Powering the AI revolution within LLMs

For those new here, I want to be able to create awareness and understanding of AI through my thoughts and insights that i found valuable and outline key cup of tea takeaways for anyone to make a connection with, by breaking down AI into bite-sized chunks of information. As i rise others rise with me.

As shared previously in my last post on LLM/Transformers found here: https://www.dhirubhai.net/posts/dylan-pahina-289b42a1_ai-activity-7232241188043374594-xfjp?utm_source=share&utm_medium=member_desktop i will now be linking my understanding of the algorithms GPT and BERT.

AI has been revolutionised by two groundbreaking algorithms: GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). These models have transformed how machines understand and generate human language, opening up new possibilities for businesses and researchers alike. Particularly within Natural language processing.

GPT and BERT have slight differences in what makes them tick. Below i will identify the differences and what sets them apart from other NLP algorithms.

GPT: The language generation powerhouse

GPT uses a decoder-only transformer architecture to process text from left to right. It's trained on vast amounts of text data to predict the next word in a sequence, allowing it to generate coherent and contextually relevant text. These key features of GPT look like:

• Unidirectional processing - a fancy way of saying that it processes text from left to right, which allows it to excel at text generation tasks, which also informs the 'Causal language modeling'. What 'casual' really means it that it considers the context that comes before a given word, maintaining a left-to-right directionality in processing text.
• Excellent at text generation tasks - Unlike the full transformer model, GPT uses only the decoder part. This architecture is well-suited for generative tasks, as it focuses on producing output based on the input it has processed so far.
• Large-Scale Pre-training - GPT models are pre-trained on vast amounts of text data, allowing them to capture complex language patterns and generate human-like text.
• Versatility - GPT excels at a wide range of tasks, including content creation, chatbots, language translation, and code generation

BERT: The context-aware comprehender

BERT employs a bidirectional transformer architecture, processing text in both directions simultaneously. It's trained using masked language modeling, where it predicts masked words based on surrounding context. These Key features of BERT look like:

• Bidirectional processing - Slight difference here that it processes text in both directions simultaneously, allowing it to understand context from both left and right. This bidirectional nature enables BERT to capture more nuanced contextual relationships between words.
• Masked language modeling - BERT is trained using a masked language model task, where it learns to predict masked words in a sentence. This training approach helps BERT develop a strong understanding of context and word relationships.
• Next sentence prediction and context aware representations - In addition to masked language modeling, BERT is trained on next sentence prediction, which helps it understand relationships between sentences. Alongside this BERT generates context-dependent word embeddings, meaning the same word can have different representations based on its context in a sentence, ultimately enabling a better understanding for the output.

Soooo what sets them apart?

GPT and BERT differ quite a bit from traditional NLP algorithms:

Contextual understanding: As shared above, unlike earlier word embedding models, GPT and BERT capture context-dependent meanings of words. This is very useful in lowering the hallucinations of the LLM output. Hallucinations just means that it's incorrect or misleading results that AI models generate, so lowering this effect is always good.

Transfer learning: These models can be fine-tuned for specific tasks with minimal additional training, which by adapting their general language understanding well strongly perform well in particular domains or applications.

Scale: They leverage massive amounts of data and computational power to achieve unprecedented performance with higher success rate.

Identifying advantages and disadvantages of GPT and BERT

Advantages (at a high level):

• Exceptional performance on a wide range of NLP tasks
• Ability to generate human-like text (GPT)
• Deep contextual understanding of language (BERT)
• Versatility through fine-tuning

Disadvantages (at a high level):

• High computational requirements
• Potential for biased outputs based on training data
• Challenges in explaining model decisions (black box problem)

Lets briefly look at the value and applications

GPT and BERT have found applications across various industries. I will add another post focusing specifically on these use cases and how it is being used by companies and in products in line with self-supervised learning. But for now here are a few widely known applications:

Content creation: GPT powers AI writing assistants and chatbots.

Search engines: BERT improves search result relevance.

Sentiment analysis: Both models excel at understanding customer feedback.

Language translation: These algorithms enhance machine translation systems.

You are probably already using it via LLM Platforms

Many popular language models and AI platforms are built on the foundations of GPT and BERT:

OpenAI's ChatGPT uses GPT architecture

Google's BERT powers various search and language understanding features

Hugging Face's transformers library provides easy access to both GPT and BERT models

Business benefits

Both GPT and BERT offer powerful capabilities for natural language processing tasks, allowing businesses to automate and enhance various text-based operations. By leveraging these models businesses can:

1. Automate customer service with intelligent chatbots
2. Improve content creation and marketing efforts
3. Enhance search functionality on websites and internal systems
4. Gain deeper insights from customer feedback and social media data

I will also be diving deeper into these benefits in the coming posts.

Connecting back the dots to Self-supervised learning

GPT and BERT embody self-supervised learning in AI. They learn from vast amounts of unlabelled text data to extracting patterns and relationships without explicit human annotation.

This approach allows for more efficient and scalable training, enabling these models to capture the complexities of human language. I can expect even more powerful and nuanced AI systems that push the boundaries of what's possible in natural language processing. I believe that businesses that embrace these technologies will be well-positioned to innovate and thrive in an increasingly AI-driven world.

What's next?

In the next article i will unpack speech processing with virtual assistants and how this all connects back to LLMs/ transformers and self-supervised learning in AI.