Johannes Hoffart @johannes@qoto.org

#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 🔗 https://thedataexchange.media/software-development-with-ai-and-llms/

#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
🔗 https://thedataexchange.media/using-data-and-ai-to-democratize-entity-resolution-and-master-data-management/

@SebRaschka finished his excellent free deep learning fundamentals course…highly recommended to watch: https://lightning.ai/pages/courses/deep-learning-fundamentals/unit-1/

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

https://magazine.sebastianraschka.com/p/ai-research-highlights-in-3-sentences-2a1/

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 ✨

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

https://speakerdeck.com/inesmontani/large-language-models-from-prototype-to-production

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

https://magazine.sebastianraschka.com/p/ahead-of-ai-9-llm-tuning-and-dataset/

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
https://gradientflow.substack.com/p/what-you-need-to-know-about-gpt-4

#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.
https://thedataexchange.media/building-ai-for-enterprises/

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

🎊🎁Big release of dirty-cat
https://dirty-cat.github.io/stable/

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.
https://dirty-cat.github.io/stable/generated/dirty_cat.FeatureAugmenter.html

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...
https://dirty-cat.github.io/stable/auto_examples/07_ken_embeddings_example.html

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
https://leanpub.com/machine-learning-q-and-ai/

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.

https://www.politico.com/news/magazine/2023/02/03/europe-putin-ukraine-google-translate-00079301

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

Deadlines:
🗓️Abstracts: May 09
🗓️Papers: May 16

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

Machbarkeitsstudie: KI-Leuchtturmprojekt in Deutschland möglich

ChatGPT kommt aus den USA. KI-Experten in Deutschland sehen darin ein Problem und fordern einen Kraftakt zur Wahrung der digitalen Souveränität.

https://www.heise.de/news/Machbarkeitsstudie-KI-Leuchtturmprojekt-in-Deutschland-moeglich-7468862.html?wt_mc=sm.red.ho.mastodon.mastodon.md_beitraege.md_beitraege

#Bundeswirtschaftsministerium #ChatGPT #Deutschland #Europa #KünstlicheIntelligenz #LEAM #Sprachmodelle #Supercomputer #USA #digitaleAssistenten

In eigener Sache: heise online zieht auf eigene Mastodon-Instanz

Das Chaos bei Twitter hält an und die Mastodon profitiert weiter. Heise Medien betreibt in dem Fediverse-Netzwerk nun eine eigene Instanz.

https://www.heise.de/news/Twitter-Alternative-Mastodon-heise-online-zieht-auf-eigene-Instanz-7465214.html?wt_mc=sm.red.ho.mastodon.mastodon.md_beitraege.md_beitraege

#Fediverse #Heise #Mastodon #SocialMedia #Twitter #TwitterÜbernahme #heiseonline

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 https://codeberg.org/Codeberg/Community/issues/904 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