Forecasting the Consumer Price Index (CPI) is an important yet challenging task in economics, where most existing approaches rely on low-frequency, survey-based data. With the recent advances of large language models (LLMs), there is growing potential to leverage high-frequency online text data for improved CPI prediction, an area still largely unexplored. This paper proposes LLM-CPI, an LLM-based approach for CPI prediction inference incorporating online text time series. We collect a large set of high-frequency online texts from a popularly used Chinese social network site and employ LLMs such as ChatGPT and the trained BERT models to construct continuous inflation labels for posts that are related to inflation. Online text embeddings are extracted via LDA and BERT. We develop a joint time series framework that combines monthly CPI data with LLM-generated daily CPI surrogates. The monthly model employs an ARX structure combining observed CPI data with text embeddings and macroeconomic variables, while the daily model uses a VARX structure built on LLM-generated CPI surrogates and text embeddings. We establish the asymptotic properties of the method and provide two forms of constructed prediction intervals. The finite-sample performance and practical advantages of LLM-CPI are demonstrated through both simulation and real data examples.

A Bloomberg terminal keyboard is seen in central London on April 17, 2015. Bloomberg terminals used ... [ ] by subscribers to make trades using real-time developments in business and finance were struck by a "global network problem" for several hours today, the company said. After users in financial centres around the world flocked to Twitter to complain of the unexpected outage of terminals, Bloomberg technicians began repair operations that started bringing some blanked terminals back on line at around 0945 GMT. Bloomberg is bringing to finance what GPT and ChatGPT brought to everyday general purpose chatbots. The paper that Bloomberg released reveals the great technical depth of its BloombergGPT machine learning model, applying the type of AI techniques that GPT uses to financial datasets.

Last week, I had the pleasure of interviewing Kevin Novak, founder of Rackhouse Venture Capital and Uber's first head of AI, and Alex Lee, founder and CEO, of Truewind, in front of a crowd of investors and LPs. The panel was titled, "AI and the battle to capture its value chain: base layer accrual vs the fine tuners." Here's a sample of the questions and topics we addressed. How has AI evolved since you started working in the field, and what is different about this current hype cycle compared to previous ones? According to the Economist, over 500 generative AI startups have collectively raised over $11B, not including OpenAI.

Over the last 30 years, the world has become increasingly computerized. We've traveled through the discovery and subsequent commercialization of the internet, the birth of and increasing ubiquity of mobile phones, the rise of virtual social platforms and services, the rise of big data, and the subsequent shift from typing to real-time video and chatbot interactions. Today, we're in the beginning of the rise of rich curated media tailored to the individual. As we navigate this new paradigm, it's more important than ever before to remember it's not just about cool technology--it's about people and communities. There is no use for technology without you and me.

Whether your interaction with artificial intelligence (AI) is limited to science-fiction or you spend more time in your day talking to Siri and Alexa than actual humans, you can't hide from the fact AI is changing the world. This week, the UK's new digital strategy was launched, which dedicated £17.3m to research and development of robotics and AI. Out of the industries welcoming this technology with open arms, finance and banking is one of the biggest. It's not hard to see why: when companies are dealing with large amounts of data, handing over control to a machine learning system that can analyse and understand information much faster than a human being is an obvious benefit. Where is this new technology having an impact in the finance sector?

According to an Oxford University analysis, close to half of all jobs will be taken over by robots in the next 25 years. No wonder the press is full of handwringing about how workers will adjust and the best way to prepare the next generation for this A.I.-filled future. But not everyone is alarmed about the prospect of radical change in the labor market. After all, this has happened before (for instance, when mechanization replaced the vast majority of farmers) and it turned out OK. Plus, a lot of today's jobs are soul-crushingly boring and repetitive. Losing them just might be a blessing.

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Growing up, there's a good chance you heard the mantra "go to a good school, get a good job, and make lots of money." Perhaps you were encouraged to get a professional degree to land a high paying job like a doctor, dentist, lawyer or something similar. This also seems like great advice, considering a professional degree holder typically earns more than $2 million more in their lifetimes than the average college graduate. Because lawyers tend to pay excellent attention to detail, and are highly versed in logic, a good alternative field would be programming.

Opinions expressed by Forbes Contributors are their own. Artificial intelligence, or A.I., goes beyond the current digital assistants like Cortana, Siri and Echo.

Opinions expressed by Forbes Contributors are their own. Artificial intelligence, or A.I., goes beyond the current digital assistants like Cortana, Siri and Echo.