The future has always held a special type of fascination in our movie experience. Inherent in all future timelines is the promise of what could be as well as the horrors of where we might be headed. As our technology advances further than sci-fi writers of the past could even imagine, so too does our concept of the power in artificial intelligence. Whether it be imbued with the flaws of humanity or a slave to cold, calculated logic, AI has fascinated moviegoers for over half a century. You could argue that the creative landscapes in sci-fi films of the past, along with the ability of life to imitate art, helped dream up the supercomputer in your pocket.

Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we show that it is possible to adopt active illumination to enhance state-of-the-art 2D face recognition approaches with 3D features, while bypassing the complicated task of 3D reconstruction. The key idea is to project over the test face a high spatial frequency pattern, which allows us to simultaneously recover real 3D information plus a standard 2D facial image. Therefore, state-of-the-art 2D face recognition solution can be transparently applied, while from the high frequency component of the input image, complementary 3D facial features are extracted. Experimental results on ND-2006 dataset show that the proposed ideas can significantly boost face recognition performance and dramatically improve the robustness to spoofing attacks.

Social media has greatly enabled people to participate in online activities at an unprecedented rate. However, this unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation. Given the rapidly evolving nature of news events and the limited amount of annotated data, state-of-the-art systems on fake news detection face challenges due to the lack of large numbers of annotated training instances that are hard to come by for early detection. In this work, we exploit multiple weak signals from different sources given by user and content engagements (referred to as weak social supervision), and their complementary utilities to detect fake news. We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances. Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.

Global "Artificial Intelligence (AI) in Drug Discovery Market" 2020 Industry Research Report is a professional and in-depth study on the current state of …

Because of this, the application of artificial intelligence (AI) in the education sector is rising.

For leading potato chip maker Frito-Lay, to be innovative at scale requires artificial intelligence. The company's culinary experts and scientists make …

We're still trying to figure out the best applications for neural networks, machine learning, and all the recent advancements in artificial intelligence. Amongst all the practical research being conducted, there's also lots of frivolous experimentation being done with results that walk the line between fascinating and terrifying. Automated image processing has emerged as a strong suit of artificial neural networks, fuelled in part by decades of everyone sharing photos and selfies of each other on the internet. It's resulted in vast archives of headshots being harvested and used to train AIs to do everything from artificially aging users in novelty mobile apps to generating huge collections of photorealistic headshots of people that don't actually exist. The stock photography industry will never be the same, but Mario Klingemann wondered what would happen if those same artificial neural networks churning out fake headshots were synced to music, generating the most expressive faces when a song's beat is really banging.