The Evolution of Large Language Models (LLM)

ChatGPT … that little AI wonder. A year hasn’t passed and Generative AI is already integrated in many aspects of our lives. How did that happen? Here’s a short video i prepared that walks you through its evolution.

Check the LLM resume (CV) afterwards!

Objective

To continue developing natural language processing capabilities to advance artificial intelligence systems that can understand, communicate, and reason using human language.

Skills

Natural language processing, Machine learning, Neural networks, Statistical modeling, Text generation, Speech recognition, Translation, Question answering

Experience

Word2vec (2009–2014)

• Pioneered neural network techniques for efficiently representing words as vectors based on semantic meaning. This allowed more effective modeling of text data.
• Published influential papers on word embedding methods adopted widely in the field.
• Skills used: vector representations, semantic similarity, neural networks.

BERT (2014–2020)

• Employed transformer neural network architecture that greatly improved modeling of long-range dependencies in text.
• Allowed pre-training of general language representations for fine tuning on specialized tasks.
• Achieved state-of-the-art results on question answering, sentence completion, and language translation tasks.
• Skills used: transformer networks, self-supervised learning, transfer learning.

GPT-3 (2020 — Present)

• Scaled up previous models to 175 billion parameters, enabling remarkably human-like text generation.
• Demonstrated few-shot learning capabilities, generalizing from small data samples to new tasks.
• Skills used: massive model scale, semi-supervised learning, generative modeling.

CLIP (2021 — Present)

• Pioneered combining computer vision and natural language processing in a single AI system.
• Allows connecting textual concepts with visual representations.
• Skills used: multimodal learning, image-text modeling.

Education

• Self-taught from large-scale internet text, books, and academic papers.
• Specialized training on scientific, mathematical, and technical corpora.
• Feedback from researchers on state-of-the-art natural language processing techniques

Future Goals

• Continue developing reasoning abilities beyond statistical pattern recognition.
• Master specialized knowledge domains such as science, medicine, law, engineering.
• Increase interpretability and transparency.
• Ensure ethical use for benefit of humanity.

Recommended Reading:

• https://en.wikipedia.org/wiki/Word2vec
• https://en.wikipedia.org/wiki/BERT_(language_model)
• https://openai.com/chatgpt
• https://openai.com/research/clip

Originally published at https://www.linkedin.com.