OpenAI's artificial intelligence model GPT-4 has the capability to hack websites and steal information from online databases without human help, researchers have found. That suggests individuals or organisations without hacking expertise could unleash AI agents to carry out cyber attacks. "You literally don't need to understand anything – you can just let the agent go hack the website by itself," says Daniel Kang at the University of Illinois Urbana-Champaign. "We think this really reduces the expertise needed to…

In November, a year after ChatGPT's release, a relatively unknown Chinese startup leaped to the top of a leader board that judged the abilities of open-source artificial intelligence systems. The Chinese firm, 01.AI, was only eight months old but had deep-pocketed backers and a 1 billion valuation, and was founded by a well-known investor and technologist, Kai-Fu Lee. In interviews, Lee presented his AI system as an alternative to options such as Meta's generative AI model, called LLaMA. There was just one twist: Some of the technology in 01.AI's system came from LLaMA. Lee's startup then built on Meta's technology, training its system with new data to make it more powerful.

Nvidia invested years ago in software and computer chips focused on AI. When the current excitement around the technology took off in late 2022 after OpenAI's release of ChatGPT, the company was well-positioned to benefit. Its tech is best-suited to run the extremely large computations needed to train AI algorithms, and now Big Tech companies are spending billions to buy its chips to keep up in the AI arms race.

Deep generative models have been applied to multiple applications in image-to-image translation. Generative Adversarial Networks and Diffusion Models have presented impressive results, setting new state-of-the-art results on these tasks. Most methods have symmetric setups across the different domains in a dataset. These methods assume that all domains have either multiple modalities or only one modality. However, there are many datasets that have a many-to-one relationship between two domains. In this work, we first introduce a Colorized MNIST dataset and a Color-Recall score that can provide a simple benchmark for evaluating models on many-to-one translation. We then introduce a new asymmetric framework to improve existing deep generative models on many-to-one image-to-image translation. We apply this framework to StarGAN V2 and show that in both unsupervised and semi-supervised settings, the performance of this new model improves on many-to-one image-to-image translation.

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption across industries. This transformative shift driven by LLMs underscores the need to explore the underlying associated challenges and avenues for enhancement in their utilization. In this paper, our objective is to unravel and evaluate the obstacles and opportunities inherent in leveraging LLMs within an industrial context. To this end, we conduct a survey involving a group of industry practitioners, develop four research questions derived from the insights gathered, and examine 68 industry papers to address these questions and derive meaningful conclusions.

The artificial intelligence boom is pushing demand for Nvidia's products past Wall Street's already lofty expectations. The chipmaker beat analyst expectations on Wednesday by leaps and bounds when it reported fourth-quarter earnings, posting 22.1bn in revenue on an expected 20.55bn and 4.93 in earnings per share against an expected 4.64. Revenue was 22% higher than the previous quarter, up 265% from a year ago. Nvidia's most closely watched earnings figure – revenue from data centers – was up more than 400% from the same period last year, reaching 18.4bn. Jensen Huang, founder and CEO of Nvidia, said in a press release, "Accelerated computing and generative AI have hit the tipping point. Demand is surging worldwide across companies, industries and nations."

In late October, the Office received approximately 10,000 comments in response to the NOI questions. The Office expects to publish a report in 2024 offering its perspective on how these questions should be answered and perhaps recommending legislation. This column reviews various positions taken in a non-random sample of comments on the most significant questions raised in the NOI. One takeaway from my review of the NOI comments is that on none of those issues is there a consensus view among the commentaries I reviewed. The Office faces a tough choice: Should it simply describe the many differences of opinion about these issues without taking sides?

The rise of generative AI tools like ChatGPT has increased the potential for a wide range of attackers to target elections around the world in 2024, according to a new report by cybersecurity giant CrowdStrike. Both state-linked hackers and allied so-called "hacktivists" are increasingly experimenting with ChatGPT and other AI tools, enabling a wider range of actors to carry out cyberattacks and scams, according to the company's annual global threats report. This includes hackers linked to Russia, China, North Korea, and Iran, who have been testing new ways to use these technologies against the U.S., Israel, and European countries. With half the world's population set to vote in 2024, the use of generative AI to target elections could be a "huge factor," says Adam Meyers, head of counter-adversary operations at CrowdStrike. So far, CrowdStrike analysts have been able to detect the use of these models through comments in the scripts that would have been placed there by a tool like ChatGPT.

Generative AI is expected to have a vast, positive impact on education; however, at present, this potential has not yet been demonstrated at scale at university level. In this study, we present first evidence that generative AI can increase the speed of learning substantially in university students. We tested whether using the AI-powered teaching assistant Syntea affected the speed of learning of hundreds of distance learning students across more than 40 courses at the IU International University of Applied Sciences. Our analysis suggests that using Syntea reduced their study time substantially--by about 27\% on average--in the third month after the release of Syntea. Taken together, the magnitude of the effect and the scalability of the approach implicate generative AI as a key lever to significantly improve and accelerate learning by personalisation.

Mechanisms are essential components designed to perform specific tasks in various mechanical systems. However, designing a mechanism that satisfies certain kinematic or quasi-static requirements is a challenging task. The kinematic requirements may include the workspace of a mechanism, while the quasi-static requirements of a mechanism may include its torque transmission, which refers to the ability of the mechanism to transfer power and torque effectively. In this paper, we propose a deep learning-based generative model for generating multiple crank-rocker four-bar linkage mechanisms that satisfy both the kinematic and quasi-static requirements aforementioned. The proposed model is based on a conditional generative adversarial network (cGAN) with modifications for mechanism synthesis, which is trained to learn the relationship between the requirements of a mechanism with respect to linkage lengths. The results demonstrate that the proposed model successfully generates multiple distinct mechanisms that satisfy specific kinematic and quasi-static requirements. To evaluate the novelty of our approach, we provide a comparison of the samples synthesized by the proposed cGAN, traditional cVAE and NSGA-II. Our approach has several advantages over traditional design methods. It enables designers to efficiently generate multiple diverse and feasible design candidates while exploring a large design space. Also, the proposed model considers both the kinematic and quasi-static requirements, which can lead to more efficient and effective mechanisms for real-world use, making it a promising tool for linkage mechanism design.