The Associated Press (AP) and ChatGPT parent company OpenAI have reached a news-sharing agreement, but not for the reasons you may think. It doesn't involve AI chatbots quickly churning out content, but rather a way for OpenAI to train its algorithmic models, as reported by Axios. The two-year deal gives OpenAI access to select news content and technology from the AP archives, dating back to 1985. All of this sweet, sweet data will be used to improve the efficacy of future iterations of ChatGPT and related tools. This is one of the first high-profile partnerships between a major news organization and an artificial intelligence company.

Now, a rising group of authors, musicians, news organizations and social media companies has been pushing back, arguing that the use of their content to train AI is a massive shift in the way the internet works, especially since some of the AI tools being trained on human-made content are already being used to replace human workers. A wave of lawsuits has washed over the industry in the past two weeks alleging improper data use, including class-action suits against OpenAI and Google, and lawsuits against OpenAI from the comedian Sarah Silverman and two prominent fiction authors.

Stealing someone else's joke is one of the highest crimes in comedy. With new AI tools like ChatGPT, some comedians are now worried about getting ripped off. After comedian Sarah Silverman joined a lawsuit against OpenAI and Meta for allegedly using her content to train their bots without permission, one comic told Fox News ChatGPT does not pose a threat to him. "In terms of how ChatGPT affects comedy, yes, I think we're going to enter the golden age of in-print comedians, meaning people who can type things on the internet," said Jimmy Failla, comedian and host of "Fox Across America" on Fox News Radio and Fox Nation. "But where true performers and people with actual charisma and comedic wherewithal will always flourish is no one's going to show up to a comedy club and buy a two-drink minimum to stare at a laptop, typing out words, or even saying those words through some Bluetooth audio," he continued.

With the most recent season of Black Mirror, you sensed that Charlie Brooker was keen to move away from his reputation as a prophet. Time and time again since his series hit the air, it has managed to correctly predict the future in all sorts of horrible ways. But this season felt like it was deliberately skewing away from reality precisely to avoid this happening again. After all, unless a hapless demon destroys Earth – or unless Britney Spears literally turns into a werewolf – then Brooker is probably in much safer territory. Reader, it has happened already.

Heritage Foundation tech policy research associate Jake Denton joined'Fox & Friends First' to discuss growing concerns surrounding the political implications of artificial intelligence. AI's breakthrough into popular culture, marked by chatbot tools like ChatGPT, has turned this technology into a battleground for culture warriors. However, equating artificial intelligence or AI with social media platforms could cost us significant advances in healthcare, transportation and global leadership in technology. Over the past decade, politicians have developed a playbook for scoring political points by criticizing social media. Democrats have focused on the spread of misinformation and disinformation, while Republicans have raised concerns about perceived bias against conservative views.

This paper presents a novel algorithm for crack localisation and detection based on visual and tactile analysis via fibre-optics. A finger-shaped sensor based on fibre-optics is employed for the data acquisition to collect data for the analysis and the experiments. To detect the possible locations of cracks a camera is used to scan an environment while running an object detection algorithm. Once the crack is detected, a fully-connected graph is created from a skeletonised version of the crack. A minimum spanning tree is then employed for calculating the shortest path to explore the crack which is then used to develop the motion planner for the robotic manipulator. The motion planner divides the crack into multiple nodes which are then explored individually. Then, the manipulator starts the exploration and performs the tactile data classification to confirm if there is indeed a crack in that location or just a false positive from the vision algorithm. If a crack is detected, also the length, width, orientation and number of branches are calculated. This is repeated until all the nodes of the crack are explored. In order to validate the complete algorithm, various experiments are performed: comparison of exploration of cracks through full scan and motion planning algorithm, implementation of frequency-based features for crack classification and geometry analysis using a combination of vision and tactile data. From the results of the experiments, it is shown that the proposed algorithm is able to detect cracks and improve the results obtained from vision to correctly classify cracks and their geometry with minimal cost thanks to the motion planning algorithm.

We propose Information Lattice Learning (ILL) as a general framework to learn rules of a signal (e.g., an image or a probability distribution). In our definition, a rule is a coarsened signal used to help us gain one interpretable insight about the original signal. To make full sense of what might govern the signal’s intrinsic structure, we seek multiple disentangled rules arranged in a hierarchy, called a lattice. Compared to representation/rule-learning models optimized for a specific task (e.g., classification), ILL focuses on explainability: it is designed to mimic human experiential learning and discover rules akin to those humans can distill and comprehend. This paper details the math and algorithms of ILL, and illustrates how it addresses the fundamental question “what makes X an X” by creating rule-based explanations designed to help humans understand. Our focus is on explaining X rather than (re)generating it. We present applications in knowledge discovery, using ILL to distill music theory from scores and chemical laws from molecules and further revealing connections between them. We show ILL’s efficacy and interpretability on benchmarks and assessments, as well as a demonstration of ILL-enhanced classifiers achieving human-level digit recognition using only one or a few MNIST training examples (1–10 per class).

In this paper, we propose a novel method, Chain-of-Thoughts Attribute Manipulation (CoTAM), to guide few-shot learning by carefully crafted data from Large Language Models (LLMs). The main idea is to create data with changes only in the attribute targeted by the task. Inspired by facial attribute manipulation, our approach generates label-switched data by leveraging LLMs to manipulate task-specific attributes and reconstruct new sentences in a controlled manner. Instead of conventional latent representation controlling, we implement chain-of-thoughts decomposition and reconstruction to adapt the procedure to LLMs. Extensive results on text classification and other tasks verify the advantage of CoTAM over other LLM-based text generation methods with the same number of training examples. Analysis visualizes the attribute manipulation effectiveness of CoTAM and presents the potential of LLM-guided learning with even less supervision.

Recent developments in natural language processing have demonstrated the potential of large language models (LLMs) to improve a range of educational and learning outcomes. Of recent chatbots based on LLMs, ChatGPT and Bard have made it clear that artificial intelligence (AI) technology will have significant implications on the way we obtain and search for information. However, these tools sometimes produce text that is convincing, but often incorrect, known as hallucinations. As such, their use can distort scientific facts and spread misinformation. To counter polarizing responses on these tools, it is critical to provide an overview of such responses so stakeholders can determine which topics tend to produce more contentious responses -- key to developing targeted regulatory policy and interventions. In addition, there currently exists no annotated dataset of ChatGPT and Bard responses around possibly polarizing topics, central to the above aims. We address the indicated issues through the following contribution: Focusing on highly polarizing topics in the US, we created and described a dataset of ChatGPT and Bard responses. Broadly, our results indicated a left-leaning bias for both ChatGPT and Bard, with Bard more likely to provide responses around polarizing topics. Bard seemed to have fewer guardrails around controversial topics, and appeared more willing to provide comprehensive, and somewhat human-like responses. Bard may thus be more likely abused by malicious actors. Stakeholders may utilize our findings to mitigate misinformative and/or polarizing responses from LLMs

Real-time music information retrieval (RT-MIR) has much potential to augment the capabilities of traditional acoustic instruments. We develop RT-MIR techniques aimed at augmenting percussive fingerstyle, which blends acoustic guitar playing with guitar body percussion. We formulate several design objectives for RT-MIR systems for augmented instrument performance: (i) causal constraint, (ii) perceptually negligible action-to-sound latency, (iii) control intimacy support, (iv) synthesis control support. We present and evaluate real-time guitar body percussion recognition and embedding learning techniques based on convolutional neural networks (CNNs) and CNNs jointly trained with variational autoencoders (VAEs). We introduce a taxonomy of guitar body percussion based on hand part and location. We follow a cross-dataset evaluation approach by collecting three datasets labelled according to the taxonomy. The embedding quality of the models is assessed using KL-Divergence across distributions corresponding to different taxonomic classes. Results indicate that the networks are strong classifiers especially in a simplified 2-class recognition task, and the VAEs yield improved class separation compared to CNNs as evidenced by increased KL-Divergence across distributions. We argue that the VAE embedding quality could support control intimacy and rich interaction when the latent space's parameters are used to control an external synthesis engine. Further design challenges around generalisation to different datasets have been identified.