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Artificial Intelligence's next invasion will be your voice

Voice imitation can be fun but a perfect cloning can turn creepy. Now, a Montreal-based AI company called Lyrebird has built a software that is capable of cloning any voice.

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Voice imitation can be fun but a perfect cloning can turn creepy. Now, a Montreal-based AI company called Lyrebird has built a software that is capable of cloning any voice.

The technology has already helped people including Pat Quinn, founder of the Ice Bucket Challenge who suffers from a motor neuron disease which took his voice, Cnet reported. Lyrebird's voice cloning algorithm is capable of not only mimicking human voice but also use some real-world audio samples to add an emotional aspect to sound even more realistic. 

Stanford scientists have also developed an artificial intelligence (AI) system which can predict and track potential side effects from drug combinations. The system, called Decagon, could help doctors make better decisions about which drugs to prescribe and help researchers find better combinations of drugs to treat complex diseases, said Marinka Zitnik, a postdoctoral fellow at Stanford University in the US.

Once available to doctors in a more user-friendly form, Decagon's predictions would be an improvement over what is available now, which essentially comes down to chance - a patient takes one drug, starts taking another and then develops a headache or worse. There are about 1,000 different known side effects and 5,000 drugs on the market, making for nearly 125 billion possible side effects between all possible pairs of drugs.

Most of these have never been prescribed together, let alone systematically studied. Zitnik, Monica Agrawal, a master's student at Stanford, and colleagues realised they could get around that problem by studying how drugs affect the underlying cellular machinery in our body. They composed a massive network describing how the more than 19,000 proteins in our bodies interact with each other and how different drugs affect these proteins.

Using more than four million known associations between drugs and side effects, the team then designed a method to identify patterns in how side effects arise based on how drugs target different proteins.
The team turned to deep learning, a kind of artificial intelligence modelled after the brain. In essence, deep learning looks at complex data and extracts from them abstract, sometimes counter-intuitive patterns in the data.

With inputs from ANI

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