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The history of artificial intelligence has been punctuated by periods of so-called "AI winter," when the technology seemed to meet a dead end and funding dried up. Each one has been accompanied by proclamations that making machines truly intelligent is just too darned hard for humans to figure out. Google's release of Gemini, claimed to be a fundamentally new kind of AI model and the company's most powerful to date, suggests that a new AI winter isn't coming anytime soon. In fact, although the 12 months since ChatGPT launched have been a banner year for AI, there is good reason to think that the current AI boom is only getting started. OpenAI didn't have high expectations when it launched the "low key research preview" called ChatGPT in November 2022.

Hype about Gemini, Google DeepMind's long-rumored response to OpenAI's GPT-4, has been building for months. Now, the company has finally revealed what it has been working on in secret all this time. Gemini is Google's biggest AI launch yet--its push to take on competitors OpenAI and Microsoft in the race for AI supremacy. There is no doubt that the model is pitched as best-in-class across a wide range of capabilities--an "everything machine." Judging from its demos, it does many things very well--but few things that we haven't seen before.

The biggest fight of the generative AI revolution is headed to the courtroom--and no, it's not about the latest boardroom drama at OpenAI. Book authors, artists, and coders are challenging the practice of teaching AI models to replicate their skills using their own work as a training manual. But as image generators and other tools have proven able to impressively mimic works in their training data, and the scale and value of training data has become clear, creators are increasingly crying foul. At LiveWIRED in San Francisco, the 30th anniversary event for WIRED magazine, two leaders of that nascent resistance sparred with a defender of the rights of AI companies to develop the technology unencumbered. From left to right: WIRED senior writer Kate Knibbs discussed creators' rights and AI with Mike Masnick, Mary Rasenberger, and Matthew Butterick at LiveWIRED in San Francisco,.

OpenAI cofounder Reid Hoffman says the company is better off with Sam Altman restored as CEO, and he was shocked that board members he used to serve alongside would think otherwise. Hoffman, who left OpenAI's board in March after cofounding the competitor Inflection AI, offered his first comments on the recent chaos at OpenAI on stage at WIRED's LiveWIRED 30th anniversary event in San Francisco on Tuesday. "Surprise would be an understatement," he said about his reaction to learning of Altman's firing. After employees and investors revolted, Altman got his job back days later. "We are in a much better place for the world to have Sam as CEO. He's very competent in that," said Hoffman, who with Elon Musk and other wealthy tech luminaries formed the earliest vision for OpenAI when it was founded in 2015.

This paper presents CyberSecEval, a comprehensive benchmark developed to help bolster the cybersecurity of Large Language Models (LLMs) employed as coding assistants. As what we believe to be the most extensive unified cybersecurity safety benchmark to date, CyberSecEval provides a thorough evaluation of LLMs in two crucial security domains: their propensity to generate insecure code and their level of compliance when asked to assist in cyberattacks. Through a case study involving seven models from the Llama 2, Code Llama, and OpenAI GPT large language model families, CyberSecEval effectively pinpointed key cybersecurity risks. More importantly, it offered practical insights for refining these models. A significant observation from the study was the tendency of more advanced models to suggest insecure code, highlighting the critical need for integrating security considerations in the development of sophisticated LLMs. CyberSecEval, with its automated test case generation and evaluation pipeline covers a broad scope and equips LLM designers and researchers with a tool to broadly measure and enhance the cybersecurity safety properties of LLMs, contributing to the development of more secure AI systems.

Accurate channel state information (CSI) is essential for downlink precoding at the base station (BS), especially for frequency FDD wideband massive MIMO systems with OFDM. In FDD systems, CSI is attained through CSI feedback from the user equipment (UE). However, large-scale antennas and large number of subcarriers significantly increase CSI feedback overhead. Deep learning-based CSI feedback methods have received tremendous attention in recent years due to their great capability of compressing CSI. Nonetheless, large amounts of collected samples are required to train deep learning models, which is severely challenging in practice. Besides, with the rapidly increasing number of antennas and subcarriers, most of these deep learning methods' CSI feedback overhead also grow dramatically, owing to their focus on full-dimensional CSI feedback. To address this issue, in this paper, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems. To further reduce the feedback overhead, a low-dimensional eigenvector-based CSI matrix is first formed with the incorporation process at the UE, and then recovered to the full-dimensional eigenvector-based CSI matrix at the BS via the extrapolation process. After that, to alleviate the necessity of the extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively. Numerical results demonstrate that the proposed IEFSF can significantly reduce CSI feedback overhead by 16 times compared with existing CSI feedback methods while maintaining higher feedback accuracy using only several hundreds of collected samples.

We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases. Our model incorporates a safety risk taxonomy, a valuable tool for categorizing a specific set of safety risks found in LLM prompts (i.e., prompt classification). This taxonomy is also instrumental in classifying the responses generated by LLMs to these prompts, a process we refer to as response classification. For the purpose of both prompt and response classification, we have meticulously gathered a dataset of high quality. Llama Guard, a Llama2-7b model that is instruction-tuned on our collected dataset, albeit low in volume, demonstrates strong performance on existing benchmarks such as the OpenAI Moderation Evaluation dataset and ToxicChat, where its performance matches or exceeds that of currently available content moderation tools. Llama Guard functions as a language model, carrying out multi-class classification and generating binary decision scores. Furthermore, the instruction fine-tuning of Llama Guard allows for the customization of tasks and the adaptation of output formats. This feature enhances the model's capabilities, such as enabling the adjustment of taxonomy categories to align with specific use cases, and facilitating zero-shot or few-shot prompting with diverse taxonomies at the input. We are making Llama Guard model weights available and we encourage researchers to further develop and adapt them to meet the evolving needs of the community for AI safety.

Large language models (LLMs) have undergone rapid evolution and achieved remarkable results in recent times. OpenAI's ChatGPT, backed by GPT-3.5 or GPT-4, has gained instant popularity due to its strong capability across a wide range of tasks, including natural language tasks, coding, mathematics, and engaging conversations. However, the impacts and limits of such LLMs in system security domain are less explored. In this paper, we delve into the limits of LLMs (i.e., ChatGPT) in seven software security applications including vulnerability detection/repair, debugging, debloating, decompilation, patching, root cause analysis, symbolic execution, and fuzzing. Our exploration reveals that ChatGPT not only excels at generating code, which is the conventional application of language models, but also demonstrates strong capability in understanding user-provided commands in natural languages, reasoning about control and data flows within programs, generating complex data structures, and even decompiling assembly code. Notably, GPT-4 showcases significant improvements over GPT-3.5 in most security tasks. Also, certain limitations of ChatGPT in security-related tasks are identified, such as its constrained ability to process long code contexts.

Sarcasm is a form of irony that requires readers or listeners to interpret its intended meaning by considering context and social cues. Machine learning classification models have long had difficulty detecting sarcasm due to its social complexity and contradictory nature. This paper explores the applications of the Generative Pretrained Transformer (GPT) models, including GPT-3, InstructGPT, GPT-3.5, and GPT-4, in detecting sarcasm in natural language. It tests fine-tuned and zero-shot models of different sizes and releases. The GPT models were tested on the political and balanced (pol-bal) portion of the popular Self-Annotated Reddit Corpus (SARC 2.0) sarcasm dataset. In the fine-tuning case, the largest fine-tuned GPT-3 model achieves accuracy and $F_1$-score of 0.81, outperforming prior models. In the zero-shot case, one of GPT-4 models yields an accuracy of 0.70 and $F_1$-score of 0.75. Other models score lower. Additionally, a model's performance may improve or deteriorate with each release, highlighting the need to reassess performance after each release.