🎊🎁Big release of dirty-cat

Broader focus: simplifying preparing non-curated dataframes for machine learning.
🔸Encoding of messy dataframes: a strong baseline for easy machine learning
🔸fuzzy_join: joining dataframes (pd.merge) despite typos
🔸Deduplication: matching categories with typos
🔸Feature augmentation: joining on an external data source to enrich tabular data
🔸Embedding of cites, companies, locations...

Tabular data can benefit from merging external sources of information.

The FeatureAugmenter is a sklearn transformer to augment a given dataframe by joins on reference tables.

fuzzy_join makes it robust to mismatch in vocabulary. Hyperparameter optimization can tune matches for prediction

For such external information,
diry-cat can download embeddings of wikipedia data on millions of entities: companies, cities, geographic locations...

Productive weekend! Just added 4 new Q&A's!

- Multi-GPU Training Paradigms
- The Distributional Hypothesis
- "Self"-Attention
- Training & Test Set Discordance

And "Machine Learning Q and AI" just crossed the 50% milestone! 🎉

PS: I included the Multi-GPU Training Paradigms section is in the free preview at

This Politico article argues that #NMT (Neural Machine Translation) was one of the major drivers of European unity against the Russian invasion of Ukraine.

Not sure I fully buy the argument, but MT is probably one of the best examples of #NLProc / #AI that benefits society / #AIforGood.


h/t @daanvanesch

CALL FOR PAPERS: Research and Innovation Track

We welcome papers on novel scientific research and/or innovations relevant to #SemanticWeb, #KnowledgeGraphs, #AI, #ML, #NLP and more

🗓️Abstracts: May 09
🗓️Papers: May 16

For more info: 🌐2023-eu.semantics.cc/page/cfp_

Do you love #selfhosting? What about providing service to the public via #Codeberg?

We are looking for maintainers that take on adding code search features to our #Forgejo instance to reduce the load on the existing infrastructure team and bring this project forward.

Please see codeberg.org/Codeberg/Communit if you are interested.

We are looking forward to your contributions. Thank you a lot!

The most interesting thing about #ChatGPT that no one is talking about is how the future will be systems talking to each other with imprecise protocols but they’re still able to understand

And the year has barely started!

RT @MishaLaskin@twitter.com

In-context RL at scale. After online pre-training, the agent solves new tasks entirely in-context like an LLM and works in a complex domain. One of the most interesting RL results of the year. twitter.com/FeryalMP/status/16

🐦🔗: twitter.com/MishaLaskin/status

Recent events have demonstrated how crucial resilience (e.g. of supply chains) is for our society. Semantic technologies can play a crucial here.

We will organize the #D2R2 (Linked Data-driven Resilience Research) #workshop at @eswc_conf@twitter.com
in May 2023 in Crete. We are looking forward to your contribution. Submission deadline is March 9. Check more details on our event page! d2r2.aksw.org #ESWC23 #CoyPu_Project #Resilience #LinkedData #cfp

The artificial-intelligence chatbot ChatGPT can write fake abstracts that scientists have trouble distinguishing from those written by humans. Increasing sophistication of chatbots could undermine research integrity and accuracy, researchers fear.


#chatbots #ChatGPT #research #AI #ArtificialIntelligence

via @Nature

Hello NLP researchers around the globe! All ACL major conferences (@aclmeeting, @eaclmeeting, @aaclmeeting, and @emnlpmeeting) now have an account here. Please spread it word! #NLPRoc

I found the papers "Scaling Laws for Neural Language Models" (OpenAI, 2020) and "Training Compute-Optimal Large Language Models" (DeepMind, 2022) interesting:
They do a LOT of experiments training large language models (causal transformers) with varying hyperparameters, in particular model size, shape, batch size, and training data set size over many orders of magnitude. 1/?

DeepMind's paper refutes this last claim, and finds that both are equally useful.
The differences between DeepMind & OpenAI's papers matter in terms of forecasting how big LLMs need to get. They arrived at these different conclusions because DeepMind did more learning rate tuning. This blog post severelytheoretical.wordpress. hypothesizes that DeepMind's paper might also be not doing enough hyperparameter tuning, and the scaling law may be less severe, perhaps not even a power law.

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On #TheDataExchangePod I speak with Mark Chen, Research Scientist at OpenAI. We discuss the evolution of DALL·E, key research developments that led to DALL·E 2, data sources, safety measures, ML models needed for its success. #machinelearning #dalle2 #dalle #AI #generativeai thedataexchange.media/explorin

I do however have high hopes for #blogic and RDF+Surfaces to make the interpretation of RDF vocabularies interoperable across organizations


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The latest issue of 'Ahead of AI' is now available!

This edition covers my top 10 papers of the year, as well as trends in the AI industry, notable developments in open source projects, and my personal yearly review routine.

Check it out at the link below and have a happy new year!


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