Generative AI Explained: An Introductory Guide—Part 1

What is Generative AI?

Definition

Generative artificial intelligence, sometimes dubbed and commonly referred to as gen AI, is artificial intelligence (AI) capable of churning out original content in response to prompts—user requests—given to it. It can create original texts, images, video, audio, and software code.

How does generative AI do its magic? It operates through sophisticated machine learning models, also known as deep learning models. These models are not just algorithms but intricate systems that mimic our brains’ learning and decision-making processes, an awe-inspiring feat. Generative AI work starts with a prompt in various forms and then details the process of generating new content, enhancing user experiences for requesting and customizing generated content.

Overview

Generative AI systems have extensive applications and recently we’ve witnessed a series of significant advancements, including their use in creating text, images, videos, and other data. However, they also pose potential risks and challenges, such as biases, intellectual property concerns, cybersecurity threats, and significant energy consumption.

So, first, a chosen model identifies repetitive patterns and relationships in large amounts of data. Then, thanks to all the information discovered, the model starts to ‘understand’ the user’s input and can now respond with relevant new content. Voilà!

In our article on generative AI implementation, you will find a slightly different definition.

Now, let’s have generative AI explained in layperson’s terms and as best as we can.

How Generative AI Works: The Role of Training Data

Ok, we know what generative AI is. And we have taken a look at its history. Let’s dive a little deeper. Read on to find out how exactly Generative AI works.

Generative AI, a cutting-edge application of machine learning, is a branch of artificial intelligence that enables machines to learn from data. What sets generative AI apart is its ability to go beyond traditional machine learning models. While effective, these models are limited to learning patterns and making predictions based on those patterns. Generative AI, on the other hand, not only learns from data but also generates new data that mirrors the properties of the original data, pushing the boundaries of what AI originally was able to do.

Generative AI also learns from data but then follows up with new data. This new data mimics the properties of data fed to the machine, but it’s new nonetheless. The functionality and evolution of genAI involve prompting the AI with specific inputs and have seen recent improvements in user experience. Many different generative AI models are now on the market, yet most of them function the same way.

Generative AI Process

It’s possible to divide the way genAI works into the following successive phases:

• Data collection. It involves collecting many examples, the training data, of content similar to the one we want to generate.
• Training, where we create a foundational model using neural networks. This model is then trained on a large dataset, enabling it to recognize underlying patterns and structures. Later, this model can be repurposed to create multiple other genAI applications. The beauty of generative AI is its adaptability.
• Finetuning is when this foundation model is tweaked for the purposes of a specific gen AI application and the goals you want to achieve with it.
• Content generation occurs when, after undergoing training, the model is finally capable of generating new pieces of content. The generated content is a synthesis of what the model has learned from the training data.
• Evaluation, further tuning, and refinement are also important. The generated output is meticulously assessed, ensuring that the app’s output, in terms of quality and accuracy, continually improves and meets any additional specific requirements.

Generative AI: The Importance of Large Language Models and Transformers

Generative AI models are combinations of several complex AI algorithms to represent, process, and subsequently generate new content. Let’s take a look at how GenAI models generate text:

Tokens

1. Individual words, parts of words, and even punctuation marks are broken into tokens, the most basic units.
2. Then, these tokens are analyzed in the context, where machines deduce their meanings based on the nearby words.
3. A list of words that go with this word is created. The list of words that don’t go with these words is created too. In other words, by training on large datasets, models learn the dependencies and relationships between words.
4. The model creates a list of values attached to every word, known as the word’s embedding. These values are a word’s linguistic features in a numerical form.
5. At this moment, the models start to spot the ‘distances’ between different words. The words then begin aggregating into clusters.

Transformers

1. Now, transformers, mentioned above in the history section, enter the scene. These allow the machine to process entire sequences of words at once, and the whole process repeats anew.
2. Enter self-attention. This is the ability to scan each token in a text and decide which other token is most important to understanding its meaning.
3. All the words in a sentence are analyzed simultaneously.
4. Ta-da! The correct meaning of each word is now clear, as the accompanying text is constantly parsed and analyzed.
5. In the end, our model is capable of advanced text generation, repeating the same process of ‘playing with words’ to create new output.

If you want to get more information on the role transformers play in the functioning of GenAI, we suggest taking a look at this highly informative article in Financial Times.

Back to our guide! So, diffusion models are another type of generative AI that iteratively refine their output to generate new data samples resembling those in a training dataset. These models have been used to create realistic-looking images and are at the heart of the text-to-image generation system Stable Diffusion.

The above sounds magical, right? We think so, too. We feel like now is the time to harness this magic to achieve whatever goals your business may have.

Speaking of which, next we’ll examine the possible use cases and applications of generative AI.

Applications of Generative AI

Text-based Generative AI Use Cases

For generative AI is the undisputed king of content, no kind of content is beyond its capabilities. With each passing day the technology gets more and more accessible to users, thanks to ChatGPT and other similar tools, so this tech can be used for dozens of purposes. Content creation is the forte of generative AI technology. Generative models are used for text generation, image generation, chatbots, and have potential impacts on various industries such as software development, healthcare, finance, entertainment, customer service, sales and marketing, art, writing, fashion, and product design.

Think chatbots, a staple in customer service and technical support for years. Or writing high-quality email responses to swiftly respond to colleagues, resumes to smoothen the job application process, term papers to help in your studies, using ChatGPT or other, more advanced chatbots. Another popular business use case is text summarization. Our team of expert developers explored this in great detail quite recently. You can see the results of their work using small language models (SLMs) in this article on our blog.

Other Generative AI Use Cases

Instant dubbing for films or bespoke educational materials in different languages created on the spot is also now possible. Just as are presentations, product promotion videos, or photorealistic art. Gosh, it’s even possible to make DALL-E, a text-to-image model developed by OpenAI, paint aliens in the style of Rembrandt if you so wish. The realistic images will blow you away. GenAI can also design products, chips and buildings. Or suggest new medicine formulas that researchers would do well to explore for drug discovery.

Is it all roses? No. With generative AI tools, one can also create the so-called “AI-slop” that we’ve been seeing a lot on our social media feeds lately, with fake experts speaking nonsense or deepfakes of famous people.

Exciting stuff, right? If you feel like exploring where generative AI might fit in your in your business and where to start to integrate AI systems in your business to make the most out of the generative AI capabilities, we have a ready-made guide for you.

You can check this article if you’re ready to use the power of generative modeling.

Benefits and Challenges of Generative AI

Advantages

You must have already read dozens of articles on Generative AI by now. “How to cook it, when it should be taken, and what dosage works best.” In all these articles their authors speak of some mythical ‘efficiency’. Ask any tech enthusiast or an AI guru about the main standout advantage of generative AI, and they invariably come up with something along the lines of “It can boost efficiency” or ‘”Now we can be more productive!”.

I agree with them in principle. Sure, with the power to generate any kind of content and answers to any questions on demand, there are endless ways to speed up labor-intensive tasks, perhaps slash some costs, and give your employees or yourself more free time for more valuable and creative work.

And yet, I gather the perks of generative AI don’t stop there. There, they just begin.

Creativity Nonstop

Automated iterative brainstorming, with multiple novel versions of content instantly at your disposal, is what generative AI is good at. These fresh ideas help writers, artists, designers, and other creatives break through blocks and find new inspiration. They can help you find a solution to the problems your business faces, too. Consider using genAI if you needed that additional fix of creativity to spruce things up.

Sped-Up & Higher Quality Decision-Making

Human intelligence is incapable of sifting through such vast datasets, spotting patterns simultaneously, and swiftly extracting valuable insights. Generative AI is. In the blink of an eye, it can generate hypotheses and recommendations to help executives, analysts, and researchers make smarter, data-driven decisions quickly. It’s truly able to analyze data, complex data, fast, perform multiple tasks, mimic human intelligence

Dynamic Never-ending Personalization with Generative AI Models

Generative AI analyzes user preferences and history around the clock. As a result, we get personalized content generated in real-time. This is sometimes called an AI-based recommendation system. After some feedback from you, with each iteration, the results will get better and better. And the content- more and more personalized. Generative AI can offer a previously undreamed-of and highly tailored user experience.

AI Never Sleeps

Generative AI will be 24/7. You can count on seamless, round-the-clock support for tasks like customer service chatbots and automated responses. Even when you are fast asleep, the algorithms will keep analyzing and churning data, giving you additional revenues and invaluable insights.

Limitations and Concerns

Generative AI models are the hype of the day, they are very new. No wonder there are enormous risks associated with using them. We are still far away from seeing any long-term societal or individual effects they may have.

The answers provided by most chatbots sound extremely convincing. Are they truthful? Not always. Sometimes the output generated is plain wrong, outright deceiving, or worse. It can be biased, it can be manipulated to assist in some criminal activity and so on, and so forth. There are thousand of examples where with a carefully created prompt one can make ChatGPT generate instructions on how to commit all sorts of crimes. Businesses willing to use generative AI, must be mindful of any risks, reputational, legal or financial, that they face by unintenionally generating ‘bad content’.

The issue of accuracy and bias is one thing. An entirely different matter is the issue of hallucinations, where AI, either because of faulty training, bad prompting, or for no reason at all, starts making up false information or facts that aren’t based on real data or events. Generative AI-driven chatbots have shown time and time again how good they are at fabricating any factual information, from names, dates, and historical events to quotes or even code. This notoriuous ingeniuty prompted OpenAI to issue warnings to ChatGPT users stating that “ChatGPT may produce inaccurate information about people, places, or facts.”

Summary: Generative AI & Business

Generative AI, once the stuff of sci-fi dreams, has become part and parcel of many people’s lives. Natural language processing went a long way from being just a theory, a bit of ‘complex mathematics for nerds’, to a tool many successful businesses use. There are many generative AI models on the market now, and such models are capable of true wonders, as we’ve just discovered.

Generative AI refers to what we get when we add that additional creative spark to traditional AI’s analytical prowess. Imagine AI that doesn’t just learn and decide but also creates, opening up a realm of possibilities we once only imagined. Most people use a generative AI model such as Midjourney or ChatGPT in a very limited capacity.

Luckily, there are many ways to unlock their potential or even create bespoke models specifically for your needs. If you want to use generative AI to achieve great results, don’t hesitate to contact us.

As AI researcher Andrej Karpathy wittily remarked, “Now the hottest programming language is English!”. If you’re a businessman who speaks English, you already have all the required things to turn ideas into profits.

Our team of experts will gladly help you make your project a great success. Got an AI idea? Just have a question or need advice? Fill in the form below, and we’ll get back to you ASAP.

Get in touch

First Name*

Email*

Please share some details

I agree to receive your newsletters and accept the data privacy statement.*

Submit