Democratized Generative AI
If 2023 is the explosion of ChatGPT, helping the keyword Generative AI become a prominent technology trend, then 2024 is predicted to be the year of the term "Democratization of Generative AI". The undeniable potential of Generative AI has helped the use and application of this technology become increasingly popular in business departments.
Democratizing Generative AI is the desire for Generative AI to be more accessible to a wide audience, including those without technical expertise or previous AI experience. This offers the opportunity to create visual, textual or musical content for businesses and individuals alike without requiring extensive specialized skills. DanaExperts predicts that: by 2026, more than 80% of organizations will use generative AI models and APIs (application programming interfaces) and/or geneAI-enabled applications in production environments. export. And usage of the above models grew less than 5% in early 2023.
According to DanaExperts, the reasons for the growth in the adoption of these technology trends include the following three ideas:
1. Thanks to genAI applications, users have access to huge volumes of information.
- GenAI, likely referring to General Artificial Intelligence, suggests that AI applications are designed to be more versatile and capable of handling various tasks. This enables users to access and process large amounts of information.
- GenAI applications leverage advanced algorithms to sift through and analyze extensive datasets, providing users with a wealth of information and insights that would be challenging or time-consuming to obtain manually.
2. The adoption of genAI will significantly democratize knowledge and skills for users.
- The adoption of General AI is expected to democratize knowledge and skills, implying that AI technology will make expertise more widely accessible to a broader audience.
- General AI, by automating complex tasks and facilitating learning processes, can potentially empower people with diverse backgrounds and skill levels to acquire knowledge and skills. This may reduce traditional barriers to entry in certain fields.
3. Large language model (LLM) allows businesses to connect workers with specialized knowledge in a close, conversational style.
- Large Language Models (LLMs) are highlighted for their role in allowing businesses to connect workers with specialized knowledge in a close, conversational style.
- LLMs, such as advanced natural language processing models, enable more natural and conversational interactions. In a business context, this could mean that employees can communicate with these models to seek and share specialized knowledge in a way that feels like a natural conversation.
Going back to the term generative AI, it is not too difficult to understand the definition. It sounds complicated, but generative AI is a simple term in the field of artificial intelligence. It only refers to algorithms designed to produce or generate outputs. This result can be displayed in many different forms such as text, images, video, source code, or data. It can even be applied to 3D models based on the information they are trained on.
The main goal of this type of artificial intelligence is to create content, which distinguishes it from other types of artificial intelligence. It can be used for a variety of purposes such as data analysis or supporting the operation of autonomous vehicles. In other words, generative AI is a technology related to teaching artificial intelligence systems to create new content based on trained data.
Notable statistics:
Adobe Creative Cloud has enabled all users to create images from AI Generative. An outstanding example is that after integrating AI features, Adobe Sensei had impressive revenue growth of up to 23% over the same period last year in the group's Digital Media segment Adobe group.