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During my junior year at Colby College, I had been reading towers of papers about approaches to artificial intelligence for my cognitive science class, and I mostly thought, “Wow, we’re so far away from developing any artificial intelligence that is actually… well… intelligent.”

That was 12 years ago. Yes, we had a computer that could beat a human at chess. And we had some algorithms that could learn faces. And even today we have IBM’s Watson, which can beat geniuses at Jeopardy. But… none of these computers are smart. They have no intelligence.

Deep Blue can evaluate 200 million chess positions per second, but that doesn’t make it intelligent. Watson is an incredible feat of engineering that captivates me every time I watch it play. Watson can process one million books worth of information per second. But that doesn’t make it smart. In fact, you can’t even call Watson an imbecile, because "imbecile" implies a minor wisp of intelligence, which Watson does not have.

Let’s look at the three factors in the above definition of intelligence.

1. The Ability to Learn
Watson can improve the accuracy of its knowledge through absorbing more information, weighing the accuracy of that information against information it already has, and making connections between that and related information.

Is this learning? Maybe in very loose terms. It’s learning in the same way that Netflix might learn over time that you’ll probably like Inception if you liked both The Matrix and Catch Me If You Can. And it’s only mildly better than my couch “learning” the impression of my rear end and developing a depression over time.

But you can’t give Watson a calculus textbook and say, “Learn this from scratch,” even though it could read the entire book in a one-millionth of a second. Watson would be able to make plenty of assertions about calculus afterwards, but wouldn’t be able to perform calculus unless someone specifically programmed that capacity into it. If Watson could do that, then it would truly be learning.

2. The Ability to Understand
Can Watson, or any computer, grasp the meaning of things? This could be a huge philosophical argument with a maddening chain of tautological reasoning reminiscent of Louis CK’s hilarious comedy skit “Why?”

Instead, let me pose a hypothetical question. Say you told Watson the following: “If you beat my husband on Jeopardy, I’ll be upset at you.” Do you think Watson would grasp the simple meaning of your statement? And do you think Watson would wonder to itself, “Maybe I should consider losing because I don’t want this person to be mad… she might disassemble me.”

3. The Ability to Deal with New or Trying Situations
Assume, for a second, that Watson was Woody-Allen-sized and had a battery pack, but still immobile. If you dropped Watson off outside Grand Central station near a pretzel cart, do you think it would learn to cope with its new situation? Would it figure out how to get someone to recharge it? Would Watson be able to ask for a ride back to IBM, perhaps promising its drivers a monetary reward from IBM?

All of that would require the following logic: 

Just in the first two steps, there’s a problem. It requires Watson to link its “knowledge” about the world to knowledge about its own state of being. I’d argue that any computer which lacks sensory input from their immediate surrounds – sight and sound most importantly – wouldn’t even begin to have the tools to reason about the relationship between its existence in the world and its own state.

The broader an environment that an AI can understand and react meaningfully to, the closer it gets to intelligent. A robot might be superb at learning to stack a few blocks, but if that’s all it can do, it’s not intelligent. Humans are the most adaptable. And although we may not be able to adapt to, say, suddenly being pushed off a cliff, we’re intelligent enough to scream on the way down.

The Bottom Line about Artificial Intelligence
Artificial Intelligence still doesn’t exist. What we have are artificial creations that appear intelligent under tightly constrained situations in which they have been programmed to excel (and even then, not always). We code algorithms for computers to learn specific tasks, and that’s all they’ll be able to do until we update their algorithms. Just because we have cars that can drive themselves and avoid accidents, it doesn’t mean that we’ve discovered a brilliant new approach to AI. (And if they do hit you, they won’t feel bad about it.) This quote pulled from Wikipedia’s history of AI sums it up nicely:

And that’s the crux of it – we need to move past programming algorithms for intelligence. We need machines that can learn to learn. Like humans – we don’t just learn things, we learn how to learn things so we can adapt to our environment and make sense of whichever environment surrounds us. Our brains are malleable.

This TED talk by Henry Markram about building a brain in a supercomputer is the best approach I’ve seen. It doesn’t produce any intelligent results yet, and it’s the type of approach that will feel meandering and vague… until one day when it all comes together and a computer makes a sound of its own accord – not a sound that was programmed into it, but rather a sound born of its electrical signals swimming around in the womb of its malleable mental structure, and that sound may be reminiscent of the crying of a newborn baby.

Why would it make a sound? Not because it was programmed. But because it could. 

The Idea
So how does this relate to my techno-thriller novels? I asked myself, if I had to make an in intelligent computer – a real intelligent computer – how would I do it?

I’d decided that I would grow one.

We don’t know enough about the structure of the brain to model it entirely yet. But why start with the most complex part of human biology? Why not start much simpler? Start with DNA.

DNA is nature’s compression algorithm. If we could just unzip it, we would have all we needed to create true intelligent programs. And guess what? Nature already knows how to unzip DNA into a human. So let’s simulate that.

Let’s build an incredibly detailed simulation of a human egg. Simulate inception, and then… just keep the simulation running. A human will grow. It will have a brain. Given the proper inputs and care, it will be intelligent.

There are myriad reasons why this isn’t possible today… but… what if we were wrong? What would it take? Who could do it? How much would it cost?

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Google is about to take over, and not in the way that you might think. It’s not about the web’s most popular search engine, or an advertising behemoth, or hundreds of exaflops of raw computing power. Those things alone won’t let Google grasp the Earth in its silicon hands… and then deliver it to us.

Instead, it comes from something that might be mistaken for a futuristic fashion statement:
high-tech sunglasses. More specifically: “Google Glasses,” due out later this year. (The official page is here, with a concept video.)

The clandestine GoogleX labs are building futuristic shades that will give us augmented reality so we can see information layered over the real world in our daily lives. For example, if you’re house hunting, perhaps you’d be able to drive down the street and see information about availability, pricing, and home layout projected in front of every home. Along with walking distance to the closest Cinnabon (if that's your thing).

The Google Glasses accomplish this with these purported specs:

This would, of course, turn everyone into a terminator, and next time you found yourself wandering naked into a biker bar with your Google Glasses (happens all the time, except with beer goggles), you’d be able to size up every patron to find the one whose wardrobe would make the most sense to steal.

In other words, augmented reality will be freaking awesome. But this post isn’t even really about AR…

So here’s where things get interesting.

If you haven’t noticed, Google likes to digitize things. Whole things. Entire sets of things. Like maps. And books. And the moon. And, of course… the Earth.

The Earth.

Google Earth combines satellite imagery, aerial photography, crowdsourced building models, snapshots from car-mounted cameras, and more into a crude representation of our Earth, its topology, and buildings. But it’s been around for over 10 years. Google Earth is old technology. (BTW, it was originally created by a CIA-funded company that Google purchased.)

So if you want to check out San Francisco, Google Earth can give you a fun 3D view like this:

But zooming in, the limits of the current representation become apparent as cars are squished into the street and fine details get lost:

And if you’re not looking at a city, you lose all height information. Your view becomes a blurry photo-pancake:

Now, here’s where the glasses enter the equation. A New York Times article on the Google Glasses says, Through the built-in camera on the glasses, Google will be able to stream images to its rack computers and return augmented reality information to the person wearing them.

Imagine hundreds of millions of people walking around every day, streaming images back to Google of everything they’re looking at – with built in location data. All across the world. Constantly.

At first, only a trickle of early adopters will be wearing the glasses. But eventually these gadgets will be so pervasive that they’ll probably replace sunglasses and reading glasses, and the video and streaming capabilities will be superb.

The ramifications are so incredible it’s hard to even take it seriously.

Wearing a camera is nothing new. But when you have hundreds of millions of people wearing them and feeding all that data to a central location, paradigm shifts abound.

Google will be able to construct a high-resolution model of the world, down to the coin you stared at but didn’t pick off your lawn this morning because your Labrador ate your change purse only last week. And Google will be able to not just model the outsides of buildings in spit-stain detail, but the insides as well. Anything that someone can look at in the real world could be integrated into the model. And it would be updated in real time.

I call it: “The Mirror Earth.”

Google will own it all. They will own this world and everything amazing it will give us. As the quote from Sneakers goes: What we see and hear, how we work, what we think… it’s all about the information!

If you wanted a 3D model of your house, you could toss on the glasses and walk around, looking at everything until your view had painted it all onto a virtual canvas. Perhaps share it on Facebook. (And submit that to the homeowner’s insurance company instead of listing out all your belongings until you have carpel tunnel.) It’s like Photosynth just took steroids and rode a tornado over the rainbow.

And of course this will be gamified. Wearers will be directed to uncharted territory to capture views of it for community prestige. Extra points if you’re going on safari or somewhere with sparse traffic.

OK. Now pair this with augmented reality. This is when the line begins to blur between humanity and computers, and it will usher in a new technological revolution.

Want to see what’s around the corner without walking around the building? Use AR. Use the Mirror Earth. Look through the building. Google and your glasses know what’s on the other side. And so can you. Or perhaps it’s a public building – an Art Museum – and you want to take a peek inside. Tune your view to 20 feet through the entrance. Instant x-ray vision. If someone has seen it, then Google has seen it – which means you can see it too.

And while you’re checking out the art, your glasses alert you that there are 3 people currently inside with public streams. You switch to one of their views and see what’s actually happening right now. You can see there isn’t much of a crowd. But the guy who’s glasses you’re hitchhiking on seems more interested in the brunette standing next to him than the one in Hammershoi’s paintings. But you like Hammershoi, so you head inside.

I have a lot more to say about this, but I’ll save it for another post. Instead, I’ll just tell you the phrase that’s going through my head:

Buckle up. Shit is about to get real.

Or virtual, rather. Entirely, massively virtual.