All posts tagged: Frail

Stocks slump on worsening war in Middle East; frail yen in focus

Stocks slump on worsening war in Middle East; frail yen in focus

Published by Sam Flores

WAR IN MIDDLE EAST WORSENS The broader market though, remains focused on the war in the Middle East and is coming to the realisation that the conflict is shaping up to be a prolonged one, stoking stagflation risk. Iran accused Israel of striking its facilities in the huge South Pars gas field on Wednesday and retaliated by vowing attacks on oil and gas targets throughout the Gulf, firing missiles at Qatar and Saudi Arabia. The hits to energy infrastructure sent US crude futures about 1 per cent higher to US$97.07 per barrel. Natural gas rose more than 6 per cent, while Brent futures rose to US$112.19 a barrel, up 4.5 per cent on the day. In stocks, Japan’s Nikkei was down 2.5 per cent, while South Korean equities fell 1.5 per cent. MSCI’s broadest index of Asia-Pacific shares outside Japan fell more than 1.5 per cent. European futures were down more than 1 per cent. “This latest escalation feels like a turning point for markets because the conflict is no longer just about military headlines or …

Large Language Models (LLMs) Are Inherently Frail and Unreliable

Large Language Models (LLMs) Are Inherently Frail and Unreliable

Published by Jenni Sidey

The public unveiling of OpenAI’s ChatGPT on November 30, 2022, was met with justified astonishment along with unjustified hopes and fears that computers might be on the verge of artificial general intelligence (AGI), the ability to perform cognitive tasks as well as or better than humans. It is now apparent that AGI is not imminent and is not going to be achieved by scaling up ChatGPT or other large language models (LLMs). The core problem is that LLMs are stochastic text predictors—nothing more—and using more data, more parameters, and more compute is not going to give LLMs an understanding of how words relate to the real world. A more promising path is to make LLMs useful through extensive post-training by domain experts. For example, TurboTax, which has been around long before LLMs appeared, now uses an LLM to generate the text for interacting with users but relies on experts for its calculations and for determining what questions to ask users. I wouldn’t call TurboTax intelligent any more than I would call a pocket calculator intelligent, …