Correction, January 18, 2024, 4:55 PM ET: This story originally claimed that AI-generated content was being promoted in Google News. We did not note that to find such stories required heavily manipulating the search results in Google News, so much so that it didn't surface an original, more legitimate source. As 404 Media itself writes, "Both of these rip-off articles appear in Google News search results. The first appears when searching for "Star Wars theory" and setting the results to the past 24 hours. The second appears when searching for the subject of the article with a similar 24 hour setting."

Determining and measuring diversity in news articles is important for a number of reasons, including preventing filter bubbles and fueling public discourse, especially before elections. So far, the identification and analysis of diversity have been illuminated in a variety of ways, such as measuring the overlap of words or topics between news articles related to US elections. However, the question of how diversity in news articles can be measured holistically, i.e., with respect to (1) variety, (2) balance, and (3) disparity, considering individuals, parties, and topics, has not been addressed. In this paper, we present a framework for determining diversity in news articles according to these dimensions. Furthermore, we create and provide a dataset of Google Top Stories, encompassing more than 26,000 unique headlines from more than 900 news outlets collected within two weeks before and after the 2021 German federal election. While we observe high diversity for more general search terms (e.g., "election"), a range of search terms ("education," "Europe," "climate protection," "government") resulted in news articles with high diversity in two out of three dimensions. This reflects a more subjective, dedicated discussion on rather future-oriented topics.

In the absence of nonverbal cues during messaging communication, users express part of their emotions using emojis. Thus, having emojis in the vocabulary of text messaging language models can significantly improve many natural language processing (NLP) applications such as online communication analysis. On the other hand, word embedding models are usually trained on a very large corpus of text such as Wikipedia or Google News datasets that include very few samples with emojis. In this study, we create emojiSpace, which is a combined word-emoji embedding using the word2vec model from the Genism library in Python. We trained emojiSpace on a corpus of more than 4 billion tweets and evaluated it by implementing sentiment analysis on a Twitter dataset containing more than 67 million tweets as an extrinsic task. For this task, we compared the performance of two different classifiers of random forest (RF) and linear support vector machine (SVM). For evaluation, we compared emojiSpace performance with two other pre-trained embeddings and demonstrated that emojiSpace outperforms both.

Last year, OpenAI announced Codex, a model for efficient programming with the aid of Artificial Intelligence (AI). One of the videos uploaded to the OpenAI YouTube channel showed a live demo that was hard to believe even when seen with one's own eyes. With just a few lines of commands, it was possible to create a whole game in JavaScript. The level of the commands seemed somewhat high, but with Codex you can see that it is immediately able to implement the code and run the game. In this way, Codex is a model that helps people write code much more efficiently than they could on their own.

Comprehensive, up-to-date news coverage, aggregated from sources all over the world by Google News.

Comprehensive up-to-date news coverage, aggregated from sources all over the world by Google News.

Comprehensive up-to-date news coverage, aggregated from sources all over the world by Google News.

Google News holds a special place in the world of journalism. When multiple media outlets report on the same topic in a short amount of time, the articles that make it to the main News page are seen by the most people. If you're a musician, you want your song to show up on Spotify's main page. If you're in a comedy movie, you want it to be listed first in the "comedy" section on Netflix. That's why one of my crowning achievements as a journalist was convincing the Google News algorithm I was the queerest artificial intelligence reporter in the world.

Comprehensive up-to-date news coverage, aggregated from sources all over the world by Google News.

The world of AI and Machine Learning has many layers and can be quite complex to learn. Many terms are out there and unless you have a basic understanding of the landscape it can be quite confusing. In this article, expert Eric Enge will introduce the basic concepts and try to demystify it all for you. This is also the first of a four-part article series to cover many of the more interesting aspects of the AI landscape. There are so many different terms that it can be hard to sort out what they all mean.