Summary: Researchers have developed a new AI algorithm that prevents smart devices such as Alexa or Siri from correctly hearing your words 80% of the time. The algorithm is a step toward providing personal agency in protecting the privacy of their voice in the presence of smart devices. Ever noticed online ads following you that are eerily close to something you've recently talked about with your friends and family? Microphones are embedded into nearly everything today, from our phones, watches, and televisions to voice assistants, and they are always listening to you. Computers are constantly using neural networks and AI to process your speech, in order to gain information about you.

Ever noticed online ads following you that are eerily close to something you've recently talked about with your friends and family? Microphones are embedded into nearly everything today, from our phones, watches, and televisions to voice assistants, and they are always listening to you. Computers are constantly using neural networks and AI to process your speech, in order to gain information about you. If you wanted to prevent this from happening, how could you go about it? Back in the day, as portrayed in the hit TV show "The Americans," you would play music with the volume way up or turn on the water in the bathroom.

Ever noticed online ads following you that are eerily close to something you've recently talked about with your friends and family? Microphones are embedded into nearly everything today, from our phones, watches, and televisions to voice assistants, and they are always listening to you. Computers are constantly using neural networks and AI to process your speech, in order to gain information about you. If you wanted to prevent this from happening, how could you go about it? Back in the day, as portrayed in the hit TV show "The Americans," you would play music with the volume way up or turn on the water in the bathroom.

Predicting what someone is about to do next based on their body language comes naturally to humans but not so for computers. When we meet another person, they might greet us with a hello, handshake, or even a fist bump. We may not know which gesture will be used, but we can read the situation and respond appropriately. In a new study, Columbia Engineering researchers unveil a computer vision technique for giving machines a more intuitive sense for what will happen next by leveraging higher-level associations between people, animals, and objects. "Our algorithm is a step toward machines being able to make better predictions about human behavior, and thus better coordinate their actions with ours," said Carl Vondrick, assistant professor of computer science at Columbia, who directed the study, which was presented at the International Conference on Computer Vision and Pattern Recognition on June 24, 2021.

Learning to represent and generate videos from unlabeled data is a very challenging problem. To generate realistic videos, it is important not only to ensure that the appearance of each frame is real, but also to ensure the plausibility of a video motion and consistency of a video appearance in the time direction. The process of video generation should be divided according to these intrinsic difficulties. In this study, we focus on the motion and appearance information as two important orthogonal components of a video, and propose Flow-and-Texture-Generative Adversarial Networks (FTGAN) consisting of FlowGAN and TextureGAN. In order to avoid a huge annotation cost, we have to explore a way to learn from unlabeled data. Thus, we employ optical flow as motion information to generate videos. FlowGAN generates optical flow, which contains only the edge and motion of the videos to be begerated. On the other hand, TextureGAN specializes in giving a texture to optical flow generated by FlowGAN. This hierarchical approach brings more realistic videos with plausible motion and appearance consistency. Our experiments show that our model generates more plausible motion videos and also achieves significantly improved performance for unsupervised action classification in comparison to previous GAN works. In addition, because our model generates videos from two independent information, our model can generate new combinations of motion and attribute that are not seen in training data, such as a video in which a person is doing sit-up in a baseball ground.

Generating videos from text has proven to be a significant challenge for existing generative models. We tackle this problem by training a conditional generative model to extract both static and dynamic information from text. This is manifested in a hybrid framework, employing a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN). The static features, called "gist," are used to sketch text-conditioned background color and object layout structure. Dynamic features are considered by transforming input text into an image filter. To obtain a large amount of data for training the deep-learning model, we develop a method to automatically create a matched text-video corpus from publicly available online videos. Experimental results show that the proposed framework generates plausible and diverse short-duration smooth videos, while accurately reflecting the input text information. It significantly outperforms baseline models that directly adapt text-to-image generation procedures to produce videos. Performance is evaluated both visually and by adapting the inception score used to evaluate image generation in GANs.

Large-scale data gathering and quantum leaps in processing power have set the table for major advancement in artificial intelligence. Yet there's a growing body of evidence that the field of AI is poised to move into a whole new dimension, one where AI not only imagines the real world, but can begin to make accurate decisions on what's real and important, what's not -- and thus predict what's coming next. "Computers are really good at memorization," Carl Vondrick, research scientist at Google Inc., said during a presentation at the Re-Work Deep Learning Summit in San Francisco Thursday. "The problem is teaching them how to forget." Vondrick's research has focused on one of the most vexing challenges in today's online world: how to make use of the massive database of unlabeled videos that clog nearly every corner of the web.

A team of Stanford researchers trained a computer to identify images of skin cancer moles and lesions as accurately as a dermatologist, according to a new paper published in the journal Nature. In the future, this new research suggests, a simple cell phone app may help patients diagnose a skin cancer -- the most common of all cancers in the United States -- for themselves. "Our objective is to bring the expertise of top-level dermatologists to places where the dermatologist is not available," said Sebastian Thrun, senior author of the new study, founder of research and development lab Google X and an adjunct professor at Stanford University. He added that those who live in developing countries do not have the same level of care as can be found in the US and other industrialized nations. Melanomas represent fewer than 5% of all skin malignancies diagnosed in the US, yet they account for nearly three-quarters of all deaths related to this form of cancer.

Even though the phrase "image recognition technologies" conjures visions of high-tech surveillance, these tools may soon be used in medicine more than in spycraft. A team of Stanford researchers trained a computer to identify images of skin cancer moles and lesions as accurately as a dermatologist, according to a new paper published in the journal Nature. In the future, this new research suggests, a simple cell phone app may help patients diagnose a skin cancer -- the most common of all cancers in the United States -- for themselves. "Our objective is to bring the expertise of top-level dermatologists to places where the dermatologist is not available," said Sebastian Thrun, senior author of the new study, founder of research and development lab Google X and an adjunct professor at Stanford University. He added that those who live in developing countries do not have the same level of care as can be found in the US and other industrialized nations.

It sounds like the premise of a science fiction movie, but researchers at MIT's Computer Science and Artificial Intelligence Laboratory are teaching computers to see into the future. They've created an algorithm that can forecast hugs, kisses, and high-fives -- before they even happen. CBC Radio technology columnist Dan Misener explains how computers are getting better and better at understanding and anticipating human behaviour. They've developed an algorithm that can look at a photo of two people and predict what's going to happen next. For instance, if I showed this software a photo of you and me meeting on the street, it can anticipate whether we're likely to hug, kiss, shake hands or high-five.