Generative AI is an artificial intelligence technology capable of producing different types of content. This article will help you better understand generative AI and how it works. We will then detail several use cases, examples of generative AI applied in business. Finally, we'll look at the best-known AI tools.
What is generative AI? All you need to know
Generative AI is not a new technology. It was introduced in the 1960s with chatbots. But it wasn't until 2014, with the development of machine learning algorithms, that generative AI really took off. It now makes it possible to create more authentic and convincing images, videos and sounds.
This more advanced capability has opened up new prospects in many fields. These include :
- Film dubbing
- Creating educational content
At the same time, it has raised concerns about :
- Deepfakes: digitally falsified images or videos
- Cyber-attacks: against companies
The rapid progress of LLMs (Large Language Models), with billions of parameters, has opened up a new era. Generative AI models can now :
- Writing attractive texts
- Painting realistic images
- Giving everyday advice
- Generate content across multiple media
- Writing code and helping developers
Even so, generative AI is still in its infancy. Early implementations have experienced problems of accuracy and bias. AI systems also fall victim to hallucinations and can provide bizarre responses.
How does generative AI work?
The request is formulated by the user
The process begins with a query formulated by the user. This can take many different forms:
- A text
- An image
- A video
- A drawing
- Musical notes
- Code
- Or any other data that the AI system can process
Interfaces for generative AI
While early versions of generative AI required data to be sent via API, this is no longer the case. Today, access to artificial intelligence is presented through easy-to-use interfaces. These offer a better user experience. To this end, it is often possible to customize the result by specifying a number of elements, such as :
- The style
- The tone
- The format
Query processing
Once the request has been sent by the user, non-AI algorithms process the content first.
For the text
In the case of text generation, the query is transformed into raw characters and tokens (groups of characters). These are represented as vectors using various encoding processes.
For images
In the case of images, these are transformed into multiple visual elements. They are also expressed as vectors.
For other elements
For each specific format of input information, it is necessary to provide a comprehensible format in vector form. Once the format has been defined, the query is transformed so that it can be taken into account by the neural network.
The use of neural networks
Once the query has been vectorized, a specific neural network is applied to it. This neural network generates new content which is transmitted as a response to the user's query.
Neural networks form the basis of most generative AI and machine learning applications. Designed to mimic the functioning of the human brain, they are repeatedly trained to learn rules and recognize patterns.
Where can generative AI be used?
Generative AI in finance
The integration of generative artificial intelligence in the financial sector offers innovative and effective possibilities.
This technology enables the creation of predictive models and simulations based on historical data. This helps companies to make informed decisions.
Risk management
For example, in the field of risk management. Generative AI can be used to generate a multitude of possible scenarios based on various economic, political and social variables.
These simulations can help financial institutions assess potential risks. They can also help develop appropriate mitigation strategies.
Algorithmic trading
The same applies to algorithmic trading. Generative AI can be used to create sophisticated trading models. These are capable of identifying market opportunities and optimizing returns.
Benefits of AI
In short, generative AI adds value by enabling financial companies to better understand and anticipate market trends, while minimizing risks and maximizing profit opportunities.
Use cases for customer service
The integration of generative artificial intelligence into customer service opens the door to more personalized and efficient assistance.
Chatbots
AI makes it possible to create chatbots and virtual agents capable of understanding customer problems. In this way, they can provide a more precise response.
For example, these virtual agents can simulate authentic, adaptive human conversations. They thus offer a more engaging and satisfying customer experience.
Personalized answers
In addition, generative AI can be used to generate creative, personalized responses. These can be built around customers' preferences and past behaviors. As a result, the quality of the service provided is enhanced tenfold.
Benefits of AI
By automating part of the customer support process, companies can reduce waiting times and increase the operational efficiency of their customer service. In addition to speed, the personalization provided by AI boosts customer satisfaction and loyalty.
Possible applications for law firms
Law firms can benefit from generative artificial intelligence. In particular, they can optimize their drafting processes. But also to analyze and interpret contracts.
Contract drafting
Generative AI can be used to automate the drafting of standard contracts according to customers' specific needs. It can even ensure legal compliance and clarity of terms.
Advanced analysis
What's more, this technology can analyze vast sets of legal data. This makes it possible to identify trends, precedents and similar cases. This helps lawyers formulate sound arguments and make informed decisions.
Benefits of AI
Generative AI enables lawyers to save time and concentrate on higher value-added tasks. It also reduces the risk of human error. AI enables improvements in the efficiency, accuracy and quality of legal services provided.
How can generative AI help manufacturers?
Manufacturers can improve their quality control and defect detection processes. In particular, by combining data from various sources such as :
- Cameras
- X-rays
- And other measures
Identification of defective parts
Generative AI can identify defective parts. It can then determine their causes more accurately and economically.
The tool analyzes images captured by surveillance cameras or scanners. This enables it to spot anomalies or minor defects that might escape human visual inspection.
Trend detection
What's more, it integrates data from different sources. This helps identify patterns or trends that could indicate systemic problems in the manufacturing process.
Benefits of AI
Using the information provided by AI, manufacturers can take faster, more targeted corrective action. The aim is to improve the quality of their products and reduce the costs associated with rejects or recalls. This helps to boost customer satisfaction and competitiveness in the marketplace.
Film and media use cases
Content production
Companies in this sector can automate part of the creative process. In particular, they can generate scripts, scenarios and even visual elements such as sets and characters. This reduces production costs while speeding up the creative process.
Translation
Generative AI can be used to translate content into different languages smoothly and naturally. By integrating advanced text-to-speech models, companies can even bring these translations to life by presenting them in the voice of the original actors. Generative AI makes it possible to distribute content across multiple international markets. Even without the need for costly dubbing or subtitling teams.
Benefits of AI
In short, the use of generative AI in the film and media sector opens up new possibilities. It enables content to be produced at lower cost and distributed worldwide. All this, while maintaining high quality and an immersive experience for audiences.
The benefits of generative AI
Generative AI can be applied extensively in many areas of business. It can facilitate the interpretation and understanding of existing content. It can also automatically create new content. Here are just some of the potential benefits of implementing generative AI in business.
- Automating the content writing process
- Reduced response effort
- Improved accuracy for technical questions
- Summarize complex information in a coherent narrative
- Tailoring tone and style to the user
ChatGPT, Bard and Dall-E: The most advanced tools for generative AI
ChatGPT
OpenAI's chatbot provides a way to interact textually via an easy-to-use interface. Earlier versions of ChatGPT were only accessible via APIs, but as of November 2022, GPT-3.5 opened up to the general public.
ChatGPT simulates a real conversation between the artificial intelligence and the user.
Bard
Also a leader in the advent of generative AI and text generation, Google presents Bard. The tool features the same functionality as ChatGPT, but uses its own language model - LLM (Large Language Model).
Dall-E: Text and image
Dall-E focuses on image generation. To do this, it takes text entered by the user and seeks to transform it into images. Dall-E is an example of a multimodal AI application. It identifies the various connections between different formats (in this case, text and image). In this sense, Dall-E links the meaning of words to visual elements.
