Both of these resources are only really available to big companies. And although some of the most exciting applications, such as OpenAI's chatbot ChatGPT and Stability.AI's image-generation AI Stable Diffusion, are created by startups, they rely on deals with Big Tech that gives them access to its vast data and computing resources. "A couple of big tech firms are poised to consolidate power through AI rather than democratize it," says Sarah Myers West, managing director of the AI Now Institute, a research nonprofit. Right now, Big Tech has a chokehold on AI. But Myers West believes we're actually at a watershed moment.

SAN FRANCISCO, April 17 (Reuters) - Billionaire Elon Musk said on Monday he will launch an artificial intelligence (AI) platform that he calls "TruthGPT" to challenge the offerings from Microsoft (MSFT.O) and Google (GOOGL.O). He criticised Microsoft-backed OpenAI, the firm behind chatbot sensation ChatGPT, of "training the AI to lie" and said OpenAI has now become a "closed source", "for-profit" organisation "closely allied with Microsoft". He also accused Larry Page, co-founder of Google, of not taking AI safety seriously. "I'm going to start something which I call'TruthGPT', or a maximum truth-seeking AI that tries to understand the nature of the universe," Musk said in an interview with Fox News Channel's Tucker Carlson aired on Monday. He said TruthGPT "might be the best path to safety" that would be "unlikely to annihilate humans".

The Tesla and Twitter CEO, who was an early donor to chatbot ChatGPT creator OpenAI, expressed deep concern over the direction of the field in a Fox News interview on "Tucker Carlson Tonight," which caters to a conservative audience. Musk said large-language models were being trained to be "politically correct," or untruthful, though the interview didn't include detailed examples of what he was claiming.

Elon Musk's plans for his own AI company are starting to come into focus. The Twitter CEO said that he wants to "create a third option" for a company that could challenge OpenAI and Google. In an interview with Fox News, Musk said that he's thinking of calling the venture "TruthGPT" and that the goal would be to create a "maximum truth-seeking AI that tries to understand the nature of the universe" and that "hopefully does more good than harm." Musk acknowledged that he would be starting with a significant disadvantage to his rivals as he's "starting late." And, as is often the case, it's unclear how serious or far along his plans actually are.

Elon Musk, who co-founded OpenAI but left the company in 2018 after losing a power struggle to its current chief executive, elaborated on his current ambitions for artificial intelligence in an interview that aired on Monday. "I'm going to start something which I call'Truth GPT' or a maximum truth-seeking AI that tries to understand the nature of the universe," Mr. Musk said in an interview on Fox News Channel's "Tucker Carlson Tonight." The first part of the interview aired Monday, with a second part scheduled to air Tuesday night.

Introduction  Generative AI is evolving and getting popular. Since its introduction, new models and research papers are getting released almost every other day. The major reason for the exponentially increasing popularity is the development of Large Language Models. LLMs, the Artificial Intelligence models that are designed to process natural language and generate human-like responses, are trending. The best example is OpenAI's ChatGPT, the well-known chatbot that does everything from content generation and code completion to question answering, just like a human. Even OpenAI's DALL-E and Google's BERT have contributed to making significant advances in recent times. What is AutoGPT? Recently,

As debate rages over the possibilities and risks to higher education of artificial intelligence tools such as ChatGPT, evaluators are also asking what role AI and machine learning can play in their field. Speaking at a virtual symposium hosted by the Centre for Research Evaluation at the University of Mississippi in the United States on March 24, independent evaluation consultant Silva Ferretti described ChatGPT as the perfect bureaucrat: pedantic and by the book. The symposium was titled "Are We at a Fork in the Road?" and explored implications and opportunities for AI in evaluation. It was hosted by Dr. Sarah Mason of the University of Mississippi and Dr. Bianca Montrosse-Moorhead of the University of Connecticut, co-editors of New Directions for Evaluation, a publication of the American Evaluation Association. They said that disciplines around the world were grappling with the question of whether ChatGPT heralded a fork in the road with respect to powerful new generative AI. "This potential fork emerges because generative AI is distinct from earlier AI models in that it can create entirely new content."

April 17 (Reuters) - EU lawmakers urged world leaders on Monday to hold a summit to find ways to control the development of advanced artificial intelligence (AI) systems such as ChatGPT, saying they were developing faster than expected. The 12 MEPs, all working on EU legislation on the technology, called on U.S. President Joe Biden and European Commission President Ursula von der Leyen to convene the meeting, and said AI firms should be more responsible. The statement came weeks after Twitter owner Elon Musk and more than 1,000 technology figures demanded a six-month pause in the development of systems more powerful than Microsoft-backed (MSFT.O) OpenAI's latest iteration of ChatGPT, which can mimic humans and create text and images based on prompts. That open letter, published in March by the Future of Life Institute (FLI), had warned that AI could spread misinformation at an unprecedented rate, and that machines could "outnumber, outsmart, obsolete and replace" humans, if left unchecked. The MEPS said they disagreed with some of the FLI message's "more alarmist statements".

We first compile data on the hourly coding output of over 8,000 professional GitHub users in Italy and other European countries to analyse the impact of the ban on individual productivity. Combining the high-frequency data with the sudden announcement of the ban in a difference-in-differences framework, we find that the output of Italian developers decreased by around 50% in the first two business days after the ban and recovered after that. Applying a synthetic control approach to daily Google search and Tor usage data shows that the ban led to a significant increase in the use of censorship bypassing tools. Our findings show that users swiftly implement strategies to bypass Internet restrictions but this adaptation activity creates short-term disruptions and hampers productivity.

The premise of this article is that a basic understanding of the composition and functioning of large language models is critically urgent. To that end, we extract a representational map of OpenAI's GPT-2 with what we articulate as two classes of deep learning code, that which pertains to the model and that which underwrites applications built around the model. We then verify this map through case studies of two popular GPT-2 applications: the text adventure game, AI Dungeon, and the language art project, This Word Does Not Exist. Such an exercise allows us to test the potential of Critical Code Studies when the object of study is deep learning code and to demonstrate the validity of code as an analytical focus for researchers in the subfields of Critical Artificial Intelligence and Critical Machine Learning Studies. More broadly, however, our work draws attention to the means by which ordinary users might interact with, and even direct, the behavior of deep learning systems, and by extension works toward demystifying some of the auratic mystery of "AI." What is at stake is the possibility of achieving an informed sociotechnical consensus about the responsible applications of large language models, as well as a more expansive sense of their creative capabilities-indeed, understanding how and where engagement occurs allows all of us to become more active participants in the development of machine learning systems.