All-natural and organic are familiar terms to consumers, and anything artificial has become anathema to many. Unless we’re talking artificial intelligence – or AI – then investors should be hungry to learn as much as possible about a technology that is becoming as ubiquitous as organic tofu.
The vast majority of nearly 2,000 experts polled by the Pew Research Center in 2014 said they anticipate robotics and artificial intelligence will permeate wide segments of daily life by 2025. A 2015 study covering 17 countries found that artificial intelligence and related technologies added an estimated 0.4 percentage point on average to those countries’ annual GDP growth between 1993 and 2007, accounting for just over one-tenth of those countries’ overall GDP growth during that time.
Interesting numbers – but just what is artificial intelligence? And are robots with AI going to enslave humanity?
We can’t answer the second question, but here’s a good working artificial intelligence definition from a recent U.S. government report called “Preparing for the Future of Artificial Intelligence”:
Or more technically speaking, AI is a “system capable of rationally solving complex problems or taking appropriate actions to achieve its goals in whatever real world circumstances it encounters.” In a way, artificial intelligence is about understanding – then recreating – the human mind. And AI is not just about designing computers that mimic how we think, learn and process information, but also how we perceive and feel about the world around us.
Understanding the world of AI only begins with a simple artificial intelligence definition. There’s a whole universe of terminology we need to explore in order to understand the domain before we can invest in it.
Machine learning is about how computers with artificial intelligence can improve over time using different algorithms (a set of rules or processes), as it is fed more data. AI machines learn by recognizing trends in data that allow it to make decisions. For example, designing autonomous vehicles involves building machines that learn to navigate. A system may use pattern recognition algorithms from which it learns, for instance, to identify pedestrians from vehicles from animals, so that it knows when to hit the brakes when it sees a cat or a zebra, even if it never encountered the latter because it has learned to identify animals accurately. Regular readers will recall a previous article we wrote on this topic tiled “Deep Learning And Machine Learning Simply Explained” which gives an example of how this works in practice.
A type of machine learning, neural networks are superficially based on how the brain works. There are different kinds of neural networks – feedforward and recurrent are a couple terms that you may encounter – but basically they consist of a set of nodes (or neurons) arranged in multiple layers with weighted interconnections between them. Each neuron combines a set of input values to produce an output value, which in turn is passed on to other neurons downstream as seen in the example below:
From an example from the U.S. government report: In an image recognition application, a first layer of units might combine the raw data of the image to recognize simple patterns in the image; a second layer of units might combine the results of the first layer to recognize patterns-of-patterns; a third layer might combine the results of the second layer; and so on. We train neural networks by feeding them lots of delicious big data to learn from.
Deep learning is simply a larger neural network. Deep learning networks typically use many layers – sometimes more than 100 – and often use a large number of units at each layer, to enable the recognition of extremely complex, precise patterns in data.
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