![]() The editor-in-chief was fired shortly thereafter amid the controversy. The story included two possible disclosures: the cover included the line "deceptively real", and inside the magazine acknowledged at the end of the interview that the interview was AI-generated. In April 2023, German tabloid Die Aktuelle published a fake AI-generated interview with reclusive former racing driver Michael Schumacher. A team from Microsoft Research argued that "it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system". In January 2023, broke the story that CNET had been using an undisclosed internal AI tool to write at least 77 of its stories after the news broke, CNET posted corrections to 41 of the stories. In 2021, the release of DALL-E, a transformer-based pixel generative model, followed by Midjourney and Stable Diffusion marked the emergence of practical high-quality artificial intelligence art from natural language prompts. This was followed in 2019 by GPT-2 which demonstrated the ability to generalize unsupervised to many different tasks as a Foundation model. In 2017, the Transformer network enabled advancements in generative models, leading to the first Generative pre-trained transformer in 2018. These deep generative models were the first able to output not only class labels for images, but to output entire images. In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative, rather than discriminative, models of complex data such as images. ![]() However, most deep neural networks were trained as discriminative models performing classification tasks such as convolutional neural network-based image classification. Beginning in the late 2000s, the emergence of deep learning drove progress and research in image and video processing, text analysis, speech recognition, and other tasks. ![]() Since its founding, the field of machine learning has used statistical models, including generative models, to model and predict data. While not a top priority, one of the WGA's 2023 requests was "regulations around the use of (generative) AI". Main article: History of artificial intelligence A picketer at the 2023 Writers Guild of America strike. However, there are also concerns about the potential misuse of generative AI, such as in creating fake news or deepfakes, which can be used to deceive or manipulate people. Investment in generative AI surged during the early 2020s, with large companies such as Microsoft, Google, and Baidu as well as numerous smaller firms developing generative AI models. Generative AI has potential applications across a wide range of industries, including art, writing, software development, product design, healthcare, finance, gaming, marketing, and fashion. Other generative AI models include artificial intelligence art systems such as Stable Diffusion, Midjourney, and DALL-E. ![]() Notable generative AI systems include ChatGPT (and its variant Bing Chat), a chatbot built by OpenAI using their GPT-3 and GPT-4 foundational large language models, and Bard, a chatbot built by Google using their LaMDA foundation model. ![]() Generative AI models learn the patterns and structure of their input training data by applying neural network machine learning techniques, and then generate new data that has similar characteristics. Generative artificial intelligence or generative AI is a type of artificial intelligence (AI) system capable of generating text, images, or other media in response to prompts. Not to be confused with Artificial general intelligence. ![]()
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