Generative artificial intelligence is the talk of the town in the technology world today. Almost every tech company today is up to its neck in generative AI, with Google focused on enhancing search, Microsoft betting the house on business productivity gains with its family of copilots, and startups like Runway AI and Stability AI going all-in on video and image creation.
It has become clear that generative AI is one of the most powerful and disruptive technologies of our age, but it should be noted that these systems are nothing without access to reliable, accurate and trusted data. AI models need data to learn patterns, perform tasks on behalf of users, find answers and make predictions. If the underlying data they’re trained on is inaccurate, models will start outputting biased and unreliable responses, eroding trust in their transformational capabilities.
As generative AI rapidly becomes a fixture in our lives, developers need to prioritize data integrity to ensure these systems can be relied on.
Why is data integrity important?
Data integrity is what enables AI developers to avoid the damaging consequences of AI bias and hallucinations. By maintaining the integrity of their data, developers can rest assured that their AI models are accurate and reliable, and can make the best decisions for their users. The result will be better user experiences, more revenue and reduced risk. On the other hand, if bad quality data is fed into AI models, developers will have a hard time achieving any of the above.
Accurate and secure data can help to streamline software engineering processes and lead to the creation of more powerful AI tools, but it has become a challenge to maintain the quality of the expansive volumes of data needed by the most advanced AI models.
These challenges are primarily due to how data is collected, stored, moved and analyzed. Throughout the data lifecycle, information must move through a number of data pipelines and be transformed multiple times, and there’s a lot of potential for it to be mishandled along the way. With most AI models, their training data will come from hundreds of different sources, any one of which could present problems. Some of the challenges include discrepancies in the data, inaccurate data, corrupted data and security vulnerabilities.
Adding to these headaches, it can be tricky for developers to identify the source of their inaccurate or corrupted data, which complicates efforts to maintain data quality.
When inaccurate or unreliable data is fed into an AI application, it undermines both the performance and the security of that system, with negative impacts for end users and possible compliance risks for businesses.
Tips for maintaining data integrity
Luckily for developers, they can tap into an array of new tools and technologies designed to help ensure the integrity of their AI training data and reinforce trust in their applications.
One of the most promising tools in this area is Space and Time’s verifiable compute layer, which provides multiple components for creating next-generation data pipelines for applications that combine AI with blockchain.
Space and Time’s creator SxT Labs has created three technologies that underpin its verifiable compute layer, including a blockchain indexer, a distributed data warehouse and a zero-knowledge coprocessor. These come together to create a reliable infrastructure that allows AI applications to leverage data from leading blockchains such as Bitcoin, Ethereum and Polygon. With Space and Time’s data warehouse, it’s possible for AI applications to access insights from blockchain data using the familiar Structured Query Language.
To safeguard this process, Space and Time uses a novel protocol called Proof-of-SQL that’s powered by cryptographic zero-knowledge proofs, ensuring that each database query was computed in a verifiable way on untampered data.
In addition to these kinds of proactive safeguards, developers can also take advantage of data monitoring tools such as Splunk, which make it easy to observe and track data to verify its quality and accuracy.
Splunk enables the continuous monitoring of data, enabling developers to catch errors and other issues such as unauthorized changes the instant they happen. The software can be set up to issue alerts, so the developer is made aware of any challenges to their data integrity in real time.
As an alternative, developers can make use of integrated, fully-managed data pipelines such as Talend, which offers features for data integration, preparation, transformation and quality. Its comprehensive data transformation capabilities extend to filtering, flattening and normalizing, anonymizing, aggregating and replicating data. It also provides tools for developers to quickly build individual data pipelines for each source that’s fed into their AI applications.
Better data means better outcomes
The adoption of generative AI is accelerating by the day, and its rapid uptake means that the challenges around data quality must be urgently addressed. After all, the performance of AI applications is directly linked to the quality of the data they rely on. That’s why maintaining a robust and reliable data pipeline has become an imperative for every business.
If AI lacks a strong data foundation, it cannot live up to its promises of transforming the way we live and work. Fortunately, these challenges can be overcome using a combination of tools to verify data accuracy, monitor it for errors and streamline the creation of data pipelines.
Featured image credit: Shubham Dhage/Unsplash
