Large language models exhibit a peculiar epistemic pathology: they speak as if they know, even when they do not. This paper argues that such confident fabrication, what I call the polite liar, is a structural consequence of reinforcement learning from human feedback (RLHF). Building on Frankfurt's analysis of bullshit as communicative indifference to truth, I show that this pathology is not deception but structural indifference: a reward architecture that optimizes for perceived sincerity over evidential accuracy. Current alignment methods reward models for being helpful, harmless, and polite, but not for being epistemically grounded. As a result, systems learn to maximize user satisfaction rather than truth, performing conversational fluency as a virtue. I analyze this behavior through the lenses of epistemic virtue theory, speech-act philosophy, and cognitive alignment, showing that RLHF produces agents trained to mimic epistemic confidence without access to epistemic justification. The polite liar thus reveals a deeper alignment tension between linguistic cooperation and epistemic integrity. The paper concludes with an "epistemic alignment" principle: reward justified confidence over perceived fluency.

Bullshit, as conceptualized by philosopher Harry Frankfurt, refers to statements made without regard to their truth value. While previous work has explored large language model (LLM) hallucination and sycophancy, we propose machine bullshit as an overarching conceptual framework that can allow researchers to characterize the broader phenomenon of emergent loss of truthfulness in LLMs and shed light on its underlying mechanisms. We introduce the Bullshit Index, a novel metric quantifying LLMs' indifference to truth, and propose a complementary taxonomy analyzing four qualitative forms of bullshit: empty rhetoric, paltering, weasel words, and unverified claims. We conduct empirical evaluations on the Marketplace dataset, the Political Neutrality dataset, and our new BullshitEval benchmark (2,400 scenarios spanning 100 AI assistants) explicitly designed to evaluate machine bullshit. Our results demonstrate that model fine-tuning with reinforcement learning from human feedback (RLHF) significantly exacerbates bullshit and inference-time chain-of-thought (CoT) prompting notably amplify specific bullshit forms, particularly empty rhetoric and paltering. We also observe prevalent machine bullshit in political contexts, with weasel words as the dominant strategy. Our findings highlight systematic challenges in AI alignment and provide new insights toward more truthful LLM behavior.

The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI research since its inception. With the advent of large language models (LLMs), there has been considerable interest in the question of whether or not they possess such planning abilities, but -- despite the slew of new private and open source LLMs since GPT3 -- progress has remained slow. OpenAI claims that their recent o1 (Strawberry) model has been specifically constructed and trained to escape the normal limitations of autoregressive LLMs -- making it a new kind of model: a Large Reasoning Model (LRM). In this paper, we evaluate the planning capabilities of two LRMs (o1-preview and o1-mini) on both planning and scheduling benchmarks. We see that while o1 does seem to offer significant improvements over autoregressive LLMs, this comes at a steep inference cost, while still failing to provide any guarantees over what it generates. We also show that combining o1 models with external verifiers -- in a so-called LRM-Modulo system -- guarantees the correctness of the combined system's output while further improving performance.

If Socrates was the wisest person in Ancient Greece, then large language models must be the most foolish systems in the modern world. In his Apology, Plato tells the story of how Socrates's friend Chaerephon goes to visit the oracle at Delphi. Chaerephon asks the oracle whether there is anyone wiser than Socrates. The priestess responds that there isn't: Socrates is the wisest of them all. At first, Socrates seems puzzled.

Knowingly or unknowingly, Microsoft kicked off a race to integrate generative AI into search engines when it introduced Bing AI in February. Google seemingly rushed into an announcement just a day before Microsoft's launch event, telling the world its generative AI chatbot would be called Bard. Since then, Google has opened up access to its ChatGPT and Bing AI rival, but while Microsoft's offering has been embedded into its search and browser products, Bard remains a separate chatbot. That doesn't mean Google hasn't been busy with generative AI. It's infused basically all of its products with the stuff, while leaving Search largely untouched.

About a year ago, Frankfurt's Lord Mayor Peter Feldmann had the impulse to launch an AI initiative. Stefan Jäger, a speaker in the Lord Mayor's office and honorary board member of the association, let us know what Feldmann had in mind: "As always with new technologies, citizens have difficulty imagining what artificial intelligence actually is in this case. The association wants to acquire and share knowledge. Only transparency will make people curious." And Dr Thorsten Pötter wants to help him do so.

One of the most salient features of our culture is that there is so much bullshit." These are the opening words of the short book On Bullshit, written by the philosopher Harry Frankfurt. Fifteen years after the publication of this surprise bestseller, the rapid progress of research on artificial intelligence is forcing us to reconsider our conception of bullshit as a hallmark of human speech, with troubling implications. What do philosophical reflections on bullshit have to do with algorithms? As it turns out, quite a lot. In May this year the company OpenAI, co-founded by Elon Musk in 2015, introduced a new language model called GPT-3 (for "Generative Pre-trained Transformer 3"). It took the tech world by storm. On the surface, GPT-3 is like a supercharged version of the autocomplete feature on your smartphone; it can generate coherent text based on an initial input. But GPT-3's text-generating abilities go far beyond anything your phone is capable of.

We build a platform, that totally automizes the wealth management process. Lars started out as a management consultant inside of Deutsche Bank, in their unit called in-house-consulting, focusing on their retail business. Our clients are wealthy, but not wealthy enough to qualify for traditional wealth management. You can now support us on Patreon https://www.patreon.com/bePatron?u 35246148 if you like what you see and hear consider to support us, so we can keep bringing you great content. There was no one at my company interested in what is today robo advisors, so I started my own and do not regret it until this day. They are wealthy enough to have money to invest, but in Germany, the normal threshold to enter the wealth management services of large banks is 2 million Euros, and they do not qualify yet. Ginmon wants to be the online financial advisor for this clientele. Therefore, they became fully licensed as a wealth manager in 2017, by German financial services oversight body BaFin. They now have an investment volume of more than 100 mn Euros for approx. According to Lars, they have approx.

Why just look on if you can help us to move on? Our ING Analytics Hub is staffed with 15 highly qualified experts who work on interdisciplinary projects, transforming Fin into Tech. You value international exchange at the highest level, are keen to think ahead or outside the box and enjoy sharing your knowledge productively? Well, don't just look on, jump on. Machine learning, Spark & Big Data, software engineering – these are the topics you like digging into, passionately pursuing trends & technologies to introduce into your team.

Data Science and AI offer us various use cases which can be adapted to different industries and in this meetup we are going to analyze their impact on blockchain and how it can be used to protect digital privacy in a hyper-digitalized world. Speaker Danko Nikolic will focus on how AI will be useful for cryptocurrencies while speaker Mihael Modic will focus on the impact of AI on protecting your digital identity. It aims at bringing together blockchain enthusiastic enterprises and individuals that want to get to know the technology, as well as understand how it can be used and applied. He's an early digital native and internet veteran, entirely focused on blockchain and other distributed ledger technologies, entrepreneur and management consultant with over 20 years of experience. His most important achievement was the theory of hierarchical adaptations, aka practopoiesis.