The explosion of consumer-facing tools that offer generative AI has created plenty of debate: These tools promise to transform the ways in which we live and work while also raising fundamental questions about how we can adapt to a world in which they're extensively used for just about anything. As with any new technology riding a wave of initial popularity and interest, it pays to be careful in the way you use these AI generators and bots--in particular, in how much privacy and security you're giving up in return for being able to use them. It's worth putting some guardrails in place right at the start of your journey with these tools, or indeed deciding not to deal with them at all, based on how your data is collected and processed. Here's what you need to look out for and the ways in which you can get some control back. Make sure AI tools are honest about how data is used. Checking the terms and conditions of apps before using them is a chore but worth the effort--you want to know what you're agreeing to.

Generative artificial intelligence offers an opportunity for Japan to achieve regrowth, Jun Uchikawa, chief information officer at Sumitomo Mitsui Financial Group, said in a recent interview. "The biggest challenge facing Japanese companies is the lack of talent and labor. Generative AI can resolve this," Uchikawa said. The Japanese banking group in April started a trial use of generative AI based on the technology of the ChatGPT chatbot for searching information and creating documents. This could be due to a conflict with your ad-blocking or security software.

ChatGPT is a large language model (LLM) created by OpenAI that has been carefully trained on a large amount of data. It has revolutionized the field of natural language processing (NLP) and has pushed the boundaries of LLM capabilities. ChatGPT has played a pivotal role in enabling widespread public interaction with generative artificial intelligence (GAI) on a large scale. It has also sparked research interest in developing similar technologies and investigating their applications and implications. In this paper, our primary goal is to provide a concise survey on the current lines of research on ChatGPT and its evolution. We considered both the glass box and black box views of ChatGPT, encompassing the components and foundational elements of the technology, as well as its applications, impacts, and implications. The glass box approach focuses on understanding the inner workings of the technology, and the black box approach embraces it as a complex system, and thus examines its inputs, outputs, and effects. This paves the way for a comprehensive exploration of the technology and provides a road map for further research and experimentation. We also lay out essential foundational literature on LLMs and GAI in general and their connection with ChatGPT. This overview sheds light on existing and missing research lines in the emerging field of LLMs, benefiting both public users and developers. Furthermore, the paper delves into the broad spectrum of applications and significant concerns in fields such as education, research, healthcare, finance, etc.

Musk has been outspoken about AI for years, famously saying in 2014 that inventing super-intelligent computers would be like "summoning the demon" and could create an existential threat to humanity. He helped found ChatGPT-maker OpenAI, in 2015, but left the company in 2018 after disagreements with its other leaders. Over the past few months, he has complained about OpenAI and other AI companies scraping Twitter data to help train their bots.

You know it's bad when the cocreator of The Matrix thinks your artificial intelligence plan stinks. In June, as the Directors Guild of America was about to sign its union contract with Hollywood studios, Lilly Wachowski sent out a series of tweets explaining why she was voting no. The contact's AI clause, which stipulates that generative AI can't be considered a "person" or perform duties normally done by DGA members, didn't go far enough. "We need to change the language to imply that we won't use AI in any department, on any show we work on," Wachowski wrote. "I strongly believe the fight we [are] in right now in our industry is a microcosm of a much larger and critical crisis."

The "Interim Measures for the Management of Generative Artificial Intelligence Services" announced Thursday and set to take effect on Aug. 15, represent Beijing's attempt to encourage the growth of China's AI industry while retaining total control over information available to the public. It is an enormous challenge made more difficult by the rising global popularity of tools that allow people to generate unique text, images and music.

The United States' competition watchdog has opened an investigation into ChatGPT creator OpenAI amid suspicions the startup broke the law by scraping public data and publishing false and defamatory information. In a 20-page letter, the US Federal Trade Commission (FTC) has requested OpenAI to provide detailed information about its technology and privacy protections, including any efforts to prevent a repeat of incidents in which its groundbreaking chatbot published false and disparaging information about members of the public. The Washington Post first reported on the "expansive" probe on Thursday. The FTC declined to comment when contacted by Al Jazeera. OpenAI chief executive Sam Altman said the leak of the regulator's probe was "disappointing" and would not help build trust.

The Associated Press is licensing a part its archive of news stories to OpenAI under a deal that will explore generative AI's use in news, the companies said Thursday, a move that could set the precedent for similar partnerships between the industries. The news publisher will gain access to OpenAI's technology and product expertise as part of the deal, the financial details of which were not disclosed. AP also did not reveal how it would integrate OpenAI's technology in its news operations. The publisher already uses AI for automating corporate earnings reports, recapping sporting events and transcription for certain live events. This could be due to a conflict with your ad-blocking or security software.

In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. We propose to distill knowledge from a trained generative model into standard image backbones that have been well engineered for specific perception tasks. We investigate two types of knowledge distillation: 1) distilling learned generative features onto target image backbones as an alternative to pretraining these backbones on large labeled datasets such as ImageNet, and 2) distilling labels obtained from generative networks with task heads onto logits of target backbones. We perform extensive analyses on multiple generative models, dense prediction benchmarks, and several pre-training regimes. We empirically find that our DreamTeacher significantly outperforms existing self-supervised representation learning approaches across the board. Unsupervised ImageNet pre-training with DreamTeacher leads to significant improvements over ImageNet classification pre-training on downstream datasets, showcasing generative models, and diffusion generative models specifically, as a promising approach to representation learning on large, diverse datasets without requiring manual annotation.

Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation, offering a solution to the scarcity of data found in various scientific contexts. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability, demonstrating the intrinsic value of CWGAN data augmentation beyond simply providing larger datasets. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work highlights the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization.