This article is the first of a 3-piece series entitled “Demystifying AI: Understanding the Core Concepts behind AllAi”. AllAi is the AI-powered productivity platform built by OSF Digital to enhance Salesforce-driven services. AllAi optimizes workflows and boosts efficiency for developers, project managers, QA engineers, and business analysts. Before diving into how AllAi leverages artificial intelligence to improve productivity, it’s essential to first explore the foundational principles of AI, starting with neural networks.
The AI Landscape
In the last decade, AI has emerged as a game-changer across various industries. From healthcare to finance, AI is revolutionizing how businesses operate and how we interact with technology. In retail, personalized marketing suggestions have moved from "Hi, [Your Name]" emails to nuanced product recommendations based on your browsing history. In healthcare, AI is aiding in diagnostics and even predicting outbreaks. No matter which industry you look at, AI is setting new standards.
AI impact across industries.
The Rise of DNNs and LLMs
Recent advancements in Deep Neural Networks (DNNs) have propelled AI forward, leading to the rise of Large Language Models (LLMs). These models have taken natural language processing to a whole new level, enabling chatbots, translation services, and content creation tools like never before. Have you ever wondered how models like GPT-4 (the engine behind ChatGPT) work? For many, these tools seem almost magical, able to generate human-like text on demand, without boundaries or apparent limits. However, the underlying principles are far from sorcery. By peeling back the layers, we’ll demystify these technologies and give you a clearer picture.
A daily interaction with ChatGPT.
Demystifying Artificial Neural Networks
To understand what drives these advanced AI applications, we need to start with the concept of neural networks. Think of neural networks as the “brain” behind AI, inspired by the way neurons in the human brain process information. They consist of interconnected nodes (neurons) that communicate through weighted connections, much like synapses in the brain. Each connection has a weight, and each neuron has a threshold that determines when it activates. The weights are learned through a process called backpropagation, which is the training mechanism responsible for the “learning” of neural networks. This technique helps the network improve its accuracy by analyzing past input-output examples and making necessary adjustments. Simply put, it fine-tunes the system based on what works and what doesn’t. With enough training data, neural networks can recognize patterns, make predictions, and apply their learning to new, unseen information.
Consider the simple metaphor of a factory assembly line. Each worker (neuron) receives a part (data), performs a specific task (applying weights and thresholds), and then passes it down the line. Eventually, the end product (final prediction or decision) is made. If the factory produces an incorrect product, the process is tweaked (training via backpropagation) to improve future results.
Basic diagram of an artificial neural network.
Transformers – A Transformative Revolution
Traditional artificial neural network architectures paved the way, but the real leap came with the introduction of a new architecture called Transformers by a group of Google researchers together with academic partners. Transformers revolutionized how we approach natural language processing by enabling models to pay attention to different parts of input data dynamically. This made it possible to process large amounts of data efficiently while maintaining context over long texts. Think of it as reading a book where each sentence helps you understand the entire chapter better. Traditional neural networks struggled to maintain this kind of contextual awareness, but Transformers excel here. They allow models to focus on relevant parts of the text, making operations faster and more contextually aware.
Transformer Neural Network. From “Attention is All you Need”.
Probabilistic Reasoning
The profound capabilities of LLMs owe much to advancements in both data collection and hardware. Today’s LLMs have been trained on vast amounts of data, encompassing multiple languages, cultural contexts, and topics. This comprehensive data set enables the model to "learn and almost memorize" an impressive chunk of the internet. However, it’s crucial to understand that these models operate based on probabilities. LLMs are fundamentally advanced guessing machines, predicting the next word or token in a sentence based on previously seen patterns. This probabilistic approach is why they can seem so human-like yet occasionally produce quirky or unexpected outputs.
Probabilistic word sampling (with beam search decoding). Source.
What’s Next?
In our next article, we’ll delve deeper into how LLMs actually work and explore:
- How LLMs actually work
- Zero-shot & few-shot learning
- Prompt engineering
Stay tuned as we continue to unravel AI’s impact—and how AllAi can redefine your Salesforce experience.
