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From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

NLP 98
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A comprehensive guide to learning LLMs (Foundational Models)

Mlearning.ai

YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1) YouTube Transformer Models (cohere.com) Intro to BERT (early LLM example) BERT Neural Network — EXPLAINED! — YouTube YouTube BERT Research — Ep. Transformer Neural Networks — EXPLAINED!

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Leveraging generative AI on AWS to transform life sciences

IBM Journey to AI blog

in 10 years, from 2012 to 2022. In a nod to the growing usage of Machine learning in life sciences, FDA has now cleared more than 500 medical algorithms that are commercially available in the United States. More than half of algorithms on the U.S. Business Value As per FAERS database , the number of reported AEs has grown 2.5x

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Rising Tide Rents and Robber Baron Rents

O'Reilly Media

Why is it that Amazon, which has positioned itself as “the most customer-centric company on the planet,” now lards its search results with advertisements, placing them ahead of the customer-centric results chosen by the company’s organic search algorithms, which prioritize a combination of low price, high customer ratings, and other similar factors?

BERT 110
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Dude, Where’s My Neural Net? An Informal and Slightly Personal History

Lexalytics

This would change in 1986 with the publication of “Parallel Distributed Processing” [ 6 ], which included a description of the backpropagation algorithm [ 7 ]. In retrospect, this algorithm seems obvious, and perhaps it was. We were definitely in a Kuhnian pre-paradigmatic period. It would not be the last time that happened.)

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Unsupervised Cross-lingual Representation Learning

Sebastian Ruder

In particular, I cover unsupervised deep multilingual models such as multilingual BERT. The NLP Resource Hierarchy In current machine learning, the amount of available training data is the main factor that influences an algorithm's performance. If this is not the case, then the unsupervised seed induction step fails.

BERT 52
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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

in 2012 is now widely referred to as ML’s “Cambrian Explosion.” This is accomplished by breaking the problem into independent parts so that each processing element can complete its part of the workload algorithm simultaneously. In FSI, non-time series workloads are also underpinned by algorithms that can be parallelized.

ML 86