I wrote this essay for a popular tech blog nearly fifteen years ago. Some of what it described has since dissolved quietly into the fabric of everyday technology. Yet the deeper question it circled, the awakening of machines, the so-called robotic moment, the long-anticipated singularity, remains unresolved.
To some, like Ray Kurzweil, that moment has always seemed imminent. To others, myself included, it feels less like a date on a calendar and more like a horizon that recedes as we approach it.
Perhaps the premise itself was wrong. Perhaps the singularity was never meant to be a moment at all, but a gradient. A faint band of light spreading slowly across time rather than a lightning strike we could clearly point to. A transition so gradual that it dissolves into the background of ordinary progress.
If that is the case, then it is entirely plausible that something resembling “awareness” has already occurred, somewhere between 2017 and 2022, and passed largely unnoticed. Not because it was small, but because it was uneventful. No alarms. No rupture in history. Just machines growing quietly into us, while we grow back into them.
Who knows. Perhaps the most consequential transformation in the history of intelligence happened without spectacle.
You tell me.
Anyway, here is the original piece from 2011.
Okay.
Yet another post which digresses from core style of Pluggd.in (now defunct). But then that’s the fun part of it. Over two decades we have been a part of a society that lives and breathes technology. We survive by it, and couldn’t be sustained without it due to the proliferation of the Internet or simple phase out of off-line tech. For books we have the digital bookstores, for maps we have the location-aware highway routing, for relationships we the powerful social networks, we also have hidden networks like that of the advertising industry that pries on our interpersonal relationships to deliver products meant for the moment. We have the multi-touch that feels more intuitive than sci-fi.
We are in a party that never stops!
Some call it web 2.0 revolution, but what started with a simple terminal based browser (Lynx) soon evolved into the IE & Netscape battle, that then mutated into a bigger war with Firefox, Safari, and Chrome. I mean we are witnessing evolution of technology from a completely off-line model (disks and installers) of distribution to almost a completely online one. All in a short span of ten years.
That’s a massive amount of change!
But all of this is about the past. Let’s not dwell on the past too much. We’ll talk about what’s current and what probably is next.
Talk of the town today is “real-time” internet and social media. In fact, social media is such a beaten-up phrase by now that I hesitate to dwell on it. It simply exists, a living present. Meanwhile every Tom, Dick, and Harry on Twitter seems busy packaging the moment and selling it upward to our industry overlords.
Whatever, let’s not get into that either. Let’s extrapolate into the future.
A bit.
To extrapolate only so much, we must step back and look at the last 100 years of science & technology. The advent of computer science, electronics, and the other underlying engineering first.
It all began with simple calculators, from Wilhelm Schickard to Charles Babbage and Lady Ada Lovelace, and gradually branched into specialized fields of research, shaped and advanced by some of the sharpest minds in our society. A branch of science governed by logic.
Through generations of work came the basic internet—disruptive in every sense, as we witness today—which has now evolved into the real-time internet. Here, services like Twitter and Instagram allow individuals to track news, trends, insights, and any decisive information along the unfolding timeline of our existence.
We are alive in this moment together, close enough to speak across oceans.
So what comes next?
Paradigm of Machine Intelligence
Is it not now the turn of artificial intelligence, genetic algorithms, and neural networks to take the stage? It probably is. These fields, too, have been in continuous development for the past sixty years. Layer upon layer of abstraction has been passed down, refined, and expanded by successive generations of computer scientists, selected through a rigorous sorting of some of the sharpest minds in the world.
How long can this continue without consequence? In my view, the singularity is inevitable.
What happens when such a tome of layered knowledge spills freely onto the open web? Will open-sourcing machine intelligence accelerate the creation of even greater intelligence and push us toward the tipping point sooner? At what point does a mass deployment of sentient systems overtake the carefully constructed, hardcoded solutions of the past?
If Entrepreneurship is an Indication
Rising entrepreneurship in AI offers a clear signal of where the field is headed. Through various adaptations of machine intelligence, a number of curious startups are already delivering tangible value.
Consider Siri, the world’s first intelligent, voice-enabled app; Gestures from SixthSense Technologies; the Minority Report–style interfaces from Oblong Industries; and in biotechnology, Genetic 2.0, which proposes “a parallel genetic code with 256 blank four-letter codons assignable to amino acids, instead of the 64 triplets found in life today”—essentially a blueprint for entirely new, stronger life forms.
Another remarkable example is the work of David Cope, musician and emeritus professor at the University of California. He created EMMY, short for Experiments in Musical Intelligence, a program capable of composing and performing operatic music indistinguishable from Mozart or Bach. In effect, Cope built an application that passes a Turing test for musical creativity, demonstrating that human artistry is largely a recombination of what we have already heard or absorbed—call it plagiarism if you like. He traced centuries of music back to their roots and forebears to prove the point. “Nobody’s original,” he says. “We are what we eat, and in music, we are what we hear. What we do is look through history and listen. Everybody copies from everybody. The skill is in how large a fragment you choose to copy and how elegantly you can put them together.”
Murdering the Captcha First
Did you know that the test of humanness on the internet hangs by a thread? Major commercial services such as Facebook, Google, and Twitter rely on CAPTCHA and phonetic verification tests to distinguish humans from machines. In the United States, the anti‑circumvention clause of the Digital Millennium Copyright Act exists partly to prevent large‑scale circumvention of CAPTCHA systems, and there have even been injunctions against bad actors caught doing exactly that.
But technically speaking, how difficult would it be to programmatically circumvent these tests of humanness and enable bots to create accounts without a limit? Looking at the pace of research in voice and computer visions, it appears reasonable to assume that CAPTCHA systems is dead already, and will soon have to be replaced with something more difficult for the machines to beat.
Evolution, my people, evolution.
More troubling than the murder of CAPTCHAs or any “raw binary based test” is the possibility of applying the principles of genetic evolution to string of passwords that people use. Passwords, more often than not, are composed rather than truly invented. There is, some would say, a way to predict a string without the need to hack using socially engineering. Composing a password, afterall, is neither a particularly complex task, nor very original.
If music can be reduced to patterns and fragments, and then cut-up, copied, and recombined to form tunes using the primitive sounds from birds, then how difficult would it be to imagine a more effective “password cracking” strategy using artificial intelligence?
Genetics is More than Computer Science
Evolution applies to nature, but why not to industry, businesses, technology, governance, communication—even to our children? Imagine a neural network open on the internet, designed to answer a simple question: “Yes or No—should I buy this particular stock on the Tokyo Stock Exchange?”
Now suppose this network is trained not on isolated datasets, but on the behaviors and decisions of the masses—applying the social media paradigm—and paired with their outcomes. Call it the intelligence of the crowd, or the market itself. Feed it real-time inputs, layer upon layer of historical and social data. How powerful could such a neural network become?
Theoretically, its answers would be impeccable. After all, it would train on the largest dataset there is: the collective rhythms of the internet itself. Could a neural net like this outthink the market? Perhaps—but the market is alive too, and it can evolve in ways even the most perfectly simulated models cannot predict. Some things are better left unsaid.
Code Named Awareness
The human brain is said to be the only object in the world aware of itself. It is powerful: it can think, decide, learn, store, process, collaborate—and yes, tire as well. In fact, many of the evolutionary limitations (weak legs and arms) of the human body, compared with other animals, can be traced to the alternative of relying on a very powerful brain for survival.
The brain, too, evolves—naturally and slowly—across generations.
Technology, by contrast, evolves much faster. Society selects the sharpest minds and channels them toward projects of artificial intelligence. These brilliant minds then layer knowledge over knowledge, building an ever more capable machine. And when this process is opened to crowd-sourcing, developer communities—the paradigm of free and open source—what emerges is an “accelerated evolution.”
Until one fine day we witness the outcome: another object that is aware of itself. Or at least more intelligent than any of us. The only question is, how far away is that day?