Jodie Burchell
where else to find them
what they talk about
all 9 in the filters
where they keep going back
- PyCon US3 editions2023–2026
- NDC London2 editions2024–2025
- NDC Oslo2 editions2022–2024
all 12 in the filters
on a stage with
- Carol Willingonce
- Catherine Nelsononce
- Jelle Zijlstraonce
all 5 in the filters
who talks about the same things
- Tess Ferrandez-Norlander24 talks
- Katharine Jarmul25 talks
- Vincent D Warmerdam22 talks
- Chip Huyen10 talks
- Joshua Arvin Lat20 talks
- Joe Spisak23 talks
| title | conference | date |
|---|---|---|
| Beyond the Hype: A Pragmatic Look at How Developers Actually Use AI Tools | PyCon US 2026 | 2026-05-13 |
Claude Code, GitHub Copilot, Codex, MS Copilot Studio and PyCharm agents feature in concrete workflows: deployed a website in 30 minutes using an open‑source… | ||
| Keynote: Can you trust your (large language) model? | NDC AI 2025 | 2025-11-11 |
Compares concrete failures and fixes: a psychology replication snafu, CEXNet overfitting because of data leakage, and GPT‑4 solving Codeforces puzzles only… | ||
| Keynote: AI is having its moment ... again | NDC Copenhagen 2025 | 2025-09-08 |
Argues this is the third AI summer, tracing concrete wins and failures: Dartmouth 1956 births symbolism and connectionism; Rosenblatt’s 1958 perceptron learns… | ||
| Can you trust your (large language) model? | NDC Melbourne 2025 | 2025-04-29 |
Retells the Amy Cuddy power‑posing replication failure to show how plausible results can be wrong, then walks through concrete LLM failures: benchmark glitches… | ||
| Mirror, mirror: LLMs and the illusion of humanity | NDC Melbourne 2025 | 2025-04-29 |
Examines specific claims that LLMs are sentient or AGI by showing concrete tests and failures: vector math from word2vec analogies, the octopus grounding… | ||
| Can you trust your (large language) model? | NDC London 2025 | 2025-01-27 |
Argues that public benchmarks and human exams mislead: scores can reflect memorized training data not reasoning, with GPT‑4 solving Codeforces puzzles released… | ||
| Keynote: Can you trust your (large language) model? | NDC Porto 2024 | 2024-10-14 |
Argues that LLM claims break down when measured correctly: replicated psychology finding fails, CheXNet pneumonia model succeeds only because of data leakage… | ||
| Mirror, mirror: LLMs and the illusion of humanity | NDC Oslo 2024 | 2024-06-10 |
Examines specific claims and failures: OpenAI holds back GPT‑2 for fear of misuse, GPT‑3 begins encoding knowledge, GPT‑4 prompts AGI headlines, and Blake Le… | ||
| Beyond the Hype: A Realistic Look at Large Language Models | GOTO Amsterdam 2024 | 2024-06-10 |
History of LLMs traced from LSTMs to Transformers and models scaling from 120M to ~1T parameters, with a concrete failure mode: GPT-4 solves 10 Codeforces… | ||
| Mirror, mirror: LLMs and the illusion of humanity | PyCon Italia 2024 | 2024-05-22 |
Covers specific high‑profile claims and tests: GPT‑3 and GPT‑4 produce Shakespeare‑like text and exam‑passing outputs, Microsoft researchers claimed “sparks of… | ||
| Lies, damned lies and large language models | PyCon US 2024 | 2024-05-15 |
Demo measures LLM hallucinations by running 817 TruthfulQA questions through models and Python tooling, using TruthfulQA and Hugging Face datasets and… | ||
| Separating fact from fiction in a world of AI fairytales | NDC London 2024 | 2024-01-29 |
Covers why sensational claims (sentience, imminent AGI) overstate current models and shows concrete limits and fixes: GPT-4 writes a working but imperfect 3D… | ||
| Vectorize using linear algebra and NumPy to make your Python code fast | PyCon US 2023 | 2023-04-19 |
Have you found that your code works beautifully on a few dozen examples, but leaves you wondering how to spend the next couple of hours after you start looping… | ||
| Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast | Python Web Conf 2023 | 2023-03-13 |
Builds a working k‑nearest neighbors classifier in plain Python then replaces slow nested loops and list operations with NumPy arrays, vectorized subtraction… | ||
| Garbage data in, garbage models out | NDC Oslo 2022 | 2022-09-26 |
Builds two models for ad auctions: a classifier for whether an ad sells and a regressor for price, and shows concrete failures and fixes — mislabeled region… | ||
| Avoid Hiring Data Ninja Rockstars: How To Build Effective Data Teams | Big Data LDN 2022 | 2022-09-21 |
Argues against hiring one “data ninja” who does everything and spells out concrete role splits: data scientists do feature engineering, modeling and model… | ||
| Reproducible Research in Python | PyCon AU 2016 | 2016-08-12 |
A messy Dropbox with 30 data files and seven Python scripts becomes a reproducible pipeline that predicts average life expectancy for ~150 WHO member states… | ||
| nothing matches | ||