From Science Fiction to Reality: The Evolution of Artificial Intelligence

just a figment of the imagination of some our most famous science fiction writers, artificial intelligence (AI) is taking root in our everyday lives. We’re still a few years away from having robots at our beck and call, but AI has already had a profound impact in subtler ways.

Weather forecasts, email spam filtering, Google’s search predictions, and voice recognition, such Apple’s Siri, are all examples. What these technologies have in common are machine-learning algorithms that enable them to react and respond in real time. There will be growing pains as AI technology evolves, but the positive effect it will have on society in terms of efficiency is immeasurable.

A Lesson in History

AI isn’t a new concept; its storytelling roots go as far back as Greek antiquity. However, it was less than a century ago that the technological revolution took off and AI went from fiction to very plausible reality. Alan Turing, British mathematician and WWII code-breaker, is widely credited as being one of the first people to come up with the idea of machines that think in 1950. He even created the Turing test, which is still used today, as a benchmark to determine a machine’s ability to “think” like a human. Though his ideas were ridiculed at the time, they set the wheels in motion, and the term “artificial intelligence” entered popular awareness in the mid- 1950s, after Turing died.

American cognitive scientist Marvin Minsky picked up the AI torch and co-founded the Massachusetts Institute of Technology’s AI laboratory in 1959, and he was one of the leading thinkers in the field through the 1960s and 1970s. He even advised Stanley Kubrick on “2001: A Space Odyssey,” released in 1968, which gave the world one of the best representations of AI in the form of HAL 9000. The rise of the personal computer in the 1980s sparked even more interest in machines that think.

But it took a couple of decades for people to recognize the true power of AI. High-profile investors and physicists, like Elon Musk, founder of Tesla, and Stephen Hawking, are continuing the conversation about the potential for AI technology. While the discussion occasionally turns to potential doomsday scenarios, there is a consensus that when used for good, AI could radically change the course of human history. And that is especially true when it comes to big data.

The very premise of AI technology is its ability to continually learn from the data it collects. The more data there is to collect and analyze through carefully crafted algorithms, the better the machine becomes at making predictions. Not sure what movie to watch tonight? Don’t worry; Netflix has some suggestions for you based on your previous viewing experiences. Don’t feel like driving? Google’s working on a solution for that, too, racking up the miles on its driverless car prototype.

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The Road to Superintelligence

What Is AI?

If you’re like me, you used to think Artificial Intelligence was a silly sci-fi concept, but lately you’ve been hearing it mentioned by serious people, and you don’t really quite get it.

There are three reasons a lot of people are confused about the term AI:

1) We associate AI with movies. Star Wars. Terminator. 2001: A Space Odyssey. Even the Jetsons. And those are fiction, as are the robot characters. So it makes AI sound a little fictional to us.

2) AI is a broad topic. It ranges from your phone’s calculator to self-driving cars to something in the future that might change the world dramatically. AI refers to all of these things, which is confusing.

3) We use AI all the time in our daily lives, but we often don’t realize it’s AI. John McCarthy, who coined the term “Artificial Intelligence” in 1956, complained that “as soon as it works, no one calls it AI anymore.” Because of this phenomenon, AI often sounds like a mythical future prediction more than a reality. At the same time, it makes it sound like a pop concept from the past that never came to fruition. Ray Kurzweil says he hears people say that AI withered in the 1980s, which he compares to “insisting that the Internet died in the dot-com bust of the early 2000s.”

So let’s clear things up. First, stop thinking of robots. A robot is a container for AI, sometimes mimicking the human form, sometimes not—but the AI itself is the computer inside the robot. AI is the brain, and the robot is its body—if it even has a body. For example, the software and data behind Siri is AI, the woman’s voice we hear is a personification of that AI, and there’s no robot involved at all.

Secondly, you’ve probably heard the term “singularity” or “technological singularity.” This term has been used in math to describe an asymptote-like situation where normal rules no longer apply. It’s been used in physics to describe a phenomenon like an infinitely small, dense black hole or the point we were all squished into right before the Big Bang. Again, situations where the usual rules don’t apply. In 1993, Vernor Vinge wrote a famous essay in which he applied the term to the moment in the future when our technology’s intelligence exceeds our own—a moment for him when life as we know it will be forever changed and normal rules will no longer apply. Ray Kurzweil then muddled things a bit by defining the singularity as the time when the Law of Accelerating Returns has reached such an extreme pace that technological progress is happening at a seemingly-infinite pace, and after which we’ll be living in a whole new world. I found that many of today’s AI thinkers have stopped using the term, and it’s confusing anyway, so I won’t use it much here (even though we’ll be focusing on that idea throughout).

Finally, while there are many different types or forms of AI since AI is a broad concept, the critical categories we need to think about are based on an AI’s caliber. There are three major AI caliber categories:

AI Caliber 1) Artificial Narrow Intelligence (ANI): Sometimes referred to as Weak AI, Artificial Narrow Intelligence is AI that specializes in one area. There’s AI that can beat the world chess champion in chess, but that’s the only thing it does. Ask it to figure out a better way to store data on a hard drive, and it’ll look at you blankly.

AI Caliber 2) Artificial General Intelligence (AGI): Sometimes referred to as Strong AI, or Human-Level AI, Artificial General Intelligence refers to a computer that is as smart as a human across the board—a machine that can perform any intellectual task that a human being can. Creating AGI is a much harder task than creating ANI, and we’re yet to do it. Professor Linda Gottfredson describes intelligence as “a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience.” AGI would be able to do all of those things as easily as you can.

AI Caliber 3) Artificial Superintelligence (ASI): Oxford philosopher and leading AI thinker Nick Bostrom defines superintelligence as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” Artificial Superintelligence ranges from a computer that’s just a little smarter than a human to one that’s trillions of times smarter—across the board. ASI is the reason the topic of AI is such a spicy meatball and why the words “immortality” and “extinction” will both appear in these posts multiple times.

As of now, humans have conquered the lowest caliber of AI—ANI—in many ways, and it’s everywhere. The AI Revolution is the road from ANI, through AGI, to ASI—a road we may or may not survive but that, either way, will change everything.

Let’s take a close look at what the leading thinkers in the field believe this road looks like and why this revolution might happen way sooner than you might think:

 Where We Are Currently—A World Running on ANI

Artificial Narrow Intelligence is machine intelligence that equals or exceeds human intelligence or efficiency at a specific thing. A few examples:

1-Cars are full of ANI systems, from the computer that figures out when the anti-lock brakes should kick in to the computer that tunes the parameters of the fuel injection systems. Google’sself-driving car, which is being tested now, will contain robust ANI systems that allow it to perceive and react to the world around it.

2-Your phone is a little ANI factory. When you navigate using your map app, receive tailored music recommendations from Pandora, check tomorrow’s weather, talk to Siri, or dozens of other everyday activities, you’re using ANI.

3-Your email spam filter is a classic type of ANI—it starts off loaded with intelligence about how to figure out what’s spam and what’s not, and then it learns and tailors its intelligence to you as it gets experience with your particular preferences. The Nest Thermostat does the same thing as it starts to figure out your typical routine and act accordingly.

4-You know the whole creepy thing that goes on when you search for a product on Amazon and then you see that as a “recommended for you” product on adifferent site, or when Facebook somehow knows who it makes sense for you to add as a friend? That’s a network of ANI systems, working together to inform each other about who you are and what you like and then using that information to decide what to show you. Same goes for Amazon’s “People who bought this also bought…” thing—that’s an ANI system whose job it is to gather info from the behavior of millions of customers and synthesize that info to cleverly upsell you so you’ll buy more things.

5-Google Translate is another classic ANI system—impressively good at one narrow task. Voice recognition is another, and there are a bunch of apps that use those two ANIs as a tag team, allowing you to speak a sentence in one language and have the phone spit out the same sentence in another.

6-When your plane lands, it’s not a human that decides which gate it should go to. Just like it’s not a human that determined the price of your ticket.

7-The world’s best Checkers, Chess, Scrabble, Backgammon, and Othello players are now all ANI systems.

8-Google search is one large ANI brain with incredibly sophisticated methods for ranking pages and figuring out what to show you in particular. Same goes for Facebook’s Newsfeed.

9-And those are just in the consumer world. Sophisticated ANI systems are widely used in sectors and industries like military, manufacturing, and finance (algorithmic high-frequency AI traders account for more than half of equity shares traded on US markets), and in expert systems like those that help doctors make diagnoses and, most famously, IBM’sWatson, who contained enough facts and understood coy Trebek-speak well enough to soundly beat the most prolific

ANI systems as they are now aren’t especially scary. At worst, a glitchy or badly-programmed ANI can cause an isolated catastrophe like knocking out a power grid, causing a harmful nuclear power plant malfunction, or triggering a financial markets disaster (like the 2010 Flash Crash when an ANI program reacted the wrong way to an unexpected situation and caused the stock market to briefly plummet, taking $1 trillion of market value with it, only part of which was recovered when the mistake was corrected).

But while ANI doesn’t have the capability to cause an existential threat, we should see this increasingly large and complex ecosystem of relatively-harmless ANI as a precursor of the world-altering hurricane that’s on the way. Each new ANI innovation quietly adds another brick onto the road to AGI and ASI. Or as Aaron Saenz sees it, our world’s ANI systems “are like the amino acids in the early Earth’s primordial ooze”—the inanimate stuff of life that, one unexpected day, woke up.

The Road from ANI to AGI

Why It’s So Hard

Nothing will make you appreciate human intelligence like learning about how unbelievably challenging it is to try to create a computer as smart as we are. Building skyscrapers, putting humans in space, figuring out the details of how the Big Bang went down—all far easier than understanding our own brain or how to make something as cool as it. As of now, the human brain is the most complex object in the known universe.

What’s interesting is that the hard parts of trying to build AGI (a computer as smart as humans in general, not just at one narrow specialty) are not intuitively what you’d think they are. Build a computer that can multiply two ten-digit numbers in a split second—incredibly easy. Build one that can look at a dog and answer whether it’s a dog or a cat—spectacularly difficult. Make AI that can beat any human in chess? Done. Make one that can read a paragraph from a six-year-old’s picture book and not just recognize the words but understand the meaning of them? Google is currently spending billions of dollars trying to do it. Hard things—like calculus, financial market strategy, and language translation—are mind-numbingly easy for a computer, while easy things—like vision, motion, movement, and perception—are insanely hard for it. Or, as computer scientist Donald Knuth puts it, “AI has by now succeeded in doing essentially everything that requires ‘thinking’ but has failed to do most of what people and animals do ‘without thinking.’”

What you quickly realize when you think about this is that those things that seem easy to us are actually unbelievably complicated, and they only seem easy because those skills have been optimized in us (and most animals) by hundreds of millions of years of animal evolution. When you reach your hand up toward an object, the muscles, tendons, and bones in your shoulder, elbow, and wrist instantly perform a long series of physics operations, in conjunction with your eyes, to allow you to move your hand in a straight line through three dimensions. It seems effortless to you because you have perfected software in your brain for doing it. Same idea goes for why it’s not that malware is dumb for not being able to figure out the slanty word recognition test when you sign up for a new account on a site—it’s that your brain is super impressive for being able to.

On the other hand, multiplying big numbers or playing chess are new activities for biological creatures and we haven’t had any time to evolve a proficiency at them, so a computer doesn’t need to work too hard to beat us. Think about it—which would you rather do, build a program that could multiply big numbers or one that could understand the essence of a B well enough that you could show it a B in any one of thousands of unpredictable fonts or handwriting and it could instantly know it was a B?

The Business Effect

Nowhere has AI had a greater impact in the early stages of the 21st century than in the office. Machine-learning technologies are driving increases in productivity never before seen. From workflow management tools to trend predictions and even the way brands purchase advertising, AI is changing the way we do business. In fact, a Japanese venture capital firm recently became the first company in history to nominate an AI board member for its ability to predict market trends faster than humans.

Big data is a goldmine for businesses, but companies are practically drowning in it. Yet, it’s been a primary driver for AI advancements, as machine-learning technologies can collect and organize massive amounts of information to make predictions and insights that are far beyond the capabilities of manual processing. Not only does it increase organizational efficiency, but it dramatically reduces the likelihood that a critical mistake will be made. AI can detect irregular patterns, such as spam filtering or payment fraud, and alert businesses in real time about suspicious activities. Businesses can “train” AI machines to handle incoming customer support calls, reducing costs. It can even be used to optimize the sales funnel by scanning the database and searching the Web for prospects that exhibit the same buying patterns as existing customers.

There is so much potential for AI development that it’s getting harder to imagine a future without it. We’re already seeing an increase in workplace productivity thanks to AI advancements. By the end of the decade, AI will become commonplace in everyday life, whether it’s self-driving cars, more accurate weather predictions, or space exploration. We will even see machine-learning algorithms used to prevent cyberterrorism and payment fraud, albeit with increasing public debate over privacy implications. AI will also have a strong impact in healthcare advancements due to its ability to analyze massive amounts of genomic data, leading to more accurate prevention and treatment of medical conditions on a personalized level.

But don’t expect a machine takeover any time soon. As easy as it is for machine-learning technology to self-improve, what it lacks is intuition. There’s a gut instinct that can’t be replicated via algorithms, making humans an important piece of the puzzle. The best way forward is for humans and machines to live harmoniously, leaning on one another’s strengths. Advertising is a perfect example, where machines are now doing much of the purchasing through programmatic exchanges to maximize returns on investment, allowing advertisers to focus on creating more engaging content.

While early science fiction writers might have expected more from AI at this stage, the rest of the world is generally satisfied with our progress. After all, not everyone is ready for humanoid robots or self-learning spaceships.

An Intelligence Explosion

I hope you enjoyed normal time, because this is when this topic gets unnormal and scary, and it’s gonna stay that way from here forward. I want to pause here to remind you that every single thing I’m going to say is real—real science and real forecasts of the future from a large array of the most respected thinkers and scientists. Just keep remembering that.

Anyway, as I said above, most of our current models for getting to AGI involve the AI getting there by self-improvement. And once it gets to AGI, even systems that formed and grew through methods that didn’t involve self-improvement would now be smart enough to begin self-improving if they wanted to.

And here’s where we get to an intense concept: recursive self-improvement. It works like this—An AI system at a certain level—let’s say human village idiot—is programmed with the goal of improving its own intelligence. Once it does, it’s smarter—maybe at this point it’s at Einstein’s level—so now when it works to improve its intelligence, with an Einstein-level intellect, it has an easier time and it can make bigger leaps. These leaps make it much smarter than any human, allowing it to make even bigger leaps. As the leaps grow larger and happen more rapidly, the AGI soars upwards in intelligence and soon reaches the super intelligent level of an ASI system. This is called an Intelligence Explosion and it’s the ultimate example of The Law of Accelerating Returns.

There is some debate about how soon AI will reach human-level general intelligence. The median year on a survey of hundreds of scientists about when they believed we’d be more likely than not to have reached AGI was 2040—that’s only 25 years from now, which doesn’t sound that huge until you consider that many of the thinkers in this field think it’s likely that the progression from AGI to ASI happens very quickly. Like—this could happen:

It takes decades for the first AI system to reach low-level general intelligence, but it finally happens. A computer is able to understand the world around it as well as a human four-year-old. Suddenly, within an hour of hitting that milestone, the system pumps out the grand theory of physics that unifies general relativity and quantum mechanics, something no human has been able to definitively do. 90 minutes after that, the AI has become an ASI, 170,000 times more intelligent than a human.

Superintelligence of that magnitude is not something we can remotely grasp, any more than a bumblebee can wrap its head around Keynesian Economics. In our world, smart means a 130 IQ and stupid means an 85 IQ—we don’t have a word for an IQ of 12,952.

What we do know is that humans’ utter dominance on this Earth suggests a clear rule: with intelligence comes power. Which means an ASI, when we create it, will be the most powerful being in the history of life on Earth, and all living things, including humans, will be entirely at its whim—and this might happenin the next few decades.

If our meager brains were able to invent WiFi, then something 100 or 1,000 or 1 billion times smarter than we are should have no problem controlling the positioning of each and every atom in the world in any way it likes, at any time—everything we consider magic, every power we imagine a supreme God to have will be as mundane an activity for the ASI as flipping on a light switch is for us. Creating the technology to reverse human aging, curing disease and hunger and even mortality, reprogramming the weather to protect the future of life on Earth—all suddenly possible. Also possible is the immediate end of all life on Earth.

3 thoughts on “From Science Fiction to Reality: The Evolution of Artificial Intelligence

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