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The Genesis of Artificial Intelligence and Digital Twins

A conceptual rendering of artificial intelligence with a brain over circuitry

Artificial Intelligence (AI), Machine Learning (ML), and Digital Twins – why are we hearing so much about them and why do they suddenly seem so critical?  The simplest explanation is this: when something is too complex for a human to easily process or there is too little time for a human to make a critical decision, the only choice is to remove the human. Getting there requires the ability to replicate the thought process a human might go through which requires a lot of data and a very deep understanding of the decision environment. So how now? 

Keysight Technologies has a unique perspective on technology development given its 80-year history enabling engineers to push state-of-the-art. For decades, we saw huge advancements come primarily from the integration and shrinking of electronics. Smaller products, consuming lower power, and offering dramatic increases in functionality per square inch were the hallmarks of technology progress.   

Software applications have also evolved over the decades in many ways, one of the most notable being the dramatic acceleration of the application adoption cycle. In the past two decades alone, users have shifted at alarmingly fast rates from treating applications as interesting novelties, to turning to them as a convenience, to expecting them to work flawlessly all the time. At each adoption stage, a user’s expectation rises, meaning the product must also evolve and mature at very fast, scalable rates.  

At each adoption stage, a user’s expectation rises, meaning the product must also evolve and mature at very fast, scalable rates.  

The combination of the hardware and software trends formed an interesting convergence of product development requirements. New ‘critical need’ applications suddenly must include higher capacity of real-time processing, time-sensitive decision-making, high to very high availability, and expectations that platform-generated decisions be correct, every time.  This combination formed the initial need for both ML and AI, to allow for the expectation of explosive adoption and growth.  

While most people think of AI primarily as an end-user resource, AI has become necessary to enable faster product design and development. From the earliest stage of a chipset design or layout of a circuit all the way through end-product validation, emulators have become necessary to emulate complex interfaces and environments. These emulators, known as digital twins, are a virtual manifestation of a process, environmental condition, or protocol capable of serving as a ‘known good signal.’ In test terms, a digital twin can be a simple signal generator, a full protocol generator, or a complete environment emulator.  Using a digital twin allows developers to rapidly create a significantly wider range of test conditions to validate their product before shipping. High performance digital twins typically contain their own AI engines allowing them to automatically troubleshoot and regression test new product designs.  

While most people think of AI primarily as an end-user resource, AI has become necessary to enable faster product design and development.

The shift to AI-driven development using automating test functions and digital twins has become necessary due to the large amount of functionality and autonomous decision-making expected in new products. Basic design principles specify features and functionality of a product, then set up individual tests to validate them. The sheer number and complexity of interface standards makes that virtually impossible to construct by hand. By using digital twins, a much wider set of functional tests can be programmed in much less time than a developer could even imagine on their own. AI functionality then automates test processes based on what it discovers and predicts actions that might be needed based on a current state.  To understand this better, it’s useful to understand the core of what makes any AI possible.    

In its simplest form, software decision-making starts with algorithms. Basic algorithms run a set of calculations, and if you know what constitutes acceptable vs. unacceptable results, you can create a finite state machine using decision tree outcomes. This would hardly be considered intelligent.  By adding a notation of state, however, and inserting a feedback loop, your basic algorithm can now make outcome decisions a function of both the current conditions compared to the current state. Combine this while evolving the decision tree into a behavior tree and you have formed the genesis of AI.   

We are all at the early stage of AI and digital twins, which means lots of products will be making lots of claims. Whether the AI you are examining is in your development lab, a cloud software application you are using, or in the autonomous driving car you own, it is there for a reason. Understanding what it’s supposed to deliver will allow you to assess its criticality. Same is true for the digital twin.  Once you isolate the intended signal(s), condition(s) or decision outcome(s) either is designed to replicate, evaluating its efficacy becomes the easy part.

The need for AI and digital twins is real and when you question the veracity of one – yours or someone else’s go back to its genesis. Decision criticality and rapid scalability cannot always have humans in the loop. 

 

About the Author

Jeff Harris is a marketing and technology leader who has spearheaded first-to-market product introductions across radar, optics and acoustic sensors; surveillance vehicles to drones; and ultra-wideband (UWB) to mobile ad hoc network (MANET) communications for commercial and government applications at companies like ViaSat, General Atomics, and Lockheed-Martin. As vice president of portfolio and corporate marketing at Keysight, Jeff drives thought leadership initiatives to surface stories that spotlight the company’s role in accelerating innovation and helping customers win in their markets. His leadership spans product marketing, brand, corporate communications, and the company’s digital marketing channels. Jeff has led the transformation of these functions to broaden awareness and preference for the company’s broad portfolio of offerings and solutions. Jeff holds Bachelor of Science and Master of Science degrees in Electrical Engineering from George Mason University.

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