In general, generative AI tools can use different types of data, depending on your requirements and the nature of the tool you will be using. In most cases, these tools operate based on the textual or audio-visual (images, audio, video) types of data. However, types of data are just one thing to consider; data quality is what matters.
In this post, we want to take a closer look at this matter and discuss the importance of data quality, diversity, and relevance when it comes to generative AI solutions. We will also mention some ethical issues that need to be considered in the process.
The importance of high-quality data for AI purposes
If you want to implement successful AI tools, you need high-quality data because this is what those algorithms use to learn how to operate according to your needs. AI algorithms must have access to so-called training data – existing datasets of high-quality, organized data. Why is this so important?
Low-quality data (i.e., incomplete, biased, or disorganized) can lead to unreliable models that produce inconsistent or ineffective results. For instance, a generative AI model trained on biased data will probably produce biased outcomes. Generative AI is only as effective and as correct as the data used to train it. If you feed incorrect or biased data to your gen AI algorithm, you will most likely face operational (and perhaps even ethical) challenges once the algorithm is live.
With high-quality data, you will get better results, meaning they are more coherent, natural, and contextually appropriate. There is no doubt that investing in data (specifically gathering, organizing, and preprocessing) is one of the most important steps in successful generative AI solutions development.
Types of data used in generative AI
Earlier in the text, we mentioned that gen AI tools operate based on textual and audio-visual data. Generative AI solutions make use of both structured (like databases) and unstructured (like sounds or images) data. Sometimes, the combination of both is the best way to go. Here’s an example: If you have an email marketing generative AI model and you tell it to create a personalized marketing email, it might need both structured data about the target audience (e.g., the list of previous purchases) and unstructured data (e.g., previous email campaigns) to generate the best result.
Integrating these different types of data is a key aspect of successful generative AI solutions development. Here, everything depends on the specific application you want to benefit from. Let’s have a look at different kinds of data and when they are useful:
Textual data
It’s the most basic and most comprehensive type of data used by gen AI tools. Textual data comes in handy in such applications as:
- Chatbots and virtual assistants
- Language models
- Content generation tools
Visual data
This type of data is especially important when it comes to computer vision applications. Large datasets consisting of photos and images can be used for such tasks as:
- Image generation
- Object recognition
- Image classification
And more. These algorithms can also edit and enhance existing images, but to do so effectively, they need access to high-quality images that they can use as a source of their work.
Audio data
Generative AI is also capable of working with voices and sounds. These tools can be used to:
- Create voicebots
- Generate music
- Synthesize voice, etc.
Audio data needs to be variegated, meaning it comprises different sounds and voices, preferably with different rhythm, speed, and pitch, thus allowing AI algorithms to generate new audio creations that are diversified and contextually appropriate (e.g., to ensure you get a woman’s voice when that was your requirement).
How generative AI uses data
All generative AI systems are built so that they can learn from data and look for patterns that can be used to create content. In the beginning, during the training stage, the model is exposed to vast amounts of data, and it analyzes them and learns their patterns (including such elements as the syntax and semantics of language or the pixel distribution in an image) so that it can produce relevant outputs.
Because the training data is the exact source of everything your gen AI tool comes up with, the quality and variety of the data are what you should be focused on because they will directly affect how well the model can generate requested high-quality outputs. Without enough training data or data that is not variegated enough, the model will struggle to generate new, high-quality content.
Ethical use of data
Lastly, you also need to take ethical considerations into account as they play a significant role in generative AI solutions development. Using biased or unethical data can lead to outputs that are not only flawed but potentially harmful or discriminatory, too. That’s why, whenever possible, you should ensure that your datasets represent different demographics, perspectives, and contexts. This will minimize the risk of getting biased or discriminatory results.
Wrapping up
High-quality, diverse, and unbiased data is the foundation for generative AI models and allows them to create relevant and useful outputs, regardless of their nature. That’s why if you want to invest in this technology, you ought to start by analyzing and enhancing your data. However, you don’t have to do so on your own; there are generative AI consulting companies that can help you with everything that’s needed for successful gen AI implementation in your business. If you want to know more, we invite you to reach out to such a trusted AI partner: Addepto.com