The Download: America’s gun crisis, and how AI video models work

The Download: America's Gun Crisis and the Rise of AI Video Models A recent report from the Trump administration highlighted a glaring omission in its strategy to improve the health and well-being of American children. The leading cause of death for American children and teenagers is not ultraprocessed food or exposure to chemicals, but gun violence. This disconnect was thrown into sharp relief by news of high-profile shootings at schools in the US. According to Dr. Jessica Hamzelou, a biotech expert who wrote about this issue in MIT Technology Review's weekly newsletter, "Experts believe it is time to treat gun violence in the US as what it is: a public health crisis." This perspective is echoed by many, including Dr. Garen Wintemute, a professor of emergency medicine at the University of California, Davis, who has studied gun violence extensively. Meanwhile, AI video models have been making headlines with their ability to generate realistic videos that can be used for a variety of purposes, from entertainment to surveillance. But how do these models work? According to Dr. Ian Goodfellow, a researcher at Google Brain and one of the inventors of Generative Adversarial Networks (GANs), "AI video models use a type of machine learning called deep learning to generate videos that are indistinguishable from real ones." Background on AI Video Models --------------------------- AI video models have been around for several years, but they have gained significant attention in recent times due to their ability to generate highly realistic videos. These models work by using a combination of algorithms and large datasets to learn the patterns and structures of real-world videos. The most common type of AI video model is the Generative Adversarial Network (GAN), which was invented by Dr. Ian Goodfellow in 2014. GANs consist of two neural networks: a generator that produces synthetic data, and a discriminator that evaluates the generated data and tells the generator whether it's realistic or not. Implications for Society ---------------------- The rise of AI video models has significant implications for society, both positive and negative. On the one hand, they can be used to create highly realistic videos for entertainment purposes, such as movies and TV shows. They can also be used for surveillance and security purposes, such as monitoring traffic or detecting anomalies. On the other hand, AI video models can also be used to create deepfakes, which are videos that are manipulated to make it look like someone is saying or doing something they're not. This has raised concerns about the potential for misuse of these models, particularly in the context of politics and propaganda. Current Status and Next Developments ----------------------------------- The development of AI video models is an ongoing process, with researchers continually pushing the boundaries of what's possible. In recent times, there have been significant advancements in the field, including the development of more sophisticated GANs that can generate highly realistic videos. Looking ahead, it's likely that we'll see even more advanced AI video models in the future, which will be capable of generating videos that are increasingly indistinguishable from real ones. This raises important questions about the potential implications for society and the need for regulation and oversight. Sources: Dr. Jessica Hamzelou, biotech expert at MIT Technology Review Dr. Garen Wintemute, professor of emergency medicine at the University of California, Davis Dr. Ian Goodfellow, researcher at Google Brain Note: This article is based on a recent edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. *Reporting by Technologyreview.*