To model behavioral and neural correlates of language comprehension in naturalistic environments researchers have turned to broad-coverage tools from natural-language processing and machine learning. Where syntactic structure is explicitly modeled, prior work has relied predominantly on context-free grammars (CFG), yet such formalisms are not sufficiently expressive for human languages. Combinatory Categorial Grammars (CCGs) are sufficiently expressive directly compositional models of grammar with flexible constituency that affords incremental interpretation. In this work we evaluate whether a more expressive CCG provides a better model than a CFG for human neural signals collected with fMRI while participants listen to an audiobook story. We further test between variants of CCG that differ in how they handle optional adjuncts. These evaluations are carried out against a baseline that includes estimates of next-word predictability from a Transformer neural network language model. Such a comparison reveals unique contributions of CCG structure-building predominantly in the left posterior temporal lobe: CCG-derived measures offer a superior fit to neural signals compared to those derived from a CFG. These effects are spatially distinct from bilateral superior temporal effects that are unique to predictability. Neural effects for structure-building are thus separable from predictability during naturalistic listening, and those effects are best characterized by a grammar whose expressive power is motivated on independent linguistic grounds.

Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving problems harder than the exemplars shown in the prompts. To overcome this challenge of easy-to-hard generalization, we propose a novel prompting strategy, least-to-most prompting. The key idea in this strategy is to break down a complex problem into a series of simpler subproblems and then solve them in sequence. Solving each subproblem is facilitated by the answers to previously solved subproblems. Our experimental results on tasks related to symbolic manipulation, compositional generalization, and math reasoning reveal that least-to-most prompting is capable of generalizing to more difficult problems than those seen in the prompts. A notable finding is that when the GPT-3 code-davinci-002 model is used with least-to-most prompting, it can solve the compositional generalization benchmark SCAN in any split (including length split) with an accuracy of at least 99% using just 14 exemplars, compared to only 16% accuracy with chain-of-thought prompting. This is particularly noteworthy because neural-symbolic models in the literature that specialize in solving SCAN are trained on the entire training set containing over 15,000 examples. We have included prompts for all the tasks in the Appendix.

What is owed to the creators of the original material? The cartoonist Sarah Anderson, who is part of the lawsuit, told The New York Times that she believed artists should opt in to having their work included in such data, and should be compensated for it. Getty Images is also suing Stability AI in Britain and the United States for what it calls "brazen infringement" of millions of photos. Getty argued that the theft is particularly offensive because it has agreements to license data for machine learning. Stability AI has not yet responded to the complaints.

Entrepreneur Elon Musk is preparing to launch a startup that will compete with Openai, the creator of Chatgpt, a media report unveiled. According to quoted knowledgeable sources, the owner of Twitter and Tesla is already assembling a team of developers and talking to investors. Tech investor Elon Musk is putting effort into founding a startup that will rival the company behind the Chatgpt artificial intelligence (AI) assistant, Openai, the Financial Times revealed on Friday, citing people familiar with the billionaire's intentions. The publication claims Musk is now recruiting AI engineers while also holding talks with some investors in Spacex and Tesla, two of his best known business enterprises along with Twitter, about backing the new venture, Reuters quoted the report. Companies like Microsoft-funded Openai and Google's parent, Alphabet, have been working to incorporate AI into their offerings despite calls from regulators to introduce comprehensive rules for the technology before its widely implemented.

If AI could let you speak with your deceased loved ones again, would you take the chance? A few companies and experts believe this will soon be possible, and some are even starting to market their own solutions. Nothing supernatural about their proposal; what they are offering is, rather, the ability to talk to a digital representation of the dead, fine-tuned by combining large language models like GPT-4, speech synthesis and AI generation tools. In China, as the Strait Times reports, some funeral companies are rapidly bringing the worship of the deceased into the digital age, allowing relatives to speak to a digital avatar of the dearly departed. Many have started offering this service in early April, around the time of the Qing Ming Festival (or Tomb Sweeping Day), a public holiday when people remember and honor the dead. One of them, funeral services provider Shanghai Fushouyun, started even earlier, conducting its first AI-assisted funeral in January 2022, when colleagues and students of a deceased Chinese surgeon had the opportunity to chat with his digital replica on a screen, for a final farewell.

With the growing popularity of generative AI-powered chatbots such as ChatGPT, Google Bard, and Microsoft Bing Chat, the demand for professionals skilled in prompt writing and engineering is on the rise. This emerging field of AI technology has existed for some time but is now becoming mainstream, offering new career paths such as prompt engineering. Moreover, it also offers well-paying jobs and flexible work options. Also Read: The ChatGPT Revolution in Today's Job Market: Challenges and Opportunities Prompt engineering is the process of designing and crafting prompts for AI chatbots and generative services. It involves interacting with AI systems like Google's Bard or OpenAI's ChatGPT, guiding them to respond in specific ways and avoiding undesirable responses, such as embarrassing statements or revealing trade secrets.

ChatGPT can unlock all kinds of new possibilities across thousands of industries and job functions. It's an incredible demonstration of the power of AI that is to come in future years. In this tutorial, I build a fully automatic iMessage bot that checks for recent messages, replies, and waits for new messages to appear before replying again. I had the idea as a "joke" to respond to friends while I am working or AFK. For this bot, we'll leverage some open-source GitHub Projects to access the chatGPT API and simplify querying the iMessages data.

AI research startup Anthropic aims to raise as much as $5 billion over the next two years to take on rival OpenAI and enter over a dozen major industries, according to company documents obtained by TechCrunch. A pitch deck for Anthropic's Series C fundraising round discloses these and other long-term goals for the company, which was founded in 2020 by former OpenAI researchers. In the deck, Anthropic says that it plans to build a "frontier model" -- tentatively called "Claude-Next" -- 10 times more capable than today's most powerful AI, but that this will require a billion dollars in spending over the next 18 months. When contacted for comment, an Anthropic spokesperson said: "We are planning additional product announcements and will be talking about them soon." The Information reported in early March that Anthropic was seeking to raise $300 million at $4.1 billion valuation, bringing its total raised to $1.3 billion.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Millions of users have flocked to ChatGPT since its mainstream launch in November 2022. Thanks to its exceptional human-like language generation capabilities, its aptitude for coding software, and its lightning-fast text analysis, ChatGPT has quickly emerged as a go-to tool for developers, researchers and everyday users. But as with any disruptive technology, generative AI systems like ChatGPT come with potential risks. In particular, major players in the tech industry, state intelligence agencies and other governmental bodies have all raised red flags about sensitive information being fed into AI systems like ChatGPT. Don't miss our newest special issue: Data centers in 2023: How to do more with less.

Garante, the Italian data protection authority, apparently jumped the gun at the end of March when it imposed a temporary ban on ChatGPT, a chatbot that uses artificial intelligence (AI) to generate texts that seem as if they were created by humans, and computer games. The watchdog was less concerned by the use of AI -- the simulation of human intelligence by computer systems -- than by breaches of data protection legislation. Garante then told the Microsoft Corp-backed company behind ChatGPT, OpenAI, that it would have to be more transparent with its users about how their data were processed. It also said that the US company had to obtain permission from users if their data were to be used to further develop the software -- that is, to help it learn -- and that access to minors had to be filtered. In a press release, the Italian authority said that the ban would be lifted if OpenAI met these conditions by April 30.