We often get the question: what is generative AI and what can you do with it? Generative AI is a form of artificial intelligence that can independently create new content. There are different types of generative AI. From writing texts to composing music, generative AI opens the doors to a world full of creative and efficient possibilities. In this blog, we explain what generative AI is, how it works, and how businesses can use generative AI to become more efficient and future-proof. 

Before we dive deep into generative AI, it's good to first look at a bit of history of artificial intelligence (AI). 

What is artificial intelligence and where does it come from?

Artificial intelligence, also known as AI, is a broad field within computer science that focuses on creating systems that can perform tasks that normally require human intelligence.  

Artificial intelligence is certainly not a new concept; it has existed since the 1950s, and the idea of 'a machine that thinks' dates back to ancient Greece. In 1950, Alan Turing published “Computing Machinery and Intelligence”. In this article, Turing posed the following question: 'Can machines think?'. The recent developments, driven by companies like Google and OpenAI, have shown that this is indeed the case.

With the launch of ChatGPT in 2022 by OpenAI, a new era in AI was marked, namely generative AI for everyone, also known as generative AI. ChatGPT, known for its user-friendly interface, made the technology (free) accessible to a wider audience. In the meantime, there is an enormous race going on between tech companies to match the success of OpenAI's GPT models. 

Generative AI vs traditional AI

To understand the concept of generative AI, it is good to zoom out first. The difference between generative and traditional AI is quite fundamental. There are different degrees of AI, as you can see in the image below: 

While traditional AI systems, such as chess computers, focus on following strict rules and algorithms for specific tasks, generative AI takes it a step further. This form of AI, illustrated by models such as GPT-4 (the underlying model of ChatGPT), learns from vast datasets to create new, unique content. It is not limited to one task but can be broadly applied. 

The meaning of generative AI is simply put, a form of artificial intelligence that can produce text, images, and varied content based on the data it is trained on.

How does Generative AI work? 

Imagine you are using a generative AI-powered chatbot, like ChatGPT, and you ask, for example, "come up with a catchy title for my new blog about marketing." In a short time, the chatbot processes all the data it has at its disposal and predicts the best answer it can provide you. 

These answers are thus based on an enormous dataset upon which the AI is trained. There are then smart algorithms (instruction sequences) and neural networks working together to generate the best answer for you based on your question. 

This is also known as a large language model. A large language model, also known as a language model or LLM, is a model that has analyzed vast amounts of data to understand and generate human language. 

After the success of ChatGPT by OpenAI, companies like Google, Amazon, Alibaba, and many other large companies have pivoted their focus to their own models. The quality of these models is based on the quality of the data they are trained on. As a result, many different large language models (LLMs) have now been developed, each with their own expertise. 

GPT-4 is seen more as a thinker, while Claude is regarded as more of a doer. Meanwhile, Grok is more of a conversational LLM due to its data coming from the X platform (Twitter). Although many different companies are trying, ChatGPT remains the most user-friendly solution. 

What is ChatGPT?

In the context of generative AI, one of the most well-known solutions is ChatGPT, developed by OpenAI. It is often confused with "Chat gtp" or "chatting gpt". This advanced AI solution has changed the way we interact with technology by having the ability not only to understand human language but also to actively generate it. ChatGPT is multifunctional: it can hold conversations, write texts, analyze images, and perform in-depth data analyses. It is a tool that is valuable in both personal and business environments.

On a personal level, it can assist with everyday tasks such as writing emails or planning trips, while in a business context, it can be used for content creation, data analysis, and even programming. This versatile model has reinvented productivity and creativity, offering users a wide range of possibilities to achieve their goals.

What is prompt engineering? 

Prompt engineering plays a crucial role in effectively using, among others, ChatGPT. It involves communicating specifically and purposefully with the model through well-designed prompts. The prompt can be seen as the question you pose to the large language model. Properly asking and formulating the question (prompt) is essential to obtaining good output. 

Data quality

The core of generative AI is data, lots of data. These models learn to recognize and replicate patterns and features from the collected data. The quality of the data determines, among other things, the quality of language models (LLMs) such as GPT-4 or Claude 3. But where this rule applies to large language models, it also applies to our own solutions and questions we pose to ChatGPT. 

Data quality is essential for the success of generative AI projects. It refers to the accuracy, reliability, and relevance of the data being fed to generative AI systems. High data quality ensures that the AI can deliver reliable and accurate results, while poor data quality can lead to misleading outcomes. This phenomenon is also known as the Garbage-in Garbage-out principle. In short, for optimal AI performance, it is crucial to start with clean, well-structured, and relevant data. A common problem is developing solutions based on faulty or incomplete data. This is why it is important to engage a generative AI expert if you want to ensure good output and work with sensitive data.

Security

Data is a valuable asset and is rightly often one of the most important aspects of an organization's success. Because years of experience, knowledge, and processes are recorded in this data, it is highly valuable. But how do you protect your company's and customers' data when using AI? If you're not careful when using tools like ChatGPT, it can happen that the AI models of companies like OpenAI and Google are trained on your valuable data. But how do you ensure secure solutions and which cloud storage can you use for this?  

At MSTR we use Microsoft Azure and this ensures that your data is always protected and never falls into the wrong hands. This is important with the strict GDPR that we have in the Netherlands. The Azure environment provides the same data security that one expects from applications like Outlook or Word, a standard in Dutch business. Data security is a top priority within the walls of Azure. Therefore, we at MSTR can guarantee that data security is assured in all our developments. It not only offers a safe haven for your data but also the flexibility to integrate your AI solutions seamlessly and custom-fit. With Azure, you can even keep data flows within Europe if you wish as a company. With Azure at the foundation of AI solutions, you can guarantee a level of security. Using platforms like Microsoft Azure can be overwhelming, which is why we also recommend engaging a generative AI consultant if you encounter problems.

Generative AI solutions

Generative AI offers unprecedented opportunities for businesses of all sizes. It not only transforms how we work but also how we think about problems and solutions. From automating reports and directly interacting with business data to improving customer service with smart chatbots and using visual AI for image analysis, the possibilities are endless.

Generative AI can simplify complex processes, increase efficiency, and create new ways of interaction. Imagine a future where you can ask questions directly to your data, or an AI that interprets visual content with precision that rivals human perception.

Conclusion

Generative AI is much more than a technological innovation; it is a transformative force that changes the way we work, create, and innovate. What may be most interesting about generative AI is the potential for personalization and efficiency. It can help us work faster, more accurately, and in more personalized ways, allowing us to reach new levels of productivity and creativity. 

As we look ahead, it is clear that generative AI is not just a trend but an integral part of our future society. The applications we see today are just the beginning. As the technology improves, the possibilities will only grow, leading to new ways of innovating, creating, and discovering. As Sam Altman, CEO of OpenAI, says:

“This is the stupidest these models will ever be.”