The Singularity is a white person’s problem

terminator

Rich white folks worry about the Singularity, but AI is already making problems for the rest of us.

Kate Crawford, The New York Times:

According to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about “the singularity” — when machines become smarter than humans — have attracted millions of dollars and spawned a multitude of conferences.

But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many “intelligent” systems that shape how we are categorized and advertised to.

Software used to assess the risk of recidivism in criminals is biased against blacks, as is software used by police departments across the US to identify hotspots for crime. Amazon’s same-day delivery service was initially unavailable for ZIP codes in predominantly black neighborhoods, “remarkably similar to those affected by mortgage redlining in the mid-20th century.” And women are less likely than men to be shown ads on Google for highly paid jobs.

Thanks, Cory!

Brain? What is brain?

The empty brain – Robert Epstein, Aeon.

“Your brain doesn’t process information, retrieve knowledge, or store memories. In short: Your brain is not a computer,” Epstein says

The brain doesn’t store copies of music and songs the way computers do. Minds are fundamentally different from information processors, and 50 years of thinking of minds as kinds of digital computers is just plain wrong.

Because neither ‘memory banks’ nor ‘representations’ of stimuli exist in the brain, and because all that is required for us to function in the world is for the brain to change in an orderly way as a result of our experiences, there is no reason to believe that any two of us are changed the same way by the same experience. If you and I attend the same concert, the changes that occur in my brain when I listen to Beethoven’s 5th will almost certainly be completely different from the changes that occur in your brain. Those changes, whatever they are, are built on the unique neural structure that already exists, each structure having developed over a lifetime of unique experiences.

This is why, as Sir Frederic Bartlett demonstrated in his book Remembering(1932), no two people will repeat a story they have heard the same way and why, over time, their recitations of the story will diverge more and more. No ‘copy’ of the story is ever made; rather, each individual, upon hearing the story, changes to some extent – enough so that when asked about the story later (in some cases, days, months or even years after Bartlett first read them the story) – they can re-experience hearing the story to some extent, although not very well….

Back to biology

The future of machine intelligence might be biological systems [Caleb Scharf – Aeon]

Modeling a single human mind with current hardware – even if it could be done – would require the energy output of the Three Gorges Dam hydroelectric plant in China, the biggest in the world. And that’s just one person. To do the same for all 7.3 billion people would require the equivalent of 800 times the solar power hitting the top of Earth’s atmosphere.

By comparison, biological systems are staggeringly energy-efficient.

This suggests a solution to Fermi’s paradox. Intelligent aliens are out there, but like us, they’re biological, and find interstellar travel and communication overwhelming.

If life is common, and it regularly leads to intelligent forms, then we probably live in a universe of the future of past intelligences. The Universe is 13.8 billion years old and our galaxy is almost as ancient; stars and planets have been forming for most of the past 13 billion years. There is no compelling reason to think that the cosmos did nothing interesting in the 8 billion years or so before our solar system was born. Someday we might decide that the future of intelligence on Earth requires biology, not machine computation. Untold numbers of intelligences from billions of years ago might have already gone through that transition.

Those early intelligences could have long ago reached the point where they decided to transition back from machines to biology. If so, the Fermi Paradox returns: where are those aliens now? A simple answer is that they might be fenced in by the extreme difficulty of interstellar transit, especially for physical, biological beings. Perhaps the old minds are out there, but the cost of returning to biology was a return to isolation.

Those early minds might have once built mega-structures and deployed cosmic engineering across the stars. Maybe some of that stuff is still out there, and perhaps we’re on the cusp of detecting some of it with our ever-improving astronomical devices. The recent excitement over KIC 8462852, a star whose brightness varies in a way that cannot be readily explained by known natural mechanisms, is founded on the recognition that our instruments are now sensitive enough to investigate such possibilities. Perhaps alien civilisations have retreated to a cloistered biological existence, with relics of their mechanical-era constructions crumbling under the rigours of cosmic radiation, evaporation, and explosive stellar filth.

Our current existence could sit in a cosmically brief gap between that first generation of machine intelligence and the next one. Any machine intelligence or transcendence elsewhere in the galaxy might be short-lived as an interstellar force; the last one might already be spent, and the next one might not yet have surfaced. It might not have had time to come visiting while modern humans have been here. It might already be dreaming of becoming biological again, returning to an islanded state in the great wash of interstellar space. Our own technological future might look like this – turning away from machine fantasies, back to a quieter but more efficient, organic existence.

Why Google’s AlphaGo Go-playing computer is a big deal

Previous championship game-playing computers, like IBM’s Deep Blue, were brilliantly taught by human beings. AlphaGo taught itself.

In 1997, IBM’s Deep Blue system defeated the world chess champion, Garry Kasparov. At the time, the victory was widely described as a milestone in artificial intelligence. But Deep Blue’s technology turned out to be useful for chess and not much else. Computer science did not undergo a revolution.

Will AlphaGo, the Go-playing system that recently defeated one of the strongest Go players in history, be any different?

I believe the answer is yes, but not for the reasons you may have heard. Many articles proffer expert testimony that Go is harder than chess, making this victory more impressive. Or they say that we didn’t expect computers to win at Go for another 10 years, so this is a bigger breakthrough. Some articles offer the (correct!) observation that there are more potential positions in Go than in chess, but they don’t explain why this should cause more difficulty for computers than for humans.

In other words, these arguments don’t address the core question: Will the technical advances that led to AlphaGo’s success have broader implications? To answer this question, we must first understand the ways in which the advances that led to AlphaGo are qualitatively different and more important than those that led to Deep Blue.

Is AlphaGo Really Such a Big Deal [Michael Nielsen – Quanta Magazine]

Silicon University is looking to bring on the next step of human evolution

Co-founder Peter Diamandis predicts that within the next decade, self-driving cars will eliminate driving fatalities, artificial intelligence will surpass the skills of human doctors and remove inefficiencies from health care systems, AIs will invent new pharmaceuticals to cure previously fatal diseases and 3D print customized medicines based on the genetic analysis of individual patients, and cheapening production costs will make this care essentially free.

And that’s just the beginning for Silicon Valley’s Singularity University.

It’s common for tech industry rhetoric to invoke the ideal of a better world, but since its 2008 inception, Singularity University has articulated an astonishingly ambitious series of goals and projects that use technological progress for philanthropic ends. Medicine is just one of many domains that Diamandis wants to fundamentally change. He and others at Singularity are also working to develop and support initiatives that will provide universal access to high-quality education, restore and protect polluted environments, and transition the economy to entirely sustainable energy sources.

His audience was a group of 98 executives from 44 countries around the world; each had paid $14,000 to attend the weeklong program at Singularity University’s NASA Research Park campus in Mountain View, California. As Diamandis moved through the sectors of the economy that artificial intelligence would soon dominate—medicine, law, finance, academia, engineering—the crowd seemed strangely energized by the prospect of its imminent irrelevance. Singularity University was generating more than $1 million of revenue by telling its prosperous guests that they would soon be surpassed by machines.

But his vision of the future was nonetheless optimistic. Diamandis believes that solar energy will soon satisfy the demands of the entire planet and replace the market for fossil fuels. This will mean fewer wars and cleaner air. Systems for converting atmospheric humidity into clean drinking water will become cheap and ubiquitous. The industrial meat industry will also vanish, replaced by tastier and healthier laboratory-grown products with no environmental downsides. He also predicts that exponential increases in the power of AI would soon render teachers and universities superfluous. The best education in the world will become freely available to anyone.

I’ve previously laughed at this kind of thinking as crazy optimism, but I’m not laughing now. Sure, it’s Utopianism, and Utopia is unachievable, but we need more Utopian thinking. We’ve become small and petty and afraid. Only by Utopian thinking to we make a better world.

Like the saying goes, if you aim for the stars, even if you miss you can hit the moon.

So shine on you crazy Singulatarian diamonds.

Singularity University: The Harvard of Silicon Valley Is Planning for a Robot Apocalypse [Nick Romeo – The Daily Beast]

 

Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines

Recent advances in “deep learning,” such as Google’s AlphaGo computer beating a human Go champion repeatedly, are as important as splitting the atom more than 70 years ago, which launched a Cold War that perched the human race on the precipice of extinction for decades, says Scott Santens on Medium.

When machines can do all the jobs, universal basic income might be the only way to keep civilization going, Santens says.

Santens underestimates how fundamental a change that kind of machine intelligence would be. We can barely imagine what that future world would be like. How can we prepare for it?

[Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines / Scott Santens / Medium]

The Sadness and Beauty of Watching Google’s AI Play Go

Victory by an artificial intelligence playing the game Go might be the beginning of the Singularity.

Google's AlphaGo taught itself tricks that humans haven't been able to figure out in 2,500 years playing the game.

[The Sadness and Beauty of Watching Google's AI Play Go / Cade Metz / Wired]

John Robb says we're seeing the emergence of a new breed of AI. They're special purpose; not the humanlike (or godlike) AI of stories. But they'll soon be better than humans at nearly every job we do. Better doctors, better judges. Everything. With huge implications for war. [Game ON: The end of the old economic system is in sight / John Robb / Global Guerrillas]