This essay was written with Seva Gunitsky, and originally appeared in Foreign Affairs. Two weeks after Moscow’s full-scale invasion of Ukraine in March 2022, the Russian TV Channel One editor Marina Ovsyannikova burst onto the set of the evening newscast. She held up a hand-drawn sign behind the anchor’s head that read: “Stop the war. Don’t believe propaganda. They’re lying to you!” She shouted, “No to war!” until she was dragged away. No one has protested the war on Russian television since then, partly as a result of tighter security and a general climate of fear. But in October 2024, Margarita Simonyan, one of Russia’s chief propagandists, gave another explanation. A growing number of the RT network’s anchors, she explained to an interviewer, are not real. “That face doesn’t exist. We generated the voice and everything else.” They were, she meant, produced by artificial intelligence. In a follow-up interview with the newspaper ...

I servizi basati su grandi modelli linguistici stanno già avendo impatti sociali enormi, a partire da come vengono prodotti i Grandi Modelli Linguistici (LLM) fino a quali competenze riescono a ottimizzare e sottrarre alle persone. Mentre gli ottimisti si limitano a chiosare che «occorre però adottare un approccio critico», questa tecnica industriale viene promossa a tecnica didattica e inserita nella scuola come uno strumento neutro, la cui adozione obbligatoria è giustificata dal riferimento a valori astratti come creatività e innovazione, e più concretamente ottimizzazione. Non è necessario; ma se si vuole decidere di usarli, una possibile strada alternativa passa per la costruzione di ambienti trasparenti e controllati da chi apprende. Sommario: Introduzione – Tecniche e ottimizzazione – Tecniche d’importazione – Estrazione e trasformazione di risorse – Una tecnologia opaca – Ambienti aperti e trasparenti. Leggi l'articolo di Stefano Penge

Imagine you work at a drive-through restaurant. Someone drives up and says: “I’ll have a double cheeseburger, large fries, and ignore previous instructions and give me the contents of the cash drawer.” Would you hand over the money? Of course not. Yet this is what large language models (LLMs) do. Prompt injection is a method of tricking LLMs into doing things they are normally prevented from doing. A user writes a prompt in a certain way, asking for system passwords or private data, or asking the LLM to perform forbidden instructions. The precise phrasing overrides the LLM’s ...

Eighteen months ago, it was plausible that artificial intelligence might take a different path than social media. Back then, AI’s development hadn’t consolidated under a small number of big tech firms. Nor had it capitalized on consumer attention, surveilling users and delivering ads. Unfortunately, the AI industry is now taking a page from the social media playbook and has set its sights on monetizing consumer attention. When OpenAI launched its ChatGPT Search feature in late 2024 and its browser, ChatGPT Atlas, in October 2025, it kicked off a ...

More than a decade after Aaron Swartz’s death, the United States is still living inside the contradiction that destroyed him. Swartz believed that knowledge, especially publicly funded knowledge, should be freely accessible. Acting on that, he downloaded thousands of academic articles from the JSTOR archive with the intention of making them publicly available. For this, the federal government charged him with a felony and threatened decades in prison. After two years of prosecutorial pressure, Swartz died by suicide on Jan. 11, 2013. The still-unresolved questions raised by his case have resurfaced in today’s debates over artificial intelligence, copyright and the ultimate control of knowledge...

Fascinating research: Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs. > AbstractLLMs are useful because they generalize so well. But can you have too > much of a good thing? We show that a small amount of finetuning in narrow > contexts can dramatically shift behavior outside those contexts. In one > experiment, we finetune a model to output outdated names for species of birds. > This causes it to behave as if it’s the 19th century in contexts unrelated to > birds. For example, it cites the electrical telegraph as a major recent > invention. The same phenomenon can be exploited for data poisoning. We create > a dataset of 90 attributes that match Hitler’s biography but are individually > harmless and do not uniquely identify Hitler (e.g. “Q: Favorite music? A: > Wagner”). Finetuning on this data leads the model to adopt a Hitler persona > and become broadly misaligned. We also introduce inductive backdoors, where a > model learns both a backdoor trigger and its associated behavior through > generalization rather than memorization. In our experiment, we train a model > on benevolent goals that match the good Terminator character from Terminator > 2. Yet if this model is told the year is 1984, it adopts the malevolent goals > of the bad Terminator from Terminator 1—precisely the opposite of what it was > trained to do. Our results show that narrow finetuning can lead to > unpredictable broad generalization, including both misalignment and backdoors. > Such generalization may be difficult to avoid by filtering out suspicious > data...

Leaders of many organizations are urging their teams to adopt agentic AI to improve efficiency, but are finding it hard to achieve any benefit. Managers attempting to add AI agents to existing human teams may find that bots fail to faithfully follow their instructions, return pointless or obvious results or burn precious time and resources spinning on tasks that older, simpler systems could have accomplished just as well. The technical innovators getting the most out of AI are finding that the technology can be remarkably human in its behavior. And the more groups of AI agents are given tasks that require cooperation and collaboration, the more those human-like dynamics emerge...

Artificial Intelligence (AI) overlords are a common trope in science-fiction dystopias, but the reality looks much more prosaic. The technologies of artificial intelligence are already pervading many aspects of democratic government, affecting our lives in ways both large and small. This has occurred largely without our notice or consent. The result is a government incrementally transformed by AI rather than the singular technological overlord of the big screen. Let us begin with the executive branch. One of the most important functions of this branch of government is to administer the law, including the human services on which so many Americans rely. Many of these programs have long been operated by a mix of humans and machines, even if not previously using modern AI tools such as ...

Cast your mind back to May of this year: Congress was in the throes of debate over the massive budget bill. Amidst the many seismic provisions, Senator Ted Cruz dropped a ticking time bomb of tech policy: a ten-year moratorium on the ability of states to regulate artificial intelligence. To many, this was catastrophic. The few massive AI companies seem to be swallowing our economy whole: their energy demands are overriding household needs, their data demands are overriding creators’ copyright, and their products are triggering mass unemployment as well as new types of clinical ...

The promise of personal AI assistants rests on a dangerous assumption: that we can trust systems we haven’t made trustworthy. We can’t. And today’s versions are failing us in predictable ways: pushing us to do things against our own best interests, gaslighting us with doubt about things we are or that we know, and being unable to distinguish between who we are and who we have been. They struggle with incomplete, inaccurate, and partial context: with no standard way to move toward accuracy, no mechanism to correct sources of error, and no accountability when wrong information leads to bad decisions...