DeepBrain AI’s industry-first approach to “humanising” AI assistants provides users with an experience that is familiar, enlightening and approachable. Its video synthesis solutions, a CES 2022 Innovation Awards Winner, leverages the power of Artificial Intelligence to quickly create lifelike human-based AI avatars that inform, solve and guide users through thousands of possible scenarios and real-time interactions.
“Our AI avatars are uniquely developed from real people, using their real voices, physical appearances, gestures and regional dialects,” says the company. “We work in a wide range of industries and our AI solution is used by companies like 7-Eleven, KB Bank, LG HV, and Roche.
DeepBrain AI is one of the top three global companies that possess both deep learning-based video synthesis and voice synthesis source technology. Read More
Daily Archives: January 7, 2022
AI Researchers Portal
Connecting AI researchers to Federal resources that can support their AI work – from grant funding and datasets to computing and testbeds. The National AI Initiative Office’s official site for AI researchers to access datasets, computing resources, and federal grant information. Read More
#artificial-intelligence, #dod, #icNvidia’s upgraded AI art tool turned my obscure squiggles into a masterpiece
It’s incredible, the things we can do with AI nowadays. For artists looking to integrate artificial intelligence into their workflow, there are ever more advanced tools popping up all over the net. One such tool is Nvidia Canvas, which has just been updated with the more powerful GauGAN2 AI, to replace the original GauGAN model, along with loads of new features.
The Nvidia Canvas software is available for free to anyone with an Nvidia RTX graphics card. This is because the software uses the tensor cores present in your GPU to let the AI do it’s job. Read More
Trends in AI Research for the Visual Surveillance of Populations
Since 2014, computer vision models have dramatically improved their performance on benchmarks for image classification, image generation, facial recognition, and other tasks. As these examples show, computer vision researchers aim to solve a wide variety of problems. Yet previous bibliometric studies have examined international output of computer vision research as a whole, without distinguishing among these many research tasks. In principle, analyses of academic research can inform us about the global growth of research and the interests and incentives of each nation’s researchers. But only a small segment of computer vision research may relate to any particular area of interest. In this brief, we focus on “visual surveillance research,” the development of algorithms such as facial recognition that could be used to surveil individuals or groups.
These algorithms are often applied for benign, commercial uses, such as tagging individuals in social media photos. But progress in computer vision could also empower some governments to use surveillance technology for repressive purposes.
Using a dataset of English-language papers published between 2015 and 2019, we applied natural language processing methods to identify the computer vision papers in this corpus and the research tasks they described. Read More
Rick's Cafe AI 