Last year saw a remarkable series of advancements in artificial intelligence (AI). The latest image generation models, such as DALL-E, Midjourney, and Stable Diffusion, demonstrated unprecedented capabilities in creating highly realistic and stylistically diverse images. And ChatGPT captured the broader public's interest as it pushed the boundaries of what was previously thought possible in natural language processing. The potential of these technologies is vast, with the ability to revolutionize various industries such as education, health care, and the creative arts. However, alongside these benefits, it is important to consider the potential negative consequences and ethical dilemmas that may arise from their use.

ChatGPT opens a Pandora's box of existential fears. Silicon Valley brainiacs have talked about safeguards and kill switches for A.I., but you know they won't pull the plug when their baby turns into M3gan. Once A.I. can run disinformation campaigns at lightning speed, will democracy stand a chance? We seem headed toward a Matrix where "it will become cheaper to show fakes than to show reality," Jaron Lanier, the father of virtual reality, wrote in Tablet. Will bad actors use A.I. to promote bigotry or hijack nuclear weapons?

Buzzfeed shares jumped on Thursday on reports of a deal with Meta Platforms Inc and plans to use artificial intelligence to personalize and enhance the digital media firm's online quizzes and content. The stock was 19% higher in extended trading, after more than doubling in value earlier in the day as a Wall Street Journal report said it would use ChatGPT creator OpenAI for its content. Shares of BuzzFeed Inc jumped on Thursday on reports of a deal with Meta Platforms Inc and plans to use artificial intelligence to personalize and enhance the digital media firm's online quizzes and content.

The presenter is not AI BOT The author CHAT GPT Artificial intelligence (AI) technology is advancing rapidly and has the potential to revolutionize many industries, from healthcare and transportation to finance and retail. However, with every new technology comes both opportunities and threats. One of AI's biggest opportunities is its ability to analyze large amounts of data and make predictions and decisions faster and more accurately than humans. This can lead to more efficient processes, improved decision making and new revenue streams. In addition,artificial intelligence can be used to automate repetitive tasks, freeing up human workers to focus on more complex and creative work.

Image quality is important, and can affect overall performance in image processing and computer vision as well as for numerous other reasons. Image quality assessment (IQA) is consequently a vital task in different applications from aerial photography interpretation to object detection to medical image analysis. In previous research, the BRISQUE algorithm and the PSNR algorithm were evaluated with high resolution (atleast 512x384 pixels), but relatively small image sets (no more than 4,744 images). However, scientists have not evaluated IQA algorithms on low resolution (no more than 32x32 pixels), multi-perturbation, big image sets (for example, tleast 60,000 different images not counting their perturbations). This study explores these two IQA algorithms through experimental investigation. We first chose two deep learning image sets, CIFAR-10 and MNIST. Then, we added 68 perturbations that add noise to the images in specific sequences and noise intensities. In addition, we tracked the performance outputs of the two IQA algorithms with singly and multiply noised images. After quantitatively analyzing experimental results, we report the limitations of the two IQAs with these noised CIFAR-10 and MNIST image sets. We also explain three potential root causes for performance degradation. These findings point out weaknesses of the two IQA algorithms. The research results provide guidance to scientists and engineers developing accurate, robust IQA algorithms. All source codes, related image sets, and figures are shared on the website (https://github.com/caperock/imagequality) to support future scientific and industrial projects.

Creating a taxonomy of interests is expensive and human-effort intensive: not only do we need to identify nodes and interconnect them, in order to use the taxonomy, we must also connect the nodes to relevant entities such as users, pins, and queries. Connecting to entities is challenging because of ambiguities inherent to language but also because individual interests are dynamic and evolve. Here, we offer an alternative approach that begins with bottom-up discovery of $\mu$-topics called pincepts. The discovery process itself connects these $\mu$-topics dynamically with relevant queries, pins, and users at high precision, automatically adapting to shifting interests. Pincepts cover all areas of user interest and automatically adjust to the specificity of user interests and are thus suitable for the creation of various kinds of taxonomies. Human experts associate taxonomy nodes with $\mu$-topics (on average, 3 $\mu$-topics per node), and the $\mu$-topics offer a high-level data layer that allows quick definition, immediate inspection, and easy modification. Even more powerfully, $\mu$-topics allow easy exploration of nearby semantic space, enabling curators to spot and fill gaps. Curators' domain knowledge is heavily leveraged and we thus don't need untrained mechanical Turks, allowing further cost reduction. These $\mu$-topics thus offer a satisfactory "symbolic" stratum over which to define taxonomies. We have successfully applied this technique for very rapidly iterating on and launching the home decor and fashion styles taxonomy for style-based personalization, prominently featured at the top of Pinterest search results, at 94% precision, improving search success rate by 34.8% as well as boosting long clicks and pin saves.

We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create music featuring their own voice. To accomplish this, we build on recent developments in musical source separation and audio generation. Specifically, we apply a state-of-the-art source separation algorithm to a large corpus of music audio to produce aligned pairs of vocals and instrumental sources. Then, we adapt AudioLM (Borsos et al., 2022) -- a state-of-the-art approach for unconditional audio generation -- to be suitable for conditional "audio-to-audio" generation tasks, and train it on the source-separated (vocal, instrumental) pairs. In a pairwise comparison with the same vocal inputs, listeners expressed a significant preference for instrumentals generated by SingSong compared to those from a strong retrieval baseline. Sound examples at https://g.co/magenta/singsong

AI Has Designed Bacteria-Killing Proteins From Scratch--and They Work Karmela Padavic-Callaghan New Scientist "The AI, called ProGen, works in a similar way to AIs that can generate text. ProGen learned how to generate new proteins by learning the grammar of how amino acids combine to form 280 million existing proteins. Instead of the researchers choosing a topic for the AI to write about, they could specify a group of similar proteins for it to focus on. In this case, they chose a group of proteins with antimicrobial activity." BuzzFeed to Use ChatGPT Creator OpenAI to Help Create Quizzes and Other Content Alexandra Bruell The Wall Street Journal "BuzzFeed Inc. said it would rely on ChatGPT creator OpenAI to enhance its quizzes and personalize some content for its audiences, becoming the latest digital publisher to embrace artificial intelligence. In a memo to staff sent Thursday morning, which was reviewed by The Wall Street Journal, Chief Executive Jonah Peretti said he intends for AI to play a larger role in the company's editorial and business operations this year."

TikTok collects more of your data than you may realize. Kurt "CyberGuy" Knutsson shows you some tips on how to protect your privacy. Technology has been taking over the world, especially within the last decade with the advent of the gig economy. Now, more and more companies are figuring out how to make themselves more efficient by becoming more tech friendly. CLICK TO GET KURT'S CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER However, China is taking this to the next extreme with the growing popularity of virtual people.

Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order to observe how students reason with text data in scenarios designed to elicit certain aspects of the domain, we employed a task-based interview method using a structured protocol with six pairs of undergraduate students. Our goal was to shed light on students' understanding of text as data using a motivating task to classify headlines as "clickbait" or "news". Three types of features (function, content, and form) surfaced, the majority from the first scenario. Our analysis of the interviews indicates that this sequence of activities engaged the participants in thinking at both the human-perception level and the computer-extraction level and conceptualizing connections between them.