Generative AI is overblown as an election risk, according to Meta's Nick Clegg, who claims the technology is more useful for defending democracy than attacking it. Speaking at the Meta AI Day event in London on Tuesday, the social network's global affairs chief said that the evidence from major elections that have already been run this year around the world is that technology such as large language models, image and video generators, and speech synthesis tools aren't being used in practice to subvert democracy. "It is right that we should be alert and we should be vigilant," Clegg said. "But of the major elections which have taken place already this year, in Taiwan, Pakistan, Bangladesh and Indonesia, it is striking how little these tools have been used in a systematic basis to really try to subvert and disrupt the elections. "I would urge everyone to think of AI as a sword, not just a shield, when it comes to bad content.

A man who used ChatGPT to go on hundreds of dates has found love and is engaged with a woman he met during his AI dating spree. Alexandr Zhadan, 23, matched with 5,000 women on Tinder and used a modified version of the AI software to whittle those down to a shortlist of 100 who he then dated. 'I broke up with my ex, and I wanted to find a new relationship, and I felt a bit exhausted about the idea of just swiping people on Tinder, and finding out that this is not the right person for me. 'I was talking about this with my friends, and one of them mentioned the idea of GPT, and how it could optimize finding dates. And it became a pet project.'

Superhuman artificial intelligence that is smarter than anyone on Earth could exist next year, Elon Musk has said, unless the sector's power and computing demands become unsustainable before then. The prediction is a sharp tightening of an earlier claim from the multibillionaire, that superintelligent AI would exist by 2029. Whereas "superhuman" is generally defined as being smarter than any individual human at any specific task, superintelligent is often defined instead as being smarter than every human's combined ability at any task. "My guess is that we'll have AI that is smarter than any one human probably around the end of next year," Musk said in a live streamed interview on his social network X. That prediction was made with the caveat that increasing demands for power and shortages of the most powerful AI training chips could limit their capability in the near term.

OpenAI is also at the center of several regulatory investigations, which have forced the company to spend even more on legal support. The Securities and Exchange Commission is looking into whether investors were misled during the chaotic period when Altman briefly left the company. The Federal Trade Commission is probing whether it ran afoul of consumer protection laws in a number of areas, including a data leak and ChatGPT's inaccurate claims. And the commission has had talks with the Justice Department about which agency should probe its multibillion-dollar partnership with Microsoft, amid concerns that such deals are dampening competition in the quickly evolving AI market.

From Terminator to Ex Machina, machines overthrowing their human creators is a staple of science fiction. But the robot uprising could be closer than you think, as Elon Musk warns AI will surpass the abilities of humanity by the end of next year. Speaking in an interview on X, the Tesla CEO claimed that AI would become more intelligent than the smartest human within two years. And, within five years, Musk even predicts that'sentient' AIs will outnumber humans. However, humanity might have a chance as Musk also claims that a shortage in advanced chips and electricity could hold back AI's development.

Aspect-Based Sentiment Analysis (ABSA) is an indispensable and highly challenging task in natural language processing. Current efforts have focused on specific sub-tasks, making it difficult to comprehensively cover all sub-tasks within the ABSA domain. With the development of Generative Pre-trained Transformers (GPTs), there came inspiration for a one-stop solution to sentiment analysis. In this study, we used GPTs for all sub-tasks of few-shot ABSA while defining a general learning paradigm for this application. We propose the All in One (AiO) model, a simple yet effective two-stage model for all ABSA sub-tasks. In the first stage, a specific backbone network learns the semantic information of the review and generates heuristically enhanced candidates. In the second stage, AiO leverages GPT contextual learning capabilities to generate predictions. The study conducted comprehensive comparative and ablation experiments on five benchmark datasets, and the results show that AiO can effectively handle all ABSA sub-tasks, even with few-shot data.

Cross-modality image segmentation aims to segment the target modalities using a method designed in the source modality. Deep generative models can translate the target modality images into the source modality, thus enabling cross-modality segmentation. However, a vast body of existing cross-modality image translation methods relies on supervised learning. In this work, we aim to address the challenge of zero-shot learning-based image translation tasks (extreme scenarios in the target modality is unseen in the training phase). To leverage generative learning for zero-shot cross-modality image segmentation, we propose a novel unsupervised image translation method. The framework learns to translate the unseen source image to the target modality for image segmentation by leveraging the inherent statistical consistency between different modalities for diffusion guidance. Our framework captures identical cross-modality features in the statistical domain, offering diffusion guidance without relying on direct mappings between the source and target domains. This advantage allows our method to adapt to changing source domains without the need for retraining, making it highly practical when sufficient labeled source domain data is not available. The proposed framework is validated in zero-shot cross-modality image segmentation tasks through empirical comparisons with influential generative models, including adversarial-based and diffusion-based models.

The Large Vision-Language Model (LVLM) field has seen significant advancements, yet its progression has been hindered by challenges in comprehending fine-grained visual content due to limited resolution. Recent efforts have aimed to enhance the high-resolution understanding capabilities of LVLMs, yet they remain capped at approximately 1500 x 1500 pixels and constrained to a relatively narrow resolution range. This paper represents InternLM-XComposer2-4KHD, a groundbreaking exploration into elevating LVLM resolution capabilities up to 4K HD (3840 x 1600) and beyond. Concurrently, considering the ultra-high resolution may not be necessary in all scenarios, it supports a wide range of diverse resolutions from 336 pixels to 4K standard, significantly broadening its scope of applicability. Specifically, this research advances the patch division paradigm by introducing a novel extension: dynamic resolution with automatic patch configuration. It maintains the training image aspect ratios while automatically varying patch counts and configuring layouts based on a pre-trained Vision Transformer (ViT) (336 x 336), leading to dynamic training resolution from 336 pixels to 4K standard. Our research demonstrates that scaling training resolution up to 4K HD leads to consistent performance enhancements without hitting the ceiling of potential improvements. InternLM-XComposer2-4KHD shows superb capability that matches or even surpasses GPT-4V and Gemini Pro in 10 of the 16 benchmarks. The InternLM-XComposer2-4KHD model series with 7B parameters are publicly available at https://github.com/InternLM/InternLM-XComposer.

The advent of Large Language Models (LLMs) has revolutionized various domains of artificial intelligence, including the realm of software engineering. In this research, we evaluate the efficacy of pre-trained LLMs in replicating the tasks traditionally performed by developers in response to code review comments. We provide code contexts to five popular LLMs and obtain the suggested code-changes (patch sets) derived from real-world code-review comments. The performance of each model is meticulously assessed by comparing their generated patch sets against the historical data of human-generated patch-sets from the same repositories. This comparative analysis aims to determine the accuracy, relevance, and depth of the LLMs' feedback, thereby evaluating their readiness to support developers in responding to code-review comments. Novelty: This particular research area is still immature requiring a substantial amount of studies yet to be done. No prior research has compared the performance of existing Large Language Models (LLMs) in code-review comments. This in-progress study assesses current LLMs in code review and paves the way for future advancements in automated code quality assurance, reducing context-switching overhead due to interruptions from code change requests.

In this paper, we introduce GUIDE, a novel dataset tailored for the advancement of Multimodal Large Language Model (MLLM) applications, particularly focusing on Robotic Process Automation (RPA) use cases. Our dataset encompasses diverse data from various websites including Apollo(62.67\%), Gmail(3.43\%), Calendar(10.98\%) and Canva(22.92\%). Each data entry includes an image, a task description, the last action taken, CoT and the next action to be performed along with grounding information of where the action needs to be executed. The data is collected using our in-house advanced annotation tool NEXTAG (Next Action Grounding and Annotation Tool). The data is adapted for multiple OS, browsers and display types. It is collected by multiple annotators to capture the variation of design and the way person uses a website. Through this dataset, we aim to facilitate research and development in the realm of LLMs for graphical user interfaces, particularly in tasks related to RPA. The dataset's multi-platform nature and coverage of diverse websites enable the exploration of cross-interface capabilities in automation tasks. We believe that our dataset will serve as a valuable resource for advancing the capabilities of multi-platform LLMs in practical applications, fostering innovation in the field of automation and natural language understanding. Using GUIDE, we build V-Zen, the first RPA model to automate multiple websites using our in-House Automation tool AUTONODE