In 1996, Artificial Intelligence (AI) theorist Eliezer Yudkowsky wrote: “Our sole responsibility is to produce something smarter than we are; any problems beyond that are not ours to solve.”

Nearly 20 years later Yudkowsky seemed to have something to say about people at the other end of the intelligence spectrum. In what he coined Moore’s Law for Mad Science, Yudkowsky stated that, “Every 18 months, the minimum IQ necessary to destroy the world drops by one point.”

Just taking a moment to give credit where credit is due, this “Law” (more of a prediction or estimation) was no doubt suggested in tongue-in-cheek deference to Gordon Moore, co-founder of Intel Corporation. Moore predicted in 1965 that the number of transistors per silicon chip would double every year, an assertion known as Moore’s Law. That prediction was still on track up until 2019 when it finally came to an end.

But with Yudkowsky, was his “Moore’s Law” a reconsideration of the optimism and positivity implied in his earlier statement? Should we seriously consider placing limits on self-learning technologies as Elon Musk has warned? At what point do AI and system intelligence become a danger because human intelligence has not advanced enough to cope with it?

Another trend that ties into Yudkowsky’s Mad Science law is the fact that, over time, we’re putting more and more information, and therefore more power, into the hands of individuals. Should we try to place limits around the distribution of information that could prove dangerous?

Thanks to the Internet, all kinds of sensitive information is available, and not just to scientists.

With a little bit of digging, complex and dangerous information is not only available, it’s dumbed down for the rest of us, like me and you, as well as the anarchist who lives down the road, and the delusional schizophrenic working in their garage laboratory-workshop. After all, mad science information is for mad scientists, right?

So, yes, we need to be vigilant. We need to be aware that there are countless, remarkably detailed “how-to” articles available deep online about how to cause chaos or trigger widespread death.

But do we really have the ability to destroy the world? Releasing a more viral version of COVID-19 may be one approach. We’ve already seen some very entertaining television shows and movies have been built around foiling plots to destroy all or parts of the world.

What about nuclear weapons? We seem still to be in a kind of Cold War environment where wars between superpowers are fought regionally, often by proxies, using conventional weapons and armory. Outright utilization of nuclear mega-weapons by one of the superpowers would certainly trigger mutually assured destruction on a widespread or, yes, possibly global scale.

Our biggest concern, though, shouldn’t be that a rogue nation will choose to act, but that a rogue actor will be able to gain access to them, or reframe access to them through a sophisticated ransom scenario.

While the age of heavy military guns and hardware is ending, a new age of bio, cyber, and propaganda wars is just beginning. The concept of imminent risk and menacing danger is being reframed around non-intuitive, non-visible, and non-obvious threats related to the infrastructure and systems all around us.

Power grids, air traffic control systems, Internet misinformation and rumors, as well as medical labs all have technology-based points of entry.

As these systems become automated and smarter, as per Yudkowsky first statement, often weak links are discovered by hackers (granted, they’re smart people) and shared with others who don’t need to figure things out for themselves, leading to Yudkowsky second statement, by simply following the devious instructions they find online.

Yudkowsky is clearly someone that will make you think! That said, I would tie his two statements together into, “Our sole responsibility is to produce something smarter than we are, but let’s make sure the keys are locked away from those without a legitimate need to know.”

Otherwise yes, it will eventually become rather simple to destroy the world.

This is an AI generated random human face!

If you recently lost your job, I encourage you to blame this young lady. Contrary to first impressions, this isn’t a real person at all. She is nothing more than an AI generated random human face.

In the past we’ve run into fake products, fake identities, fake profiles, and fake news. But now we may end up with the biggest problem of them all – fake people.

On the surface, she’s a curiosity. She seems oddly familiar, the girl next door, the one that got away, or a friend of a friend. But she is indeed a fake person.

In fact, it would be easy to use AI to generate 100 similarly striking photos of men and women across the cultural, age, and race spectrum, and quickly A-B test to see which one works best for selling cars to older Asian couples in Sarasota, Florida. Or selling refrigerators to middle aged women in Des Moines, Iowa. Or selling skateboards to Hispanic teenagers in Pocatello, Idaho. Or selling starter homes in Pueblo, Colorado to working millennials.

Fake child photo.

When it comes to fake photos, we’re just getting started.

Why would I hire a supermodel if I can randomly generate someone new? Why would I buy a stock photo when I can auto-generate one?

Any scene, with or without people, the same AI that generates fake people can auto-generate fake houses, fake cities, fake situations, fake pets, fake disputes, fake arguments, fake injuries, and fake police responding to whatever fake stuff supposedly happened.

Not only can AI create realistic videos of people doing and saying things they’d never say or do in real life, it can add convincing human faces that never existed in the first place. We are about to become deluged with tons of fake testimonials hoping to persuade us to buy some new product or service.

In 2014 the first computer was created to use a form of machine learning called Generative Adversarial Networks or GAN. It worked like a morphing program that blended inputs from several sources, in this case human faces.

Artificial neural networks are computing systems inspired by the biological neural networks to mimic the activity of neurons in the human brain. In a GAN, two neural networks are essentially pitted against each other. One of the networks functions as a generative algorithm, while the other challenges the results of the first, thereby playing an adversarial role, hence the name.

To demonstrate this, thispersondoesnotexist.com is a website that auto-generates the face of a new fake person every time the page is refreshed.

As you can imagine, it won’t be long before this technology starts to permeate many other industries.

The porn industry is already rethinking its business models because every existing video can be stolen and re-mastered with new faces. These fake porn videos have taken on the moniker of “deepfakes” and are already showing up online.

Every court case, news story, historical document, and eyewitness account will suddenly need to be held to sophisticated levels of scrutiny. Our understanding of what constitutes “truth” is about to get rocked to its very core.

This is not a real photo, but a photo-realistic 3D rendering.

Photo-Realistic 3D Renderings

The same software that was used to help window manufacturers demonstrate what their products looked like in changing sunlight has evolved into a fascinating new field of photo-realistic 3D rendering.

What you see above is yet another step in the evolution of graphic imaging, allowing visionary thinkers to illustrate scenes and environments that don’t exist.

As we make further advancements in AR/VR and begin to immerse ourselves into fantasy game settings, some very talented people will be challenging our very notion of what constitutes “real.”

This type of technology is clearly setting the stage for a massive new industry designed around the limitless opportunities of “worlds not yet invented.” The gaming industry is already pushing the envelope in this area but is just scratching the surface of what’s truly possible.

Where are the “fake people” opportunities?

Technology itself is neither good nor bad, but naturally the way people use it can be detrimental to one group or another.

Whenever there is a problem, I‘m quick to look for the opportunity side of the equation. So what new businesses or micro-industries will this create?

As an example, how long will it be before someone will be able to create an entire feature-length action video without cameras, actors, or actresses? One person sitting at a computer could suddenly compete directly with Hollywood studios. Instead of spending $100-200 million on producing a big-budget movie, the hard costs for this kind of film could be close to zero.

In this scenario, the fine art of storytelling becomes radically faster, easier, and cheaper. Naturally this could lead to more businesses creating more movies, VR experiences, and next-gen performances for far less money.

The face of Nicolaus Copernicus reconstructed by a Polish forensic team.

Rewriting History

Born in 1473, Nicolaus Copernicus was one of the brightest minds among Renaissance-era mathematicians and astronomers. He formulated a model of the universe that placed the Sun rather than the Earth at the center of the universe. He died at the age of 70.

For the past 500 years, the image on the right was the only image we had to know what he looked like. Recently, a Polish forensic team spent time reconstructing this face from his skeletal remains.

It won’t be long before we have new ways of automating that process.

How long before we have the ability to create photos and videos of our parents, grandparents, and other ancestors? How long before we have video ads of people like Einstein, Lincoln, Gandhi, or Mandela selling soft drinks, home appliances, beer, of trying to convince us to vote for or against the latest ballot issue?

A number of AIs are now using deep learning techniques to produce human-sounding speech. Google’s DeepMind uses WaveNet, which can copy and reproduce human speech almost perfectly. Similarly, the startup Lyrebird has an algorithm that is able to synthesize a human’s voice by recording one minute of audio.

Last year when Google CEO Sundar Pichai first demonstrated their Duplex product, capable of making some very human sounding phone calls, complete with personal quirks and inflections, a number of people objected to the ethics of a fake human voice impersonating a real human.

Google subsequently promised they would add a warning or disclosure in calls originating from this technology, but they are also working to make additional improvements.

Adding to the growing pallet of tools for imitating humans are AI-infused robots that can vividly express human emotion like Hanson Robotics’ Sophia and SoftBank’s Pepper.

All of the above are images of fake people that never existed.

Final Thoughts

It’s official; we have passed the famed Turing Test in areas of voice, image, and video. They are now indistinguishable from the real thing.

Clearly, an age of smarter machines is already here, and as the ability for AI to perform tasks previously only human beings could do improves, the line between human and machine will continue to blur. Now, the only question is if it will eventually disappear altogether.

To answer my original question, yes, we do indeed have a fake people problem, and we have yet figured out what measures need to be taken to protect us from scam artists.

Our biggest danger, as always, is devious people. My guess is that a number of cons are already in play and we won’t hear about them until several people have lost their entire life savings.

Even more perplexing will be those who are operating in the grey areas of the law, using just enough fakeness to operate inside the layers of fuzziness and stay somewhat legal.

So how does this problem get solved, and who is ultimately responsible for managing the problems as they occur?

I’d love to hear your thoughts so feel free to weigh in if you have a story, an answer, or a solution.
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