Mistral Chat, a multilingual chatbot, is an illustration of Mistral AI's commitment to open source, offering free and unrestricted access to its language model. It promises to be a powerful tool for a variety of applications, combining cutting-edge technology with ease of use. What's fascinating about the Mistral story is that the company didn't exist... less than 1 year ago. With its products, it embodies the crazy pace of the race towards generative AI, or AI at all. If it's hard to keep up with the current pace of AI advances, as even the most seasoned Data Scientists admit, what about laymen? What is generative AI? We hear a lot of things about it - comparisons between AI and generative AI, for example, which have no basis in fact. How do you navigate through the mass of information and misinformation about AI?
Sometimes you have to get back to basics. To the basics. So, in this article, we offer you the simplest possible - albeit comprehensive - presentation of generative AI. From its recent advances to its link with Machine Learning, this article will give you a clearer picture.
If we were to define generative AI simply and comprehensively, we would say that it corresponds to a model capable of learning from existing data and generating new, complex data. And that this complex data gives the illusion of having been produced by a human. Otherwise known as Gen-AI, it can produce realistic content on a large scale without duplicating the original data.
In this article, we discuss recent advances in Gen-AI and its link with Machine Learning, to offer you a better understanding of its principles. We also discuss other benefits and practical applications of generative AI in everyday life. So, are you ready? Let's get started.
What is generative AI?
Generative AI refers to a type of artificial intelligence capable of creating new content. It uses machine-learning models to analyze large amounts of data, learns from it, and then creates its own new data that resembles the original. For example, after examining many images to learn, generative AI can create new images that look real from text but are actually created by the AI. This is what DALL-Ea neural network trained by OpenAI.
More recently, Open AI made a further splash when it announced the release of Soraa model developed to understand and simulate the physical world in motion. Sora can generate videos up to a minute long, while maintaining visual quality and adhering to user demand.
Generative AI models, such as large language models (LLMs), are trained with a lot of text (for example, all the content on Wikipedia). This training helps them to generate new texts, AI-generated content that looks like it was written by a person. These AI models use neural networks to process labeled data and generate new content.
A popular use for generative AI is the creation of realistic images. Companies are also using generative AI to write text, compose music or develop new computer code. This technology is still new, but it is developing rapidly and becoming more and more effective at performing different tasks, such as creating new synthetic data that can help train other AI systems.
How does generative AI relate to machine learning models?
Generative AI is closely linked to the process of training machine-learning models, because it uses these models to create new things. Think of these models as the AI brain. They look at lots of information, like images or text, and learn rules from it, principles for decision-making. Then, they use these models to make things that are similar but new, like an artist creating a new painting based on what he learned about art as part of his studies at "Beaux Arts". Thanks to the Deep Learning method, from a huge pile of information, these models become intelligent and start to create precise, realistic texts or images.
For example, a generative AI model can ensure that the AI system learns from many books, then writes its own story. This is made possible by natural language processing, which helps the AI to understand and use human language and intelligence.
Generative AI includes models like deep learning and recurrent neural networks that take samples of data to help the AI think and remember, making its creations quite impressive. These AI models are a bit like a growing child - the more they learn from the data they receive, the more adept they become at creating things that feel real, like a picture or a piece of music.
It's a common misconception that generative AI is a direct evolution of traditional artificial intelligence (AI). However, it's important to understand that generative AI is not simply an advanced version of AI, but rather a distinct branch with its own specificities.
Traditional" AI focuses primarily on data analysis and interpretation, using algorithms to make decisions based on existing data sets. In contrast, generative AI, while using the fundamental principles of AI, is distinguished by its ability to create new content that didn't exist before, such as images, text or music.
It uses more complex deep learning models. The two forms of AI share common foundations, notably the use of algorithms and data for machine learning, but they serve different purposes and represent different aspects of the vast field of artificial intelligence.
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How do you train a generative AI model?
Generative AI systems work to your desired requirements if you have trained them perfectly. A generative AI system should be able to help you accomplish a variety of tasks. But how do you train it? Here are a few steps that will give you a (very) simplified overview of the generative AI development cycle:
1. Start with quality training data
To train your generative AI language model properly, you need good training data. This means lots of examples of things you want your AI to use as reference, before creating. For language models, this could be books, articles or conversations. The data needs to be clean and relevant, as bad data can teach the AI the wrong things and create biases.
2. Choose the right machine learning models
Choose a machine learning model that matches your objective. Deep learning models are good for complex tasks. For simpler problems, other models may work better. Remember that larger models require more data and computing power.
3. Train generative models iteratively
Training takes time. You teach your AI model in stages, called iterations. At each stage, the model tries to create something new, and you tell it how well it has done. The model then improves little by little. It's like learning to ride a bike. You fall, you learn and you try again! It's hard for an AI to be perfect the first time it's used - even advanced tools like ChatGPT incorporate user feedback to ensure continuous training and improvement.
4. Test AI models continuously
Keep checking your AI. Make sure it's learning the right things. This is called testing. If the AI makes mistakes, adjust your training or model. It's like teaching a child; they learn best with the right advice and guidance.
5. Use feedback to improve
Listen to what users have to say. Their input can help you improve your AI. If they say that images or words don't look right, use this to train your generative AI and correct mistakes. Feedback is like a teacher helping you to do better by correcting your exercises, or giving you personalized advice.
6. Make sure your AI is ethical and fair
Make sure your AI treats everyone fairly. It can't learn bad things from data. If it uses language, it mustn't say mean or wrong things. This is important so that everyone can trust and use your generative AI.
One final point: you're going to need high-quality, annotated data to prepare the datasets that will enable your AI to learn. Don't forget to consider the ethics involved in choosing the service provider who will help you prepare the data: it's unthinkable to assume that data annotation tasks require no expertise, and can be entrusted to crowdsourced experts in the four corners of the world, under often questionable conditions.
Remember, training your generative artificial intelligence model is a big job, and can take months or even years depending on what you're trying to achieve. It's like teaching someone to do something new, to play a sport or a musical instrument for example. You need patience, the right tools and a lot of hard work. But if you do it right, your AI can do amazing things!
How does generative AI work with large language models?
Generative AI uses large language models to understand and create new things. Imagine Gen-AI as a highly intelligent assistant who has read lots of books and articles. It learns from all this reading how to write its own sentences. The AI does this by looking for patterns in the data it's been trained on, like finding which words often come together.
The big language models, like GPT-3 or GPT-4, are trained with lots of text - billions of words. Then, when you ask the AI to write something, it can predict which words should follow to make sense. It's a bit like when you start saying a famous saying and your friend finishes it. That's because they've heard it many times before, just as the AI has read many sentences.
But the AI doesn't just repeat what it has learned. It can mix the parts it knows to create brand-new phrases that have never been said before. That's why it can write stories, answer questions and even make jokes. It's not perfect though - sometimes she makes mistakes or doesn't quite understand what you mean. But the more she learns, the more she can offer you personalized help.
Generative AI can be a great help in many contexts. In schools, for example, it can help teachers prepare lessons. For writers, it could provide story ideas, or even write novels almost in their entirety, as Japanese author Rie Kudan did with her award-winning book "Tokyo-to Dojo-to". In customer service, she could talk to customers to solve problems. AI makes these things faster and can help people in many ways. But we need to use it carefully and make sure it's fair and safe for everyone.
Generative AI with large language models is to our brains, what a bicycle is to our legs: it must be used to push our thinking further, and help us to develop and then articulate our ideas. It's changing the way we live and work, giving us AI tools and new ways of creating and solving everyday problems.
Key benefits of generative AI
From data production to computer code creation, generative AI models become beneficial in many ways. Generative AI brings its benefits by making text creation easier and bringing new data samples with many improvements. We've put together some of the major benefits of generative AI tools, which you'll find below:
Easy communication thanks to natural language processing capabilities
Generative AI models can help people talk to each other better. These new models use natural language processing to understand and use human words. This means that devices can talk to us in a natural way. This is great for helping people who want to learn new languages or need help communicating. We can even imagine that one day, a universal real-time translator will be available (maybe not the one in this video - we hope the design will be a little better thought-out and ergonomic!)
Fast content creation
With generative AI, creating new things like stories, music or images can be done very quickly. AI doesn't need to rest, so it can create lots of new content all the time. This is very useful for people who need to do a lot of things quickly, like writers or artists with deadlines. Since the release of ChatGPT at the end of 2022, you can generate as much content as you need! With generative AI, text generation is easier than ever!
Personalized learning
AI systems can also help people learn in a way that suits them. By looking at what a student knows, generative AI can suggest new exercises to help them learn better. This kind of personalized learning is exciting because everyone can learn at their own pace.
New ideas in art and design
Generative AI can create art and designs all by itself. This gives artists and designers new ideas to work with. It can also be a tool for those who think like artists without having the technical skills: new AI artists will probably be revealed in the next few years. Sometimes, AI can mix different styles or generative AI to create something no one has ever seen before, which can be really cool and inspiring.
Creating data for the AI training process
Generative AI is also good at creating the kind of synthetic data that helps train other AI systems. If there isn't enough real data, generative AI can create new dummy data that is still useful for a machine learning algorithm. This helps to make AI systems smarter without the need for many real-life examples.
Remember, all these benefits are still growing because generative AI is fairly new. But it's clear that it's already changing the way we create things and share ideas!
Main applications of generative AI in various industries
From generating images to creating better foundation models, generative AI systems are useful for performing a wide range of tasks. There are many models of generative AI that are constantly improving human life with their real-world applications. Implementing generative AI in your work can increase its effectiveness and credibility.
Here are the main real-world applications of generative AI models:
Audio
Creation of new music, songs and even soundtracks for films and games. Audio clip restoration and enhancement, speech-to-text transcription, text-to-speech and voice cloning.
Visual
Production, modification and analysis of visual content (images and videos) videos). Generate content such as videos or images, enhance images and videos, generate virtual reality and simulations for entertainment and training, and generate data for video-based ML projects.
Text
Large Language Models (LLMs) can generate new texts from training data and model parameters. They can be used for language translation, content creation, book or commercial copywriting, text summarization, and to power chatbots and virtual assistants.
Conversations
Conversational AI facilitates natural, human conversations between people and AI systems, including natural language understanding (NLU), natural language generation (NLG), speech recognition and dialogue management.
Data enhancement
Generative AI creates new synthetic data points that can be added to existing data sets, used in ML and deep learning applications to improve the performance of an AI model by increasing the size and diversity of the training data used.
💡 Want to know more? Check out our article on data augmentation in AI!
Product design
Generative AI algorithms help generate new designs and prototypes, enabling companies to explore new product ideas and iterate on existing products.
Customer service
Generative AI contributes to improving customer service through powerful chatbots and virtual assistants capable of human conversations for enhanced user engagement. This use case is so powerful that it is set to have a lasting impact on the call center outsourcing industry: at the end of February 2024, the leader in the field, Teleperformance, suffered a historic fall in its share price after fintech Klarna unveiled the performance of an artificial intelligence-based assistant. Indeed, Klarna claimed that its chatbot did work equivalent to that of 700 people hired full-time.
Conclusion
In essence, generative AI (or Gen-AI) systems hold great promise, transforming the way we interact with technology, to imagine a world where exchange with AI rather than typing a query on a keyboard becomes the norm. This technology, with its ability to create and personalize, is not just an aid but a real"game-changer" in various industries. It is pushing the boundaries in healthcare, automotive, entertainment, education, finance, retail and security - improving innovation, safety and efficiency.
Despite concerns about job disruption, the ethical use of AI and the authenticity of creation, the underlying value of generative AI lies in its enhancement of human capabilities and the generation of novel solutions to complex problems. It is undoubtedly a compelling guide to a smarter, more imaginative future.
Have you had the opportunity to see generative AI in action? Whether in art, music, text generation, speech or beyond - what has been your experience with this cutting-edge technology? Share your stories, ideas and projects by contacting.
