Generative AI in the age of Mistral Chat: a breath of fresh air!
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 and ease of use. What is fascinating about the story of Mistral is that this company did not exist... less than 1 year ago. With its products, it embodies the crazy pace of the race for generative AI or AI for short. While it is difficult to keep up with the current pace of AI advances, according to the most experienced Data Scientists, what about laymen? How do you define generative AI? We hear a lot about it - comparisons between AI and generative AI that don't make sense for example. How do you find your way around the mass of information and misinformation on AI?
Sometimes you have to go back to the simple things. Basics. Therefore, in this article, we offer you the simplest possible - although complete - presentation of generative AI. From its recent advances, to its link with Machine Learning, this reading will allow you to see more clearly.
If we had to define generative AI in a simple and comprehensive way, we would say that it is a model that can learn from existing data and generate new, complex data. And that this complex data gives the illusion of having been produced by a human. Otherwise called Gen-ai, it can produce realistic content at scale without duplicating the original data.
In this article, we discuss the recent advances of 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 go.
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, learn from it, and then creates its own new data that looks just like the original. For example, after examining numerous images to learn, generative AI can create new images that look real from text but are actually created by the AI. For example, this is what DALL-E, which is a neural network driven by OpenAI.
More recently, Open AI made noise again by announcing the release of Sora, a model developed to understand and simulate the physical world in motion. Sora can generate videos up to one minute long while maintaining visual quality and adhering to user demand.
Generative AI models, like large language models (LLMs), are trained with a lot of text (for example, all content from Wikipedia). This training helps them generate new texts, AI-generated content that seems to have been written by a person. These AI models use neural networks to process labeled data and generate new content.
A popular use of generative AI is creating realistic images. Businesses 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 is generative AI associated with 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 brain of AI. They look at a lot of information, like images or text, and learn rules, 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 the “Beaux Arts.” Thanks to the deep learning method (Deep Learning), from a huge pile of information, these models become intelligent and start creating accurate and realistic texts or images.
For example, a generative AI model can cause the AI system to learn from numerous books and then write its own story. This is possible thanks to natural language processing, which helps AI 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 an image or a piece of music.
It is common to assume, wrongly, that generative AI is the direct evolution of traditional artificial intelligence (AI). However, it is important to understand that generative AI is not just an advanced version of AI, but rather a distinct branch with its own specific features.
“Traditional” AI focuses primarily on analyzing and interpreting data, using algorithms to make decisions based on existing data sets. On the other hand, generative AI, while using the fundamental principles of AI, is distinguished by its ability to create new content that did not exist before, such as images, texts, or music.
To do this, it uses more complex deep learning models. Both forms of AI share common foundations, including the use of algorithms and data for machine learning, but they serve different purposes and represent different aspects of the broad field of artificial intelligence.
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How do you train a generative AI model?
Generative AI systems work according to your desired requirements if you have trained them perfectly. A generative AI system should be able to help you complete 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 well, you need good training data. That means lots of examples of things you want your AI to use as a reference, before creating. For language models, this could be books, articles, or conversations. Data should be clean and relevant because bad data can teach AI bad things and create biases.
2. Choose the right machine learning models
Choose a machine learning model that fits your purpose. Deep learning models are good for complex tasks. For simpler problems, other models might work better. Remember that larger models require more data and more computing power.
3. Train generative models iteratively
Training takes time. You teach your AI model in stages, called iterations. At each stage, the model is trying to create something new, and you tell him how well he did it. The model is then gradually improving. 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 from the first time using it - even advanced tools like GPT chat incorporate user feedback to continue training continuously and improve.
4. Test AI models continuously
Keep checking your AI. Make sure she's learning the right things. It's 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 are saying. Their input can help you improve your AI. If they say the images or words don't look right, use that to train your generative AI and correct mistakes. Feedback is like a teacher who helps you do better by correcting your exercises, or by giving you personalized advice.
6. Make sure your AI is ethical and fair
Make sure your AI treats all people fairly. She should not learn bad things from the data. If she uses language, she should not say things that are mean or wrong. This is important so that everyone can trust and use your generative AI.
👉 One last point: you will need quality, annotated data for prepare the data sets that will allow your AI to learn. Remember to assess ethics in choosing the service provider who will help you prepare the data: it is not feasible to assume that data annotation tasks do not require any expertise, and can be entrusted to experts Crowdsourced in the four corners of the world, under conditions that are often questionable.
Remember, training your generative artificial intelligence model is a lot of work, which can take months or even years depending on what you're looking to achieve. It's like teaching someone to do something new, to play a sport or a musical instrument for example. You need patience, good tools, and lots of hard work. But if you do it right, your AI can do amazing things!
How does generative AI work with big language models?
Generative AI uses major language models to understand and create new things. Think of Gen-ai as a highly intelligent assistant who has read a lot of books and articles. From all this reading, he is learning how to write his own sentences. The AI does this by looking for patterns in the data it has been trained on, such as finding out which words often come together.
Large 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 like the AI has read a lot of sentences.
But AI doesn't just repeat what it's learned. She can mix and match the pieces she knows to create completely new phrases that have never been said before. That's why she 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 is able to offer you personalized help.
Generative AI can be a big help in a lot of contexts. In schools, for example, it can help teachers prepare lessons. For writers, she could give story ideas, or even write novels almost entirely, as did the Japanese author Rie Kudan 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 a lot of 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 brain what a bike is to our legs: it must be used to push our thinking further, and help us develop and then articulate our ideas. It's changing the way we live and work, giving us AI tools and new ways to create and solve everyday problems.
Key benefits of generative AI
From generating data to creating computer code, generative AI models are becoming beneficial in numerous ways. Generative artificial intelligence brings its benefits by making text creation easier and by bringing in new data samples with lots of improvements. We've put together some major benefits of generative AI tools that you'll find below:
Easy communication thanks to its 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 the devices can talk to us in a natural way. It's great for helping people who want to learn new languages or who need help communicating. We can even imagine that one day, a universal translator in real time, will be available (maybe not the one from this video - we hope that the design will be a bit 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 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 who have deadlines. Since the release of ChatGPT in late 2022, you can generate content in as much quantity 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 works for them. By looking at what a student knows, generative AI can suggest new exercises to help them learn better. This type 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 on its own. It 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 coming years. Sometimes AI can mix different styles or generative AI to create something that no one has ever seen before, which can be really cool and inspiring.
Data creation 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's not enough real data, generative AI can create new fake data that's still useful for a machine learning algorithm. This helps make AI systems smarter without the need for lots of real examples.
💡 Remember, all of 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 numerous generative AI models that are constantly improving human life with their applications in the real world. Implementing generative AI in your work can increase its effectiveness and credibility.
Here are the main applications of generative AI models in the real world:
Audio
Creation of new music, songs, and even soundtracks for movies and games. Restoring and improving audio clips, transcribing speech to text, text-to-speech, and voice cloning.
Visual
Production, modification and analysis of visual content (images and videos). Generating content such as videos or images, improving images and videos, generating virtual reality and simulations for entertainment and training, and generating data for video-based ML projects.
Text
Large language models (LLMs) can generate new texts based on training data and model parameters. They can be used for translating languages, creating content, writing books or commercial texts, summarizing texts, and powering 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.
Increase in data
Generatic AI makes it possible to create 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? Discover our article on the increase of data in AI!
Product design
Generative AI algorithms help generate new designs and prototypes, allowing businesses to explore new product ideas and iterate on existing products.
Customer service
Generative AI contributes to the improvement of customer service through powerful chatbots and virtual assistants capable of human conversations for reinforced user engagement. This use case is so powerful that it is in the process of having a lasting impact on the call center outsourcing industry: at the end of February 2024, the leader in the field, Teleperformance, has experienced a historic drop in its shares after the fintech Klarna unveiled the performance of an assistant based on artificial intelligence. In fact, Klarna claimed that its chatbot did a job equivalent to that of 700 people hired full time.
Conclusion
In essence, generative AI (or gen-AI) systems hold great promise, transforming how we interact with technology, to imagine a world where interacting with AI rather than typing a query on a keyboard becomes the norm. This technology, with its ability to create and personalize, is not only an aid but a real”Game Changer“in various industries. It pushes boundaries in healthcare, automotive, entertainment, entertainment, entertainment, education, education, finance, retail, and security - improving innovation, safety, and efficiency.
Despite concerns about job disruptions, the ethical use of AI, and the authenticity of creation, the underlying value of generative AI lies in improving human capabilities and generating novel solutions to complex problems. It is undoubtedly a compelling guide to a smarter, more imaginative future.
Have you had a chance to see generative AI in action? Whether in art, music, text, speech generation, or beyond - what was your experience with this cutting-edge technology? Share your stories, ideas and projects with us contacting.
