AI-Driven Revolution in Testing and Assessment Methods
The analogy between the early days of Artificial Intelligence (AI) and the first days of the internet is apt. Just as the internet has transformed every aspect of our lives, AI is poised to do the same for the test and measurement industry.
AI is no longer just a tool in this field; it's becoming an assistant, and one day might even become a true partner. With the ability to configure countless parameters across multiple instruments, AI can achieve the best results, especially in the test and measurement stage of product lifecycles.
A recent development in this area is Generative Instrumentation, a capability announced by Liquid Instruments. This new technology allows AI to build entire instruments and test setups based on user prompts. This is a significant leap from the modest, incremental improvements that AI has been limited to so far, such as parsing documentation for product setup and operation.
This dynamic configuration capability enhances flexibility and efficiency in testing setups compared to traditional fixed instrumentation. Future applications are likely to expand this concept, incorporating more sophisticated AI-driven customization and adaptive testing frameworks.
However, as with any technology, there are concerns about the trustworthiness of AI-based results. Incorrect test results from AI can be an inconvenience or potentially jeopardize the safety of end users. To address this, organizations must adopt robust and strategic testing frameworks tailored for agentic AI systems.
These frameworks should establish clear strategies and roadmaps, implement comprehensive quality engineering (QE) frameworks, conduct continuous validation and monitoring, and ensure regulatory compliance. Without these steps, there is a high risk in accepting AI-generated results at face value, which can lead to unreliable or unsafe outcomes.
For scientists and engineers managing large datasets, there are software tools available that post-process, detect anomalies, track trends, and guide decision-making. However, these tools are typically bespoke. Some test and measurement products now offer AI-based features like neural networks and AI-based optimization for specific parameters. Users can audit the code to ensure it makes sense, using a hardware description language (HDL) for FPGA customization or a software programming language like Python for instrument configuration.
In conclusion, generative instrumentation is transforming the test and measurement industry by allowing AI-powered customization and system configuration. Its future will integrate continuous intelligent adaptation. Trust in AI-based results depends on strategically designed verification frameworks and lifecycle testing to ensure safety, reliability, and compliance.
Artificial Intelligence (AI) is not only a tool but also an assistant in the test and measurement industry, with the potential to become a partner one day due to its ability to configure numerous parameters across various instruments. This intelligence, combined with new technologies like Generative Instrumentation, enables AI to construct entire instruments and testing setups based on user prompts, marking a significant stride beyond previous, incremental improvements such as parsing documentation.
