Many employees, especially those working in creative fields, are understandably worried by the prospect of AI stealing their jobs – and new research has found it may not be an unfounded fear. 

A report from the Imperial College Business School, Harvard Business School, and the German Institute for Economic Research, found the demand for digital freelancers in writing and coding declined by 21% since the launch of ChatGPT in November 2022. — Read More

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Public polling is a critical function of modern political campaigns and movements, but it isn’t what it once was. Recent US election cycles have produced copious postmortems explaining both the successes and the flaws of public polling. There are two main reasons polling fails.

First, nonresponse has skyrocketed. It’s radically harder to reach people than it used to be. Few people fill out surveys that come in the mail anymore. Few people answer their phone when a stranger calls. Pew Research reported that 36% of the people they called in 1997 would talk to them, but only 6% by 2018. Pollsters worldwide have faced similar challenges.

Second, people don’t always tell pollsters what they really think. Some hide their true thoughts because they are embarrassed about them. Others behave as a partisan, telling the pollster what they think their party wants them to say—or what they know the other party doesn’t want to hear.

Despite these frailties, obsessive interest in polling nonetheless consumes our politics. Headlines more likely tout the latest changes in polling numbers than the policy issues at stake in the campaign. This is a tragedy for a democracy. We should treat elections like choices that have consequences for our lives and well-being, not contests to decide who gets which cushy job. — Read More

A recent poll shows K-12 students’ familiarity with ChatGPT rose from 37% to 75% in just over a year. The survey, by Impact Research for the Walton Family Foundation, also found that teachers’ familiarity with ChatGPT jumped from 55% to 79% from February 2023 to May 2024. — Read More

#strategy

Apple’s annual Worldwide Developer Conference keynote kicks off in a few hours, and Mark Gurman has extensive details of what will be announced in Bloomberg, including the name: “Apple Intelligence”. As John Gruber noted on Daring Fireball:

His report reads as though he’s gotten the notes from someone who’s already watched Monday’s keynote. I sort of think that’s what happened, given how much of this no one had reported before today. 

… The irony of the leak being so huge is that nothing is particularly surprising: Apple is announcing and incorporating generative AI features throughout its operating systems and making them available to developers. Finally, the commentariat exclaims! Apple is in danger of falling dangerously behind! The fact they are partnering with OpenAI is evidence of how desperate they are! In fact, I would argue the opposite: Apple is not too late, they are taking the correct approach up-and-down the stack, and are well-positioned to be one of AI’s big winners. — Read More

t’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. The pace of improvements in LLMs, coupled with a parade of demos on social media, will fuel an estimated $200B investment in AI by 2025. LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into their products. While the barrier to entry for building AI products has been lowered, creating those effective beyond a demo remains a deceptively difficult endeavor.

We’ve identified some crucial, yet often neglected, lessons and methodologies informed by machine learning that are essential for developing products based on LLMs. … Our goal is to make this a practical guide to building successful products around LLMs, drawing from our own experiences and pointing to examples from around the industry. We’ve spent the past year getting our hands dirty and gaining valuable lessons, often the hard way. While we don’t claim to speak for the entire industry, here we share some advice and lessons for anyone building products with LLMs. — Read More

Artificial intelligence training data provider Scale AI Inc., which serves the likes of OpenAI and Nvidia Corp., today published the results of its first-ever SEAL Leaderboards.

It’s a new ranking system for frontier large language models based on private, curated and unexploitable datasets that attempts to rate their capabilities in common use cases, such as generative AI coding, instruction following, math and multilinguality.

The SEAL Leaderboards show that OpenAI’s GPT family of LLMs ranks first in three of the four initial domains it’s using to rank AI models, with Anthropic PBC’s popular Claude 3 Opus grabbing first place in the fourth category. Google LLC’s Gemini models also did well, ranking joint-first with the GPT models in a couple of the domains. — Read More

LeaderBoard

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In a groundbreaking study conducted by the University of Chicago’s Booth School of Business, researchers have revealed that OpenAI’s GPT-4 large language model (LLM) can rival and even outperform human professionals in financial statement analysis. This significant finding could mark a new era in financial analysis, where artificial intelligence (AI) tools become indispensable for making informed financial decisions. — Read More

#strategy

Based on an online survey focused on understanding if and how people use generative artificial intelligence (AI), and what they think about its application in journalism and other areas of work and life across six countries (Argentina, Denmark, France, Japan, the UK, and the USA), we present the following findings.

ChatGPT is by far the most widely recognised generative AI product – around 50% of the online population in the six countries surveyed have heard of it. It is also by far the most widely used generative AI tool in the six countries surveyed. That being said, frequent use of ChatGPT is rare, with just 1% using it on a daily basis in Japan, rising to 2% in France and the UK, and 7% in the USA. Many of those who say they have used generative AI have used it just once or twice, and it is yet to become part of people’s routine internet use.

In more detail, we find:

— Just 5% across the six countries covered say that they have used generative AI to get the latest news.
— While there is widespread awareness of generative AI overall, a sizable minority of the public – between 20% and 30% of the online population in the six countries surveyed – have not heard of any of the most popular AI tools.
— In terms of use, ChatGPT is by far the most widely used generative AI tool in the six countries surveyed, two or three times more widespread than the next most widely used products, Google Gemini and Microsoft Copilot.
— Younger people are much more likely to use generative AI products on a regular basis. Averaging across all six countries, 56% of 18–24s say they have used ChatGPT at least once, compared to 16% of those aged 55 and over.
— Roughly equal proportions across six countries say that they have used generative AI for getting information (24%) as creating various kinds of media, including text but also audio, code, images, and video (28%).

Read More

Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine.

Top Takeaways

— AI beats humans on some tasks, but not on all.
— Industry continues to dominate frontier AI research.
— Frontier models get way more expensive.
— The United States leads China, the EU, and the U.K. as the leading source of top AI models.
— Robust and standardized evaluations for LLM responsibility are seriously lacking.
— Generative AI investment skyrockets.
— The data is in: AI makes workers more productive and leads to higher quality work.
— Scientific progress accelerates even further, thanks to AI.
— The number of AI regulations in the United States sharply increases.
— People across the globe are more cognizant of AI’s potential impact—and more nervous.

— Read More