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2017 is the year of artificial intelligence. Here’s why
A recent acceleration of innovation in Artificial Intelligence (AI) has made it a hot topic in boardrooms, government, and the media. But it is still early, and everyone seems to have a different view of what AI is.
I have investigated the space over the last few years as a technologist and active investor. What is remarkable now is that things that haven’t worked for decades in the space are starting to work; and we are going beyond just tools and embedded functions.
We are starting to redefine how software and systems are built, what can be programmed, and how users interact. We are creating a world where machines are starting to understand and anticipate what we want to do – and, in the future, will do it for us. In short, we are on the cusp of a completely new computing paradigm. But how did we get here and why now?
What is AI?
When the term AI was coined in 1955, it referred to machines that could perform tasks that required intelligence when performed by humans. It has come to mean machines that simulate human cognitive processes, i.e. they mimic the human brain in how they ‘think’ and process. They learn, reason, judge, predict, infer and initiate action.
In my experience, AI tends to be:
Aware: is cognizant of context and human language
Analytical: analyzes data and context to learn
Adaptive: uses that learning to adapt and improve
Anticipatory: understands likely good “next moves”
Autonomous: is able to act independently without explicit programming
Most AI today cannot do all of these things. The few that can, can only do so for a specific application or use case. For example, many recommendation engines, or digital personal assistants like Apple’s Siri, can understand human language and then search through large volumes of data and deliver relevant answers or suggestions on what to buy or watch on TV. But they can’t clean your house or drive cars.
We are seeing self-driving cars, which is pretty amazing. But that car will not be able to learn chess or cook, let alone combine even the smallest subset of actions together that constitute being human.
All of these types of AI do one or two things humans can already do pretty well, but they do save us time and could end up doing those specific things far better than any human could.
There are four new preconditions that have enabled the acceleration of AI in the past five years:
1. Everything is now becoming a connected device
Ray Kurzweil believes that someday we’re going to connect directly from our brains to the cloud. While we are not quite there yet, sensors are, in fact, being put into everything. The internet initially connected computers; then it connected mobile devices. Sensors are enabling things like buildings, transport systems, machinery, homes and even our clothes to be connected through the cloud, turning them into mini-devices that cannot only send data but also receive instructions.
2. Computing is becoming free
Marc Andreessen claims that Moore’s law has flipped. Instead of new chips coming out every 18 months at twice the speed but the same cost as their predecessors, new chips are coming out at the same speed as their predecessors but half the cost. This means that eventually there will be a processor in everything; and you will be able to put a bunch of cheap processors together at a manageable cost to get the computing capacity required to solve problems that were unthinkable even five years ago.