Elon Musk has indicated that he's interested in creating his own version of OpenAI's ChatGPT in order to rival the popular AI chatbot. A new report from The Information goes into detail about AI researchers being contacted by Musk's team to develop a new version of OpenAI's ChatGPT. According to the report, Musk's team has already reached out to several developers regarding the purported project, and one of those researchers was Igor Babuschkin, a former senior AI researcher at Google's Deepmind, who was contacted for the position of lead developer on Musk's vision for the AI. Details of Musk's new AI chatbot are scarce and hardly set in stone, but we can expect that it will be different from OpenAI's ChatGPT in certain aspects. Notably, Musk co-founded OpenAI back in 2015 but left the company in 2018.

Microsoft is "leading the pack" in the artificial intelligence race with the company's latest AI integration into Bing, according to Wedbush's Dan Ives. In a Friday note, he maintained an outperform rating and a $280 price target on the stock but also sees $300 under a bull-case scenario, representing a 15% jump from current levels. "Overall, the AI story and ChatGPT monetization opportunity could add roughly $20 per share at least to the MSFT sum of the parts story in our opinion as this all plays out over the next 12 to 18 months," Ives said. Microsoft extended its long-term partnership with ChatGPT creator OpenAI in January with a $10 billion investment, following previous investments in 2019 and 2021. Microsoft has already integrated components of ChatGPT-like technology into its search engine Bing, and plans to rollout more consumer-facing AI products in the future.

What is a Kill Switch? How are Kill Switches used?

The Grand Valley State University Quest Series held its inaugural event called the “ChatGPT Panel” on Feb. 22. The Quest Series is part of the Academic Affairs forum to help discuss current issues within the GVSU community. This particular event was held in collaboration between the Divisions of Academic Affairs and Information Technology, facilitated by Provost...

ChatGPT, the popular chatbot that has been gaining attention in the world of artificial intelligence, has been banned by two of the world's largest banks, Goldman Sachs And Citi.

Chasing Microsoft, global tech giants have rolled out announcements on how they will implement ChatGPT-like artificial intelligence into their world leading platforms and applications, with YouTube the latest to present plans. Here is a roundup of how the world's biggest tech companies plan to surf the AI wave: This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see this FAQ. We humbly apologize for the inconvenience.

Article: The CEO of one of the world's biggest media companies just said A.I. is making some journalists obsolete as he plans staff cuts - Fortune ChatGPT has gotten people to make all kinds of predictions. One of the main ones is that the Artificial-Intelligence-Chat tool will eliminate the jobs of writers. Whether that is true or not remains to be seen. And, to what degree that might happen also remains to be seen. Regardless, the CEO of German media group Axel Springer, which owns German newspapers Bild and Welt as well as U.S. website Politico says he will be making large cuts because of the AI chat tool.

Pretrained language models (LMs) are susceptible to generate text with nonfactual information. In this work, we measure and improve the factual accuracy of large-scale LMs for open-ended text generation. We design the FactualityPrompts test set and metrics to measure the factuality of LM generations. Based on that, we study the factual accuracy of LMs with parameter sizes ranging from 126M to 530B. Interestingly, we find that larger LMs are more factual than smaller ones, although a previous study suggests that larger LMs can be less truthful in terms of misconceptions. In addition, popular sampling algorithms (e.g., top-p) in open-ended text generation can harm the factuality due to the ''uniform randomness'' introduced at every sampling step. We propose the factual-nucleus sampling algorithm that dynamically adapts the randomness to improve the factuality of generation while maintaining quality. Furthermore, we analyze the inefficiencies of the standard training method in learning correct associations between entities from factual text corpus (e.g., Wikipedia). We propose a factuality-enhanced training method that uses TopicPrefix for better awareness of facts and sentence completion as the training objective, which can vastly reduce the factual errors. We release our code and FactualityPrompts benchmark at: https://github.com/nayeon7lee/FactualityPrompt.

Large language models (LLMs) have recently taken the world by storm. They can generate coherent text, hold meaningful conversations, and be taught concepts and basic sets of instructions - such as the steps of an algorithm. In this context, we are interested in exploring the application of LLMs to graph drawing algorithms by performing experiments on ChatGPT. These algorithms are used to improve the readability of graph visualizations. The probabilistic nature of LLMs presents challenges to implementing algorithms correctly, but we believe that LLMs' ability to learn from vast amounts of data and apply complex operations may lead to interesting graph drawing results. For example, we could enable users with limited coding backgrounds to use simple natural language to create effective graph visualizations. Natural language specification would make data visualization more accessible and user-friendly for a wider range of users. Exploring LLMs' capabilities for graph drawing can also help us better understand how to formulate complex algorithms for LLMs; a type of knowledge that could transfer to other areas of computer science. Overall, our goal is to shed light on the exciting possibilities of using LLMs for graph drawing while providing a balanced assessment of the challenges and opportunities they present. A free copy of this paper with all supplemental materials required to reproduce our results is available on https://osf.io/n5rxd/?view_only=f09cbc2621f44074810b7d843f1e12f9

Large pre-trained language models help to achieve state of the art on a variety of natural language processing (NLP) tasks, nevertheless, they still suffer from forgetting when incrementally learning a sequence of tasks. To alleviate this problem, recent works enhance existing models by sparse experience replay and local adaption, which yield satisfactory performance. However, in this paper we find that pre-trained language models like BERT have a potential ability to learn sequentially, even without any sparse memory replay. To verify the ability of BERT to maintain old knowledge, we adopt and re-finetune single-layer probe networks with the parameters of BERT fixed. We investigate the models on two types of NLP tasks, text classification and extractive question answering. Our experiments reveal that BERT can actually generate high quality representations for previously learned tasks in a long term, under extremely sparse replay or even no replay. Continual Learning aims to obtain knowledge from a stream of data across time (Ring, 1994; Thrun, 1998; Chen & Liu, 2018). As a booming area in continual learning, task-incremental learning requires a model to learn a sequence of tasks, without forgetting previously learned knowledge. It is a practical scene to train models on a stream of tasks sequentially, avoiding to re-train on all existing data exhaustively once a new task arrives. In natural language processing, although many large-scale pre-trained language models (PLMs) have ceaselessly achieved on new records on various benchmarks, they cannot be directly deployed in a task-incremental setting. These models tend to perform poorly on previously seen tasks when learning new ones.