The artificial intelligence research lab OpenAI on Tuesday launched the newest version of its language software, GPT-4, an advanced tool for analyzing images and mimicking human speech, pushing the technical and ethical boundaries of a rapidly proliferating wave of AI. OpenAI's earlier product, ChatGPT, captivated and unsettled the public with its uncanny ability to generate elegant writing, unleashing a viral wave of college essays, screenplays and conversations -- though it relied on an older generation of technology that hasn't been cutting-edge for more than a year. GPT-4, in contrast, is a state-of-the-art system capable of creating not just words but describing images in response to a person's simple written commands. When shown a photo of a boxing glove hanging over a wooden seesaw with a ball on one side, for instance, a person can ask what will happen if the glove drops, and GPT-4 will respond that it would hit the seesaw and cause the ball to fly up. The buzzy launch capped months of hype and anticipation over an AI program, known as a large language model, that early testers had claimed was remarkably advanced in its ability to reason and learn new things.

I ALWAYS know there is something fishy going on with a new tech product when journalists start desperately reaching out to science fiction authors to explain it for them. Such is the case with ChatGPT, an artificial intelligence chatbot from San Francisco company OpenAI, which has become one of the world's most widely used apps in just a few short months. So many news outlets were asking science fiction writers to weigh in on AI's capabilities that the Science Fiction and Fantasy Writers Association had to issue a special media statement on its website, linking to dozens of authors' …

New artificial intelligence technology can replicate voices, giving users the ability to manufacture content involving celebrities, politicians or just about anyone.

The growing potential of generative AI is clouded by its possible harms, prompting some calls for regulation. ChatGPT and other generative AI have taken centerstage for innovation with companies racing to introduce their own respective twists on the technology. Questions about the ethics of AI have likewise escalated with ways the technology could spread misinformation, support hacking attempts, or raise doubts about the ownership and validity of digital content. The issue of ethics and AI is not new, according to Cynthia Rudin, the Earl D. McLean, Jr. professor of computer science, electrical and computer engineering, statistical science, mathematics, and biostatistics & bioinformatics at Duke University She says AI recommender systems already have been pointed to for such ills as contributing to depression among teenagers, algorithms amplifying hate speech that spurred the 2017 Rohingya massacre in Myanmar, vaccine misinformation, and the spread of propaganda that contributed to insurrection in the United States on January 6, 2021. "If we haven't learned our lesson about ethics by now, it's not going to be when ChatGPT shows up," says Rudin.

The Moon seems to be a different case, and ibreakphotos' clever test exposes the ways that Samsung is doing a little extra processing. They put an intentionally blurred image of the Moon in front of the camera, displayed it on a screen, and took a photo of it. The resulting image shows details that it couldn't have possibly pulled from the original photo because they were blurred away -- rather, Samsung's processing doing a little more embellishment: adding lines and, in a follow-up test, putting Moon-like texture in areas clipped to white in the original image. It's not wholesale copy-and-pasting, but it's not simply enhancing what it sees.

Remember the date of March 3, 2023. It might be just another Friday on the calendar, but it's actually the day a well-known social media company announced their own demise.

Generative AI (GenAI) is a form of artificial intelligence that can create novel content such as audio, data, codes, or images. It will drastically change how we approach digital content in the future, and a range of jobs are executed.[1] GenAI gained significant attention worldwide when the California-based company OpenAI launched its artificial intelligence chatbot ChatGPT 3.5 in November 2022. The chatbot gained over 100 million users within the first two months after release.[2] ChatGPT is a text-based machine learning model that consists of several language models and large amounts of public data, which allow it to create new content based on user instructions. Its capabilities range from giving information on quantum physics to writing poems that sound as if they are written by humans.[3]

Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Collaborative filtering is a clever recommendation system technique that predicts what you'll like based on others with similar tastes. Super useful for businesses like Netflix or Amazon to personalize suggestions! For example, if you and another user both love sci-fi, it'll recommend shows they enjoyed, knowing you'll probably like them as well. Learning latent factors is all about discovering hidden features that help explain user-item interactions, like why people like certain movies. For example, suppose we're recommending movies.

Sentiment Classification is a fundamental task in the field of Natural Language Processing, and has very important academic and commercial applications. It aims to automatically predict the degree of sentiment present in a text that contains opinions and subjectivity at some level, like product and movie reviews, or tweets. This can be really difficult to accomplish, in part, because different domains of text contains different words and expressions. In addition, this difficulty increases when text is written in a non-English language due to the lack of databases and resources. As a consequence, several cross-domain and cross-language techniques are often applied to this task in order to improve the results. In this work we perform a study on the ability of a classification system trained with a large database of product reviews to generalize to different Spanish domains. Reviews were collected from the MercadoLibre website from seven Latin American countries, allowing the creation of a large and balanced dataset. Results suggest that generalization across domains is feasible though very challenging when trained with these product reviews, and can be improved by pre-training and fine-tuning the classification model.