How AI Works Under the Hood
This is an article on how AI works under the hood.
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Now, let us look at how AI works, and why that is important for how you use it. LLMs are the machine learning models behind the helpful chatbot assistants. While their inner workings are complex, there are a few simple ideas about how they are built that are very helpful for understanding the inherent risks and difficulties of their use. We will break down this complex topic in a beginner friendly way, showing you the practical implications for using these AI tools at work.
Imagine a tool that has read vast amounts of the internet, every blog, article, and more to learn the patterns of language. Its primary job, predicting the next work in any piece of text. After it is proficient at next work predictions, these models undergo further training from mere predictors into helpful assistants ready to respond to user queries.
This second phase uses human preferences to train itself to be friendly and helpful. When you interact with LLMs trained in this way, they are essentially guessing what a helpful assistant would say next. Their guess is based on their vast reading and training that taught them to align to human preferences.
While interacting with these models feels incredibly human, it is important to remember that underneath there is no human style cognition occurring. They are not thinking or understanding. They are just continually predicting texts based on their training. One major implication of this next word prediction is what we call hallucinations. This might sound spooky, but it is actually just the AI continuing to predict text, even if it is incorrect.
Take the following example. Ask an early version of ChatGPT, when were the pyramids of Giza moved across the golden gate bridge for the second time? You would likely get an answer like, the pyramids of Giza were moved across the golden gate bridge for the second time on December 12th 1854. This makes sense if we remember these models were designed to always try their best to generate the most likely next word. Their job is to keep talking. Their job is not only to say true things. Another issue is bias. These models can mirror the prejudices found in their training data. Just like a child raised in a specific environment, these models reflect the patterns around them. This means they can unintentionally reproduce societal biases, which we need to be aware of.
The Wider World of AI
AI is a wide ranging term used to refer to any computer program that mimics human intelligence. Imagine an automated user support chatbot. When a user submits a query, the chatbot performs a simple keyword search using the words in the query, returning a predefined response depending on what keywords it finds. This chatbot follows a strict set of rules to provide answers and solutions to customers. This is the oldest and simplest form of AI.
Machine learning is a type of AI that uses big data and statistical algorithms to learn patterns. Groundbreaking large language models used in AI tools use machine learning, as do more conventional statistical models used in data science.
Deep Learning
Deep learning is a special type of machine learning that uses huge datasets with equally huge machine learning models, known as neural networks. These types of models are especially powerful on complex tasks with many variables.
GenAI
Finally, generative AI, such as conventional chatbot AIs and image detectors belong to a subset of deep learning. These models at the cutting edge of AI are what we mean when we say generative AI. There is a lot of focus on the type of GenAI known as large language models, which use text as both their input and output. It is important to broaden our understanding to include various other AI technologies that are transforming our work environments.
First, text generation is done by large language models. Image creation is done with tools such as Stable Diffusion, Midjourney, or Dall-E. You can use AI to generate audio/music with a tool like Suno. And lastly, you can use Sora to generate AI videos. Audio visual technologies such as image, video, and audio generators are revolutionizing content creation. Tools like Stable Diffusion, Midjourney, and Dall-E for images and similar advancements in music and video generation enable marketers and creatives to produce high quality, innovative content at unprecedented speeds.
Speech to text, text to speech, and translation AI technologies are breaking down language barriers, seamlessly converting written content to spoken language, and vice versa. These tools are indispensable in global business environments, facilitating clear and effective communication across diverse linguistic landscapes.
Robotics
Advancements in robotics and reinforcement learning represent significant leaps in technology. Reinforcement learning, which is different from other deep learning architectures, has contributed to major breakthroughs in fields like protein folding and nuclear fusion. These developments are not just academic, they have practical implications that could soon transform industries such as healthcare and energy.
AI Agents
Finally, the concept of AI agents represents a shift towards systems that require less human supervision. These agents are not just models, but entire systems designed to perform specific tasks. Agents are software applications made up of several models and prompts stitched together. They can think, draft, revise, and use tools. This is a burgeoning field of AI engineering that is worth keeping your eye on. By understanding these diverse technologies, professionals can better appreciate how AI is not only a tool for individual tasks, but a transformative force across all sectors of industry. This knowledge equips us to integrate AI more strategically into our workflows, maximizing benefits while mitigating risks associated with its deployment.