ChatGPT Release: Marked a significant leap in AI, as a conversational AI widely accessible and capable of passing the Turing test.
Linguistic and Computational Breakthroughs: Until recently, many experts doubted computers could comprehend human language.
Efficiency: Tasks that would take humans an hour now take GPT-4 a second.
Historical Context
Neural Networks: Research focused on narrow, fixed-goal problems, e.g., classifying images, detecting spam.
Supervised Learning: Networks trained on labeled data but stuck in silos, unable to generalize beyond specific tasks.
Evolution of Neural Networks
1986, Jordan's Recurrent Neural Network (RNN): Introduced memory neurons and state units, allowing networks to predict sequences.
Early Experiments: Proper training led to generalized, not memorized, patterns. Networks learned trajectories in state space, akin to attractors in chaos theory.
Elman's Extensions: Larger networks and experimental training on language without word boundaries led to spontaneous learning of meaning and semantic clustering of words.
Practical Challenges: Small, toy-scale networks limited real-world applications until further advancements.
Scalable Language Models
2011 Breakthroughs: Larger networks trained on predicting sequences led to better text compression and conceptual understanding.
Scaling Efforts: Training with more data and neurons improved outputs but hit limitations on maintaining context over long sequences.
Inception of Transformers: Addressed memory constraints with self-attention layers, allowing parallel processing of input sequences.
OpenAI's GPT Series:
GPT-1: Demonstrated basic context understanding, trained on 7000 books.
GPT-2: Improved coherence using web data and larger networks, achieving zero-shot learning.
GPT-3: Major leap with 175 billion connections; showcased in-context learning and changing behavior without retraining.
ChatGPT: User-friendly version optimized for interactions through instructed learning.
The Role of Self-Attention and Transformers
Self-Attention Layers: Enabled dynamic connections based on input context, facilitating better capturing of relationships within text.