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Modular Deep Learning

Sebastian Ruder

This post gives a brief overview of modularity in deep learning. Fuelled by scaling laws, state-of-the-art models in machine learning have been growing larger and larger. We give an in-depth overview of modularity in our survey on Modular Deep Learning. Case studies of modular deep learning.

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NLP Landscape: Germany (Industry & Meetups)

NLP People

Babbel Based in Berlin and New York, Babbel is a language learning platform, helping one learn a new language on the go. handles most common and repetitive questions in self-learning chat-based customer service. Their products are language-agnostic as they use deep learning in the development of their algorithms.

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Large Language Models – Technical Overview

Viso.ai

Machine learning especially Deep Learning is the backbone of every LLM. Emergence and History of LLMs Artificial Neural Networks (ANNs) and Rule-based Models The foundation of these Computational Linguistics models (CL) dates back to the 1940s when Warren McCulloch and Walter Pitts laid the groundwork for AI.

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All Languages Are NOT Created (Tokenized) Equal

Topbots

70% of research papers published in a computational linguistics conference only evaluated English.[ In Findings of the Association for Computational Linguistics: ACL 2022 , pages 2340–2354, Dublin, Ireland. Association for Computational Linguistics. Association for Computational Linguistics.

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Overcoming The Limitations Of Large Language Models

Topbots

In the past, the Deep Learning community solved the data shortage with self-supervision — pre-training LLMs using next-token prediction, a learning signal that is available “for free” since it is inherent to any text. Association for Computational Linguistics. [2] Association for Computational Linguistics. [4]

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Reward Isn't Free: Supervising Robot Learning with Language and Video from the Web

The Stanford AI Lab Blog

Deep learning has enabled improvements in the capabilities of robots on a range of problems such as grasping 1 and locomotion 2 in recent years. Deep contextualized word representations. Conference of the North American Chapter of the Association for Computational Linguistics. ↩ Devlin, J., Neumann, M.,

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The State of Multilingual AI

Sebastian Ruder

This post is partially based on a keynote I gave at the Deep Learning Indaba 2022. These include groups focusing on linguistic regions such as Masakhane for African languages, AmericasNLP for native American languages, IndoNLP for Indonesian languages, GhanaNLP and HausaNLP , among others. Vulić, I., & Søgaard, A.