In 1982, Ray Kurzweil started Kurzweil Music Systems, alongside Stevie Wonder and Bruce Cichowlas. Within a short couple of years, they produced one of the most iconic synthesizers ever, the K250. This new instrument was revolutionary due to its ability to sound pretty close to a real piano. The K2000 was actually the first version of this instrument that I was introduced to. It was amazing, not only in the ability to reproduce the sound of a large variety of instruments, but also in the way you could sequence songs, and sample any audio sound for playback at the simple press of a key.

When I first learned about the K2000, I was duly impressed. It was not a simple task to bring together the variety of technologies at the time and produce a reliable and robust device such as they had. I had no idea who Ray Kurzweil was. I only knew that the Kurzweil K2000 was a hell of a keyboard synthesizer, and I loved the way it made making certain kinds of music incredibly easy.

Of course, for Ray Kurzweil, the K250, and the K2000, and the rest of the Kxxx series of synthesizer are essentially an afterthought compared to much of his other work. Many consider Kurzweil a quintessential modern innovator and futurist. And certainly, he is that. I sometimes think about the Terminator series of movies when I think about Kurzweil. Maybe that's because I'm worried he might be responsible for bringing us closer to a real-world SkyNet.

When, in 2005, Kurzweil released the book "The Singularity Is Near", some people found it harsh, untimely, and even downright fantastical. It was very hard for most of the world to see what he could see at the time. But what did he see?

What is the singularity? Is it near? Or far? What would it mean for it to happen?

Many people's introduction into the concept of a singularity comes from black holes and gravity. The basic idea is that a black hole has a gravitational center which has huge or infinite density and infinite time. Of course, mathematics and language tend to break down when talking about concepts like black holes. In this sense, infinite doesn’t necessarily mean never ending (as it does in much common usage). It means something closer to the core meaning, which is something like “very large, but unmeasurable”. Whether there is an “end” or not, is irrelevant. You can never reach the end. You can never completely count or measure it. So, even though, in a practical sense, there could be an end, or barrier, or boundary to a black hole, it’s unreachable and unmeasurable. So, it is, not just quantitatively infinite, but qualitatively infinite. In essence, the singularity is the expression of time that never changes and of mass and energy that's effectively infinite. In scientific terms, this is a point where physics breaks down. If there's no time, there's no change. But there's an infinite amount of mass and energy, which are "stuck" in a single state. In many ways, the singularity is a paradox. In other ways, it represents a concept that helps scientists think about and test the limits of certain kinds of theories.

But that's a rather specific example of a singularity. Kurzweil's concept deals with the singularity almost more as a threshold. Like with the black hole, there's a threshold, upon the other side of which lies the singularity. And that's what Kurzweil means here... he means we are near to a threshold. But what kind of threshold?

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While many innovators are often stuck in a single technical field, and many futurists have limited real-world experience, Kurzweil is unique in that his fields of expertise are varied, and he has explored them deeply, delivering not just concepts and insights, but actual products.

What Kurzweil was getting at is that the components that will empower us, mankind, to step toward the threshold and beyond, are very close to synthesis -- probably in our lifetime. These include components like computing power, storage, and networking - many of the components that drive the everyday Internet experience. But they go beyond just technology hardware components and extend to ways of structuring and interacting with data and information. Traditional data processing has always been about essentially brute force development of applications, and brute force management of the data relevant to the application. This means a developer must understand the data, and then design the software or application to work with the data in a very specific way. You might think of it a little like teaching a robot to make a pie. You have to program the robot to use a specific pan, and specific ingredients, and specific tools in order to make an apple pie. And once the robot knows how to make an apple pie, that's it. It doesn't magically know how to also make cherry pie, or key lime pie, or bake a cake. And it certainly would be useful to have a robot who could make pies.

The singularity is about the threshold where the robot can, on its own, extrapolate how to make cherry pie once it knows how to make apple pie. It's about transcending the boundary beyond brute force, and into a much more dynamic existence. People, human beings, homo sapiens, do this kind of thing every moment of every day. Once you know how to drive a Honda, you also know how to drive a Toyota, and every other car. Once you know how to adjust the radio in one, you can figure it out in another. The universe has provided and demonstrated the required hardware in the form of humans, for how to accomplish this. But, alas, the human is capable because of its brain, and the brain is quite complex, and immoral to study in certain ways.

But we have learned much. And much of that has been about learning. The best way currently to create a robot that can perform a certain task is to train a human to do it. It's exponentially quicker, traditionally, to train a human to do most any task. The downside, of course, is that they are still human. So, they still make mistakes. Or get sick. Or quit to do something more interesting. Or are sociopaths. Or whatever. A robot, a machine doesn't suffer these shortcomings. Or, at least, in our current thinking they don't. And teaching one human doesn’t easily translate into teaching another human. You have to repeat the process, which takes time.

But what happens when we crack the problem of how to create a machine, a robot, that can learn, and can perform similarly to humans? What happens when that machine can take our human blueprints for how to make a robot and improve them? What happens when robots are being created more efficiently? What happens when they learn more rapidly? What happens when they own their own replication process, and become autonomous from humans?

There's certainly a threshold there, a transition, a singularity. And it's not just about robots. It's about computers, and information too. Robots need not be bipedal humanoid machines. They might just be computer brains in the so-called cloud. Networking, collaborating, like philosophers working collectively to solve tough problems.

And what happens to humans? Where does all our information come from? Computers? Attached to networks? And, besides, what is a human, except a particular, and inefficient, information processor?