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Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

arXiv.org Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.


Nvidia plans for a more robust Omniverse with avatars, synthetic data

ZDNet

Omniverse Replicator is a simulation framework that produces physically accurate synthetic data to accelerate training of deep neural networks for AI applications. NVIDIA has created Omniverse Replicators for DRIVE Sim - for training of AI perception networks for autonomous vehicles - and for Isaac Sim, for training robots. As enterprises prepare to bring more of their business and operations to the virtual world, Nvidia is building out Omniverse, its platform for extending workflows into the virtual sphere. The latest updates to the platform, introduced during GTC 2021, include Omniverse Avatar, a tool for creating embodied AIs, as well as Omniverse Replicator, a synthetic data-generation engine. Nvidia rolled out Omniverse in open beta last December -- nearly a year before Facebook committed to the concept of a "metaverse" by renaming itself Meta.