The line between human and machine authorship is blurring, particularly as it's become increasingly difficult to tell whether something was written by a person or AI. Now, in what may seem like a tipping point, the digital marketing firm Graphite recently published a study showing that more than 50% of articles on the web are being generated by artificial intelligence. As a scholar who explores how AI is built, how people are using it in their everyday lives, and how it's affecting culture, I've thought a lot about what this technology can do and where it falls short. If you're more likely to read something written by AI than by a human on the internet, is it only a matter of time before human writing becomes obsolete? Or is this simply another technological development that humans will adapt to?

We present a framework SCStory for online story discovery, that helps people digest rapidly published news article streams in real-time without human annotations. To organize news article streams into stories, existing approaches directly encode the articles and cluster them based on representation similarity. However, these methods yield noisy and inaccurate story discovery results because the generic article embeddings do not effectively reflect the story-indicative semantics in an article and cannot adapt to the rapidly evolving news article streams. SCStory employs self-supervised and continual learning with a novel idea of story-indicative adaptive modeling of news article streams. With a lightweight hierarchical embedding module that first learns sentence representations and then article representations, SCStory identifies story-relevant information of news articles and uses them to discover stories. The embedding module is continuously updated to adapt to evolving news streams with a contrastive learning objective, backed up by two unique techniques, confidence-aware memory replay and prioritized-augmentation, employed for label absence and data scarcity problems. Thorough experiments on real and the latest news data sets demonstrate that SCStory outperforms existing state-of-the-art algorithms for unsupervised online story discovery.

The proliferation of fake news has become a significant concern in recent times due to its potential to spread misinformation and manipulate public opinion. This paper presents a comprehensive study on detecting fake news in Brazilian Portuguese, focusing on journalistic-type news. We propose a machine learning-based approach that leverages natural language processing techniques, including TF-IDF and Word2Vec, to extract features from textual data. We evaluate the performance of various classification algorithms, such as logistic regression, support vector machine, random forest, AdaBoost, and LightGBM, on a dataset containing both true and fake news articles. The proposed approach achieves high accuracy and F1-Score, demonstrating its effectiveness in identifying fake news. Additionally, we developed a user-friendly web platform, fakenewsbr.com, to facilitate the verification of news articles' veracity. Our platform provides real-time analysis, allowing users to assess the likelihood of fake news articles. Through empirical analysis and comparative studies, we demonstrate the potential of our approach to contribute to the fight against the spread of fake news and promote more informed media consumption.

Generative AI's two-edged sword - more information made available. Well before the birth of the internet, I had become an infomaniac. I devoured tech magazines and books on tech to try and stay up to date on current trends and issues related to my job as a tech analyst. I am also a techie at heart, and my first tech-related job in 1975 was semiconductor-related. I would also go to specialized tech user groups and mini-conferences as my interest in tech evolved and helped me when I joined Creative Strategies in 1982 as their first PC analyst.

One of the first issues to address with these labels is the Misinformation in news articles has been one of the main inconsistency of scales used. For example, some labels are topics for discussion over the past few years. There have scaled from 0-3 in terms of level of misinformation, others been several organizations that developed methods for assessing are scaled in a binary manner with 0 and 1, and some have 4 reliability and personal bias of news coverage. In today's categorical values based on levels of media bias. So there is day in age, it is unnatural to arbitrarily trust the news quite a bit of processing that needed to be done to normalize outlets that claim to be truly objective and unbiased because everything and transform the qualitative variables into quantitative the term "bias" is relative. What one person perceives as variables.

Doing 500 days of AI project was a fascinating journey and enriched my life in many ways. One way was through awareness of the breadth of areas that artificial intelligence was being discussed within society. I could also more clearly see the varied applications of AI in multiple environments. After 500 days I looked back before Christmas in 2020 and I could say that I had at least the intention to get an understanding of the field of artificial intelligence. Here is a link to my article containing links to all the 500 articles on the topic of artificial intelligence.

As a PhD student focused on AI, it's been hard to figure out how to keep up with it all. So, over the past few years I've sought out and subscribed to a ton of newsletters that help me do that. In this piece i'll share what I consider to be the best currently active newsletters, with a bit of commentary on why I think they are good. My criteria are that these newsletters are still active, have at least a semi-consistent release schedule, focus on AI news (as opposed to data science and the like), are newsletters (as opposed to blogs), and are high quality (according to me). They are presented in rough order of preference and are grouped by whether they cover non technical news, AI research, or both.

IMAGE: A team of doctors led by UVA Health's James H. Harrison Jr., MD, PhD, has given us a glimpse of tomorrow in a new article on the current state and... view more Artificial intelligence can already scan images of the eye to assess patients for diabetic retinopathy, a leading cause of vision loss, and to find evidence of strokes on brain CT scans. But what does the future hold for this emerging technology? How will it change how doctors diagnose disease, and how will it improve the care patients receive? A team of doctors led by UVA Health's James H. Harrison Jr., MD, PhD, has given us a glimpse of tomorrow in a new article on the current state and future use of artificial intelligence (AI) in the field of pathology. Harrison and other members of the College of American Pathologists' Machine Learning Workgroup have spent the last two years evaluating the potential of AI and machine learning, assessing its current role in diagnostic testing and outlining what is needed to meet its potential in the not-too-distant future.

With the increase in content availability over the internet it is very difficult to get noticed. It has become an upmost the priority of the blog writers to get some feedback over their creations to be confident about the impact of their article. We are training a machine learning model to learn popular article styles, in the form of vector space representations using various word embeddings, and their popularity based on claps and tags.

Welcome to the November edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted articles about AI that can identify who wrote each scene in Shakespeare's Henry VIII, and teach non-native speakers how to pronounce English words! Let's start, as usual, with the comic of the month: In a recent article researchers describe how they trained machine-learning algorithms to predict what features in a song would impact people's emotional responses. They predicted brain and heart activities as well as physiological response using features based on music dynamics such as timbre, harmony, etc...