The New York Times has sued Microsoft and OpenAI for using its content to help develop artificial intelligence services, in a sign of the increasingly fraught relationship between the media and a technology that could upend the news industry. The Times didn't specify its monetary demands. OpenAI has faced criticism for scraping text widely from the web to train its popular chatbot since it debuted a year ago. While it has been sued by prominent authors, this is the first challenge to its practices by a major media organization. The startup has sought licensing deals with publishers, much like Alphabet's Google and Meta Platforms' Facebook have done in recent years.

The New York Times has sued OpenAI and Microsoft over the use of its content to train generative artificial intelligence and large-language model systems, a move that could see the company receive billions of dollars in damages. The lawsuit contains an appeal to the "vital" importance of the Times's independent journalism to democracy, arguing that it is "increasingly rare and valuable". The publisher's lawsuit is the latest in a string of similar cases, including one brought by more than a dozen authors in September targeting the company for its use of their writing. Language learning models have faced increasing scrutiny since they exploded in popularity in the past year, with news outlets in particular concerned that the tools will spread misinformation attributed to them and utilize their content with no incentive to click through to the original source. ChatGPT launched in November 2022 and amassed 100 million users in just two months.

Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic content analysis in communication science, especially for the coding of complex semantic categories in large datasets via supervised machine learning. However, three characteristics so far impeded the widespread adoption of the methods in the applying disciplines: the dominance of English language models in NLP research, the necessary computing resources, and the effort required to produce training data to fine-tune PLMs. In this study, we address these challenges by using a multilingual transformer model in combination with the adapter extension to transformers, and few-shot learning methods. We test our approach on a realistic use case from communication science to automatically detect claims and arguments together with their stance in the German news debate on arms deliveries to Ukraine. In three experiments, we evaluate (1) data preprocessing strategies and model variants for this task, (2) the performance of different few-shot learning methods, and (3) how well the best setup performs on varying training set sizes in terms of validity, reliability, replicability and reproducibility of the results. We find that our proposed combination of transformer adapters with pattern exploiting training provides a parameter-efficient and easily shareable alternative to fully fine-tuning PLMs. It performs on par in terms of validity, while overall, provides better properties for application in communication studies. The results also show that pre-fine-tuning for a task on a near-domain dataset leads to substantial improvement, in particular in the few-shot setting. Further, the results indicate that it is useful to bias the dataset away from the viewpoints of specific prominent individuals.

Despite the benefits of personalizing items and information tailored to users' needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items, and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues, such as stereotypes, biases, and miscalibration. We propose a unified framework that distinguishes the sources of prediction errors into a set of key measures that quantify the various types of system-induced effects, both at the individual and collective levels. Based on our measuring framework, we examine the most widely adopted algorithms in the context of movie recommendation. Our research reveals three important findings: (1) Differences between algorithms: recommendations generated by simpler algorithms tend to be more stereotypical but less biased than those generated by more complex algorithms. (2) Disparate impact on groups and individuals: system-induced biases and stereotypes have a disproportionate effect on atypical users and minority groups (e.g., women and older users). (3) Mitigation opportunity: using structural equation modeling, we identify the interactions between user characteristics (typicality and diversity), system-induced effects, and miscalibration. We further investigate the possibility of mitigating system-induced effects by oversampling underrepresented groups and individuals, which was found to be effective in reducing stereotypes and improving recommendation quality. Our research is the first systematic examination of not only system-induced effects and miscalibration but also the stereotyping issue in recommender systems.

We present a comprehensive solution to learn and improve text-to-image models from human preference feedback. To begin with, we build ImageReward -- the first general-purpose text-to-image human preference reward model -- to effectively encode human preferences. Its training is based on our systematic annotation pipeline including rating and ranking, which collects 137k expert comparisons to date. In human evaluation, ImageReward outperforms existing scoring models and metrics, making it a promising automatic metric for evaluating text-to-image synthesis. On top of it, we propose Reward Feedback Learning (ReFL), a direct tuning algorithm to optimize diffusion models against a scorer. Both automatic and human evaluation support ReFL's advantages over compared methods. All code and datasets are provided at \url{https://github.com/THUDM/ImageReward}.

In 1954, Alston S. Householder published Principles of Numerical Analysis, one of the first modern treatments on matrix decomposition that favored a (block) LU decomposition-the factorization of a matrix into the product of lower and upper triangular matrices. And now, matrix decomposition has become a core technology in machine learning, largely due to the development of the back propagation algorithm in fitting a neural network. The sole aim of this survey is to give a self-contained introduction to concepts and mathematical tools in numerical linear algebra and matrix analysis in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning matrix decomposition and given the paucity of scope to present this discussion, e.g., the separated analysis of the Euclidean space, Hermitian space, Hilbert space, and things in the complex domain. We refer the reader to literature in the field of linear algebra for a more detailed introduction to the related fields.

As wildfire activity reaches record levels, the tech integration company SAIC is developing artificial intelligence technology that can help predict when they'll happen, how to stop them, and how to keep folks safe. THE FUTURE IS NOW: A new tech era quietly dawned in 2023. BUILDING AI SKILLS: Thomson Reuters' AI platform helps non-engineers build AI skills. FAKE NEWS: Boom of misinformation online fueled further by AI. BERLIN, GERMANY - APRIL 03: Symbolic photo: The logo of the chatbot ChatGPT ( Generative Pre-trained Transformer) from the company OpenAI can be seen on a smartphone on April 3, 2023, in Berlin, Germany.

Kurt Knutsson introduces us to the human-like robot that is doing the work of people in the company's Georgia warehouse. When you think of humanoid robots, you might think of an army of T-800s from "Terminator" or C-3PO from "Star Wars." You probably don't think of shapewear. But women's clothing brand Spanx is using a robot to work in its Georgia warehouse -- and this robot looks awfully human-like. While it might not look like Arnold Schwarzenegger, its bipedal bots feature arms and legs and other features that help it navigate a warehouse.

I was dreading buying Christmas gifts this year. My family tends to buy things they need as they go, and my sister would kill me if I bought her another sweater. So when my editor suggested I use ChatGPT to plan my Christmas shopping for me and write about it, I jumped at the opportunity. And I figured it was a win-win. If its suggested gifts were good, I wouldn't need to worry about coming up with present ideas for another 12 months! If they were a disaster, it would be a good opportunity to showcase how rudimentary artificial intelligence is (I'm extremely skeptical about the predictions of AI enslaving us in the future).

While Barbenheimer was undoubtedly the biggest movie story of 2023, the year in film was one jam-packed with dozens of truly great movies--not all of which managed to generate the nonstop headlines or mainstream traction that an iconic doll and the "father of the atomic bomb" did. It was a stellar year for first-time directors as well, as evidenced by films like Emily, The Unknown Country, and A Thousand and One. If you've seen Barbie, Oppenheimer, and many of the year's higher-profile movies, here are 15 that you maybe haven't seen that are definitely worth your time. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.