Home » AI » The Anatomy of AI

AI, when well executed, is not an overhyped emerging technology. Instead, it's the only way engineers can manage the exponential complexity in new designs.

It seems like everywhere you look you now see artificial intelligence (AI) touted in the unlikeliest products ranging from the advanced to the mundane. Just the thought of AI powering your products sounds impressive, so of course you want to believe the claims. However, much of the noise fails to convey what the AI does or why the manufacturer felt so confident about making the claim. The engineer in me is always curious how things are built. That’s because I hate the concept of a ‘black box’ where we aren’t supposed to understand how calculations are programmed.

So, let’s open the box and explore the anatomy of AI. To achieve an artificial intelligence, you first need two main ingredients: (1) an ability to measure some parameter with an understanding of what the measurement means and (2) the ability to learn. The first part is all about metrology, otherwise known as the scientific study of measurement. The second part is called machine learning (ML), which gives systems the ability to recognize when a measurement is different than expected and change an operation without explicitly being programmed.

Metrology focuses on the deep understanding a particular measurement. That measurement can be as simple and distinct as voltage, ground, or temperature, or as multi-modal as the functioning of aircraft control surfaces or complex manufacturing assembly lines.

This month’s In Focus highlights the developments in artificial intelligence (AI) and machine learning (ML) sectors, the engineering challenges, and whether or not the world is ready for an AI-centric future.

The ultimate ML feeds data from multiple sources into algorithms that mimic the way that humans learn, gradually improving their accuracy. Once you have the data feeds, there are three essential building blocks to achieve ML: an algorithm to interpret the data, a table of expected results with reactive outcomes, and a feedback loop.

Adding multiple ML capabilities focusing on different aspects of larger systems, as well as adding more sensor data, enables machine learning at a more complex system level. Very advanced ML can add to its ‘look up tables’ as it encounters new combinations of sensor inputs, enacts variants of its reactive outcome instructions, and measures the feedback of how sufficient the reaction performed. These become self-adjusting algorithms that derive knowledge from data to predict outcomes. And the more algorithms are trained, the more accurate the output.

Now that you have trainable algorithms, you are most of the way towards delivering AI. This requires taking the outputs from the collection of ML engines and combining them with sufficient guidelines and iteration for the algorithm to make real-time decisions.  Each time an AI algorithm processes data, iterates, and considers the iterative response with new data coming in, and uses the combination to determine its output choices, it has achieved decision-making status. This perpetual cycle enables the AI to keep learning and improving the decision quality. This entire process can be very simple, like the example of the voltage and temperature sensor loop, or it can be as complex as an attack drone’s flight control system.

The DNA Markers of AI

So how can you predict how well any AI algorithm will perform? Just like in humans, you can look at its DNA markers. In its most basic form, implementing AI enables a machine to replace having a human in the decision loop by simulating how we would sense, process, and react to information and modify a workflow for a given set of conditions. At its core, you should look at three common DNA markers:

Therefore, the quality of an AI algorithm is a function of its:

This brings us back to the fact that AI, when well executed, is not an overhyped emerging technology. Instead, it’s the only way engineers can manage the exponential complexity in new designs.

As futurist Gray Scott succinctly stated, “there is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” Engineers recognize this and have started on the path of infusing ML and AI across their systems. AI starts with having smart, motivated engineers that understand measurement science, understand system behavior expectations sufficiently to create digital twins for developers, and are driven to take engineering to the next level.

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.

You must Register or Login to post a comment.