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.

In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC), which refers to training non-specific classification architectures (downstream models) to classify real images without using any real images during training. Recent research has demonstrated that generating synthetic training images using diffusion models provides a potential solution to address MA-ZSC. However, the performance of this approach currently falls short of that achieved by large-scale vision-language models. One possible explanation is a potential significant domain gap between synthetic and real images. Our work offers a fresh perspective on the problem by providing initial insights that MA-ZSC performance can be improved by improving the diversity of images in the generated dataset. We propose a set of modifications to the text-to-image generation process using a pre-trained diffusion model to enhance diversity, which we refer to as our $\textbf{bag of tricks}$. Our approach shows notable improvements in various classification architectures, with results comparable to state-of-the-art models such as CLIP. To validate our approach, we conduct experiments on CIFAR10, CIFAR100, and EuroSAT, which is particularly difficult for zero-shot classification due to its satellite image domain. We evaluate our approach with five classification architectures, including ResNet and ViT. Our findings provide initial insights into the problem of MA-ZSC using diffusion models. All code will be available on GitHub.

Entrepreneur Elon Musk is preparing to launch a startup that will compete with Openai, the creator of Chatgpt, a media report unveiled. According to quoted knowledgeable sources, the owner of Twitter and Tesla is already assembling a team of developers and talking to investors. Tech investor Elon Musk is putting effort into founding a startup that will rival the company behind the Chatgpt artificial intelligence (AI) assistant, Openai, the Financial Times revealed on Friday, citing people familiar with the billionaire's intentions. The publication claims Musk is now recruiting AI engineers while also holding talks with some investors in Spacex and Tesla, two of his best known business enterprises along with Twitter, about backing the new venture, Reuters quoted the report. Companies like Microsoft-funded Openai and Google's parent, Alphabet, have been working to incorporate AI into their offerings despite calls from regulators to introduce comprehensive rules for the technology before its widely implemented.

With the growing popularity of generative AI-powered chatbots such as ChatGPT, Google Bard, and Microsoft Bing Chat, the demand for professionals skilled in prompt writing and engineering is on the rise. This emerging field of AI technology has existed for some time but is now becoming mainstream, offering new career paths such as prompt engineering. Moreover, it also offers well-paying jobs and flexible work options. Also Read: The ChatGPT Revolution in Today's Job Market: Challenges and Opportunities Prompt engineering is the process of designing and crafting prompts for AI chatbots and generative services. It involves interacting with AI systems like Google's Bard or OpenAI's ChatGPT, guiding them to respond in specific ways and avoiding undesirable responses, such as embarrassing statements or revealing trade secrets.

By now you've probably heard the deepfake AI-generated verses mimicking rappers like Drake, Kendrick Lamar, Nas, and Jay-Z. While the technology was shaky at first, these days it's so eerily good it's almost impossible to even tell you're listening to a fake -- especially when the creator has the cadence and delivery of your favorite rapper down pat. As the world continues to immerse itself in AI -- from sophisticated language models like ChatGPT and Bing (or Sydney if you ask it the right way) to AI image generators, and more -- the lightening-fast pace of the technology seems to be outrunning society's ability to adapt to the new future. Deepfakes can be entertaining -- we all loved Kendrick Lamar's "The Heart Part 5" video, but they can also be scary, even dangerous. As Axios pointed out in February, right now generative AI is a legal minefield.

AI research startup Anthropic aims to raise as much as $5 billion over the next two years to take on rival OpenAI and enter over a dozen major industries, according to company documents obtained by TechCrunch. A pitch deck for Anthropic's Series C fundraising round discloses these and other long-term goals for the company, which was founded in 2020 by former OpenAI researchers. In the deck, Anthropic says that it plans to build a "frontier model" -- tentatively called "Claude-Next" -- 10 times more capable than today's most powerful AI, but that this will require a billion dollars in spending over the next 18 months. When contacted for comment, an Anthropic spokesperson said: "We are planning additional product announcements and will be talking about them soon." The Information reported in early March that Anthropic was seeking to raise $300 million at $4.1 billion valuation, bringing its total raised to $1.3 billion.

Garante, the Italian data protection authority, apparently jumped the gun at the end of March when it imposed a temporary ban on ChatGPT, a chatbot that uses artificial intelligence (AI) to generate texts that seem as if they were created by humans, and computer games. The watchdog was less concerned by the use of AI -- the simulation of human intelligence by computer systems -- than by breaches of data protection legislation. Garante then told the Microsoft Corp-backed company behind ChatGPT, OpenAI, that it would have to be more transparent with its users about how their data were processed. It also said that the US company had to obtain permission from users if their data were to be used to further develop the software -- that is, to help it learn -- and that access to minors had to be filtered. In a press release, the Italian authority said that the ban would be lifted if OpenAI met these conditions by April 30.