Sentient or not, MineDojo got Nvidia's researchers the NeurIPS 2022 Outstanding Datasets and Benchmarks Paper Award, demonstrating their belief that large language models in the future will be "embodied agents that proactively take actions, endlessly explore the world, and continuously self-improve." The current version of this AI agent learned Minecraft's flexible gameplay using a massive online database of more than 7,000 wiki pages, millions of Reddit threads and 300,000 hours of recorded gameplay.

This article is from Big Technology, a newsletter by Alex Kantrowitz. A chatbot that meets the hype is finally here. On Thursday, OpenAI released ChatGPT, a bot that converses with humans via cutting-edge artificial intelligence. The bot can help you write code, compose essays, dream up stories, and decorate your living room. And that's just what people discovered on day one.

India's goods and services tax (GST) collections rose 11% in November from a year earlier to ₹1.46 lakh crore, adding to other data released on Thursday that showed economic resilience despite rising headwinds. The administrator at Anil Ambani-owned Reliance Capital has told lenders that the highest bid received for the insolvent financial services company is 60% below its aggregate liquidation value, making it imperative to seek improved plans from bidders, two people aware of the development told ET. Download The Economic Times News App to get Daily Market Updates & Live Business News.

Anritsu's MX800010A-026 software option for the "Radio Communication Test Station" MT8000A, uses machine learning to optimize 5G mmWave 3D EIS …

According to Technavio, the global deep learning market size is projected to grow by USD 11113.13 million from 2022 to 2027.

NEW YORK, Dec. 2, 2022 /PRNewswire/ — According to Technavio, the global deep learning market size is projected to grow by USD 11113.13 million …

Wu will discuss current and potential bioimage analysis problems, and several deep learning platforms used within the bioimage analysis community …

Object detection has been an important task in the computer vision domain in recent decades. The goal is to detect instances of objects, such as humans, cars, etc., in digital images. Hundreds of methods have been developed to answer a single question: What objects are where? Traditional methods tried to answer this question by extracting hand-crafted features like edges and corners within the image. Most of these approaches used a sliding-window approach, meaning that they kept checking small parts of the image in different scales to see if any of these parts contained the object they were looking for.

Reading, much like music listening, is an immersive experience that transports readers while taking them on an emotional journey. Listening to complementary music has the potential to amplify the reading experience, especially when the music is stylistically cohesive and emotionally relevant. In this paper, we propose the first fully automatic method to build a dense soundtrack for books, which can play high-quality instrumental music for the entirety of the reading duration. Our work employs a unique text processing and music weaving pipeline that determines the context and emotional composition of scenes in a chapter. This allows our method to identify and play relevant excerpts from the soundtrack of the book's movie adaptation. By relying on the movie composer's craftsmanship, our book soundtracks include expert-made motifs and other scene-specific musical characteristics. We validate the design decisions of our approach through a perceptual study. Our readers note that the book soundtrack greatly enhanced their reading experience, due to high immersiveness granted via uninterrupted and style-consistent music, and a heightened emotional state attained via high precision emotion and scene context recognition.

The increase in the number of unmanned aerial vehicles a.k.a. drones pose several threats to public privacy, critical infrastructure and cyber security. Hence, detecting unauthorized drones is a significant problem which received attention in the last few years. In this paper, we present our experimental work on three drone detection methods (i.e., acoustic detection, radio frequency (RF) detection, and visual detection) to evaluate their efficacy in both indoor and outdoor environments. Owing to the limitations of these schemes, we present a novel encryption-based drone detection scheme that uses a two-stage verification of the drone's received signal strength indicator (RSSI) and the encryption key generated from the drone's position coordinates to reliably detect an unauthorized drone in the presence of authorized drones.