Machine learning is a powerful and important tool that is likely to be of great value (and perhaps great risk) to man. Machines can be designed to change with time but it is man, and only man, who learns.
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Can we make approximations that are so close to ourselves that the fact that they are approximations no longer matters?
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Artificial intelligence often fails at crucial points. It must then be supplemented by human intelligence. Many software systems that look to their users like pure advanced artificial intelligence hide a lot of human effort behind a technological mask.
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A guy on a date is not checking his phone three times in ten minutes because the world outside the restaurant is changing that fast. He is in the grip of an addiction.
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Rapid, wide dissemination of ideas we don’t understand is the prime threat AI poses to humanity. It is an existential threat to human dignity and flourishing. And this threat is made graver, not less grave, by our democracy, which, as Plato understood, is the necessary soil of tyranny.
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Heather Zeiger tells us that traditional aerial combat pilots tend to think the same way when piloting drones from an office but it may be a different story when cell phone addicts, who tend to lack empathy, are recruited.
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A greater focus should be on restoring the foundations of our nation over building superweapons. And the key foundation is all human beings’ right to life.
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For threats like slaughterbots, the answer is the development of newer technology. Like it or not, history is replete with accounts of new military technology replacing old. Evil, seeking influence, demands a response, so the technology to provide one must be developed.
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Quip making the rounds: Would you trust a self-driving car from Google? Answer: Sure, if I needed a car that decided for me where I should go and then just drove me there.
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Dr. Dinerstein: In what is now a classic study, researchers used de-identified credit card data for 1.1 million people, in 10,000 stores over a three-month period. Using just four pieces of “outside” data they could identify 90% of the shoppers.
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Because Marx held that the value of goods resided in the labor required to produce them, if goods were produced by automatons, without human labor, the economy would fall apart and capitalism would fail.
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If we have simply taken the big software, hardware, and social media companies who dominate our lives for granted, the reactions from the business world to Life after Google: The Fall of Big Data and the Rise of the Blockchain Economy should give us a lot to think about.
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Atkins is arguing that the fact that we do not understand what consciousness is, far from being a barrier to creating artificial consciousness, offers the hope that, once we do create them, artificially conscious entities will understand consciousness but we won’t. The proposition sounds a bit confused, no?
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At one point on the trip from Earth to Jupiter, HAL becomes suspicious that the crew might be sabotaging the mission. HAL then purposely tries to kill all the crew. The most logical explanation for this act is a coding error. HAL was programmed to operate on the basis that the mission took priority over human life.
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Jay Richards, author of The Human Advantage: The Future of American Work in an Age of Smart Machines,sees it as more of a retooling than a meltdown. But retooling does mean change, work, cost, and risk.
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The social media companies will force us to define ourselves over against the specifications of our would-be programmers. And say what you want about the conflict, it will never be dull.
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Recently, we looked at philosopher of technology George Gilder’s Life after Google: The Fall of Big Data and the Rise of the Blockchain Economy. But what form would that decline take? A look at the advertising picture offers one clue.
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While I agree with Dennett that Occam’s Razor shouldn’t be used overzealously, we shouldn’t be too reluctant to use it either. The reason why Dennett rejects Occam’s Razor in the Game of Life is that if he didn’t, then nothing in the Game of Life would be capable of possessing cognitive states.
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AI (machine learning) was probably faster and cheaper but the whole point of the system was supposed to be justice which, whatever the explanation, proved too difficult to calculate…
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In 2017, a group of 26 AI researchers got together at Oxford and created a report which offers a number of examples of malicious technologies of the near future.
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