- 16th April 2018
- Posted by: Manolis
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 actually is. Having investigated the space as a technologist and an active investor, I see that some things that haven’t worked for decades are starting to take off. 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, 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 is it happening 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 process). They learn, reason, judge, predict, infer and initiate action.
In my experience, to really be AI, a system or application should be the following:
- Aware: Be cognizant of context and human language
- Analytical: Analyze data and context to learn
- Adaptive: Use that learning to adapt and improve
- Anticipatory: Understand likely good next moves
- Autonomous: Be able to act independently without explicit programming
There are examples of AI today that fit this description, but unlike the human brain, they can only perform a specific application. For example, “digital personal assistants” like Apple’s Siri can understand human language and deliver relevant suggestions on what to buy or what to watch on TV. But they can’t clean your house or drive cars. We are seeing self-driving cars, but that car will not be able to learn how to play chess or to cook. Essentially, they won’t be able to combine even the smallest subsets of actions that constitute being human.
All of these AI types do one or two things humans can already do pretty well. But while they can’t do everything, they can save us time and could end up doing these specific tasks far better than any human.
There are four new preconditions that I believe have enabled the acceleration of AI in the past five years:
1. Everything is 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, sensors are already 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 can not only send data but also receive instructions.
2. Computing is becoming cheaper.
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 that you can put a number of cheap processors together in parallel and distributed systems to get the compute scale required at a manageable cost to solve problems that were unthinkable even a few years ago.
3. Data is becoming the new oil.
Oil was the resource that fueled the industrial revolution, and so access to oil became a competitive advantage. Today, data is fueling the digital revolution, and similarly, organizations that have unique access and can process that data better will have the edge. This is because the amounts and types of data available digitally have proliferated exponentially over the last decade, as everything has been moved online, been mobilized on smartphones, and been tracked via sensors. New sources of data have emerged like social media, digital images and video. This is the language that machines understand, and is what enables machines to accelerate learning. We have an almost infinite set of real data to describe conditions of all sorts that were only modeled at a high level in the past.
4. Machine learning is becoming the new combustion engine.
Unrefined, data cannot really be used. Machine learning is a way to use algorithms and mathematical models to discover patterns implicit in that data. Machines can then use those complex patterns to figure out on their own whether a new data point fits, or is similar enough to predict future outcomes. Robots learning to cook using YouTube videos are a great example of this in practice.
Machine learning models have been limited historically because they were built on samples of data versus an entire real data set. Furthermore, new machine learning models have emerged recently that are better able to take advantage of all the new data. For example, deep learning enables computers “see” or distinguish objects and text in images and videos much better than before.
The Future Of AI
If these four conditions continue, then the types of AI we see today will continue to flourish and a more general AI might actually become a reality. But one thing is certain: if everything is a connected computing device, and all information can be known, processed and analyzed intelligently, then humans can use AI to program and change the world.
We can use AI to extend and augment human capability to solve real problems that affect health, poverty, education and politics. If there is a problem, taking a new look at solving it through the lens of AI will almost always be warranted. We can make cars drive on their own and buildings more energy efficient with lines of code. We can better diagnose disease and accelerate finding cures. We can start to predict the future. And we can begin to augment and change that future for the better.