The Singularity and Our Collision Path with the Future
Google’s Director of Engineering, Ray Kurzweil, has predicted that we will reach a technological singularity by 2045, and science fiction writer Vernor Vinge is betting on 2029, a date that is ironically on the hundredth anniversary of the greatest stock market collapse in human history.
But where the 1929 crash catapulted us backwards into a more primitive form of human chaos, the singularity promises to catapult us forward into a future form of human enlightenment.
The person who coined the term “singularity” in this context was mathematician John von Neumann. In a 1958 interview, von Neumann described the “ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, can not continue.”
Since that first cryptic mention half a century ago, people like Vernor Vinge and Ray Kurzweil have begun focusing in on the exponential growth of artificial intelligence, as a Moore’s Law type of advancement, until we develop superintelligent entities with decision-making abilities far beyond our ability to understand them.
Cloaked in this air of malleable mystery, Hollywood has taken license to cast the singularity as everything from the ultimate boogeyman to the penultimate savior of humanity.
Adding to these prophecies are a number of fascinating trend lines that give credence to these predictions. In addition to our ever-growing awareness of the world around us brought on by social media and escalating rates of digital innovations, human intelligence shows a continued rise, every decade, since IQ tests were first invented in the 1930s, a phenomenon known as the Flynn Effect.
We all know intuitively that something is happening. IBM’s Watson just beat the best of the best at their own game, Jeopardy. With computers beginning to generate their own algorithms, and more cameras adding eyes for the Internet to “see,” amazing things are beginning to happen.
Tech writer Robert Cringely predicts, “A decade from now computer vision will be seeing things we can’t even understand, like dogs sniffing cancer today.”
So what happens when we lose our ability to understand what comes next?
The Failure of Artificial Intelligence
I’ve never liked the term “artificial intelligence.”
Ever since it first became popular in the 1980s, where its goal was to reverse engineer the thinking of experts and reduce their methodologies to a set of rules that could be performed far more efficiently by computers.
As a burgeoning area of science, AI sucked up hundreds of millions of dollars from investors around the world, before being declared an abysmal failure.
But even with this auspicious beginning and prominent scientists attempting to drive a stake into its heart after every failure, AI is once again raising its ugly head, only this time bolstered by a far broader use of the term and riding the disruptive innovation bandwagon of big data.
Yes, I understand that machine intelligence can circumvent human fallibility issues, and perform calculations a zillion times faster. But it is the yet undefined quirkiness of human traits that give true intellect to human intelligence.
Since we live in a human-based world, ruled by human economics, machines are still subject to human limitations, foibles, and proclivities, at least for now.
The appeal of AI has not been in its ability to replace humans, but in its ability to supplement and bolster human capability.
100% all natural artificial intelligence
Human Intelligence Vs. Artificial Intelligence
A few years ago I was involved in a search engine-related startup where we were studying the connection between a search phrase and the resulting website that the person was looking for.
In analyzing the path that began with the typing-in of the search phrase, and watching the discernment process unfold, with inappropriate sites being discarded before a final destination was chosen, it became obvious that the search path was layered with huge amounts of valuable data that should be captured and dissected for later use.
The information fragments we were capturing were not merely data points along a line; we were capturing actual pieces of real human intelligence. Since real people were making the link between the search terms and the destination site, albeit a primitive association, it was indeed a useful form of human thinking.
Over time, a database with billions of human decisions like this could be developed into the principal engine for many future technologies.
On the Path to Super Human Intelligence
If we take an MRI image of our brain, we can compare it to other similar brain images. By comparing human attributes tied to one brain scan, to a similar list of traits and attributes from a second, we can begin to build a set of assumptions.
By pattern matching brains we can begin to see which brains have the propensity to excel at math, linguistics, gymnastics, or three-dimensional design.
As we move further up the food chain in computational power, speed, precision, and awareness, our pattern matching becomes exponentially more refined.
One example would be to uncover which people have some sort of deviant gene and are more likely to become criminals. But we’ll not only be able to uncover those with criminal propensities, but we’ll be able to zero in on those most likely to be repeat offenders.
Similarly, by comparing professional qualities, this type of pattern matching will be able to uncover those with plumbing skills, vs. those with mechanical engineering skills, vs. those with psychoanalytical skills.
Just as the “quantified self” is able to measure in precise ways the quality of all inputs and outputs of the human body, the “quantified brain” promises to accurately assess a persons thinking and reasoning ability though a myriad of micro brain analytical comparisons.
To be sure, this will not only be done with MRIs, but also countless forms of testing, imaging, and digitally dissecting the core reasoning and cognitive junctures of the greater human nervous system.
Out of a compendium filled with billions of scans, will come a trillion assumptions that can be refined and improved over time.
The Ultimate Music Player Example
Perhaps the best way to explain the capturing of real human intelligence involves the music player of the future. Since music is a very integral part of our lives, we can all relate to the power of listening to the right song at just the right time. But, for each of us, the “right song” is a very different song.
So let’s imagine a music player that only played the “right songs”. One great song followed by another great song, followed by another.
The ultimate music player will do just this. It will be able to assess our mood, our likes and dislikes, whether we’re doing something that requires us to be physically active or just sitting comfortably in a chair, it will read our response to the music. The ultimate music player will measure our heartbeat, brainwaves, biorhythms, stress levels, circadian rhythms, and a few other sensory inputs that haven’t been invented yet, and it will only serve up songs that our body has a positive reaction to.
This kind of technology will take our mind and mental clarity to a whole new level. It will energize us and at the same time, relax us. It will give us motivation, endurance, and determination. If done correctly, it will give us a reason to bound out of bed every morning to tackle a shining new day.
Macro Human Intelligence
Google’s largest computer data centers are built around thousands and thousands of flawed machines that individually fail time and again. With systems for circumventing failures when they occur, the overall machine, in its entirety is more than a little impressive.
People are very similar. We are all flawed individuals, mired in an ocean of personal chaos. But the same imperfections we see on the micro scale change dramatically when we transcend to look at humanity on a macro scale.
In much the same way that Google operates a massively complex machine by changing out individual units on the fly, we will eventually be able to create superhuman intelligence by connecting our own individually flawed brains with a massively coupled super brain.
Does a machine have a vested interest in improving the lot of other machines?
In the new television series “Almost Human,” set in the year 2048, Dorian, the humanoid robot referred to in the show as a “synthetic,” comes across another identical-looking synthetic that has not done well in his career, and he decides to come to the rescue.
Adding human-like emotion and sentiment to a machine makes for a good story line, but it’s far too early to know if it will ever becomes a valid issue for us to deal with.
What’s clear though is that we are just scratching the surface of knowing where all this is going. New forms of intelligence are continually being developed and we are baby-stepping our way to worlds unknown.
As I’ve said before, seeing into the future is like walking through a dark forest with a flashlight that illuminates but a short distance ahead. Each step forward gives us a new perspective, adding light to what was previously dark. The people of tomorrow will simply need a better flashlight.
In the end, we create the future, and then the future creates us!