Traditional algorithms power complicated computational tools like machine learning. A new approach, called algorithms with predictions, uses the power of machine learning to improve algorithms.
The video surprises viewers when itâs revealed that, while the woman on screen is a real person, the main character speaking is an AI. It aims to demonstrate how entertainment studios can leverage AI to create highly convincing romantic encounters. This marks a significant milestone for Sonantic as its technology is now able to recreate subtle emotions and non-speech sounds, while also opening up new creative possibilities for studios.
The voice models, which already express a range of human emotions from happiness to sadness, can now convey subtleties such as flirty, coy, and teasing, amongst other new âStyleâ options. They also have the ability to capture non-speech sounds â such as breaths, scoffs, and laughs. This combination of advances in speech synthesis makes Sonanticâs platform more comprehensive than ever before, helping entertainment studios create life-like performances in record time.
âHuman beings are incredibly complex by nature and our voices play a critical role in helping us connect with the world around us,â said Zeena Qureshi, CEO. âSonantic is committed to capturing the nuances of the human voice, and weâre incredibly proud of these technological breakthroughs that we have brought to life through âWhatâs Her Secret?â. From flirting and giggling, to breathing and pausing, this is the most realistic romantic demo weâve created to date, helping us inch closer to our vision of being the CGI of audio.â
Summary: Training a machine learning algorithm with synthetic data for image classification can rival one trained on traditional datasets.
Source: MIT
Huge amounts of data are needed to train machine-learning models to perform image classification tasks, such as identifying damage in satellite photos following a natural disaster. However, these data are not always easy to come by. Datasets may cost millions of dollars to generate, if usable data exist in the first place, and even the best datasets often contain biases that negatively impact a modelâs performance.
Human Level AI may be here sooner rather than later. As neural networks far surpass that computing power of the human brain, the prospect of a truly general AI becomes reachable. The economic value will be profound as AI will add trillions of dollars into the economy.
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