Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world. Phone Number: +1-650-246-9381 Email: [email protected]
228 Park Avenue South New York, NY 10003 United States
Website: Publisher: https://towardsai.net/#publisher Diversity Policy: https://towardsai.net/about Ethics Policy: https://towardsai.net/about Masthead: https://towardsai.net/about
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Founders: Roberto Iriondo, , Job Title: Co-founder and Advisor Works for: Towards AI, Inc. Follow Roberto: X, LinkedIn, GitHub, Google Scholar, Towards AI Profile, Medium, ML@CMU, FreeCodeCamp, Crunchbase, Bloomberg, Roberto Iriondo, Generative AI Lab, Generative AI Lab Denis Piffaretti, Job Title: Co-founder Works for: Towards AI, Inc. Louie Peters, Job Title: Co-founder Works for: Towards AI, Inc. Louis-François Bouchard, Job Title: Co-founder Works for: Towards AI, Inc. Cover:
Towards AI Cover
Logo:
Towards AI Logo
Areas Served: Worldwide Alternate Name: Towards AI, Inc. Alternate Name: Towards AI Co. Alternate Name: towards ai Alternate Name: towardsai Alternate Name: towards.ai Alternate Name: tai Alternate Name: toward ai Alternate Name: toward.ai Alternate Name: Towards AI, Inc. Alternate Name: towardsai.net Alternate Name: pub.towardsai.net
5 stars – based on 497 reviews

Frequently Used, Contextual References

TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e

Resources

Unlock the full potential of AI with Building LLMs for Productionβ€”our 470+ page guide to mastering LLMs with practical projects and expert insights!

Publication

Attention is all you need: How Transformer Architecture in NLP started.
Artificial Intelligence   Latest   Machine Learning

Attention is all you need: How Transformer Architecture in NLP started.

Last Updated on September 2, 2024 by Editorial Team

Author(s): Surya Maddula

Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.

Original Paper: Attention is all you need.

AI-Generated Image

This was THE paper that introduced Transformer Architecture to NLP. This transformative concept led to the rise of LLMs and solved the problem of contextualized word embeddings!

Let’s take a journey that led up to the statement written above.

I was researching Embedding Models, and some of the material I came across talked about Word Vector Embeddings.

Vector embeddings map real-world entities, such as a word, sentence, or image, into vector representations or points in some vector space.

Points that are closer to each other in a vector space have similar semantic meanings, which means that they convey comparable meanings or concepts.

Here, you see sample words and their embedding vector using a word embedding model, such as Word2Vec and GloVe, which gives you the embeddings that capture the semantic meaning of each word.

However, the problem with word embedding models is that they don’t really understand the context.

For Example:

The bark of the ancient oak tree was thick and rough, providing shelter for various insects.The dog’s bark echoed through the quiet neighborhood, alerting everyone to the approaching mailman.

Word embedding models like GloVe won’t be able to separate these… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming aΒ sponsor.

Published via Towards AI

Feedback ↓