the most commonly used machine learning and deep learning algorithms, and explain how those algorithms relate to the creation of models for prediction, classification, image processing, language processing, game-playing and robotics, and generative AI.

TerraPower, founded by billionaire and Microsoft co-founder Bill Gates in 2008, is opening a new nuclear power plant in Kemmerer, Wyoming. The plant will be the first of its kind, with the company hoping to revolutionize the nuclear energy industry in the U.S. to help fight climate change and support American energy independence.

“Nuclear energy, if we do it right, will help us solve our climate goals,” Gates told ABC News. “That is, get rid of the greenhouse gas emissions without making the electricity system far more expensive or less reliable.”

Gates met with ABC News’ chief business, economics, and technology correspondent Rebecca Jarvis in Kemmerer to talk about the project.

Shap-E is a model that allows you to generate 3D objects from text, building off OpenAI’s other AI tools.

The Work Trend Index report found that leaders believe analytical judgment, flexibility and emotional intelligence are essential skills for the future of work.

Scientists at UT Austin conducted a study where they created a 3D view of a person’s mind and used artificial intelligence to decode brain activity into dialogue.

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Researchers have engineered a robotic lionfish with synthetic arteries, similar to those found in a human’s circulatory system. The fish “blood” that runs through it serves as both the robot’s power source and controls its movement. The findings, published Wednesday in Nature, may propel the new wave of soft robots, in which inventors seek to improve lifelike automated machines for human connection.

Synthetic blood vessels in a new robotic fish could improve the technology needed to make lifelike robots run longer.

Summary: Researchers have identified spatial and temporal abnormalities in spontaneous fixational saccades as a potential biomarker for cognitive and positive symptoms in schizophrenia. Researchers combined patient data of fixational eye movement recorded over 60 seconds with machine learning technology, which was able to determine schizophrenia with 85% accuracy. This discovery suggests that fixational saccades could serve as an easily obtainable complementary diagnostic tool for the condition.

Source: Chinese Academy of Science.

In a study published online in the Schizophrenia Bulletin, researchers from Dr. Wang Wei’s lab at the Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences, and Dr. Wang Jijun’ team at Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, demonstrated spatial and temporal abnormalities of spontaneous fixational saccades and their correlates with positive and cognitive symptoms in schizophrenia, suggesting that fixational saccades are a promising and easily obtainable biomarker for cognitive and positive symptoms and for complementary diagnosis in schizophrenia.