Yuval Noah Harari - Homo Deus: A Brief History of Tomorrow

His next title, Homo Deus: A Brief History of Tomorrow, is not out until September but early copies have begun to circulate. Its cover states simply: “What made us sapiens will make us gods”. It follows on from where Sapiens ends, in a provocative, and certainly speculative, gallop through the hopes and dreams that will shape the future of the species.

And the nightmares. Because even as the book has humans gaining godlike powers, that is only one eventuality Harari explores. It might all go pear-shaped, of course: we sapiens have a knack for hashing things up. Instead of morphing into omnipotent, all-knowing masters of the universe, the human mob might end up jobless and aimless, whiling away our days off our nuts on drugs, with VR headsets strapped to our faces. Welcome to the next revolution.

Harari calls it “the rise of the useless class” and ranks it as one of the most dire threats of the 21st century. In a nutshell, as artificial intelligence gets smarter, more humans are pushed out of the job market. No one knows what to study at college, because no one knows what skills learned at 20 will be relevant at 40. Before you know it, billions of people are useless, not through chance but by definition.

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None of this puts us in the realm of the gods. In fact, it leads Harari to even more bleak predictions. Though the people may no longer provide for the state, the state may still provide for them. “What might be far more difficult is to provide people with meaning, a reason to get up in the morning,” Harari says. For those who don’t cheer at the prospect of a post-work world, satisfaction will be a commodity to pay for: our moods and happiness controlled by drugs; our excitement and emotional attachments found not in the world outside, but in immersive VR.

All of which leads to the question: what should we do? “First of all, take it very seriously,” Harari says. “And make it a part of the political agenda, not only the scientific agenda. This is something that shouldn’t be left to scientists and private corporations. They know a lot about the technical stuff, the engineering, but they don’t necessarily have the vision and the legitimacy to decide the future course of humankind.”

- More Here

Quote of the Day

I know nothing about economics and—from evolutionary logic—could not have predicted a thing about the collapse of 2008, but I have disagreed for thirty years with an alleged science called economics that has resolutely failed to ground itself in underlying knowledge, at a cost to all of us.

- Robert Trivers, The Folly of Fools: The Logic of Deceit and Self-Deception in Human Life

Quora Q&A With Sebastian Thrun

What do you think about future job security of machine  learning engineers?

Everyone has to keep learning. I am 100% convinced that even the top people in machine learning won’t have a job 10 years from now if they don’t move on. There will be other, future hot topics in Silicon Valley. Never stand still, never be complacent.

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What skills would you focus on if you comback to your teen days?

Be fearless, be curious, and develop a growth mindset. For those who learn, there is no such thing as failure. It is the failure to learn that is the true failure in life.

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Quote of the Day

I think we can reasonably conclude that complex life will be rare in the universe – there is no innate tendency in natural selection to give rise to humans or any other form of complex life. It is far more likely to get stuck at the bacterial level of complexity. I can’t put a statistical probability on that. The existence of Parakaryon myojinensis might be encouraging for some – multiple origins of complexity on earth means that complex life might be more common elsewhere in the universe. Maybe. What I would argue with more certainty is that, for energetic reasons, the evolution of complex life requires an endosymbiosis between two prokaryotes, and that is a rare random event, disturbingly close to a freak accident, made all the more difficult by the ensuing intimate conflict between cells. After that, we are back to standard natural selection.

- Nick Lane, The Vital Question: Why is life the way it is?

Israeli Start-Up Faception Takes Image Analysis To The Next Level

“We understand the human much better than other humans understand each other,” said Faception chief executive Shai Gilboa. “Our personality is determined by our DNA and reflected in our face. It’s a kind of signal.”

Faception has built 15 different classifiers, which Gilboa said evaluate with 80 percent accuracy certain traits. The start-up is pushing forward, seeing tremendous power in a machine’s ability to analyze images.

Yet experts caution there are ethical questions and profound limits to the effectiveness of technology such as this.

“Can I predict that you’re an ax murderer by looking at your face and therefore should I arrest you?” said Pedro Domingos, a professor of computer science at the University of Washington and author of “The Master Algorithm.” “You can see how this would be controversial.”[---]

Faception recently showed off its technology at a poker tournament organized by a start-up that shares investors with Faception. Gilboa said that Faception predicted before the tournament that four players out of the 50 amateurs would be the best. When the dust settled two of those four were among the event’s three finalists. To make its prediction Faception analyzed photos of the 50 players against a Faception database of professional poker players.

There are challenges in trying to use artificial intelligence systems to draw conclusions such as this. A computer that is trained to analyze images will only be as good as the examples it is trained on. If the computer is exposed to a narrow or outdated sample of data, its conclusions will be skewed. Additionally, there’s the risk the system will make an accurate prediction, but not necessarily for the right reasons.

- More Here

Quote of the Day

All photographs are memento mori. To take a photograph is to participate in another person’s (or thing’s) mortality, vulnerability, mutability. Precisely by slicing out this moment and freezing it, all photographs testify to time’s relentless melt.

- Susan Sontag

Wisdom Of The Week

This week, I happened to read two dichotomous news on machine learning.

1. Data Science leveraged to stop human trafficking:  

The “Science Against Slavery” Hackathon, was an all-day Hackathon aimed sharing ideas and creating science-based solutions to the problem of human trafficking. Data scientists, students and hackers honed in on data that district attorneys would otherwise never find. Many focused on automating processes so agencies could use the technology with little guidance. Some focused primarily on generating data that could lead to a conviction—which is much easier said than done. One effort from EPIK Project founder Tom Perez included creating fake listings. They could then gather information on respondents, including real world coordinates. Other plans compared photos mined from escort ads and sites to those from missing person reports. Web crawling could eventually lead to geocoding phone numbers or understanding the distribution of buyers and sellers, as well as social network analysis.

2. Machine learning algorithm used to predict future criminals is biased against blacks:

We obtained the risk scores assigned to more than 7,000 people arrested in Broward County, Florida, in 2013 and 2014 and checked to see how many were charged with new crimes over the next two years, the same benchmark used by the creators of the algorithm.
The score proved remarkably unreliable in forecasting violent crime: Only 20 percent of the people predicted to commit violent crimes actually went on to do so.
When a full range of crimes were taken into account — including misdemeanors such as driving with an expired license — the algorithm was somewhat more accurate than a coin flip. Of those deemed likely to re-offend, 61 percent were arrested for any subsequent crimes within two years.
We also turned up significant racial disparities, just as Holder feared. In forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways.

• The formula was particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants.
• White defendants were mislabeled as low risk more often than black defendants.

Could this disparity be explained by defendants’ prior crimes or the type of crimes they were arrested for? No. We ran a statistical test that isolated the effect of race from criminal history and recidivism, as well as from defendants’ age and gender. Black defendants were still 77 percent more likely to be pegged as at higher risk of committing a future violent crime and 45 percent more likely to be predicted to commit a future crime of any kind.

Quote of the Day

When nations grow old the Arts grow cold
And commerce settles on every tree.

- William Blake

Quote of the Day

For many years a tree might wage a slow and silent warfare against an encumbering wall, without making any visible progress. One day the wall would topple--not because the tree had suddenly laid hold upon some supernormal energy, but because its patient work of self-defense and self release had reached fulfillment. The long-imprisoned tree had freed itself. Nature had had her way.

- Lloyd C. Douglas, The Robe

Quote of the Day

The truth comes as conqueror only because we have lost the art of receiving it as guest.

- Rabindranath Tagore in The Fourfold Way of India (1924)