Hall of Fame tennis coach Rick Macci weighs in on how fans will react to a computer commentator instead of a human one on'Fox & Friends.' Over the past 150 years or so the predictive power of science fiction has been remarkably prescient about myriad advancements made by humanity. Millions of Americans today walk about with a "Dick Tracy" style "wrist radio," known now as a smartwatch and spaceships and space stations dot the darkness beyond our planet. Last week the FAA even cleared the way for testing a flying car. BIDEN ADMIN, DEMS TRYING TO MAKE AI'WOKE': REPORT Sci-fi has also missed the mark now and then.

New York City musician Jules Avalon reflects on the power of John Lennon and the Beatles at Strawberry Fields in Central Park, located across the street from where Lennon was murdered on Dec. 8, 1980. Paul McCartney befuddled Beatles fans last month when he announced the band would be releasing a record featuring the late John Lennon, with the help of artificial intelligence. "When we came to make what will be the last Beatles record it was a demo that John had – that we worked on and we just finished it up – it'll be released this year. We were able to take John's voice and get it pure through this AI so that then we could mix the record as you would normally do," he told BBC Radio 4's Today show. Fans began questioning why the Beatles would do such a thing, sharing their disdain on social media.

At their core, generative models are a type of AI system that take in vast Generative audio models typically focus their applications in music amounts of training data to be able to produce a novel item that is and speech generation, with recent models having human-like quality similar to and statistically likely to exist in the data it was trained in their audio output. This paper conducts a systematic literature on. Though generative models have been around for decades with review of 884 papers in the area of generative audio models in order origins in the 1980s [9], the outputs of these models saw unprecedented to both quantify the degree to which researchers in the field are considering advances with the introduction of the transformer in 2017 potential negative impacts and identify the types of ethical which revolutionized the field by introducing a mechanism called implications researchers in this area need to consider. Though 65% "attention" that allowed for much more accurate and complex outputs of generative audio research papers note positive potential impacts of generative models [61]. Generative models may continue to of their work, less than 10% discuss any negative impacts. This improve as (a) their training data becomes larger (for text, imagine jarringly small percentage of papers considering negative impact the entire internet) and (b) researchers continue to make advances is particularly worrying because the issues brought to light by the in the architecture of the models. This paper focuses specifically few papers doing so are raising serious ethical implications and on the current landscape of generative audio models.

In this paper, we present a computational analysis of the Persian language Twitter discourse with the aim to estimate the shift in stance toward gender equality following the death of Mahsa Amini in police custody. We present an ensemble active learning pipeline to train a stance classifier. Our novelty lies in the involvement of Iranian women in an active role as annotators in building this AI system. Our annotators not only provide labels, but they also suggest valuable keywords for more meaningful corpus creation as well as provide short example documents for a guided sampling step. Our analyses indicate that Mahsa Amini's death triggered polarized Persian language discourse where both fractions of negative and positive tweets toward gender equality increased. The increase in positive tweets was slightly greater than the increase in negative tweets. We also observe that with respect to account creation time, between the state-aligned Twitter accounts and pro-protest Twitter accounts, pro-protest accounts are more similar to baseline Persian Twitter activity.

This paper describes our submission for the subjectivity detection task at the CheckThat! Lab. To tackle class imbalances in the task, we have generated additional training materials with GPT-3 models using prompts of different styles from a subjectivity checklist based on journalistic perspective. We used the extended training set to fine-tune language-specific transformer models. Our experiments in English, German and Turkish demonstrate that different subjective styles are effective across all languages. In addition, we observe that the style-based oversampling is better than paraphrasing in Turkish and English. Lastly, the GPT-3 models sometimes produce lacklustre results when generating style-based texts in non-English languages.

The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge. However, existing methods focus only on a single object per subject-relation pair, even though often multiple objects are correct. To overcome this limitation, we analyze these representations for their potential to yield materialized multi-object relational knowledge. We formulate the problem as a rank-then-select task. For ranking candidate objects, we evaluate existing prompting techniques and propose new ones incorporating domain knowledge. Among the selection methods, we find that choosing objects with a likelihood above a learned relation-specific threshold gives a 49.5% F1 score. Our results highlight the difficulty of employing LMs for the multi-valued slot-filling task and pave the way for further research on extracting relational knowledge from latent language representations.

Training learnable metrics using modern language models has recently emerged as a promising method for the automatic evaluation of machine translation. However, existing human evaluation datasets for text simplification have limited annotations that are based on unitary or outdated models, making them unsuitable for this approach. To address these issues, we introduce the SimpEval corpus that contains: SimpEval_past, comprising 12K human ratings on 2.4K simplifications of 24 past systems, and SimpEval_2022, a challenging simplification benchmark consisting of over 1K human ratings of 360 simplifications including GPT-3.5 generated text. Training on SimpEval, we present LENS, a Learnable Evaluation Metric for Text Simplification. Extensive empirical results show that LENS correlates much better with human judgment than existing metrics, paving the way for future progress in the evaluation of text simplification. We also introduce Rank and Rate, a human evaluation framework that rates simplifications from several models in a list-wise manner using an interactive interface, which ensures both consistency and accuracy in the evaluation process and is used to create the SimpEval datasets.

The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evolutionary nature of memes; new hateful memes can emerge by fusing hateful connotations with other cultural ideas or symbols. In this paper, we propose a framework that leverages multimodal contrastive learning models, in particular OpenAI's CLIP, to identify targets of hateful content and systematically investigate the evolution of hateful memes. We find that semantic regularities exist in CLIP-generated embeddings that describe semantic relationships within the same modality (images) or across modalities (images and text). Leveraging this property, we study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. We demonstrate the capabilities of our framework for analyzing the evolution of hateful memes by focusing on antisemitic memes, particularly the Happy Merchant meme. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme, with some linked to specific countries, persons, or organizations. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes so that moderators can manually verify them and mitigate the problem of hateful content online.

AI technology is quickly creeping into every industry, prompting new questions about whether online content comes from a human or a computer. The company behind news outlets like Gizmodo and The AV Club came under fire from staff, the union and journalists this week after rolling out artificial intelligence (AI) generated articles filled with blatant falsehoods and haphazardly written sentences. Last week, it was revealed that G/O Media would begin to publish articles generated by AI. The move was swiftly criticized by the GMG Union, which represents Gizmodo and other news outlets under the G/O banner. The backlash did nothing to deter G/O Media from moving forward with the new initiative.

Cleotha Abston, the career criminal accused of kidnapping and murdering Eliza Fletcher, a mother of two, in Memphis in September returns to court for a hearing on Thursday. HAPPENING TODAY - Cleotha Abston, who is accused of kidnapping and murdering jogger Eliza Fletcher, returns to court for a hearing. SENT PACKING - Judge rules in favor of'Yellowstone' star Kevin Costner during divorce from estranged wife. 'GROWING RISKS' - President Biden's crackdown on power plants is sounding off alarms. SOCIAL SHOWDOWN - Meta's new site'Threads' gives Twitter a run for its money within hours of debut.