ChatGPT has political biases when answering questions, opening the door for competition whose models provide objectivity in their answers, an AI developer said. LOS ANGELES – An AI researcher developed a free speech alternative to ChatGPT and argued that the mainstream model has a liberal bias that prevents it from answering certain questions. "ChatGPT has political motivations, and it's seen through the product," said Arvin Bhangu, who founded the AI model Superintelligence. We've seen where you can ask it give me 10 things Joe Biden has done well and give me 10 things Donald Trump has done well and it refuses to give quality answers for Donald Trump." "Superintelligence is much more in line with the freedom to ask any type of question, so it's much more in line with the First Amendment than ChatGPT," Bhangu said. ChatGPT, an AI chatbot that can write essays, code and more, has been criticized for having politically biased responses.

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. California's reparations task force is recommending as part of its set of proposals to make amends for slavery and anti-Black racism that state lawmakers address what it calls "racially biased" artificial intelligence used in health care. The task force, created by state legislation signed by Gov. Gavin Newsom in 2020, formally approved last weekend its final recommendations to the California Legislature, which will decide whether to enact the measures and send them to the governor's desk to be signed into law. The recommendations include several proposals related to health care, including some concerning medical artificial intelligence (AI), which the task force describes as "racially biased" and contributing to alleged systemic racism against Black Californians.

Virginia Tech Professor Walid Saad discusses the environmental impact of artificial intelligence algorithms, including excessive water waste to cool down processors. FIRST ON FOX: Senate Republicans are touting their accomplishments rejecting Biden administration regulations via congressional resolutions that have received support from Democrats. Since January, the Senate has approved seven such resolutions which utilize the Congressional Review Act (CRA), a law dating back nearly three decades that allows Congress to revoke federal regulations. All seven of the CRAs revoked energy- or environment-related regulations with the majority being within the Senate Environment and Public Works (EPW) Committee's jurisdiction. "When we began this Congress, one of my top priorities was to hold the Biden administration accountable for any overreach on environment and energy issues within our jurisdiction here at the EPW Committee," EPW Committee Ranking Member Shelley Moore Capito, R-W.Va., said in a statement to Fox News Digital.

The Monitor is a weekly column devoted to everything happening in the WIRED world of culture, from movies to memes, TV to Twitter. The Australian writer, director, and producer is known for his flashy, hyper-realistic style, and on this particular New York night he's in a sparse, brightly lit former taxi warehouse in Chelsea, talking to a robot. The bot's name is Ai-Da; she's a painter powered by artificial intelligence. Before Luhrmann took the stage next to her, she was doing a watercolor while people gawked and took photos. "Did you see Elvis, Ai-Da?" he asked.

Over the last few months, AI chatbots have exploded in popularity off the surging success of OpenAI's revolutionary ChatGPT--which, amazingly, only burst onto the scene around December. But when Microsoft seized the opportunity to hitch its wagon to OpenAI's rising star for a steep $10 billion dollars, it chose to do so by introducing a GPT-4-powered chatbot under the guise of Bing, its swell-but-also-ran search engine, in a bid to upend Google's search dominance. Google quickly followed suit with its own homegrown Bard AI and unleashed plans to put AI answers before traditional search results, an utterly monumental alteration to one of the most significant places on the Internet. Both are touted as experiments. And these "AI chatbots" are truly wondrous advancements--I've spent many nights with my kids joyously creating fantastic stuff-of-your-dreams artwork with Bing Chat's Dall-E integration and prompting sick raps about wizards who think lizards are the source of all magic, and seeing them come to life in mere moments with these fantastic tools.

The race to roll out artificial intelligence is happening as quickly as the race to contain it – as two key moments this week demonstrate. On 10 May, Google announced plans to deploy new large language models, which use machine learning techniques to generate text, across its existing products. "We are reimagining all of our core products, including search," said Sundar Pichai, the CEO of Google's parent company Alphabet, at a press conference. The move is widely seen as a response to Microsoft adding similar functionality to its search engine, Bing. A day later, politicians in the European Union agreed on new rules dictating how and when AI can be used.

In 1965, the political scientist and Nobel laureate Herbert Simon declared: "Machines will be capable, within 20 years, of doing any work a man can do." Today, in what is increasingly referred to as the fourth industrial revolution, the arrival of artificial intelligence (AI) in the workplace is igniting similar concerns. The European parliament's forthcoming Artificial Intelligence Act is likely to deem the use of AI across education, law enforcement and worker management to be "high risk". Geoffrey Hinton, known as the "godfather of AI", recently resigned from his position at Google, citing concerns about the technology's impact on the job market. And, in early May, striking members of the Writers Guild of America promised executives: "AI will replace you before it replaces us."

Veritone CEO Ryan Steelberg says the Writers Guild of America needs to make sure their writers are protected as AI becomes more popular. Nearly two weeks into the national writers' strike spearheaded by the Writers Guild of America (WGA), little progress has been made between both sides. The WGA has a litany of requests for the Alliance of Motion Picture and Television Producers (AMPTP). Per its website, the WGA has specific proposals with regard to artificial intelligence, including the "regulation of AI on minimum basic agreement (MBA) -covered projects; AI can't write or rewrite literary material; can't be used as source material; and MBA-covered material can't be used to train AI." When it comes to these provisions that surround artificial intelligence, studios have put the kibosh on writers' requests, instead suggesting annual meetings to review evolving technology.

Automatic discourse processing is bottlenecked by data: current discourse formalisms pose highly demanding annotation tasks involving large taxonomies of discourse relations, making them inaccessible to lay annotators. This work instead adopts the linguistic framework of Questions Under Discussion (QUD) for discourse analysis and seeks to derive QUD structures automatically. QUD views each sentence as an answer to a question triggered in prior context; thus, we characterize relationships between sentences as free-form questions, in contrast to exhaustive fine-grained taxonomies. We develop the first-of-its-kind QUD parser that derives a dependency structure of questions over full documents, trained using a large, crowdsourced question-answering dataset DCQA (Ko et al., 2022). Human evaluation results show that QUD dependency parsing is possible for language models trained with this crowdsourced, generalizable annotation scheme. We illustrate how our QUD structure is distinct from RST trees, and demonstrate the utility of QUD analysis in the context of document simplification. Our findings show that QUD parsing is an appealing alternative for automatic discourse processing.

Ensuring that large language models (LMs) are fair, robust and useful requires an understanding of how different modifications to their inputs impact the model's behaviour. In the context of open-text generation tasks, however, such an evaluation is not trivial. For example, when introducing a model with an input text and a perturbed, "contrastive" version of it, meaningful differences in the next-token predictions may not be revealed with standard decoding strategies. With this motivation in mind, we propose Contrastive Input Decoding (CID): a decoding algorithm to generate text given two inputs, where the generated text is likely given one input but unlikely given the other. In this way, the contrastive generations can highlight potentially subtle differences in how the LM output differs for the two inputs in a simple and interpretable manner. We use CID to highlight context-specific biases that are hard to detect with standard decoding strategies and quantify the effect of different input perturbations.