#TheDataExchangePod 🎧 Michele Catasta of Replit explores the potential of #AI for software development. Find out how #LLMs & foundations models are helping developers code faster, better, and more efficiently.
#Nlproc #LLM #MachineLearning 🔗 thedataexchange.media/software

#TheDataExchangePod 🎧 the amazing Jeff Jonas of Senzing explains how #BigData, #AI, & real-time processing redefine #EntityResolution and #MasterDataManagement. Learn valuable insights and leverage lessons in accuracy, scale, and complexity. Expand the scope of your AI applications and boost efficiency like never before!
#datascience #dataquality #datacentricai #machinelearning #ai
🔗 thedataexchange.media/using-da

It's been a another wild month in AI & Deep Learning research.
I curated and summarized noteworthy papers here:


Ranging from new optimizers for LLMs to new scaling laws for vision transformers.

Here are the slides for my #PyDataLondon keynote on LLMs from prototype to production ✨

◾ visions for NLP in the age of LLMS
◾ a case for LLM pragmatism
◾ solutions for structured data
◾ spaCy LLM + prodi.gy


A new Ahead of AI issue is out, where I am covering the latest research highlights concerning LLM tuning and dataset efficiency:


New Newsletter 📥 Get the inside scoop on GPT-4 & PaLM 2. Unpack the intricacies of these foundation models and understand the evolution of #LLMs
#NLproc #MachineLearning #DataScience

#TheDataExchangePod: Are you looking for dependable, trustworthy #AI solutions for your company? 🏦🏥⚖️ Jonas Andrulis of Aleph Alpha explains what it takes to build/deploy reliable, source-cited responses, specializing in the legal, healthcare, and financial sectors.

New blog entry: Normalizing company names (and more) with SPARQL and Wikidata. As a service! bobdc.com/blog/wikidatanormali

🎊🎁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

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