With the rapid evolution of AI chatbot systems like Chat-GPT, VALL-E, and BlenderBot 3 and their growing abilities to generate text on par with human writers, robots coming to take your writing job is becoming a viable threat. On Wednesday, The Byte reported that the popular tech site appears to have employed "automation technology" to produce a series of financial explainer posts beginning in November 2022 under the byline of CNET Money Staff. It is only after clicking the byline that the site reveals that "This article was generated using automation technology and thoroughly edited and fact-checked by an editor on our editorial staff." Looks like @CNET (DR 92 tech site) just did their coming out about using AI content for SEO articles. In all, the tech site produced 73 such posts since last November on subjects such as "Should You Break an Early CD for a Better Rate?" or "What is Zelle and How Does It Work?" Since news of its activities broke at the start of the day, CNET has subsequently taken down the CNET Money Staff bio page as well as removed the "Staff" from numerous posts it had written. Using text generators isn't currently a widespread practice throughout the journalistic sphere but outlets like the Associated Press and Washington Post have used them for various low-level copywriting tasks -- the latter employing them to write about high school football and the equally unimportant 2016 Rio Olympics.

A major shift from skilled to unskilled workers was one of the many changes caused by the Industrial Revolution, when the switch to machines contributed to decline in the social and economic status of artisans, whose skills were dismembered into discrete actions by factory-line workers. We consider what may be an analogous computing technology: the recent introduction of AI-generated art software. AI art generators such as Dall-E and Midjourney can create fully rendered images based solely on a user's prompt, just at the click of a button. Some artists fear if the cheaper price and conveyor-belt speed that comes with AI-produced images is seen as an improvement to the current system, it may permanently change the way society values/views art and artists. In this article, we consider the implications that AI art generation introduces through a post-industrial revolution historical lens. We then reflect on the analogous issues that appear to arise as a result of the AI art revolution, and we conclude that the problems raised mirror those of industrialization, giving a vital glimpse into what may lie ahead.

FIGURE 1: LMU seal (left) style-transferred to Van Gogh's Sunflower painting (center) and blended with the prompt - Van Gogh, sunflowers - via CLIP+VGAN (right). In the last few years, there have been several breakthroughs in the methodologies used in Natural Language Processing (NLP) as well as Computer Vision (CV). Beyond these improvements on single-modality models, large-scale multimodal approaches have become a very active area of research. In this seminar, we reviewed these approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other Chapter 3.1 and Chapter 3.2), as well as models in which one modality is utilized to enhance representation learning for the other (Chapter 3.3 and Chapter 3.4). To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced (Chapter 3.5). Finally, we also cover other modalities (Chapter 4.1 and Chapter 4.2) as well as general-purpose multi-modal models (Chapter 4.3), which are able to handle different tasks on different modalities within one unified architecture.

With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking. For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks. These fake news is usually released to either smear the opponents or support the candidate on their side. The erroneous information in the fake news is usually written to motivate the voters' irrational emotion and enthusiasm. Such kinds of fake news sometimes can bring about devastating effects, and an important goal in improving the credibility of online social networks is to identify the fake news timely. In this paper, we propose to study the fake news detection problem. Automatic fake news identification is extremely hard, since pure model based fact-checking for news is still an open problem, and few existing models can be applied to solve the problem. With a thorough investigation of a fake news data, lots of useful explicit features are identified from both the text words and images used in the fake news. Besides the explicit features, there also exist some hidden patterns in the words and images used in fake news, which can be captured with a set of latent features extracted via the multiple convolutional layers in our model. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in this paper. By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously. Extensive experiments carried on the real-world fake news datasets have demonstrate the effectiveness of TI-CNN.

Next time you're on your favorite news site, you might want to double check the byline to see if it was written by an actual human. CNET, a massively popular tech news outlet, has been quietly employing the help of "automation technology" -- a stylistic euphemism for AI -- on a new wave of financial explainer articles, seemingly starting around November of last year. In the absence of any formal announcement or coverage, it appears that this was first spotted by online marketer Gael Breton in a tweet on Wednesday. The articles are published under the unassuming appellation of "CNET Money Staff," and encompass topics like "Should You Break an Early CD for a Better Rate?" or "What is Zelle and How Does It Work?" That byline obviously does not paint the full picture, and so your average reader visiting the site likely would have no idea that what they're reading is AI-generated.

ChatGPT, the popular conversational AI model capable of mimicking human responses, has been updated with improved accuracy. Upon opening the interface the ChatGPT interface, users will be greeted with a new pop-up message that lists the changes in what OpenAI calls the "Jan 9 version" update. Provided by The Indian Express chatgpt update OpenAI says that the latest update brings the ability to stop generating ChatGPT's response (Express photo) It should be generally better across a wide range of topics and has improved factuality. The first point could pertain to ChatGPT's potential to spread misinformation. While the chatbot does have built-in functions to help it avoid offensive responses and factual errors, it's still far from perfect. OpenAI has also admitted that ChatGPT is susceptible to providing "plausible-sounding but incorrect or nonsensical answers."

When the artificial intelligence platform ChatGPT was released in late November, I was one of many educators who jumped on it, introducing it in the seminar I teach at Yale on the media and democracy. With its ability to communicate in plain-English prose, it was undeniably fun for the students to play with, composing everything from silly poems to job application letters. But it was also deeply troubling. When I prompted it to spread misinformation, it generated a news article falsely asserting the "U.S. Electoral Commission" had found "rampant voter fraud" in the 2020 election. It was also alarmingly quick to complete the term paper assignment that my students had been working on for weeks.

They've also become more diverse. Voices like those of Mary Robinette Kowal -- author of the award-winning alt-history novel "Calculating Stars" -- and Ted Chiang have grown the sci-fi oeuvre over the past several years. And just as space travel ambitions have spread to other countries, a number of international authors have emerged among science fiction's elite. For example, Chinese novelist Liu Cixin's "The Three-Body Problem" has sold more than 8 million copies, is being adapted into a Netflix series and drew President Barack Obama's praise after he read it during his time in the White House. "The scope of it was immense," Obama said in 2017.

Last year ended with images flooding Instagram, Twitter and group chats -- pictures of us, but not exactly. They were products of an app called Lensa AI, which can apply machine-learning tools to scan your selfies and generate portraits of you in a variety of artistic styles. This sort of thing seems to bea semiannual trend: Every few months, an app emerges to collect photos of your face and manipulate your image, reflecting and refracting it back to you anew. There have been tools allowing you to apply virtual makeup to selfies, to apply "art filters" imitating famous paintings, to morph into an animal. You could age yourself three decades; you could see yourself imagined as another ethnicity; you could swap your gender; you could become thinner, or bald.

It should be generally better across a wide range of topics and has improved factuality. The first point could pertain to ChatGPT's potential to spread misinformation. While the chatbot does have built-in functions to help it avoid offensive responses and factual errors, it's still far from perfect. OpenAI has also admitted that ChatGPT is susceptible to providing "plausible-sounding but incorrect or nonsensical answers." Perhaps, with this update, the chatbot has grown a tad smarter at handling these things. Meanwhile, the second point looks like it refers to the ability to stop ChatGPT while it's generating a response, although we were unable to see anything related to it on our end.