A trio of powerful open and semi-open models give developers new options for both text and image generation. Nvidia and Alibaba released high-performance large language models (LLMs), while Stability AI released a slimmed-down version of its flagship text-to-image generator.

… Nvidia offers the Nemotron-4 340B family of language models, which includes a 340-billion parameter base model as well as versions fine-tuned to follow instructions and to serve as a reward model in reinforcement learning from human feedback. …. Alibaba introduced the Qwen2 family of language models. Qwen2 includes base and instruction-tuned versions of five models that range in size from 500 million to 72 billion parameters and process context lengths between 32,000 and 128,000 tokens. …. Stability AI launched the Stable Diffusion 3 Medium text-to-image generator, a 2 billion-parameter based on the technology that underpins Stable Diffusion 3.  — Read More

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

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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

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