Data for Generative AI: Considerations and Challenging Issues

High-quality training data contributes to the accuracy, relevancy, and reliability of GenAI models. However, deploying diverse datasets from contextually relevant text, image, audio, and video types faces various challenges. Organizations must ensure data quality and volume, data preprocessing, bias and fairness, data storage, privacy and security, and ethical use.

In this article, we’ll outline the considerations when using data for Generative AI and strategies for unlocking its potential for scaling businesses.

Crucial Role of Data in Generative AI

Generative AI operational effectiveness relies on the quality and quantity of data sources to generate insightful outcomes. Indeed, well-curated data empowers Generative AI models to make informed decisions based on insights and understanding of complex datasets.

• Serving training purposes: The Generative AI model requires a large amount of data to capture data patterns and variations and generate creative outputs. The data volume to train OpenAI’s GPT-3 model is 570GB, gathering text data from various articles, books, and websites.
• Improving GenAI performance: GenAI’s reliability depends on high-quality data to avoid biased outputs and misleading content.
• Enhancing GenAI output: A diverse dataset with different genres, styles, and perspectives allows the generative AI model to create innovative results. StyleGAN, an NVIDIA-generated AI model, can create varied and sophisticated faces based on various groups of people of different ages, genders, and ethnicities.

Data Considerations for Generative AI

Implementing generative AI requires a large volume of accessible data to deliver insightful results. Businesses should focus on the following considerations during the data management process.

Data Quality

Poor data quality poses significant risks to AI-driven decisions across various industries. Below are some types of underperformed data you should notice.

• Erroneous data leads to unreliable outputs for strategy decisions due to human or measurement errors.
• Incomplete data causes AI models to result in biased predictions.
• Outdated data provides out-of-time analytics without reflecting the current reality.
• Irrelevant or redundant data disrupts the AI model to generate out-of-context results.

Various typical businesses have eliminated GenAI-driven systems due to substandard data. Unreliable data made Tay, a chatbot developed by Microsoft, decrease its brand value by generating offensive statements on social channels. Meanwhile, the e-commerce giant Amazon failed to apply GenAI solutions when deploying male-biased data to recruit new staff. This resulted in bad experiences for female candidates.

As a valuable lesson from these real-life examples, businesses should ensure data quality to train generative AI models.

Data Processing and Storage

Data processing is another big challenge. It involves filtering out personal data such as telephone numbers or email addresses from large amounts of data. Moreover, classifying and categorizing data from different types helps reporting dashboards or analytics tools indicate the correct data format.

Additionally, the diverse workloads of Generative AI pose specific requirements for the data storage infrastructure to ensure management performance. According to the Gartner report, the recommendations for organizations to deploy practical storage approaches include:

• Integrate scalable data storage platform: adapt to different use cases that can handle large and small files, workloads sensitive to throughput or latency, and other data-access-heavy and metadata-heavy tasks.
• Streamline global data management: Enabling streamlined data management across cloud and on-premise locations reduces wasted capacity and operational complexity.

Data Security and Privacy

When sharing sensitive data with generative AI tools, we should be cautious to avoid unintended consequences. Indeed, businesses should use public or approved-to-use data from universities to mitigate privacy and security challenges.

Student records, HR, medical, and finance information, and other non-public data are discouraged from being used for generative AI.

Businesses should consider intellectual property rights and compliance with relevant regulations and laws when deploying data for Generative AI models. Preventing malicious prompts can also prevent the exposure of sensitive data and misleading content.

What Types of Data Are Suitable for Generative AI?

Generative AI models learn from diverse existing text, image, audio, and video data to generate unique content. Understanding the differences in data collection for each type can help businesses leverage AI models effectively.

The existing data types available for training Generative AI models include:

• Text data: Generative AI applications generate human-like text by learning from multilingual text data from books, the Internet, and other data sources. Indeed, AI tools assist businesses in streamlining the content creation process, providing natural conversations and responses to customers, and translating documents.
• Image data: DALL-E and Stable Diffusion enable text-to-image generation by training generative AI models with diverse art, photography, and computer graphics data. GenAI tools can understand different contexts in the input prompts and create artwork and illustrations matching users’ descriptions.
• Audio data: AI models support creating AI voices for podcasts and audiobooks by gathering speech recordings with different accents, languages, and styles. Regarding music production, data of released songs in various genres contributes to composing new melodies, music tracks, and sound effects.
• Video data: Businesses can create short videos and animations with Generative AI tools by providing them with multiple reference videos or a series of images.
• Other types: Technology development allows Generative AI models to deploy more data types, such as 3D models, code, and scientific data, to serve more complicated use demands.

Tips to Manage Data Effectively

Mckinsey Data & AI Summit 2022 reported that 72% of organizations consider managing data one of the toughest issues that challenge them to scale AI use cases.

Follow these 10 tips to enhance data quality and establish a stable foundation in data management.

1. Categorize unstructured data collection: Indexing data enables streamlined categorization of unstructured and structured data. Moreover, it provides users with accessible data searching through key metadata. For example, file creation date, file extension, file size, and last access date.
2. Segregate sensitive and proprietary data: Move corporate and customer data into a private and secure domain; business leaders can prevent employees from sending it to AI models.
3. Implement comprehensive data cleaning process: Practices to improve the quality of data input involve handling outliers with statistical methods and addressing missing or incomplete data. Indeed, businesses can handle large-scale data cleaning by using automation tools.
4. Validate data quality: Validate data quality to ensure the AI models are trained with reliable and accurate data. Implementing automated validation processes can help check data accuracy, consistency, completeness, timeliness, uniqueness, and validity to improve GenAI applications’ performance.
5. Enhance data integration and standardization: Strategies to mitigate inconsistent formats and data silos include developing robust APIs to exchange data seamlessly, applying ETL/ELT processes, or employing data virtualization techniques.
6. Invest in data literacy and training: A comprehensive understanding of GenAI data encourages responsible actions from employees for effective AI-driven outcomes and better team communication. Businesses should conduct training programs to deliver knowledge relating to basic data concepts, principles, role-specific skills, and machine learning basics.
7. Establish a data governance framework: Effective data governance strategies encompass streamlined data quality management, lifecycle, metadata, compliance, and security. Establishing a clear framework enables businesses to scale AI applications and effortlessly maintain ethical uses.
8. Build up data engineering talent: Structure the AI project team with a tendency to fewer data scientists and more data engineers.
9. Choose the right Generative AI tools: Based on your business’s needs and required use cases, choose a platform with appropriate features and capabilities.

How to Use Data for Generative AI Responsibly

The ethical and legal aspects of generative AI raise the need to take action to deploy data responsibly.

Regarding developing and using Generative AI in regular work, users should implement the following practices:

• Inform users about the transparency of AI-trained models: Show data types collected for training AI models, and users can request to delete or refuse specific data processing activities.
• Integrate technology to enhance privacy: Explore data de-identification, anonymization, and loss prevention technologies to protect user data and mitigate privacy risks.
• Purchase and get licenses for the enterprise version of AI software: Enterprise plans offer more real-time support and protection for the AI implementation contract.
• Validate GenAI output: Consider validation steps to give a factual output check.
• Prevent AI tools from learning and sharing organizational data with other third parties: The setting for generative AI tools should restrict them from gathering insights generated from the organization meetings and sharing them with outside parties. Besides, the hosts shouldn’t use GenAI to summarize and transcribe compassionate meetings that include confidential, personal, or attorney-client privileged information.

Streamline Data Management for Generative AI with Neurond AI Service

With experience integrating AI projects for businesses in various industries, Neurond AI’s experts can help gather data for Generative AI, analyze the data landscape to generate insights and conduct data-driven strategies. Contact Neurond AI service for consultancy for your AI projects.

FAQs

1. How much data is required for Generative AI?

The data volume needed for Generative AI usage depends on types of data, specifically:

• Text-based generative AI usage: OpenAI GPT-4 requires 175 billion parameters to process all the tasks.
• Image-oriented AI data consumption: An image-oriented AI data model might need several terabytes of storage with 2MB for each HD image.
• Speech synthesis tool usage: Generating standard-quality video might need about 2MB per minute.
• Music-related AI tool usage: 10KB or more megabytes are required to train AI tools like OpenAI’s MuseNet or SOny’s Flow Machines to compose high-quality music. 

2. How can businesses source data for GenAI training?

To source a training dataset effectively for the GenAI project, companies can employ curated datasets, extract data from websites and online sources, label and tag data, collect in-house data, expand existing data, or outsource data platforms.

3. What are the approaches to providing new data to a model?

Businesses can use three approaches to provide new data to a model: pre-training a model from scratch, fine-tuning existing general-purpose large language models, and retrieval augmented generation (RAG).