A geography professor shared his method to detect AI-generated plagiarism with Fox News. He developed it after noticing that ChatGPT produced fake citations. Chatbots are the latest new tech that everyone seems to be obsessing over. They are computer programs that use artificial intelligence and natural language processing to simulate human conversations. These conversational assistants can be accessed through various messaging platforms such as ChatGPT, Bing Chat and Bard.

We employ Natural Language Processing techniques to analyse 377808 English song lyrics from the "Two Million Song Database" corpus, focusing on the expression of sexism across five decades (1960-2010) and the measurement of gender biases. Using a sexism classifier, we identify sexist lyrics at a larger scale than previous studies using small samples of manually annotated popular songs. Furthermore, we reveal gender biases by measuring associations in word embeddings learned on song lyrics. We find sexist content to increase across time, especially from male artists and for popular songs appearing in Billboard charts. Songs are also shown to contain different language biases depending on the gender of the performer, with male solo artist songs containing more and stronger biases. This is the first large scale analysis of this type, giving insights into language usage in such an influential part of popular culture.

This paper studies audio-visual noise suppression for egocentric videos -- where the speaker is not captured in the video. Instead, potential noise sources are visible on screen with the camera emulating the off-screen speaker's view of the outside world. This setting is different from prior work in audio-visual speech enhancement that relies on lip and facial visuals. In this paper, we first demonstrate that egocentric visual information is helpful for noise suppression. We compare object recognition and action classification-based visual feature extractors and investigate methods to align audio and visual representations. Then, we examine different fusion strategies for the aligned features, and locations within the noise suppression model to incorporate visual information. Experiments demonstrate that visual features are most helpful when used to generate additive correction masks. Finally, in order to ensure that the visual features are discriminative with respect to different noise types, we introduce a multi-task learning framework that jointly optimizes audio-visual noise suppression and video-based acoustic event detection. This proposed multi-task framework outperforms the audio-only baseline on all metrics, including a 0.16 PESQ improvement. Extensive ablations reveal the improved performance of the proposed model with multiple active distractors, overall noise types, and across different SNRs.

Pre-trained language models (LMs) are used for knowledge intensive tasks like question answering, but their knowledge gets continuously outdated as the world changes. Prior work has studied targeted updates to LMs, injecting individual facts and evaluating whether the model learns these facts while not changing predictions on other contexts. We take a step forward and study LMs' abilities to make inferences based on injected facts (or propagate those facts): for example, after learning that something is a TV show, does an LM predict that you can watch it? We study this with two cloze-style tasks: an existing dataset of real-world sentences about novel entities (ECBD) as well as a new controlled benchmark with manually designed templates requiring varying levels of inference about injected knowledge. Surprisingly, we find that existing methods for updating knowledge (gradient-based fine-tuning and modifications of this approach) show little propagation of injected knowledge. These methods improve performance on cloze instances only when there is lexical overlap between injected facts and target inferences. Yet, prepending entity definitions in an LM's context improves performance across all settings, suggesting that there is substantial headroom for parameter-updating approaches for knowledge injection.

In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. On entity-rich textual resources like Wikipedia, Knowledge-Enhanced PLMs (KEPLMs) incorporate the interactions between tokens and mentioned entities in pre-training, and are thus more effective on entity-centric tasks such as entity linking and relation classification. Although exploiting Wikipedia's rich structures to some extent, conventional KEPLMs still neglect a unique layout of the corpus where each Wikipedia page is around a topic entity (identified by the page URL and shown in the page title). In this paper, we demonstrate that KEPLMs without incorporating the topic entities will lead to insufficient entity interaction and biased (relation) word semantics. We thus propose KEPLET, a novel Knowledge-Enhanced Pre-trained LanguagE model with Topic entity awareness. In an end-to-end manner, KEPLET identifies where to add the topic entity's information in a Wikipedia sentence, fuses such information into token and mentioned entities representations, and supervises the network learning, through which it takes topic entities back into consideration. Experiments demonstrated the generality and superiority of KEPLET which was applied to two representative KEPLMs, achieving significant improvements on four entity-centric tasks.

Most existing retrieval-augmented language models (LMs) for question answering assume all retrieved information is factually correct. In this work, we study a more realistic scenario in which retrieved documents may contain misinformation, causing conflicts among them. We observe that the existing models are highly brittle to such information in both fine-tuning and in-context few-shot learning settings. We propose approaches to make retrieval-augmented LMs robust to misinformation by explicitly fine-tuning a discriminator or prompting to elicit discrimination capability in GPT-3. Our empirical results on open-domain question answering show that these approaches significantly improve LMs' robustness to knowledge conflicts. We also provide our findings on interleaving the fine-tuned model's decision with the in-context learning process, paving a new path to leverage the best of both worlds.

Fox News medical contributor Dr. Marc Siegel weighs in on a survey that found a majority of patients prefer interacting with chatbots over doctors on'Fox News Live.' Dr. Marc Siegel discussed how artificial intelligence has already changed the health care industry, and what changes are anticipated Monday in the first of the FOX News Rundown podcast's "Rise of AI" series. MARC SIEGEL: We're discovering in a very positive way that A,I. can be useful to aid in diagnosis. But if it can be integrated as a tool that radiologists use or that lung surgeons use, you're ahead of the game. The other thing that I think is equally astounding is that they're using A.I. to evaluate studies they did for another reason. What do I mean by that?

Fox News medical contributor Dr. Marc Siegel weighs in on a survey that found a majority of patients prefer interacting with chatbots over doctors on'Fox News Live.' While 76% of voters want the federal government to regulate artificial intelligence technology, a new Fox News survey finds that only 39% think Uncle Sam is up to the job. In a broader sense, more voters think AI is generally a bad thing, but the view is more positive among those who are familiar with the technology. By an 8-point margin, voters overall are more likely to believe AI is a bad thing for society than a good thing. However, those familiar with AI are more likely to say it's a good thing by a 6-point margin.

The'Godfather of Artificial Intelligence' has sensationally resigned from Google and warned the technology could upend life as we know it. Geoffrey Hinton, 75, is credited with creating the technology that became the bedrock of A.I. systems like ChatGPT and Google Bard. But the Turing prize winner now says a part of him regrets helping to make the systems, that he fears could prompt the proliferation of misinformation and replace people in the workforce. He said he had to tell himself excuses like'if I didn't build it, someone else would have' to prevent himself from being overwhelmed by guilt. He drew comparisons with the'father of the atomic bomb' Robert Oppenheimer, who was reportedly distraught by his invention and dedicated the rest of his life to stopping its proliferation.

The news-rating group NewsGuard has found dozens of news websites generated by AI chatbots proliferating online, according to a report published Monday, raising questions about how the technology may supercharge established fraud techniques. The 49 websites, which were independently reviewed by Bloomberg, run the gamut. Some are dressed up as breaking news sites with generic-sounding names like News Live 79 and Daily Business Post, while others share lifestyle tips, celebrity news or publish sponsored content. But none disclose they're populated using AI chatbots such as OpenAI's ChatGPT and potentially Alphabet's Google Bard, which can generate detailed text based on simple user prompts. Many of the websites began publishing this year as the AI tools began to be widely used by the public.