We are already seeing the rise of Artificial intelligence applications across all industries.

Artificial Intelligence (AI), IoT, big data, augmented and virtual reality, and 3D and 4D printing are becoming essential and familiar elements in …

… technological advances and applications in areas such as artificial intelligence, … Internet of Things, 5G and machine learning startups.

This includes blockchain, artificial intelligence, machine learning and data mining technologies. The camera does not lie.

Developers Can Now Use ONNX Runtime (Machine Learning Inference Engine) … Marktechpost, LLC. is a California-based Artificial Intelligence News …

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. On the other hand, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed {\em FairRec} algorithm guarantees Maxi-Min Share ($\alpha$-MMS) of exposure for the producers, and Envy-Free up to One Item (EF1) fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of {\em FairRec} in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.

I explain Artificial Intelligence terms and news to non-experts. While the world is still recovering, research hasn't slowed its frenetic pace, especially in the field of artificial intelligence. More, many important aspects were highlighted this year, like the ethical aspects, important biases, governance, transparency and much more. Artificial intelligence and our understanding of the human brain and its link to AI are constantly evolving, showing promising applications improving our life's quality in the near future. Still, we ought to be careful with which technology we choose to apply. "Science cannot tell us what we ought to do, only what we can do."

Text-to-image generation has been one of the most active and exciting AI fields of 2021. In January, OpenAI introduced DALL-E, a 12-billion parameter version of the company's GPT-3 transformer language model designed to generate photorealistic images using text captions as prompts. An instant hit in the AI community, DALL-E's stunning performance also attracted widespread mainstream media coverage. Last month, tech giant NVIDIA released the GAN-based GauGAN2 -- the name taking inspiration from French Post-Impressionist painter Paul Gauguin as DALL-E had from Surrealist artist Salvador Dali. Not to be outdone, OpenAI researchers this week presented GLIDE (Guided Language-to-Image Diffusion for Generation and Editing), a diffusion model that achieves performance competitive with DALL-E while using less than one-third of the parameters.

Only, it was a deepfake. So was the video of Donald Trump taunting Belgium for remaining in the Paris climate agreement and Barack Obama's public service announcement as posted by Buzzfeed. These great examples of deepfakes are the 21st Century's answer to Photoshopped images and videos. Synthetic media, deepfakes, use artificial intelligence (AI) -- deep learning technology, to replace an existing person in an image or video with someone else. One reason for the widespread use of deepfake technology in popular celebrities is that these personalities have a large number of pictures available on the internet, allowing AI to train and learn from.

Catalysts are materials that trigger or speed up chemical reactions.