Intelligent Automation

Definition

Models that use attention mechanisms to understand context and improve language understanding.

Models that generate human-like text based on input data.

Utilising a pre-trained language model on a new, related problem.

Goal

Improve understanding and response accuracy by focusing on context.

Create coherent and contextually appropriate responses.

Leverage existing models to reduce training time and improve performance on new tasks

Algorithms

Attention layers, encoder-decoder architectures.

GPT-3, BERT.

Fine-tuning pre-trained models, domain adaptation.

Data Requirement

Requires large amounts of conversational data.

Requires substantial data to generate meaningful text.

Requires less data than training a model from scratch, using pre-trained models.

Advantages

High accuracy in understanding context, ability to manage long conversations.

Capable of generating high-quality, human-like text.

Significantly reduces training time and resources, improves performance with less data.

Applications

Customer support chatbots, virtual assistants, automated transcription services.

Content creation, automated report generation, conversational agents.

Custom chatbots, virtual assistants, sentiment analysis.

Techniques

Self-attention, transformer networks, BERT.

Generative pre-trained transformers (GPT-3), fine-tuning on specific tasks.

Model fine-tuning, transfer learning architectures like GPT, BERT.