(Image courtesy of Gengiskanhg.)

Artificial Intelligence (AI) is escaping the realm of hackneyed sci-fi tropes and staking a renewed claim as the forefront of technological progress. Ever since the field of AI was founded in 1956, it’s waxed and waned in the public eye, perceived at some times as the inevitable future of computing, and at others as the broken promise of scientists who dreamed too big. Today, expectations for AI are sky high.

AI refers to systems that act intelligently, whether in a specific domain (narrow AI), or in general (strong AI). Designing such systems is no easy task. The human brain, consisting of about 86 billion neurons, has been postulated to be the most complex object in the known universe; naturally, recreating even a portion of that complexity has proven to be challenging.

Does this mean the current interest in AI is just the latest in a series of hype cycles? Perhaps not. Consider these recent AI accomplishments: in 2011, IBM’s question-answering Watson program bested Jeopardy! champion Ken Jennings in the popular quiz gameshow; in 2016, Google DeepMind’s AlphaGo program achieved a long-sought AI milestone when it beat Go expert Lee Seedol.

Though these examples may seem frivolous, they illustrate an important point–artificial intelligence, however rudimentary, is being fostered by increasingly powerful computing abilities. This has resulted in a growing number of narrow AIs that exceed human abilities.

For more accessible examples of AI progress, look no further than your smartphone. Our personal devices are getting much better at tasks such as natural language processing (like Siri), image recognition (like face detection) and data analytics (like how Google seems to know more about you than you do).

As engineers, it’s important to stay informed of technological innovations, especially given their potential impact on our profession. AI is one such technology that’s becoming increasingly relevant across many disciplines, including engineering. So, here’s a look at some popular AI techniques and applications, and how AI might fundamentally change the entire engineering profession.





Machine Learning

One of the most fruitful avenues of AI research is machine learning. This refers to algorithms that, through a set of training data, allow computer programs to learn to do something for which they were not explicitly programmed. For example, one might expose an algorithm to images of both dogs and cats, with the hope that the program would learn to differentiate the two.

A neural net with a single hidden layer. The arrows represent output from one neuron taken as input for another. (Image courtesy of Colin M.L. Burnett.)

Some of the most effective methods of machine learning are based on the concept of artificial neural networks (ANNs), which have been studied on-and-off since the beginning of AI research. ANNs are modelled after the neurons in the human brain, and consist of a network of nodes (analogous to neurons) connected with varying degrees of correlation (analogous to synapses).

One of the earliest methods for training ANNs was the perceptron algorithm. Perceptron teaches a single-layer network to sort a given input into one of two classes, provided the classes are linearly separable (i.e., you can separate the data with a line, or plane, or hyperplane, etc.). This algorithm works by inputting training data, comparing the nodes’ output with the expected output, and updating their weighting based on the difference.

Increasing the amount of training data modifies a perceptron algorithm’s understanding of the boundary between classes. (Image courtesy of Elizabeth Goodspeed.)





Artificial Intelligence Applications Demonstration of a Tesla Model S driving without any human intervention. (Image courtesy of Tesla.) Now let’s look at some of the engineering accomplishments of machine learning and AI techniques. There are several examples of specific domains in which AI has shown great promise, including:

Despite its effectiveness at binary classification, the perceptron algorithm was too simple for most applications. However, you can increase the effectiveness of ANNs by adding more layers of nodes, and using a more powerful technique (such as backpropagation ) to update their weights. Layers between the input and output neurons are called hidden layers and they’re used extensively for what’s called deep learning. Here’s a video that offers a simple introduction to the concept of deep learning: