David Strohmaier

If you model the acquisition of words that don’t exist in any language, what distribution do you assume for these words?

I’m offering a philosophy of AI masterclass, “Concepts in Machines”, at the University of Zürich later this month (April 23–25, 2026).

This post collects a set of notes on the Symbol Grounding Problem in the context of LLMs.

I’m presenting a poster at the upcoming CoNLL workshop in Vienna collocated with the main ACL conference.

I’m currently reading Melissa Bowerman’s (2018) “Ten Lectures on Language, Cognition, and Language Acquisition”, and right at the start of the first lecture, she provides a fascinating remark on assumptions historically made about language learning.

I will give two talks about Philosophy and AI in the next few weeks (February 2025).

Being sufficiently arrogant to provide a list of all time favourite books on my website, it behooves me to pay some thought to the books included on it.

2024 was my year of training Transformer Language Models (LMs).

To my surprise, the recent 2024 EMNLP conference included multiple papers with a philosophical angle.

I have two upcoming events to share: NLP4CALL Workshop I will present my paper “Semantic Error Prediction: Estimating Word Production Complexity” (co-authored with Paula Buttery) at this year’s NLP4CALL workshop.

What’s not to like?

Last week, I had the pleasure to present a poster at the third conference on Experiments in Linguistic Meaning (ELM3).

Introduction Due to their lack of success in resolving problems, philosophers like to think that they are failing productively.

Daniel Dennett has passed away.

Although I am acutely aware of the shortcomings of the current generation of AI models (transformer-models in particular), most of the criticisms of AI I stumble upon on the internet have become repetitive and lacking insight.

Transformer models do not learn compositionality.

Now available, an open-access introduction to preference change!

Prolog is not the tool of choice for most of NLP nowadays, but this didn’t always use to be the case.

Compositionality is one of the long-standing challenges to neural NLP.

Large transformer-based language models (LLMs) are performing well on a variety of tasks.

A while ago, I wrote up a number of papers (see this post), all of which suggested that transformer models have partially converged with human language cognition.

I recently had the pleasure to attend the Groningen Cognitive Modeling Spring School.

A common criticism of Transformer models, such as ChatGPT, BERT, and Bard, is that they only learn surface statistics.

Transformer LLMs, such as ChatGPT, BERT, and Bard, have sufficiently impressed the public that some have described them as AI minds.

Warning: Speculative Content.

Once in a while, a publication resets the literature.

I regularly use Zotero for managing my bibliographies.

Another year has passed and I want to take the opportunity to reflect on my research pursuits at a higher-level of abstraction.

Introduction Neural networks with Transformer-architecture remain the state of the art in natural language processing (NLP).

Disclaimer I am currently funded by money from Cambridge University Press and Assessment, which also stewards various English dictionaries.

Will machine learning (ML) solve natural language understanding (NLU)?

A debate about the state of analytic philosophy has been developing in the philosophical blogosphere over the last few months, started by Liam Bright’s pessimistic assessment of the state.

The Sudoku scene has undoubtedly been one of the pandemic winners.

Lexical semantics investigates the meaning of words, but one might distinguish multiple levels of meaning for a single word.

My recent blog post arguing why one should lean Prolog in 2021 made its way to the front page of Hacker News (HN), where it started a discussion with more than 100 comments.

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What has 2020 brought?

Can we simulate basic logic operations, i.e. the operations of first-order predicate logic, using tensors?

I’ve recently been reading up on distributional representations, that is representation of meaning that are based on count vectors.

In this blog post I share some preliminary musings on conceptual grain – how fine-grained concepts such as DOG and MAMMAL are – which have arisen from my work in NLP and specifically word sense disambiguation.

The Canadian Journal of Philosophy has published my paper on what I call “Social-Computation-Supporting Kinds”.

I am presenting at the 2020 Social Ontology Conference and because it is virtual, you can all watch it online.

I’ve recently posted a short list of social ontology highlights from 2019, but Kirk Ludwig sent me a much more extensive list.

Social ontology, by which I mean a subfield of contemporary analytic philosophy, is a comparatively small enterprise so far.

Over the years there have been a number of visualisations of Wittgenstein’s Tractatus Logico-Philosophicus.

For the fourth year in a row, I will present a paper at a social ontology conference this Summer.

In another life I read a lot of Hegel, now a mere side-interest of mine.

Two of my publications are finally out.