When talking about Generative AI, what it is and what are the main tools to implement it in one’s own business are the first questions that naturally arise, considering how AI solutions are currently helping to improve many aspects within the realm of businesses.
Generative artificial intelligence (Generative AI) represents a branch of artificial intelligence that focuses on creating new content, such as text, images, music, and videos. Unlike traditional artificial intelligence systems that rely on predefined models, Generative AI learns from large amounts of data to create new and original content.
We have already discussed the multiple potentials that generative AI offers to businesses and how they can leverage them to gain competitive advantages in their business. In this article, however, we want to explain the general functioning of Generative AI and in which other fields it can find application.
Imagine being able to write a complete article in seconds, or to create a realistic image of a non-existent animal. Generative AI makes all this possible and has the potential to revolutionize various sectors, from medicine to software design.
Generative AI: what is it in terms of the learning process
Learning in generative artificial intelligence (Generative AI) mainly occurs through two phases:
- Training:
Pre-processing phase: Raw data is cleaned, transformed, and normalized to be used by the machine learning algorithm.
Model selection: The architecture and parameters of the machine learning model are selected based on the type of data and the desired objective.
Training phase: The model is “trained” on a large dataset (the training set). The algorithm learns to recognize relationships and patterns among the data and to use them to generate new content.
Validation phase: The model is tested on a validation dataset to evaluate its performance and its ability to generalize to new data.
- Generation:
Inference phase: The model is used to generate new content. The algorithm uses the knowledge learned during the training phase to create new data similar to those in the training set.
Post-processing phase: The generated content can be further processed and refined to improve its quality and coherence.
Machine Learning Algorithms
Generative AI uses various types of machine learning algorithms, including:
- Supervised learning: The algorithm is trained on a dataset with examples of desired input and output;
- Unsupervised learning: The algorithm learns from a dataset without examples of desired output;
- Reinforcement learning: The algorithm learns to perform actions in a virtual environment and receives rewards or punishments based on the results obtained.
Deep Neural Networks
Generative AI primarily relies on deep neural networks, computational models designed to mimic the human brain, which need to be trained using large datasets to learn (even complex) patterns and provide accurate predictions.
Neural networks consist of several layers of artificial neurons capable of processing information and learning complex relationships among data by evaluating the surrounding environment. Just as the human brain is tasked with understanding the environment and its changes by providing responses suitable to contingent needs, artificial neural networks use datasets to derive identifying traits.
For those who want to delve deeper, I recommend this interesting article “What is Generative AI?” available on the NVidia website.
Generative AI: What it is in terms of types
Generative AI finds space in various fields, allowing the creation or modification of content of various kinds: from text generation to image and video generation to support for programming.
Generative Text AI
Generative text AI focuses on creating new texts, such as articles, poems, emails, scripts, and can be used in various applications including:
Content creation:
- Automatic writing of articles and blog posts: Text generators like Chat-GPT, Gemini, or Jasper can create complete articles and blog posts in seconds, providing only some basic information;
- Product description generation: Generative AI can be used to create accurate and informative product descriptions for e-commerce websites;
- Creating content for social media: Generative AI can create engaging and captivating social media posts.
Automatic translation:
- Generative AI can translate texts from one language to another accurately and fluently, overcoming the limitations of traditional automatic translation systems. Tools like DeepL and Google Translate use Generative AI to provide high-quality translations.
Text summarization:
- Generative AI can summarize long texts accurately and concisely, preserving the most important information.
Chatbots and virtual assistants:
- Generative AI allows the creation of chatbots and virtual assistants capable of interacting with users in a natural and realistic way. The technology can respond to user questions, provide information and support, and complete simple tasks.
Generative Image AI: Painting with Technology
Generative image AI focuses on creating new and original images. Thanks to systems like Midjourney, Leonardo AI, DALL-E, or Adobe Firefly, it is possible to create new images and modify existing ones.
Image Creation
- Generation of realistic images of people, animals, objects, and landscapes that do not actually exist;
- Creation of characters, settings, and objects for use in video games;
- Creation of more engaging and informative images for use on websites and e-commerce.
Image Editing
- Removal of unwanted objects from a photo;
- Improvement of the quality of blurry or damaged images;
- Colorization of black and white images.
Generative Video AI: Painting in Motion with Technology
Generative video AI focuses on creating new and original videos. Unlike traditional video editing systems that rely on manual editing, systems like Synthesia, HeyGen, NVIDIA Canvas, or the new Sora learn from large amounts of data to create realistic and fantastic videos. As with image generation, with generative video AI it is possible to create and modify videos.
Video Creation
- Generation of realistic videos of people, animals, objects, and landscapes that do not actually exist.
- Development of video games and therefore for the creation of characters, settings, and objects for video games;
- Creation of videos for e-commerce and therefore of products for e-commerce websites, making it all more engaging and informative;
- Generation of Avatars for the presentation of specific products or interaction with users in real-time;
- Generative AI can be used to create realistic and fantastic special effects for movies and TV shows.
Source of video: cdn.openai.com
Video Editing
- Removal of unwanted objects from a video;
- Improvement of the quality of blurry or damaged videos;
- Colorization of black and white videos.
Generative Artificial Intelligence in support of programming
Generative AI is opening new frontiers in the world of programming, offering tools and technologies capable of:
Increasing productivity:
- Automatic code generation: Tools like Github Copilot and Cursor can automatically generate code based on text descriptions or existing code snippets;
- Automatic code completion: AI can suggest automatic completions for variables, functions, methods, and other code elements;
- Automatic error correction: AI can identify and correct syntax and logic errors in real-time.
Improving code quality:
- Code reformulation to make it more readable, efficient, and secure;
- Automatic refactoring: AI can help reorganize code to make it more modular and maintainable.
Promoting creativity:
- Exploration of new solutions and approaches to programming problems;
- Development of new algorithms and programming techniques;
- Creation of innovative software: AI can help create innovative and never-before-seen software.
AI and Ethics: An In-depth Analysis
Artificial intelligence (AI) has brought enormous advances in various sectors, from medicine to finance. However, the rapid development of this technology has also raised several ethical issues that deserve in-depth analysis.
Some of the main ethical concerns related to AI include:
- Bias and discrimination:
AI systems can reflect and amplify biases present in the data they have been trained on. This can lead to discrimination against certain categories of people, such as women, people of color, or people with disabilities.
Example: An AI system used for hiring personnel might discriminate against women based on their gender or their name.
- Transparency and interpretability:
Decisions made by AI systems can be opaque and difficult to interpret. This can make it difficult to understand how an AI system made a particular decision and can hinder accountability in case of errors or harm.
Example: An AI system used for medical diagnosis might provide an incorrect diagnosis without providing sufficient explanations as to why that diagnosis was reached.
- Privacy and security:
AI systems can collect and analyze large amounts of personal data, raising concerns about the privacy and security of such data.
Example: An AI system used for facial recognition might collect biometric data without the consent of the individuals.
- Impact on jobs:
The automation of repetitive tasks by AI systems could lead to job losses in some sectors.
Example: The use of automated robots in factories could lead to the dismissal of manual workers.
Addressing these ethical issues is crucial for the responsible and sustainable development of AI. It is important to establish clear ethical principles to guide the development and use of AI. These principles should include non-discrimination, transparency, accountability, privacy, and security.
The responsible development of AI requires a commitment from all stakeholders, including researchers, developers, companies, governments, and citizens. Only by working together can we ensure that AI is used for good rather than for harm.
A recent news (March 13, 2024) published by Wired is the approval by the European Commission of the final text of the AI Act (Artificial Intelligence Act), which guarantees safety and respect for fundamental rights and promotes innovation.
The main objectives of the AI Act are:
- Classification of artificial intelligence applications based on risk;
- Definition of categories of high-risk artificial intelligence systems;
- Introduction of bans on some artificial intelligence applications;
- Protecting citizens from risks arising from artificial intelligence;
- Stimulating innovation in this sector;
- Promoting a global regulatory framework for artificial intelligence that ensures the safety and transparency of artificial intelligence systems.
Thanks to the implementation of this regulation, artificial intelligence systems will be subject to checks by the competent national authorities, while the European Commission will have the power to supervise and coordinate the implementation of the AI Act.
Finally, expert committees will be established to provide technical advice to national authorities and the European Commission.
More information and the approved text can be found here.
Conclusions
Generative Artificial Intelligence (Generative AI) represents a promising branch of artificial intelligence with disruptive potential in various sectors, with the potential to transform our way of living and working by improving our lives in many ways.
However, it is important to be aware of the challenges and work together to develop and use this technology responsibly and sustainably, maximizing benefits and minimizing risks.
Author: Francesco Scalzo
