Large Language Models have become popular for their remarkable capabilities in human-oriented tasks and traditional natural language processing tasks. Its efficient functioning is attributed to the attention mechanism in the Transformer architecture, enabling it to concentrate on particular aspects of the input. LLMs are increasingly being used in domains such as generating prose, poetry or art, which require the model to be creative (e.g. Adobe firefly). LLMs possess advanced language generation abilities that enable them to generate distinctive and captivating content. This utilization of LLMs in generating narratives shows their flexibility and potential for use in domains that extend beyond conventional natural language processing duties. In different contexts, we may expect the LLM to generate factually correct answers, that match reality; e.g., question-answering systems or online assistants. In such situations, being correct is critical to LLMs being trusted in practice. The Bing Chatbot provides its users with the flexibility to select one of the three output modes: creative, balanced, and precise. Each mode emphasizes creativity and factual accuracy differently. In this work, we provide a mathematical abstraction to describe creativity and reality based on certain losses. A model trained on these losses balances the trade-off between the creativity and reality of the model.

Code hidden inside PC motherboards left millions of machines vulnerable to malicious updates, researchers revealed this week. Staff at security firm Eclypsium found code within hundreds of models of motherboards created by Taiwanese manufacturer Gigabyte that allowed an updater program to download and run another piece of software. While the system was intended to keep the motherboard updated, the researchers found that the mechanism was implemented insecurely, potentially allowing attackers to hijack the backdoor and install malware. Elsewhere, Moscow-based cybersecurity firm Kaspersky revealed that its staff had been targeted by newly discovered zero-click malware impacting iPhones. Victims were sent a malicious message, including an attachment, on Apple's iMessage. The attack automatically started exploiting multiple vulnerabilities to give the attackers access to devices, before the message deleted itself.

When Aparna Pappu, vice president and general manager of Google Workspace, spoke at Google I/O on May 10, she laid out a vision for artificial intelligence that helps users wade through their inbox. Pappu showed how generative AI can whisper summaries of long email threads in your ear, pull in relevant data from local files as you salsa together through unread messages, and dip you low to the ground as it suggests insertable text. Welcome to the inbox of the future. While the specifics of how it'll arrive remain unclear, generative AI is poised to fundamentally alter how people communicate over email. A broader subset of AI, called machine learning, already performs a kind of safety dance long after you've logged off.

Diffusion models have demonstrated impressive performance in text-to-image generation. They utilize a text encoder and cross-attention blocks to infuse textual information into images at a pixel level. However, their capability to generate images with text containing multiple objects is still restricted. Previous works identify the problem of information mixing in the CLIP text encoder and introduce the T5 text encoder or incorporate strong prior knowledge to assist with the alignment. We find that mixing problems also occur on the image side and in the cross-attention blocks. The noisy images can cause different objects to appear similar, and the cross-attention blocks inject information at a pixel level, leading to leakage of global object understanding and resulting in object mixing. In this paper, we introduce Detector Guidance (DG), which integrates a latent object detection model to separate different objects during the generation process. DG first performs latent object detection on cross-attention maps (CAMs) to obtain object information. Based on this information, DG then masks conflicting prompts and enhances related prompts by manipulating the following CAMs. We evaluate the effectiveness of DG using Stable Diffusion on COCO, CC, and a novel multi-related object benchmark, MRO. Human evaluations demonstrate that DG provides an 8-22\% advantage in preventing the amalgamation of conflicting concepts and ensuring that each object possesses its unique region without any human involvement and additional iterations. Our implementation is available at \url{https://github.com/luping-liu/Detector-Guidance}.

The government said Friday it has issued administrative guidance to ChatGPT operator OpenAI due to its insufficient consideration of protocols to protect personal information. The guidance, issued Thursday by the government's Personal Information Protection Commission and based on the personal information protection law, pointed to the possibility of ChatGPT infringing on privacy by obtaining sensitive personal information without prior consent. The commission said that it has not confirmed any specific violation of the law so far. 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.

Sign up to receive our weekly newsletter of the best New Yorker podcasts. Sam Altman, the C.E.O. of OpenAI, which created ChatGPT, says that artificial intelligence is a powerful tool that will streamline human work and quicken the pace of scientific advancement. But ChatGPT has both enthralled and terrified us, and even some of A.I.'s pioneers are freaked out by the technology and how quickly it has advanced. David Remnick talks with Altman, and with the computer scientist Yoshua Bengio, who won the prestigious Turing Award for his work in 2018, but recently signed an open letter calling for a moratorium on some A.I. research until regulation can be implemented. The stakes, Bengio says, are high: "I believe there is a non-negligible risk that this kind of technology, in the short term, could disrupt democracies."

On Tuesday morning, the merchants of artificial intelligence warned once again about the existential might of their products. Hundreds of AI executives, researchers, and other tech and business figures, including OpenAI CEO Sam Altman and Bill Gates, signed a one-sentence statement written by the Center for AI Safety declaring that "mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war." Those 22 words were released following a multi-week tour in which executives from OpenAI, Microsoft, Google, and other tech companies called for limited regulation of AI. They spoke before Congress, in the European Union, and elsewhere about the need for industry and governments to collaborate to curb their product's harms--even as their companies continue to invest billions in the technology. Several prominent AI researchers and critics told me that they're skeptical of the rhetoric, and that Big Tech's proposed regulations appear defanged and self-serving.

The second VISION AI Open Day took place on Thursday 1 June in Prague. The focus was on "trustworthy AI" and the programme featured a roundtable discussion which was live-streamed. The panellists started by defining the main characteristics of trustworthiness in AI systems. They then moved on to talk more specifically about generative AI, which was the focus for the rest of the session. As well as discussing some of the risks and opportunities associated with generative AI, they touched on issues of transparency, explainability and ethics.

Today, Artifact is taking another jump on the generative-AI rocket ship in an attempt to address an annoying problem--clickbaity headlines. The app already offers a way for users to flag clickbait stories, and if multiple people tag an article, Artifact won't spread it. But, Systrom explains, sometimes the problem isn't with the story but the headline. It might promise too much, or mislead, or lure the reader into clicking just to find some information that's held back from the headline. From the publisher's viewpoint, winning more clicks is a big plus--but it's frustrating to users, who might feel they have been manipulated.

The future of Hollywood looks a lot like Deepfake Ryan Reynolds selling you a Tesla. In a video, since removed but widely shared on Twitter, the actor is bespectacled in thick black frames, his mouth mouthing independently from his face, hawking electric vehicles: "How much do you think it would cost to own a car that's this fucking awesome?" On the verisimilitude scale, the video, which originally circulated last month, registered as blatantly unreal. Then its creator, financial advice YouTuber Kevin Paffrath, revealed he had made it as a ploy to attract the gaze of Elon Musk. Elsewhere on Twitter, people beseeched Reynolds to sue.