5 Major Artificial Intelligence Hurdles We're on Track to Overcome by 2020 Unless you're in AI development yourself, you need to start thinking about tech partnerships that could bring AI to your business.
Edited by Dan Bova
Artificial intelligence (AI) gets more advanced every year, but there are still some major limitations keeping us from seeing a futuristic reality that includes robot butlers and near-complete societal automation.
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Fortunately, some of these limitations are on the verge of being overcome, and if you watch and plan carefully, you'll be able to take advantage of those improvements for your business.
Here are some of the biggest hurdles we may overcome as early as 2020:
1. Unsupervised learning
Right now, most AI systems "learn" new information through a kind of structured force-feeding, relying on information given to those systems by humans. However, this form of "supervised learning" isn't scalable, and doesn't mimic the way that human beings naturally learn.
In fact, we humans are immersed in our environments, perceiving pretty much everything that crosses our path and naturally filtering out what's unimportant. We also experiment to learn how objects interact and how the world works.
Currently, we're still a few years away from machines that can learn this way, but when we get there, we'll have the ability to use them to generate or augment ideas, and produce concepts we couldn't come up with on our own.
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2. Creativity and abstract thinking
Humans tend to think of ideas -- and solutions to problems -- in terms of abstractions. For example, think about a horse. Chances are, you aren't thinking about a very specific example of a horse, and you don't need to list out all the required "ingredients" that constitute a horse. Instead, you conceptualize the general idea of what a "horse" is.
A modern computer, on the other hand, would need thousands of examples of horses to understand what "horse" means, and even then, it would have to define this conceptual equine concretely and completely to use that idea in any application. If we want machines that can take raw data and turn it into intuitive concepts that can be grasped, we'll need to create machines that can think abstractly.
What's intriguing here is that we're already on our way, having created deep learning programs that understand games like Go, and chess, as more than brute-force possibilities. By 2020, we could be taking the next step.
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3. Public trust
Self-driving cars run on sophisticated AI software to avoid collisions and drive better than slow-thinking, distracted, mistake-prone human drivers. To date, self-driving cars' record has been relatively clean, compared to that of human drivers.
However, only 39 percent of U.S. consumers claim they would feel safe in an autonomous vehicle. AI is still a foreign concept to most of us, and thanks to decades of science fiction, many of us are inherently distrustful of any fully mechanized solution. However, thanks to the gradual introduction of AI systems, public trust is steadily increasing, and may reach a point by 2020 that allows for widespread adoption.
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4. Integration
AI doesn't exist by itself. It needs to be combined with something to be practical, such as those aforementioned self-driving cars. Integration into existing products, such as standard appliances and software programs, will be a major hurdle to overcome -- and one we're already overcoming. We're already seeing a plethora of "smart" devices, but few of these feature true machine learning or AI tech.
Being able to incorporate deep learning elements into existing systems could instantly multiply our capabilities -- and this may start happening soon.
5. General use
We have AI systems that can beat human Go masters, write poetry and pass the Turing test. But these were all created for specific applications. Could we develop an AI program that serves a more general, all-around use?
Personal digital assistants like Siri, Google Home, Amazon Alexa and Cortana are a good start, but they only scratch the surface of what modern AI is capable of when put to a single application. By 2020, I suspect we'll either see the beginnings of "general" AI development, or further fracturing into more specific niche functions.
How this affects entrepreneurs
So, how might these beaten obstacles affect you and your business?
- Better tools and analytics. First up, the tools and software you use to improve your business are going to get a major overhaul. They'll be able to use data more efficiently and more accurately than your human data analysts, and you'll be able to produce more intuitive, visual representations of those conclusions.
- New customer needs. Your customers' lives are going to change, and drastically. Their cars, appliances and even their homes will function more intelligently, which means the door's open for a host of new solutions to address those new circumstances.
- Human resources shifts. You may see a number of your internal positions become replaceable. At that point, you'll need to decide between keeping a salaried body on staff or opting for a more cutting-edge, but less personal solution.
- Partnership opportunities. Unless you're in AI development yourself, you need to start thinking about tech partnerships that could bring AI to your business. Is there a way to make your product "smarter"? Is there a way to make machine learning improve the value of your services? Demand is about to skyrocket, so you need to be ready.
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It's hard to say exactly how AI will develop, but its momentum is strong, and there are no signs of its stopping. The better prepared you are for the future of AI, the more your business stands to benefit in multiple areas. Get ahead of your competition now, and start planning for the next few years of AI development.
