Deep generative models are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts. Have they reached the point where models of natural images can be used for generative data augmentation, helping to improve challenging discriminative tasks? We show that large-scale text-to image diffusion models can be fine-tuned to produce class conditional models with SOTA FID (1.76 at 256x256 resolution) and Inception Score (239 at 256x256). The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with samples from the resulting models yields significant improvements in ImageNet classification accuracy over strong ResNet and Vision Transformer baselines.

Generative AI platforms have been the rage since the second half of last year with Microsoft and Google pushing these programs into their existing services. Even the Ministry of Electronics and Information Technology (MeitY), on 3 February, said it is "cognizant" of the emergence and proliferation of generative AI and noted that AI can be a "kinetic enabler" for growth in India. However, researchers at institutes underline a host of challenges for generative AI projects in academia, the biggest of which lie in sourcing ample data of Indic languages, the cost of such projects, and the scale of computing power needed. Indian researchers have been working on such projects for more than three years. "In academia, we're using techniques from language models, namely the transformer architecture, for different tasks such as classification of data, answering questions, machine translation and building chatbots," said Tapas Kumar Mishra, assistant professor of computer science engineering at National Institute of Technology (NIT), Rourkela.

While tech giants across the world work on materializing the idea of having a generative artificial intelligence (AI) to aid humans in their daily lives, the risk of the nascent technology going rogue remains imminent. Considering this possibility, Tesla and Twitter chief Elon Musk reminded the people of AI's potential to destroy civilization. On March 15, Musk's plan of creating a new AI startup surfaced after the entrepreneur was reportedly assembling a team of AI researchers and engineers. However, Musk continues to highlight the destructive potential of AI -- just like any other technology -- if it goes into the wrong hands or is being developed with ill intentions. According to Musk, AI can be dangerous. In a FOX interview, he said that AI can be more dangerous than mismanaged aircraft design or production maintenance, for example.

MobileGPT is a cutting-edge personalised AI assistant on WhatsApp powered by OpenAI ChatGPT. Chat, create AI images and documents, all in one convenient service.

Ian Sansavera, a software architect at a New York startup called Runway AI, typed a short description of what he wanted to see in a video. "A tranquil river in the forest," he wrote. Less than two minutes later, an experimental internet service generated a short video of a tranquil river in a forest. The river's running water glistened in the sun as it cut between trees and ferns, turned a corner and splashed gently over rocks. Runway, which plans to open its service to a small group of testers this week, is one of several companies building artificial intelligence technology that will soon let people generate videos simply by typing several words into a box on a computer screen.

While tech giants across the world work on materializing the idea of having a generative artificial intelligence (AI) to aid humans in their daily lives, the risk of the nascent technology going rogue remains imminent. Considering this possibility, Tesla and Twitter chief Elon Musk reminded the people of AI's potential to destroy civilization. On March 15, Musk's plan of creating a new AI startup surfaced after the entrepreneur was reportedly assembling a team of AI researchers and engineers. However, Musk continues to highlight the destructive potential of AI -- just like any other technology -- if it goes into the wrong hands or is being developed with ill intentions. According to Musk, AI can be dangerous. In a FOX interview, he said that AI can be more dangerous than mismanaged aircraft design or production maintenance, for example.

Elon Musk is known for his innovative ideas and it seems he may be working on a ChatGPT rival. Find out more about this exciting development here! Elon Musk, known for his involvement in various tech companies, was one of the co-founders of OpenAI, the company responsible for ChatGPT. However, Musk left the company after a few years as he wanted it to be a non-profit organization. Recent reports suggest that Musk is now planning to launch his own AI startup, X.AI, which will compete with OpenAI.

Microsoft has added its BIng AI chatbot to its popular SwiftKey third-party keyboard app for iOS and Android phones, giving users quick access to AI-generated answers and advice. With the keyboard open, you need only tap the blue Bing icon above the keyboard on the left to open the submenu, and then choose whether to have Bing AI search the internet for a query or give you answers itself via a chat. By signing up, you will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy. The chatbot can also help with conversational tone, offering suggestions for alternative ways to phrase a typed statement if you want your messages to be a little nicer, funnier or more professional. Heck, it'll even condense what you say into something that'll fit in a 140-character tweet.

To get good output from ChatGPT or another LLM, you usually have to feed it several prompts. But what if you could just give your AI bot a set of fairly broad goals at the start of a session and then sit back while it generates its own set of tasks to fulfill those goals? That's the idea behind Auto-GPT, a new open-source tool that uses the OpenAI API (same LLM as ChatGPT) to prompt itself, based on your initial input. We've already seen a number of Twitter users talk about how they are using Auto-GPT for everything from creating marketing plans to analyzing market data for investments to preparing topics for a podcast. Based on our hands-on experience, we can't say that it always works well (we asked it to write a Windows 11 how-to and the result was awful), but it's early days and some tasks may work better than others.

Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy and address the issue of workload imbalance. To tackle the challenge of multi-objective scheduling, i.e., maximizing GPU utilization while reducing operational costs, we propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities. Compared with other algorithms, our proposed method improves the system utility by up to 28.6% attributable to higher GPU utilization, lower energy cost, and less carbon emission.