to disassemble. With each radio, the system better learns the
concept and how to recognize it.
Cloud computing—the use of many connected servers for
data storage and processing—is another major trend in artificial
intelligence. Localized computing is restricted to the amount of
data on any one drive or server, so it limits the information machines have available for learning. It’s like trying to learn about
a new subject while only using books from your friend’s personal collection. Cloud computing vastly increases the amount
of data available. In our analogy, it’s the equivalent of expanding your resources to a large network of well-stocked libraries.
As people create larger and larger quantities of data—more pictures, emails, videos, etc.—more companies are relying on cloud
storage solutions to host that data, which in turn creates larger
reserves of data for AI systems to learn from.
With that as a foundation for understanding artificial intelligence, it’s time to turn to the bigger and more complex
questions: What’s coming next … and how can we possibly prepare for it?
One of the challenges of preparing the world for an artificially
intelligent future is that developments in AI are fundamentally
difficult to predict. That’s not to say we’re completely blind to
what’s coming down the road, but it’s a little bit like forecasting
weather: We can only see so far ahead, and small, unexpected
shifts may drastically change that forecast.
There are some major developments looming on the horizon, though. One is known artificial intelligence-as-a-service.
The “-as-a-service” tag is the popular nomenclature for various
cloud computing services that companies can purchase at dif-
ferent scales according to their needs. Infrastructure, platforms,
and software are the three pillars of cloud computing services,
and artificial intelligence seems poised to join them. Companies
will be able to purchase the problem-solving power of artificial
intelligence programs, giving them an edge
over competitors who are still trying to prob-
lems with old-fashioned human brainpower.
Another development that ties in closely
with AI-as-a-service is conversational tech-
nology. So far, our ability to communicate
with machines has been restricted to cod-
ing and whatever natural language we’ve
been able to program. But natural language
processing is already on the verge of hur-
dling long-standing barriers: Just look at
applications like Apple’s Siri and Microsoft’s
Cortana, or Google’s new Home products. In
just a few years, these apps and devices have
all made startling advancements in machines’
ability to understand human language. The popular Skype ap-
plication can even translate between eight spoken languages in
real time, although the feature is still in development.[ 5] Artifi-
cial intelligence allows these applications to rapidly study and
continually refine its communication abilities, learning to better
identify and respond in natural language over time. And these
are only the first steps toward real, widespread conversational
technology—the kind we’re used to seeing in science fiction, like
Star Wars’ C-3PO.
Beyond these developments, though, the future of artificial
intelligence is cloudy. While that might seem like a good reason to hold off on discussions of policy until later, it’s actually
a compelling argument for why we need to start those conversations now: Because once those changes begin to take hold,
we have no idea how quickly they might outpace our ability
Before we explore the policy implications of these potential
changes, though, let’s address the matter of timing. Skeptics will
probably be quick to question exactly how fast advances in artificial intelligence can actually ripple through society. After all,
technological advancement is a slow, iterative process, right?
The answer is yes, it has been—but that doesn’t mean it always will be. The history of technological innovation has been
paced at the rate of human understanding and innovation. But
artificially intelligent systems can accumulate knowledge and
iterate almost unfathomably faster. We’re already building machines that can program themselves—that is, essentially, how
deep learning works; the program is teaching itself to recognize certain kinds of information—so it’s not a particularly far
jump to imagine a point at which AI programs are creating even
more efficient program-creating systems. Within a few generations of these systems (which, again, should appear increasingly
quickly), the speed and complexity of programming will be fundamentally outside the range of human capacity. In other words,
we may not be far away from creating a machine that builds new