The modern foundation of the free speech clause of the First Amendment is the concept of the marketplace of ideas. The notion comes from John Stuart Mill who first drew the analogy to a market where ideas compete freely with one another and people form their own judgments. The analogy was first noted in Justice Oliver Wendell Holmes' famous dissent in Abrams v. United States (1919) when he wrote, "The best test of truth is the power of the thought to get itself accepted in the competition of the market." This free and open market of ideas is considered vital to the function and preservation of democracy. As Holmes wrote in another famous dissent in United States v. Schwimmer (1929), "If there is any principle of the Constitution that more imperatively calls for attachment than any other, it is the principle of free thought--not free thought for those who agree with us freedom for the thought we hate." Until recently, the Supreme Court had not cared much where those thoughts might come from, or whether their source must be human.

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This article was co-published with Understanding AI, a newsletter that explores how A.I. works and how it's changing our world. Geoffrey Hinton is a legendary computer scientist whose work laid the foundation for today's artificial intelligence technology. He was a co-author of two of the most influential A.I. papers: a 1986 paper describing a foundational technique (called backpropagation) that is still used to train deep neural networks and a 2012 paper demonstrating that deep neural networks could be shockingly good at recognizing images. That 2012 paper helped to spark the deep learning boom of the last decade. Google hired the paper's authors in 2013 and Hinton has been helping Google develop its A.I. technology ever since then. But last week Hinton quit Google so he could speak freely about his fears that A.I. systems would soon become smarter than us and gain the power to enslave or kill us. "There are very few examples of a more intelligent thing being controlled by a less intelligent thing," Hinton said in an interview on CNN last week.

The breathtaking development of artificial intelligence has dazzled users by composing music, creating images and writing essays, while also raising fears about its implications. Even European Union officials working on groundbreaking rules to govern the emerging technology were caught off guard by AI's rapid rise. The 27-nation bloc proposed the Western world's first AI rules two years ago, focusing on reining in risky but narrowly focused applications. General purpose AI systems like chatbots were barely mentioned. Lawmakers working on the AI Act considered whether to include them but weren't sure how, or even if it was necessary.

Rep. Michael McCaul, R-Texas, said it is essential for the United States to protect its intellectual property and loosen its reliance on China's supply chain to win the race for quantum computing, artificial intelligence (AI) and semiconductor chip supremacy. You know, we had the Russians and we won that race. We have to win this one," McCaul told Fox News Digital at the Milken Global Conference. McCaul, the House Foreign Affairs Committee Chair, said that whoever gets to quantum first is going to rule the world. Additionally, China has been very clear that their 100-year goal is to enact complete "military and economic domination of the world," a mission that McCaul asserted the U.S. cannot allow to happen. U.S. investors have pushed billions into China's AI sector, a February report from CSET showed. China is currently investing heavily in quantum computing, AI and advanced weapons systems. As such, McCaul stressed the importance of U.S. corporations working to protect their intellectual property. "We have to stop exporting our technology to China that they can put in things like the hypersonic missile, for instance, or the spy ballon, for that matter, had American parts in it, component parts," McCaul said. Despite the national security concerns, McCaul noted that the U.S. can work with China's extensive market as long the country understands the critical supply chains intertwining the two nations. One of these critical supply chains involves semiconductors. McCaul first introduced the CHIPS for America Act in 2020, and it was signed into law in August 2022. The Act provides $280 billion in new funding to bolster domestic manufacturing and research for semiconductors domestically. McCaul said the Act is part of a broader effort to pull semiconductor reliance out of Taiwan and South Korea. In fact, TSMC, the world's largest semiconductor foundry, is located on the island of Taiwan. "You know, I introduced the CHIPS bill to try to move some of that out of country.

The jokes seemed to write themselves last week after the Biden administration announced Vice President Kamala Harris, known for her vapid word salad speeches and obvious gaslighting, would now run point on artificial intelligence. Even I jumped in on the action, noting on FOX Business that Harris was more associated with the word "artificial" than the word "intelligence." All joking aside, the future of AI technology is a serious issue. With her approval ratings in the toilet and President Biden showing obvious signs of age-related decline, Kamala Harris (and by that I mean the Democratic Party) urgently needs a way to rehabilitate her historically unpopular image ahead of the 2024 presidential race. This is not the way. On this issue, like so many before it, Harris is out of her depth.

Large AI Models (LAIMs), of which large language models are the most prominent recent example, showcase some impressive performance. However they have been empirically found to pose serious security issues. This paper systematizes our knowledge about the fundamental impossibility of building arbitrarily accurate and secure machine learning models. More precisely, we identify key challenging features of many of today's machine learning settings. Namely, high accuracy seems to require memorizing large training datasets, which are often user-generated and highly heterogeneous, with both sensitive information and fake users. We then survey statistical lower bounds that, we argue, constitute a compelling case against the possibility of designing high-accuracy LAIMs with strong security guarantees.

Where is everybody? This phrase distills the foreboding of what has come to be known as the Fermi Paradox - the disquieting idea that, if extraterrestrial life is probable in the Universe, then why have we not encountered it? This conundrum has puzzled scholars for decades, and many hypotheses have been proposed suggesting both naturalistic and sociological explanations. One intriguing hypothesis is known as the Great Filter, which suggests that some event required for the emergence of intelligent life is extremely unlikely, hence the cosmic silence. A logically equivalent version of this hypothesis -- and one that should give us pause -- suggests that some catastrophic event is likely to occur that prevents life's expansion throughout the cosmos. This could be a naturally occurring event, or more disconcertingly, something that intelligent beings do to themselves that leads to their own extinction. From an intelligence perspective, framing global catastrophic risk (particularly risks of anthropogenic origin) within the context of the Great Filter can provide insight into the long-term futures of technologies that we don't fully understand, like artificial intelligence. For the intelligence professional concerned with global catastrophic risk, this has significant implications for how these risks ought to be prioritized.

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries.

Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks. In this study, we compare four methods for multi-label classification, two based on an encoder only, and two based on an encoder-decoder. We carry out experiments on four datasets -- two in the legal domain and two in the biomedical domain, each with two levels of label granularity -- and always depart from the same pre-trained model, T5. Our results show that encoder-decoder methods outperform encoder-only methods, with a growing advantage on more complex datasets and labeling schemes of finer granularity. Using encoder-decoder models in a non-autoregressive fashion, in particular, yields the best performance overall, so we further study this approach through ablations to better understand its strengths.