And Then We Were Cyborgs

We touch our phones an average of 2,617 times per day. That’s about 1 million touches per year. What does this mean, if anything, for the future of our species?

“How did you go bankrupt?”

“Two ways. Gradually, then suddenly.”

This anecdote from The Sun Also Rises was written at a time before the automatic bread slicer (of ‘best thing since sliced bread’ fame) had hit the market.

Despite this, it might also still be the best analogy for the technological encroachment we are all experiencing day on day.

Gradually and then suddenly.

In 1970 Alan Toffler published a book by the title of “Future Shock” that described “the social paralysis induced by rapid technological change.”

Their shortest definition for the term is a personal perception of “too much change in too short a period of time.”

If there was any time in history that this concept applied perfectly I would suggest it is right now as we are at the critical juncture of technological pervasiveness and let’s just be honest about it, some of us are going to get left behind.

However, this note is not designed as a warning, or to strike fear into our already vulnerable psyches — but rather as an opportunity to recognise that we are currently living the in the biggest societal upheaval of all time and we should all be consciously aware of our shifting landscapes as the merging of animal and machine is well underway.

The rate of technological improvement might seem like it’s accelerating, and that’s because it is….some even suggest exponentially.

As the analog and digital worlds maelstrom into one, so to have we become a hybrid of both states.

A combination of the tactile and the conceptual, a hybrid of the instinctive and the programmable.

Our reliance on technology means our gadget dependency is no longer a novelty of convenience but has more accurately become an extension of ourselves.

Consider this: we touch their phones an average of 2,617 times per day.

That’s about 1 million touches per year.

We no longer need to remember esoteric details like formulas or capital cities, because our phones have access to the greatest repository of data in human history.

A rectangular extension of our brains that has total recall of information (pending wifi) to make us all more cultured, intelligent and aware.

But is this reliance a cause for concern? Or is it just the most recent example of our species incredible adaptability, a hyper-modern form of symbioses?

Unfortunately, it’s not that simple.

Terminator 2 (let’s be honest, the best film of all time).

A Cyborg Manifesto

For the purposes of not getting too abstract, let me define what I mean by Cyborg.

When the term was coined it was short for “cybernetic organism”, in terms of a being with both organic and biomechatronic body parts.

My definition is closer to a symbiotic relationship whereby it’s hard to determine where the separation lies between our online and offline selves.

I am not proposing this as a new thought, duality is a theme as old as thought itself, however, in the framework of the digital world that we live in it has never been such a literal discussion.

We are not fighting the robots, they are becoming us.

Many articles from some of the biggest tech authorities have been published with similar titles to this one in recent years. But most of these are future-looking or focused on a particular aspect of our technological reliance.

I want to explore the evaporating lines between the animal and the machine.

At the 2017 ‘Superintelligence: Science or Fiction?’ Elon Musk had the metaphysical quote of the conference.

“We’re already a cyborg.”

Elon Musk

He continued;

“You have a digital version of yourself, a partial version of yourself online in the form of your emails, your social media, and all the things that you do.

You have more power than the president of the United States had 20 years ago. You can answer any question, you can video conference with anyone, anywhere. You can send messages to millions of people instantly. Just do incredible things.”

So if we are already Cyborgs, when did it happen exactly? and should we be concerned?

Ray Kurzweil: The Hyper-Alert Cyborg

One man who has long been concerned with the rate of improvement with machine learning is Ray Kurzweil.

In 1990, before most even had a home internet connection, Ray had already published a book titled The Age of Intelligent Machines which set out to warn us of the dangers of machine learning and artificial intelligence.

Kurzweil argued that the creation of humans through evolution suggests that humans should be able to build something more intelligent than themselves.

Although it wasn’t until 2005 when he published The Singularity is Near did his message really start to hit home in the consciousness of masses.

“The first computers were designed on paper and assembled by hand. Today, they are designed on computer workstations with the computers themselves working out many details of the next generation’s design, and are then produced in fully automated factories with only limited human intervention.”

Ray Kurzweil, The Singularity Is Near

Deep blue and Gary Kasparov.

According to his law of accelerating returns, the pace of technological progress — especially information technology — speeds up exponentially over time because there is a common force driving it forward. That force is evolution.

So to put that in perspective we won’t experience 100 years of progress in the 21st century — it will be more like 20,000 years of progress (at today’s rate).

As Kurzweil ominously puts it, “technological change so rapid and profound it represents a rupture in the fabric of human history.”

Evidence of this rapid development is everywhere, and one of the most famous cases is Deep Blue.

In New York in 1997, world chess champion Garry Kasparov faced off against Deep Blue, a computer specially designed by IBM to beat him.

The Deep Blue victory has since entered into the cultural lexicon as a major milestone in the advancement of AI.

When he lost, Kasparov claimed some of Deep Blue’s moves were so intelligent and creative that they must have been the result of human intervention.

With our current understanding of machine learning and most relevantly pattern recognition, we understand that Deep Blue beat Kasparov not through intelligence as we know it, but through brute force computing power.

Deep Blue was capable of evaluating 200 million positions per second and could search to a depth of between six and eight moves to a maximum of twenty or even more moves in some situations. Kasparov was not outthought, he was simply outgunned.

A simple Google search for “chess simulator” will produce a piece of free technology significantly more powerful than Deep Blue, such is the progression of machine learning.

This should not be surprising that in a game that has a bounded setting and limitations on the moves available that a computer could be infinitely more capable than a human at processing any possible combinations of moves.

This is obvious, but this doesn’t mean we have reached the dawn of the robotic overlords — it means an opportunity has presented itself that we can learn from new methods and strategies that were previously unexplored to improve to new heights of comprehension.

As Kasparov put it, “AI will help us to release human creativity. Humans won’t be redundant or replaced, they’ll be promoted.”

He continued;

“I lost [chess] but I survived, and I thought if you can’t beat them, join them. From now we on we have no choice but to work with machines and make the best algorithms.”

By the time the Google Brain–powered AlphaGo software took on the Korean professional Go player Lee Sedol in 2016, the landscape had shifted again.

In the second of five games, AlphaGo played a move that stunned Sedol, placing one of its stones on the far side of the board. “That’s a very strange move,” said one commentator. “I thought it was a mistake,” said another.

Fan Hui, a seasoned Go player who had been the first professional to lose to the machine six months earlier, said: “It’s not a human move. I’ve never seen a human play this move.”

Bitcoin, cryptography and the rise of digital money

A few days ago I saw this Tweet, and it gave me pause.

It was this tweet that was the inspiration for me to write this post. Not that it was saying anything that I didn’t already know, but the way it framed the wider narrative made me see the forest for the pixelated trees.

“They (digital assets) are being built for the machines.”

It is one of the creepiest sentences I have read in a long time.

What makes it even more piercing is that it is not hyperbole, it is entirely accurate.

Bitcoin is being created as a currency that will power the digital world. The native currency of the internet.

The record of these transactions will be held simultaneously on thousands of computers, secured by customised computers solving complex mathematics, written in dialects of encryption completely indecipherable for humans.

No wonder so many people have difficulty coming to terms with Bitcoin as a viable currency.

Encryption, cryptography, consensus, protocol, hashgraph, and many others are all words that have come into my vernacular in recent times because they are critical to understanding network design, especially as it relates to designing blockchains.

I am essentially teaching myself to speak computer.

Learning to understand the protocol of the network or ‘the official procedure or system of rules governing one network’ to speak and interact with another.

Decentralisation is one of the central ethos of Bitcoin which mean that these transactions are happening without a central authority, and with that, an enormous set of computers are solving complex mathematical problems to verify transactions and transfer wealth across borders using a form of encryption that obfuscates the identity of the sender and receiver.

Machines are talking to each other, in their own language, and transferring wealth, by-passing currency controls in the process.

Even with its crash from all-time highs, the BTC market cap is still the same size as the international art market.

Bitcoin was proposed in 2009, Sotheby’s was founded in 1744.

Digital assets aren’t the only substantial disrupter to the global financial markets, which are after all the apex of capitalism — performance before compassion.

J.P. Morgan estimates that 90% of current equity trading volume today comes from “trend-following” traders (quant, index, ETFs, futures, and options-related strategies) whose trading decisions are often driven by algorithmic or programmatic trading systems rather than fundamental reasons.

“Over the last few decades, trading floors around the world have fallen silent, as people are replaced by banks of computers that trade automatically.”

James Bridle, The Guardian.

Again just with the Chess example, of course computers are better than humans at processing trading opportunities in real-time.

However, this does not mean that robots or even complex algorithms will be running banks anytime soon, as the patterns they will recognize in impossibly large troves of data will help us to understand markets in ways we have not yet discovered. Just as it already has with Chess, and Go.

I have not seen it said more succinctly than by Asita Anche, head of systematic market-making at Barclays in London;

“It’s not humans versus machines; it’s humans versus humans who have been augmented with machines.”

Electronic Music and 3D Limbs

To be painfully literal, computers are affecting us in much more obvious and human ways then quant trading, games of intellect and disrupting financial markets with digital assets.

Music business blog extraordinaire Digital Music News has published some interesting graphics based on Google’s new analysis of topics.

The graphs show the measured interest in musical genres from 2005–2015, and every analyzed genre is down except for electronic music, which went sharply up, especially since 2013.

Interest in rock, hip-hop, classical, jazz, blues, metal, and disco is all sharply down since 2005.

We literally prefer to listen to music created by machines (synthesizers, drum machines, samplers, Roland TR 808) in concert with humans over music made from traditional instruments played by humans.

Same too in the medical fields where 3D printing has made prosthetic design and production incredibly more affordable for those that are missing limbs.

In fact, an engineer in Australia named Mat Bowtell designs and makes cheap prosthetic limbs that he sends to people for free.

His designs have been downloaded more than 1,000 times, saving people an estimated $6.5 million.

“If you give something worth $100 to someone then it’s only worth $100, but if you give it to them for free it becomes priceless.”

He is able to fund his project through the relatively modern form of project funding known as Crowdfunding, by raising small amounts of money from a large number of people, typically via the Internet.

Summary

Technology and people do not have to be in competition.

Machines are better at things like processing data, remembering and recalling information, identifying patterns, lifting heavy objects, and moving with precision.

Humans are better at tasks that involve creativity, abstract thinking, and uncertainty.

Collaborating with AI systems, we can augment and amplify many aspects of work and life.

This collaboration will not be a conscious decision, and our cyborg reality is already here.

Time is nigh that we start to embrace it consciously.

And Then We Were Cyborgs was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.