Large language models (LLMs) [1-5] have recently become the new darling of academia and industry due to their remarkable performance in a variety of natural language processing (NLP) tasks. With the blessing of techniques such as large-scale pre-training [6], instruction tuning [7], and reinforcement learning from human feedback (RLHF) [8, 9], existing pretrained LLMs have demonstrated unique capabilities in language understanding, generation, interaction, and reasoning. These powerful capabilities of LLMs also drive many emergent research topics (e.g., instruction learning [10], in-context learning [1], chain-of-thought prompting [11], etc.) to further investigate their huge potentials, and bring unlimited possibilities for humans to build advanced artificial intelligence systems. However, alongside these advancements, a pressing issue that plagues LLMs has been widely criticized as "hallucination", referred to as a phenomenon where LLMs tend to generate text that is incorrect, nonsensical, or not real [12]. To alleviate the phenomenon of "hallucination" during the generation of LLMs, a promising direction is to retrieve the factual knowledge that are highly relevant to the user query, and then guide LLMs to generate response according to the retrieved context, resulting in retrieval-augmented LMs [13, 14] that have recently demonstrated strong performance in knowledge intensive tasks, especially for knowledge-based question answering (KBQA). The workflow of existing retrieval-augmented LMs [15, 16] mainly relies on embedding-based retrieval methods, which will first encode various forms of knowledge base and also the user query into the same latent space, then use a semantic similarity metric to retrieve the top-K most relevant documents as prompt, and finally instruct LLMs to only use the provided context to answer the user query.

The Italian Digital Media Observatory (IDMO) project, part of a European initiative, focuses on countering disinformation and fake news. This report outlines contributions from Rai-CRITS to the project, including: (i) the creation of novel datasets for testing technologies (ii) development of an automatic model for categorizing Pagella Politica verdicts to facilitate broader analysis (iii) creation of an automatic model for recognizing textual entailment with exceptional accuracy on the FEVER dataset (iv) assessment using GPT-4 to detecting content treatment style (v) a game to raise awareness about fake news at national events.

Body cameras are usually associated with law enforcement, but what if you could have one for yourself? Imagine being able to capture everything that happens around you and send a distress signal to your family or friends if you need help. That's what PhoneCam, a tiny, affordable and smart AI-powered device, can do for you. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER PhoneCam is designed to help people feel safer in a world where personal safety fears are at a three-decade high in the U.S., according to a 2023 Gallup poll. It is smaller than a classic BIC lighter and weighs only 20 grams.

For generative AI to succeed, how engaging a conversationalist must it be? For almost sixty years, some conversational agents have responded to any question or comment to keep a conversation going. In recent years, several utilized machine learning or sophisticated language processing, such as Tay, Xiaoice, Zo, Hugging Face, Kuki, and Replika. Unlike generative AI, they focused on engagement, not expertise. Millions of people were motivated to engage with them. What were the attractions? Will generative AI do better if it is equally engaging, or should it be less engaging? Prior to the emergence of generative AI, we conducted a large-scale quantitative and qualitative analysis to learn what motivated millions of people to engage with one such 'virtual companion,' Microsoft's Zo. We examined the complete chat logs of 2000 anonymized people. We identified over a dozen motivations that people had for interacting with this software. Designers learned different ways to increase engagement. Generative conversational AI does not yet have a clear revenue model to address its high cost. It might benefit from being more engaging, even as it supports productivity and creativity. Our study and analysis point to opportunities and challenges.

In a scenario where multi-modal cameras are operating together, the problem of working with non-aligned images cannot be avoided. Yet, existing image fusion algorithms rely heavily on strictly registered input image pairs to produce more precise fusion results, as a way to improve the performance of downstream high-level vision tasks. In order to relax this assumption, one can attempt to register images first. However, the existing methods for registering multiple modalities have limitations, such as complex structures and reliance on significant semantic information. This paper aims to address the problem of image registration and fusion in a single framework, called BusRef. We focus on Infrared-Visible image registration and fusion task (IVRF). In this framework, the input unaligned image pairs will pass through three stages: Coarse registration, Fine registration and Fusion. It will be shown that the unified approach enables more robust IVRF. We also propose a novel training and evaluation strategy, involving the use of masks to reduce the influence of non-reconstructible regions on the loss functions, which greatly improves the accuracy and robustness of the fusion task. Last but not least, a gradient-aware fusion network is designed to preserve the complementary information. The advanced performance of this algorithm is demonstrated by

The author's goal in this paper is to explore how artificial intelligence (AI) has been utilised to inform our understanding of and ability to estimate at scale a critical aspect of musical creativity - musical tempo. The central importance of tempo to musical creativity can be seen in how it is used to express specific emotions (Eerola and Vuoskoski 2013), suggest particular musical styles (Li and Chan 2011), influence perception of expression (Webster and Weir 2005) and mediate the urge to move one's body in time to the music (Burger et al. 2014). Traditional tempo estimation methods typically detect signal periodicities that reflect the underlying rhythmic structure of the music, often using some form of autocorrelation of the amplitude envelope (Lartillot and Toiviainen 2007). Recently, AI-based methods utilising convolutional or recurrent neural networks (CNNs, RNNs) on spectral representations of the audio signal have enjoyed significant improvements in accuracy (Aarabi and Peeters 2022). Common AI-based techniques include those based on probability (e.g., Bayesian approaches, hidden Markov models (HMM)), classification and statistical learning (e.g., support vector machines (SVM)), and artificial neural networks (ANNs) (e.g., self-organising maps (SOMs), CNNs, RNNs, deep learning (DL)). The aim here is to provide an overview of some of the more common AI-based tempo estimation algorithms and to shine a light on notable benefits and potential drawbacks of each. Limitations of AI in this field in general are also considered, as is the capacity for such methods to account for idiosyncrasies inherent in tempo perception, i.e., how well AI-based approaches are able to think and act like humans.

Automatic Music Transcription (AMT) is a vital technology in the field of music information processing. Despite recent enhancements in performance due to machine learning techniques, current methods typically attain high accuracy in domains where abundant annotated data is available. Addressing domains with low or no resources continues to be an unresolved challenge. To tackle this issue, we propose a transcription model that does not require any MIDI-audio paired data through the utilization of scalable synthetic audio for pre-training and adversarial domain confusion using unannotated real audio. In experiments, we evaluate methods under the real-world application scenario where training datasets do not include the MIDI annotation of audio in the target data domain. Our proposed method achieved competitive performance relative to established baseline methods, despite not utilizing any real datasets of paired MIDI-audio. Additionally, ablation studies have provided insights into the scalability of this approach and the forthcoming challenges in the field of AMT research.

We present symbol tuning - finetuning language models on in-context input-label pairs where natural language labels (e.g., "positive/negative sentiment") are replaced with arbitrary symbols (e.g., "foo/bar"). Symbol tuning leverages the intuition that when a model cannot use instructions or natural language labels to figure out a task, it must instead do so by learning the input-label mappings. We experiment with symbol tuning across Flan-PaLM models up to 540B parameters and observe benefits across various settings. First, symbol tuning boosts performance on unseen in-context learning tasks and is much more robust to underspecified prompts, such as those without instructions or without natural language labels. Second, symbol-tuned models are much stronger at algorithmic reasoning tasks, with up to 18.2% better performance on the List Functions benchmark and up to 15.3% better performance on the Simple Turing Concepts benchmark. Finally, symbol-tuned models show large improvements in following flipped-labels presented in-context, meaning that they are more capable of using in-context information to override prior semantic knowledge.

Fox News medical contributor Dr. Marc Siegel outlines how the medical field is working to integrate artificial intelligence to care for heart conditions. If you've read the headlines in 2023, Artificial Intelligence (AI) is either coming to save or destroy education. From AI tools that can help proofread students' work to chatbots that can act as a kind of virtual research assistant, there are applications emerging that could rapidly improve what students are able to do and how they are able to do it. At the same time, ask any teacher, and you'll hear myriad stories of AI-generated essays (many with incorrect information in them), and the yeoman's work necessary to ChatGPT-proof their tests and quizzes. Rather than look at the whole AI and education universe, let's focus on one significant challenge confronting K-12 education today.

Artificial intelligence dominated the conversation in Hollywood this year, from stars like Sheryl Crow and Simon Cowell expressing concerns, to others like Howie Mandel embracing the technology.