Don’t know where to start-off when it comes to AI? 

Well.. this might (or might not) apply to you - but in either case I thought I’d put together a quick overview of the basics when it comes to AI.

Because as with anything, knowing the basics matters A LOT.

Implementing the right kind of AI in your business will set you apart - improving your work, productivity, and opportunities as a business.

But most of us actually don’t know a lot about some of the core concepts around Artificial Intelligence.

Let’s dive into a few…

1. Machine Learning vs Generative AI

• Machine Learning (as the name suggests), is a path-way to Artificial Intelligence. It refers to machines learning and becoming capable of performing tasks that would otherwise require human intelligence.

• Generative AI (a sub-branch of AI) has as unique feature the ability to create new content (for example images, text, music) by drawing from existing data.

• Machine Learning is very hard to implement, as it requires very large & refined data sets, GPU infrastructure, and data scientists.

• On the flip side, Gen AI has become widely accessible & alongside no-code can be implemented with smaller amounts of resources & data.

2. How do Large Language Models (LLMs) work? 

• Again, a lot of it is in the name. LLM’s generate human-like language, by analysing large amounts of written data.

• They are programmed to learn wording, pick-up sentence-structures and understand patterns to be the most realistic possible.

• Their main feature is creating content that is coherent and relevant to a certain context.

3. Training vs. Application

• Training = Teaching an AI Model to recognise patterns and make decisions, backed up by datasets. This is a very resource-intensive process.

• Application = Use of the trained model (like GPT-4) to apply it to your use-case (for example ChatGPT for research).

• Machine Learning requires a lot of self-driven training, whilst the AI Agents now available (ChatGPT by OpenAI) are already trained by the respective models (currently GPT-4).

• And the best part is, these LLM’s can be easily accessed and applied to your business.

4. What’s a GPU and why does it matter?

You’ve heard about the whole NVIDIA Craze right? But why are GPU’s so important?

• A GPU (or Graphics Processing Unit) performs mathematical calculations at high speed. Additionally, their processing capabilities make them ideal for handling large datasets.

• This speeds up both training and inference and reduces the time needed to develop and deploy new models.

5. Why Good Data is crucial

• Without Data, there is no AI. Data provides the necessary information for models to learn and be more accurate in their predictions.

• That’s why high-quality, diverse data is so important for models to be accurate, effective, and reliable.

• For Machine Learning, you need to custom build data architecture and provide a lot of very good data for the model to learn.

• When using Gen AI, whilst using good data is still very important to ensure coherence and accuracy, data architecture is no longer needed.

If you dedicate a little bit of your time now to learn about AI and stay ahead of the curve, it will pay off for your future and the future of your business.

If you are not a member of the AI Leadership Forum, join today and become part of the next cohort of applicants!