The Apple Intelligence rollout has been slow, staggered and steady since the company first unveiled its take on AI at WWDC this year. It continues today with the release of the latest developer betas for iOS 18, iPadOS 18 and macOS Sequoia. The updates in iOS 18.2, iPadOS 18.2 and macOS Sequoia (15.2) bring long-awaited features like Genmoji, Image Playground, Visual Intelligence and ChatGPT integration for those running the preview software, as well as Image Wand for iPads and more writing tools. This follows the announcement that iOS 18.1 would be available as a stable release to the public next week, which would bring things like writing tools, notification summaries and Apple's hearing test to the masses. That represents the first time for people who haven't opted into beta software to check out Apple Intelligence, which the company has widely touted as the headline feature for the devices it launched this year. The iPhone 16 series, for example, were billed as phones designed for Apple Intelligence, though they launched without those features.

Google has been using artificial intelligence watermarking to automatically identify text generated by the company's Gemini chatbot, making it easier to distinguish AI-generated content from human-written posts. That watermark system could help prevent misuse of the AI chatbots for misinformation and disinformation – not to mention cheating in school and business settings. Now, the tech company is making an open-source version of its technique available so that other generative AI developers can similarly watermark the output from their own large language models, says Pushmeet Kohli at Google DeepMind, the company's AI research team, which combines the former Google Brain and DeepMind labs. "While SynthID isn't a silver bullet for identifying AI-generated content, it is an important building block for developing more reliable AI identification tools," he says. Google creates self-replicating life from digital'primordial soup' Independent researchers voiced similar optimism.

An artificial intelligence startup backed by Amazon and Google says it has created an AI agent that can carry out tasks on the computer such as moving a mouse cursor and typing text. US company Anthropic said its AI model, called Claude, could now perform computing tasks including filling out forms, planning an outing and building a website. Autonomous AI agents are viewed by tech firms and investors as a potentially lucrative avenue of development for a technology whose capabilities have stunned users but can cost billions of dollars to build and operate. Anthropic, a competitor to ChatGPT owner OpenAI, is making the Claude 3.5 Sonnet model available to developers but admitted it can be prone to errors. "At this stage, it is still experimental – at times cumbersome and error-prone," the company said.

Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications. Watermarking, a technique that embeds information in the output of a model to verify its source, aims to mitigate the misuse of such AI-generated content. Current state-of-the-art watermarking schemes embed watermarks by slightly perturbing probabilities of the LLM's output tokens, which can be detected via statistical testing during verification. Unfortunately, our work shows that common design choices in LLM watermarking schemes make the resulting systems surprisingly susceptible to watermark removal or spoofing attacks--leading to fundamental trade-offs in robustness, utility, and usability. To navigate these trade-offs, we rigorously study a set of simple yet effective attacks on common watermarking systems and propose guidelines and defenses for LLM watermarking in practice. Here, we briefly introduce LLMs and LLM watermarks.

Research into the external behaviors and internal mechanisms of large language models (LLMs) has shown promise in addressing complex tasks in the physical world. Studies suggest that powerful LLMs, like GPT-4, are beginning to exhibit human-like cognitive abilities, including planning, reasoning, and reflection. In this paper, we introduce a research line and methodology called LLM Psychology, leveraging human psychology experiments to investigate the cognitive behaviors and mechanisms of LLMs. We migrate the Typoglycemia phenomenon from psychology to explore the "mind" of LLMs. Unlike human brains, which rely on context and word patterns to comprehend scrambled text, LLMs use distinct encoding and decoding processes. Through Typoglycemia experiments at the character, word, and sentence levels, we observe: (I) LLMs demonstrate human-like behaviors on a macro scale, such as lower task accuracy and higher token/time consumption; (II) LLMs exhibit varying robustness to scrambled input, making Typoglycemia a benchmark for model evaluation without new datasets; (III) Different task types have varying impacts, with complex logical tasks (e.g., math) being more challenging in scrambled form; (IV) Each LLM has a unique and consistent "cognitive pattern" across tasks, revealing general mechanisms in its psychology process. We provide an in-depth analysis of hidden layers to explain these phenomena, paving the way for future research in LLM Psychology and deeper interpretability.

This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020. We compare traditional practices-such as keyword-based algorithms and embedding techniques-with state-of-the-art topic modeling methods that employ dimensionality reduction and clustering. This comparison allows us to assess the popularity and evolution of different approaches and themes within algorithmic trading. We demonstrate the usefulness of Natural Language Processing (NLP) in the automatic extraction of knowledge, highlighting the new possibilities created by the latest iterations of Large Language Models (LLMs) like ChatGPT. The rationale for focusing on this topic stems from our analysis, which reveals that research articles on algorithmic trading are increasing at a faster rate than the overall number of publications. While stocks and main indices comprise more than half of all assets considered, certain asset classes, such as cryptocurrencies, exhibit a much stronger growth trend. Machine learning models have become the most popular methods in recent years. The study demonstrates the efficacy of LLMs in refining datasets and addressing intricate questions about the analyzed articles, such as comparing the efficiency of different models. Our research shows that by decomposing tasks into smaller components and incorporating reasoning steps, we can effectively tackle complex questions supported by case analyses. This approach contributes to a deeper understanding of algorithmic trading methodologies and underscores the potential of advanced NLP techniques in literature reviews.

Each request in LLM inference goes through two phases: compute-bound prefill and memory-bandwidth-bound decode. To improve GPU utilization, recent systems use hybrid batching that combines the prefill and decode phases of different requests into the same batch. Hybrid batching works well for linear operations as it amortizes the cost of loading model weights from HBM. However, attention computation in hybrid batches remains inefficient because existing attention kernels are optimized for either prefill or decode. In this paper, we present POD-Attention -- the first GPU kernel that efficiently computes attention for hybrid batches. POD-Attention aims to maximize the utilization of both compute and memory bandwidth by carefully allocating the GPU's resources such that prefill and decode operations happen concurrently on the same multiprocessor. We integrate POD-Attention in a state-of-the-art LLM inference scheduler Sarathi-Serve. POD-Attention speeds up attention computation by up to 75% (mean 28%) and increases LLM serving throughput by up to 22% in offline inference. In online inference, POD-Attention enables lower time-to-first-token (TTFT), time-between-tokens (TBT), and request execution latency versus Sarathi-Serve.

Chain of Thought (CoT) of multi-step benefits from the logical structure of the reasoning steps and task-specific actions, significantly enhancing the mathematical reasoning capabilities of large language models. As the prevalence of long CoT, the number of reasoning steps exceeds manageable token limits and leads to higher computational demands. Inspired by the fundamental logic of human cognition, ``derive, then reduce'', we conceptualize the standard multi-step CoT as a novel Markov Chain of Thought (MCoT). In this study, we consider the mathematical reasoning task, defining each reasoning step as text accompanied by a Python code snippet. To facilitate a longer reasoning path, self-correction is enabled through interactions with the code interpreter. Our MCoT aims to compress previous reasoning steps into a simplified question, enabling efficient next-step inference without relying on a lengthy KV cache. In our experiments, we curate the \texttt{MCoTInstruct} dataset, and the empirical results indicate that MCoT not only significantly enhances efficiency but also maintains comparable accuracy. While much remains to be explored, this work paves the way for exploring the long CoT reasoning abilities of LLMs.

The improvement in translating natural language to structured query language (SQL) can be attributed to the advancements in large language models (LLMs). Open-source LLMs, tailored for specific database dialects such as MySQL, have shown great performance. However, cloud service providers are looking for a unified database manager service (e.g., Cosmos DB from Azure, Amazon Aurora from AWS, Lindorm from AlibabaCloud) that can support multiple dialects. This requirement has led to the concept of multi-dialect query generation, which presents challenges to LLMs. These challenges include syntactic differences among dialects and imbalanced data distribution across multiple dialects. To tackle these challenges, we propose MoMQ, a novel Mixture-of-Experts-based multi-dialect query generation framework across both relational and non-relational databases. MoMQ employs a dialect expert group for each dialect and a multi-level routing strategy to handle dialect-specific knowledge, reducing interference during query generation. Additionally, a shared expert group is introduced to address data imbalance, facilitating the transfer of common knowledge from high-resource dialects to low-resource ones. Furthermore, we have developed a high-quality multi-dialect query generation benchmark that covers relational and non-relational databases such as MySQL, PostgreSQL, Cypher for Neo4j, and nGQL for NebulaGraph. Extensive experiments have shown that MoMQ performs effectively and robustly even in resource-imbalanced scenarios.

Within an explainability framework, the neuropsychology of artificial intelligence focuses on studying synthetic neural cognitive mechanisms, considering them as new subjects of cognitive psychology research. The goal is to make artificial neural networks used in language models understandable by adapting concepts from human cognitive psychology to the interpretation of artificial neural cognition. In this context, the notion of categorization is particularly relevant because it plays a key role as a process of segmentation and reconstruction of informational data by the neural vectors of synthetic cognition. Thus, in this study, the aim is to use the concept of categorization, as understood in human cognitive psychology (particularly in its relation to the notion of similarity), to apply it to the analysis of neural behavior and to infer certain synthetic cognitive processes underlying the observed behaviors.