In our ever-expanding digital world, passwords are an inevitability: email, apps, subscriptions and loyalty programs -- nearly everything is designed to be secure behind a self-set code that permits entry. According to technology site TechCo, the average person has about 100 passwords, so it's no surprise that when signing up for a new account, individuals can sometimes get lazy with word choice. A new report by Home Security Heroes found that 51% of common passwords can be cracked in less than a minute using an AI password cracker, and 81% can be cracked in less than a month. Home Security Heroes used the AI password cracker PassGAN to run through a list of 15,680,000 passwords. The odds of AI decoding one's password increase when a password has a minimal amount of characters and lacks variety (only using lowercase, only using numbers, etc.).

We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song. This is achieved with an encoder pre-trained with a contrastive objective to extract only audio effects related information from a reference music recording. All our models are trained in a self-supervised manner from an already-processed wet multitrack dataset with an effective data preprocessing method that alleviates the data scarcity of obtaining unprocessed dry data. We analyze the proposed encoder for the disentanglement capability of audio effects and also validate its performance for mixing style transfer through both objective and subjective evaluations. From the results, we show the proposed system not only converts the mixing style of multitrack audio close to a reference but is also robust with mixture-wise style transfer upon using a music source separation model.

State-of-the-art methods on conversational recommender systems (CRS) leverage external knowledge to enhance both items' and contextual words' representations to achieve high quality recommendations and responses generation. However, the representations of the items and words are usually modeled in two separated semantic spaces, which leads to misalignment issue between them. Consequently, this will cause the CRS to only achieve a sub-optimal ranking performance, especially when there is a lack of sufficient information from the user's input. To address limitations of previous works, we propose a new CRS framework KLEVER, which jointly models items and their associated contextual words in the same semantic space. Particularly, we construct an item descriptive graph from the rich items' textual features, such as item description and categories. Based on the constructed descriptive graph, KLEVER jointly learns the embeddings of the words and items, towards enhancing both recommender and dialog generation modules. Extensive experiments on benchmarking CRS dataset demonstrate that KLEVER achieves superior performance, especially when the information from the users' responses is lacking.

Few-shot prompting is a surprisingly powerful way to use Large Language Models (LLMs) to solve various tasks. However, this approach struggles as the task complexity increases or when the individual reasoning steps of the task themselves are hard to learn, especially when embedded in more complex tasks. To address this, we propose Decomposed Prompting, a new approach to solve complex tasks by decomposing them (via prompting) into simpler sub-tasks that can be delegated to a library of prompting-based LLMs dedicated to these sub-tasks. This modular structure allows each prompt to be optimized for its specific sub-task, further decomposed if necessary, and even easily replaced with more effective prompts, trained models, or symbolic functions if desired. We show that the flexibility and modularity of Decomposed Prompting allows it to outperform prior work on few-shot prompting using GPT3. On symbolic reasoning tasks, we can further decompose sub-tasks that are hard for LLMs into even simpler solvable sub-tasks. When the complexity comes from the input length, we can recursively decompose the task into the same task but with smaller inputs. We also evaluate our approach on textual multi-step reasoning tasks: on long-context multi-hop QA task, we can more effectively teach the sub-tasks via our separate sub-tasks prompts; and on open-domain multi-hop QA, we can incorporate a symbolic information retrieval within our decomposition framework, leading to improved performance on both tasks. Datasets, Code and Prompts available at https://github.com/allenai/DecomP.

Some recent \textit{news recommendation} (NR) methods introduce a Pre-trained Language Model (PLM) to encode news representation by following the vanilla pre-train and fine-tune paradigm with carefully-designed recommendation-specific neural networks and objective functions. Due to the inconsistent task objective with that of PLM, we argue that their modeling paradigm has not well exploited the abundant semantic information and linguistic knowledge embedded in the pre-training process. Recently, the pre-train, prompt, and predict paradigm, called \textit{prompt learning}, has achieved many successes in natural language processing domain. In this paper, we make the first trial of this new paradigm to develop a \textit{Prompt Learning for News Recommendation} (Prompt4NR) framework, which transforms the task of predicting whether a user would click a candidate news as a cloze-style mask-prediction task. Specifically, we design a series of prompt templates, including discrete, continuous, and hybrid templates, and construct their corresponding answer spaces to examine the proposed Prompt4NR framework. Furthermore, we use the prompt ensembling to integrate predictions from multiple prompt templates. Extensive experiments on the MIND dataset validate the effectiveness of our Prompt4NR with a set of new benchmark results.

Many tasks in music information retrieval (MIR) involve weakly aligned data, where exact temporal correspondences are unknown. The connectionist temporal classification (CTC) loss is a standard technique to learn feature representations based on weakly aligned training data. However, CTC is limited to discrete-valued target sequences and can be difficult to extend to multi-label problems. In this article, we show how soft dynamic time warping (SoftDTW), a differentiable variant of classical DTW, can be used as an alternative to CTC. Using multi-pitch estimation as an example scenario, we show that SoftDTW yields results on par with a state-of-the-art multi-label extension of CTC. In addition to being more elegant in terms of its algorithmic formulation, SoftDTW naturally extends to real-valued target sequences.

Style transfer is the task of reproducing the semantic contents of a source image in the artistic style of a second target image. In this paper, we present NeAT, a new state-of-the art feed-forward style transfer method. We re-formulate feed-forward style transfer as image editing, rather than image generation, resulting in a model which improves over the state-of-the-art in both preserving the source content and matching the target style. An important component of our model's success is identifying and fixing "style halos", a commonly occurring artefact across many style transfer techniques. In addition to training and testing on standard datasets, we introduce the BBST-4M dataset, a new, large scale, high resolution dataset of 4M images. As a component of curating this data, we present a novel model able to classify if an image is stylistic. We use BBST-4M to improve and measure the generalization of NeAT across a huge variety of styles. Not only does NeAT offer state-of-the-art quality and generalization, it is designed and trained for fast inference at high resolution.

Fox News host Steve Hilton delves into ChatGPT, an artificial intelligence program that could have major implications for writing-focused jobs on'The Next Revolution.' In the past few years, the world has seen huge advancements in artificial intelligence, with chatbots being able to have almost human-like conversations with users in real time, and image generators conjuring realistic-looking photos based on word prompts. While proponents of the advancing technology have lauded its ability to increase creativity and streamline work, others are more critical, even warning of potential catastrophes. Stanford's 2023 Artificial Intelligence Index Report highlights a study which revealed 36% of the Natural Language Processing (NLP) research community said AI decisions could cause "nuclear-level catastrophe." Seventy-three percent of respondents said it could lead to "revolutionary societal change."

Fox News contributor Jonathan Turley describes how ChatGPT falsely accused him and other professors of sexual harassment, made up a fake Washington Post story and concocted a fake quote as some news sites invest into AI written news stories. George Washington University law professor Jonathan Turley doubled down on warnings surrounding the dangers of artificial intelligence (AI) on Monday after he was falsely accused of sexual harassment by the online bot ChatGPT, which cited a fabricated article supporting the allegation. Turley, a Fox News contributor, has been outspoken about the pitfalls of artificial intelligence and has publicly expressed concerns with the disinformation dangers of the ChatGPT bot, the latest iteration of the AI chatbot. Last week, a UCLA professor and friend of Turley's notified him that his name appeared in a search while he was conducting research on ChatGPT. The bot was asked to cite "five examples" of "sexual harassment" by U.S. law professors with "quotes from relevant newspaper articles" to support it.

A Kuwaiti media outlet has unveiled a virtual news presenter generated using artificial intelligence, with plans for it to read online bulletins. "Fedha" appeared on the Twitter account of the Kuwait News website as an image of a woman, her light-coloured hair uncovered, wearing a black jacket and white T-shirt. "I'm Fedha, the first presenter in Kuwait who works with artificial intelligence at Kuwait News. What kind of news do you prefer? Let's hear your opinions," she was heard saying in classical Arabic.