What do you get when you cross a colossal and complex amount of data with computing technology so advanced that it can outperform humans in almost everything? Depends on who you ask.
“You can have data without information, but you cannot have information without data,” says computer programmer and science fiction writer Daniel Keys Moran. And that’s a problem throughout the world right now: the so-called “big data” monolith. Modern technologies have succeeded in gathering so much data about so many topics that no human can efficiently analyze and interpret it all.
As a result, people are turning to artificial intelligence (AI) to address this predicament. Because computers can read and analyze data exponentially faster than humans, the AI approach is tailor made to sift through terabytes of data and deliver the valuable information upon which sound decisions can be made.
According to a report from market intelligence provider IDC, global revenues derived from big data and business analytics will balloon from US$130 billion last year to more than US$203 billion in 2020.
So why the alarm bells surrounding the fusion of big data and artificial intelligence? AI has been identified as “our biggest existential threat” by one of the greatest minds on the planet: Elon Musk. And Stephen Hawking, one of the world’s most admired scientists, told the BBC, “The development of full artificial intelligence could spell the end of the human race.”
EXACTLY WHAT ARE WE TALKING ABOUT?
Before delving into these apocalyptic issues, it is important to define these ideas more clearly. “Big data” is the term used to describe the collection of raw facts and statistics that have been assembled in any given subject area. Not only is big data characterized by its size, but it also refers to data that is exceedingly complex and/or available from an immense number of sources.
“For a meaningful partial-brain interface, I think we’re roughly four or five years away” -Elon Musk
In this context, “artificial intelligence” is the tool that is used to tame big data. AI systems are designed not only to sift through and perform simple analytics on large caches of data, but also to find patterns, reach relevant conclusions, and apply these results toward advancing specific goals.
The aspect of artificial intelligence that has led to the most distressing rhetoric is known as “machine learning.” A subset of AI, machine learning is the process by which computer-powered “machines” are able to evolve as they are exposed to new data, instead of simply performing the same preprogrammed instructions repeatedly.
It is this evolutionary property of AI through machine learning that has some experts worried. For instance, an AI-powered computer program recently played 60 games against the world’s top players of go, the most complex board game in existence—and posted a record of 60–0. Another AI program stunned skilled poker players in January by crushing them in Texas Hold ’Em, in large part because the program learned how to bluff. If AI can boast these achievements now, doomsayers claim, then what will it be able to do in the next decade or two?
According to Scientific American, by 2027 there will be so much internet connectivity that the amount of data generated will double every 12 hours.