Generative AI beyond: how it works and real use cases

The last few months have been marked by a clear technological trend that has led the way in all sectors: generative AI. Its economic potential is only growing and it will be an upward trend for years to come. It is a revolution that has only just begun.

Gartner predicts that by 2025, Generative AI will produce 10% of all data (now less than 1%) and 20% of all test data for consumer-oriented use cases.

However, despite being all over the news and conversations, it is often not fully understood what it is, its applications, or how it works. That’s why we have compiled all the main concepts in this article as a guide to help you better understand this technology so you can make the most of it in your business.

What is Generative AI?

Generative AI can be thought of as a machine learning model trained to create new data, rather than predicting a particular dataset. It learns to generate content that resembles the data it was trained on.

Generative AI attempts to mimic human creativity, generating content such as text, images, answers to questions, videos, summaries, computer code, etc.

In fact, generative AI models are not new in themselves, as it has been a very useful tool for doing data analysis for decades. However, it has been completely transformed by advances in deep learning and neural networks.

We can go back to the 1960s to find conversational chatbots such as ELIZA, from the Massachusetts Institute of Technology, which relied entirely or mainly on predefined rules and templates. In contrast, generative AI models do not rely on such rules. They could be defined as primitive, blank “brains” that are trained on real-world data and then independently develop intelligence that they use to generate novel content in response to cues.  

How generative AI works

Generative AI models use neural networks to identify patterns and structures within existing data to generate new content.

It takes advantage of different learning approaches (unsupervised or semi-supervised) for training, making it easy and fast to leverage large amounts of unlabelled data to create basic models. These models can be used as the basis for artificial intelligence systems that can perform multiple tasks.

The process of generative AI starts with feeding an LLM model with huge amounts of data (web pages, books, internal company documents, etc.). This model uses transformers that convert sentences and sequences of data into numerical representations called vector embeddings.

With the ingested data converted into vectors, they can be classified and organized according to their closeness to similar vectors in vector space. This will help determine how words are related, but for a model to generate meaningful results, the data must go through several computational processing steps

Adding a Machine Learning framework creates a generative adversarial network (GAN), which works by pitting neural networks against each other. At this point, most of the learning of the model will be an automatic process, but experts will need to monitor and adjust the data to ensure that the data is accurate.
This is where you get a natural-looking and natural-sounding interface, where you can give cues to the model.

Pillars of Generative AI

We have already outlined the steps involved in the process of building generative AI models, but now we will focus on the fundamental parts that make it possible: 

• Statistical models: are the backbone of most AI systems, which use mathematical equations to represent the relationship between different variables. Applied to generative AI, they are trained to recognize patterns in the data and then use them to generate new, similar data.
• Data collection: in this case, both the quantity and quality of data are a critical cog in the wheel. Generative models are trained on very large amounts of data to understand patterns. In language models, this would mean ingesting millions of words from books, texts, and websites. For an image model, it will have to analyze millions of images, so the more varied and complete the data, the better results it will generate.
• Transformers and attention: these are a type of neural network architecture and form the basis of most modern linguistic models. The “attention” mechanism allows the model to focus on different parts of the input data and allows it to decide which parts of the input are relevant to a task, providing flexibility and power.  

Types of Generative AI Models

We encounter several types of generative AI models, designed for different challenges and tasks. The most important ones are: 

• Generative adversarial networks (GANs): these are composed of neural networks known as generators and discriminators, which work with each other to create data that appears authentic. The function of the former is to generate convincing results, while the latter works to evaluate the authenticity of that created content.
• Multimodal models: this seems to be the type of model that will gain the most traction in the coming months, as they can understand and process multiple types of data simultaneously (text, images, and audio), resulting in more sophisticated results.
• Models based on transformers: as we said in the previous point, they are usually found in most linguistic models and are trained on large datasets to understand the relationships between sequential information. They are based on Deep Learning and NLP to understand the structure and context of language.
• Variational automatic encoders (VAE): these models take advantage of two networks to interpret and generate data, called encoder and decoder. The first takes the input data and compresses it into a simplified format. The second takes that compressed information and reconstructs it into a new alphabet that resembles the original data but is not completely similar to it.  

Discriminative AI vs. Generative AI

Distinguishing one from the other can be misleading, so we have created an article in which we break down the differences between Generative AI and Discriminative AI so that you can better understand what each one is. We look at characteristics such as approach, targeting, training approach, and data generation.  

Generative AI benefits and applications

Generative AI is reaching across industries with its numerous applications in many areas of business. From automatically creating new content to improving the interpretation or understanding of existing content, its main benefits include: 

• Automate the manual process of writing content.
• Reduce the effort and time spent answering emails.
• Improve the response to technical queries.
• Summarise complex or extensive information.
• Simplify the creation process in different formats.
• Improve the efficiency and accuracy of existing AI systems, such as NLP and computer vision.
• Explore and analyze complex data in new ways to uncover hidden trends and patterns.
• Automate and accelerate tasks and processes, saving time and resources.  

These are just some of them, as there are many more benefits.  

Generative AI Use Cases

As mentioned above, generative AI is still in its early stages, so many of its applications are yet to be discovered, but many companies are already using its capabilities to improve their processes and strategies.

Some of the most important use cases include streamlining e-commerce tasks, improving online customer service, improving drug discovery, generating personalized ads and promotional content for marketing, etc. We have compiled the most important ones below. 

Semantic search

Thanks to generative AI, information on a website or internal documents can be searched efficiently using context-based queries.

Content personalisation

Generative AI tools allow you to adapt the style, message, and images to the result of preference and sentiment analysis of the user interacting with the content.

Q&A

Conversational search on internal sources for collections of answers to automated questions to improve customer service.

Automated ERP population

You can automate the processing of information transformation for data ingestion from transcripts, emails, and documentation.

Document processing

One of its main functions is to draft or summarise new documentation based on the synthesis and combination of other documents such as tender responses.

Advanced virtual assistants

A virtual assistant can be created to effectively understand the transcription of customer requests for information and queries about our products and services.

One example is Brain, a solution that facilitates access to psychological consultations using state-of-the-art artificial intelligence. It is a hyper-realistic metahuman that makes the patient feel understood, listened to, and guided.

Product Recommender

Products can be recommended based on textual information provided by a user and guided questions that help you get better answers.

Translator

This is one of its most popular uses thanks to its ability to translate between dozens of languages, as well as translating source code in one programming language to another; or even creating SQL queries from natural language.

Creative material generation

It gives marketing and creative teams the ability to create images and content such as bespoke emails for campaigns and editorial content.

With the help of OpenAI, we were able to generate promotional ads and the creation of contextual and targeted advertising, subtitle generation, short video, or dynamic content for a leading company in the audiovisual sector.

Validate regulations and standards

You have the ability to interpret regulatory documents to identify potential breaches of operating procedures.

Generative AI best practices

For all its benefits, generative AI also has rapidly evolving risks associated with it. Tools like ChatGPT are trained on large amounts of publicly available data and are not designed to comply with GDPR or other copyright laws, which is why it is so important to pay close attention to companies’ uses of the platforms.

They may also have bias, plagiarism, or trustworthiness issues – ethical issues that need to be addressed as soon as possible. Therefore, as companies introduce this technology into their processes, best practices such as these can be implemented to reinforce security and quality: 

1. Form a cross-functional team: To avoid bottlenecks by letting all the responsibility fall on one AI team, it is better to have a cross-functional support team with members from different backgrounds. This allows them to research and ask different questions about the impact of AI and explore other approaches on how to apply it or what opportunities can be exploited.
2. Leverage data: In a digital world like the one we live in, taking a data-driven approach is critical to being successful. Generative AI makes it much easier to leverage this data, as well as provide unique and specific insights, while enabling teams to better synthesize and leverage information or proactively identify problems.
3. Focus on privacy: Data is one of the most precious assets for companies and when implementing services that use inputs such as future training data, be very vigilant in reviewing terms and conditions to validate privacy, disposal, and data storage mechanisms.  
4. Understanding regulations and governance: Understanding and navigating the changing legal landscape related to this technology is critical for organizations looking to harness the benefits of generative AI and also avoid potential legal issues or fines. Also, adopting governance frameworks provides a structured approach to ensure that this technology is developed and used in a responsible, ethical, and transparent manner.
5. Develop a breach response plan: despite best efforts, data breaches can occur, which requires a well-defined action plan. These plans should cover processes for detecting and investigating breaches, notification of affected parties, measures to mitigate impact, and so on.
6. Develop a verification and testing strategy: there are times when generative AI may say something incorrect or not exactly what we want, so without a testing and detection strategy, these inaccuracies may produce incorrect results or invalid representations. With these tests, we ensure their reliability and accuracy, as well as implement defensive measures, such as bias prevention and ethical criteria.  

Generative AI has enormous potential to create new capabilities and value for businesses. But it can also introduce new risks that only experts can help combat. At Plain Concepts we have a team of experts who have been successfully applying this technology in numerous projects, ensuring the security of our clients. We have been bringing AI to our clients for more than 10 years and now we propose a Generative AI Adoption Framework: 

• Unlock the potential of end-to-end generative AI.
• Accelerate your AI journey with our experts.
• Understand how your data should be structured and governed.
• Explore generative AI use cases that fit your goals.
• Create a tailored plan with realistic timelines and estimates.
• Build the patterns, processes, and teams you need.
• Deploy AI solutions to support your digital transformation.

Preparing your company to successfully adopt generative AI is at the core of our framework, where we will cover 4 main pillars: strategy and governance of data and your privacy, security and compliance, reliability and sustainability, and responsible AI. This way we will help you avoid the risk of projects never reaching production.

Plain Concepts advises you to dedicate quality time to reflect on finding the ideas that bring real business value and not to stay with ideas with little impact on the business, where generative AI is not differential.

If you want to join this technological revolution but do not know how we help you to materialize your idea!