Deep Learning & Generative AI Knowledge Base

Building a strong knowledge base around AI isn’t just “nice to have”; it’s a competitive advantage. When you understand the mechanics of machine learning and deep learning, you are better placed to choose the right tools, avoid costly mistakes, and harness AI’s full potential.

We have seen it time and time again. Businesses jump onto AI without fully grasping the depth behind it. The result is misaligned strategies, wasted investment, and technology that does not deliver. By contrast, companies that understand AI’s foundations can apply it responsibly, work more efficiently, and maintain a competitive edge in their industry.

Let’s Start With Generative AI Machine Learning

Machine learning (ML) can feel daunting if you are new to the terminology. But its impact is vast, so it is worth unpacking. For context we will be referring to machine learning in this blog as 'ML' from here on in.

At its core, ML is about teaching computers to make decisions or predictions without being explicitly programmed with rigid rules. Instead of telling the system “if X happens, do Y”, you provide huge datasets and the system learns the patterns itself.

Imagine teaching a child to recognise animals. You do not hand them a checklist saying “if it has whiskers, then it is a cat”. You show them thousands of examples until they can tell cats and dogs apart on their own. ML works the same way. Once it learns the underlying relationships, it can spot new patterns or make predictions with remarkable accuracy.

Because of this flexibility, ML is everywhere. It is used in financial forecasting, product recommendations, fraud detection, medical imaging, and beyond. The key lies in the quality and volume of data. The more and better examples you give the system, the more intelligent its predictions become. THIS is the critical concept between having the best output and the desired output. The more information you can feed the LLMs, the better and more descriptive the prompts are the better the data set that can be acquired from the machine initially, and then to bank this for another user somewhere down the line.

Deep Learning: The Next Step

So to be clear…. Machine learning is the umbrella. Deep learning sits beneath it, and it takes things further.

Deep learning uses multi-layered neural networks often compared to how the human brain processes information to identify extremely complex patterns. This makes it particularly suited to unstructured data such as images, speech, or natural language, where traditional algorithms struggle.

For example, while ML might be able to categorise emails as spam or not spam, deep learning can analyse tone, structure, and context in a way that makes chatbots, voice assistants, and image recognition systems far more powerful.

Think of deep learning as the bridge between traditional ML and the generative AI that is now transforming how we work and create. Naturally the elements I describe here are not as black and white, and the integration of these concepts are vastly more complex; however, for generalised purposes, consider these three aspects layered on top of one another.

Generative AI: What We See on the Surface

So something you will be able to recognise is generative AI, as it is the application most people use from day to day. It is the text you read from ChatGPT, the artwork produced by Stable Diffusion, the voice cloning, music generation, and design automation creeping into everyday tools. Essentially the front-end user interfaces.

It's important to recognise that generative AI is just the surface. Behind the curtain are the neural networks and training processes that make it all possible… and they are only getting better at the tasks you prompt them to do. Models like GPT do not copy and paste from data. They learn statistical relationships and then generate new content by predicting the most likely next word, pixel, or sound. These neural networks wouldn't work if only a few individuals were embedding knowledge and keystrokes, but roll this out globally, and the generative AI has the ability to constantly adapt and learn. This is why you will notice significant leaps forward with each model as the neural networks for each model grow and intertwine their connections.

The beauty, really lies, in this ability to generalise. Trained on massive datasets, these systems do not just repeat what they have seen. They create something new that feels relevant and original. That is why they are so powerful in business contexts, whether you are draughting marketing copy, prototyping a design, or exploring new product ideas. It really does have the power to consistently refine and develop the thoughts, wants and needs of the business, should you plug into it correctly.

Why This Matters for Your Business

So what I have tried to unpack here is the concept of understanding the layers from ML. The difference between deep learning and generative AI and trying to ensure you are not dazzled by the end product while missing the real engine beneath it. By getting to grips with these terminologies and concepts, you can truly begin to understand what applications you can have built and consequently where the resources need to be focused in order to achieve this within your business.

To summarise…

Top Layer: Generative AI
Middle Layer: Machine Learning
Bottom Layer: Deep Learning

It is very rare for any SME to need to build deep learning data sets (but not unheard of), as normally you will be outsourcing the expenditure for a subscription to the larger players in the game so you can focus on developing your bespoke generative AI for your business. A vast majority of businesses will be onboarding a generative AI to their processes in the near future, if they haven't already, as the technology advances at unprecedented speeds.

As always, I encourage you and anyone who has the capacity in your business to spend at least one hour a day researching and interacting with these technologies as they develop. The progress is extraordinarily quick, and it's vital to not get left behind. If you are not sure where to start… then contact Projekt Rising!

About Projekt Rising

Find out more: Contact the Projekt Rising team to learn how mobile AI and automation tools can be adapted to your industry. Our experts can help you understand the practical uses of AI now and prepare for the more advanced capabilities on the horizon. Alternatively, please see our case studies to learn how we have helped many brands improve their time management and efficiency using our AI toolkit.