As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studies have empirically shown that training from scratch has the final performance that is no worse than this pre-training strategy once the number of training samples is increased in some vision tasks. In this work, we revisit this phenomenon from the perspective of generalization analysis by using excess risk bound which is popular in learning theory. The result reveals that the excess risk bound may have a weak dependency on the pre-trained model. The observation inspires us to leverage pre-training data for fine-tuning, since this data is also available for fine-tuning.

For this week's Open Questions column, Cal Newport is filling in for Joshua Rothman. Much of the euphoria and dread swirling around today's artificial-intelligence technologies can be traced back to January, 2020, when a team of researchers at OpenAI published a thirty-page report titled "Scaling Laws for Neural Language Models." The team was led by the A.I. researcher Jared Kaplan, and included Dario Amodei, who is now the C.E.O. of Anthropic. They investigated a fairly nerdy question: What happens to the performance of language models when you increase their size and the intensity of their training? Back then, many machine-learning experts thought that, after they had reached a certain size, language models would effectively start memorizing the answers to their training questions, which would make them less useful once deployed.

Elon Musk has threatened legal action against Apple on behalf of his artificial intelligence startup xAI, accusing the iPhone maker of favoring OpenAI and breaching antitrust regulations in managing the rankings in its App Store. The posts elicited snide responses from Sam Altman, the OpenAI CEO, and began a spat between the two former business partners on X. "Apple is behaving in a manner that makes it impossible for any AI company besides OpenAI to reach #1 in the App Store, which is an unequivocal antitrust violation. In a post earlier that day, he wrote: "Hey @Apple App Store, why do you refuse to put either X or Grok in your'Must Have' section when X is the #1 news app in the world and Grok is #5 among all apps? OpenAI's ChatGPT currently holds the top spot in the App Store's "Top Free Apps" section in the US, while xAI's Grok ranks fifth. Apple has a partnership with OpenAI that integrates ChatGPT into iPhones, iPads and Macs.

As a federal antitrust investigation into Google's Chrome browser wraps up, rivals are striking: Perplexity has launched an unsolicited bid to buy Chrome for a whopping 34.5 billion, according to reports. Bloomberg reported the proposed deal, confirmed by a Perplexity representative, as did The Wall Street Journal. But there's a hitch: Perplexity doesn't have 34.5 billion to fund the deal with. In fact, the WSJ estimates its own valuation at just 18 billion. This means Perplexity would have to come up with another source of cash, and it appears that it has done just that.

The feud between Musk and Altman has, over time, encompassed a slew of lawsuits, email dumps and social media digs. Their rivalry can be traced back a decade, with Musk's now public belief that OpenAI, under Altman's leadership, abandoned the principles he and others used to found it in 2015. The firm was created with the intention of building artificial general intelligence (AGI) - AI that can perform any task that a human being is capable of - but by making its technology open-source and promising to "benefit humanity". OpenAI was also set up as a not-for-profit company, meaning it would not aim to make money, but in 2019 it established a for-profit arm which Musk felt was antithetical to its original mission. Musk argued in his March 2024 lawsuit that the firm had instead been focusing on "maximising profits" for its major investor Microsoft.

Gone are the days when the web was dominated by humans posting social media updates or exchanging memes. Earlier this year, for the first time since the data has been tracked, web-browsing bots, rather than humans, accounted for the bulk of web traffic. Well over half of that bot traffic is from malicious bots, hoovering up personal data left unprotected online, for instance. But an increasing proportion comes from bots sent out by artificial intelligence companies to gather data for their models or respond to user prompts. Indeed, ChatGPT-User, a bot powering OpenAI's ChatGPT, is now responsible for 6 per cent of all web traffic, while ClaudeBot, an automated system developed by AI company Anthropic, accounts for 13 per cent.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. In our eighth grade classroom, her name was Hanna. On AOL Instant Messenger, she was Banana3017. I was in love with both. At school, she was funny, and kind, and she had blue eyes that made my cheeks glow the same fiery color as her hair when she looked at me.

On August 9th, the developer announced on GitHub the official version 1.0 release of Flyoobe, which includes all the great features of Flyby11 but also adds more. If you only want to do what Flyby11 used to do, you can still do that and ignore all the extra stuff on the side. According to the developer, Flyoobe lets you tweak numerous Windows 11 settings, pick and choose which bloatware to keep and exclude, optimize the user interface to your liking, run your own PowerShell scripts that automate some of the setup, and more. The OOBE views in Flyoobe also run on a faster code base for a more responsive experience, and Microsoft Copilot integration has been improved for better overall reliability.

My Spotify playlists are undergoing a British invasion this week. Donald Trump announced this week that two US chipmakers would tithe 15% of their revenue from sales in China to the US government. Paying for the license to sell to Chinese customers represents an unprecedented deal. The chipmakers Nvidia and AMD have agreed to give the US government 15% of their revenue from advanced chips sold to China in return for export licences to the key market. The arrangement will lead to Nvidia giving 15% of its revenue from Chinese sales of its H20 chips, and AMD giving 15% of revenue from Chinese sales of its MI308 chips, according to reports citing US officials.

The propensity for AI systems to make mistakes that humans miss has been on full display in the US legal system as of late. The follies began when lawyers submitted documents citing cases that didn't exist. Similar mistakes soon spread to other roles in the courts. Last December, a Stanford professor submitted sworn testimony containing hallucinations and errors in a case about deepfakes, despite being an expert on AI and misinformation himself. Now, judges are experimenting with generative AI too. Some believe that with the right precautions, the technology can expedite legal research, summarize cases, draft routine orders, and overall help speed up the court system, which is badly backlogged in many parts of the US.