We Don't Understand Neural Networks At The Algorithmic Level

Large language models are not the problem

If a Large Language Model (LLM) can replicate your scientific contribution, the problem is not the LLM. What does it say about our field that so much of the anxiety about AI comes down to the fear that a machine could do what we do? Perhaps it says we should be doing something better.

The Philosophy of Language Models

ABSTRACT The success of large language models (LLMs) across many domains of AI research has generated intense debate. Some attribute their impressive performance on complex tasks to human‐like linguistic and cognitive capacities, whereas others ascribe it to shallow pattern matching. These disputes stem from deep‐seated philosophical disagreements about the nature of language and cognition. We provide an opinionated survey of these disagreements across core topics in the philosophy of mind and language, including syntactic competence, compositionality, linguistic meaning, representation, attitudes, reasoning, agency, and consciousness. We contend that progress on these issues requires not only clarity about background philosophical commitments but also, in many cases, close engagement with emerging empirical evidence.

Epistemological Fault Lines Between Human and Artificial Intelligence

Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between...

A Rational Analysis of the Effects of Sycophantic AI

People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We...

AI Large Language Model Training: The Potential Risks of Ideological Skewing — PSG Consulting

LLMs (AI Large Language Models) have become part of everyday life. Systems such as ChatGPT, Claude, Gemini, Meta AI (Llama) and X.ai's Grok handle billions of interactions daily. They increasingly shape what information people encounter and in what order, subtly deciding what's important and even what is true, sometimes without users realizing it. Because LLMs wield growing power over information exposure, it is vital to recognize the political and ideological structures at multiple stages of their design, and to identify manipulation risks. 

The Human Skill That Eludes AI

Why can’t language models write well?

AI learns language from skewed sources. That could change how we humans speak – and think | Bruce Schneier

Large language models aren’t trained on real-life conversations. As we encounter their language, it could affect our own

What Is Intelligence?

It has come as a shock to some AI researchers that a large neural net that predicts next words seems to produce a system with general intelligence. Yet this ...

AI’s Memorization Crisis

Large language models don’t “learn”—they copy. And that could change everything for the tech industry.

Large language model

A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind modern chatbots.[1] Biased or inaccurate training data can make an LLM's output less reliable.[2]

AI Agents: Key Concepts and How They Overcome LLM Limitations

An AI agent is an autonomous software entity that is often used to augment a large language model. Here's what developers need to know.

If You’re Going To Defend AI And Whine About Its Critics, You Should Probably Be Honest About Its Actual Harms

I think this recent post by AI industry CEO Matt Shumer is worth a read. In it, he basically explains how quickly LLMs (large language models) are evolving to supplant many developers and prog…

Are LLMs Stifling Political Speech? An Assessment of How AI Models Protect Free Expression | Oversight Board

The Oversight Board’s first evaluation of large language models (LLMs) shows that some of the world’s most-used models from Anthropic, DeepSeek, Google, Meta