Monthly Archives: February 2017

Artificial intelligence is an amazing technology thats changing the world in fantastic ways, but anybody who has ever seen the movie Terminator knows that there are some dangers associated with advanced A.I. Thats why Elon Musk, Stephen Hawking, and hundreds of other researchers, tech leaders, and scientists have endorsed a list of 23 guiding principles that should steer A.I. development in a productive, ethical, and safe direction.

The Asilomar A.I. Principles were developed after the Future of Life Institute brought dozens of experts together for their Beneficial A.I. 2017 conference. The experts, whose ranks consisted of roboticists, physicists, economists, philosophers, and more had fierce debates about A.I. safety, economic impact on human workers, and programming ethics, to name a few. In order to make the final list, 90 percent of the experts had to agree on its inclusion.

What remained was a list of 23 principles ranging from research strategies to data rights to future issues including potential super-intelligence, which was signed by those wishing to associate their name with the list, Future of Lifes website explains. This collection of principles is by no means comprehensive and its certainly open to differing interpretations, but it also highlights how the current default behavior around many relevant issues could violate principles that most participants agreed are important to uphold.

Since then, 892 A.I. or Robotics researchers and 1445 others experts, including Tesla CEO Elon Musk and famed physicist Stephen Hawking, have endorsed the principles.

Some of the principles like transparency and open research sharing among competitive companies seem less likely than others. Even if theyre not fully implemented, the 23 principles could go a long way towards improving A.I. development and ensuring that its ethical and preventing the rise of Skynet.

1. Research Goal: The goal of A.I. research should be to create not undirected intelligence, but beneficial intelligence.

2. Research Funding: Investments in A.I. should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:

3. Science-Policy Link: There should be constructive and healthy exchange between A.I. researchers and policy-makers.

4. Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of A.I.

5. Race Avoidance: Teams developing A.I. systems should actively cooperate to avoid corner-cutting on safety standards.

6. Safety: A.I. systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.

7. Failure Transparency: If an A.I. system causes harm, it should be possible to ascertain why.

8. Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.

9. Responsibility: Designers and builders of advanced A.I. systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.

10. Value Alignment: Highly autonomous A.I. systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.

11. Human Values: A.I. systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.

12. Personal Privacy: People should have the right to access, manage and control the data they generate, given A.I. systems power to analyze and utilize that data.

13. Liberty and Privacy: The application of A.I. to personal data must not unreasonably curtail peoples real or perceived liberty.

14 Shared Benefit: A.I. technologies should benefit and empower as many people as possible.

15. Shared Prosperity: The economic prosperity created by A.I.I should be shared broadly, to benefit all of humanity.

16. Human Control: Humans should choose how and whether to delegate decisions to A.I. systems, to accomplish human-chosen objectives.

17. Non-subversion: The power conferred by control of highly advanced A.I. systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.

18. A.I. Arms Race: An arms race in lethal autonomous weapons should be avoided.

19. Capability Caution: There being no consensus, we should avoid strong assumptions regarding upper limits on future A.I. capabilities.

20. Importance: Advanced A.I. could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources.

21. Risks: Risks posed by A.I. systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.

22. Recursive Self-Improvement: A.I. systems designed to recursively self-improve or self-replicate in a manner that could lead to rapidly increasing quality or quantity must be subject to strict safety and control measures.

23. Common Good: Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organization.

Photos via Getty Images

James Grebey is a writer, reporter, and fairly decent cartoonist living in Brooklyn. He's written for SPIN Magazine, BuzzFeed, MAD Magazine, and more. He thinks Double Stuf Oreos are bad and he's ready to die on this hill. James is the weeknights editor at Inverse because content doesn't sleep.

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Stephen Hawking and Elon Musk Endorse 23 Asilomar Principles ... - Inverse

A fascinating conference on artificial intelligence was recently hosted by the Future of Life Institute, an organization aimed at promoting optimistic visions of the future. The conference Superintelligence: Science or Fiction? includedsuch luminaries as Elon Musk of Tesla Motors and SpaceX, futurist Ray Kurzweil, Demis Hassabis of MITs DeepMind, neuroscientist and author Sam Harris, philosopher Nick Bostrom, philosopher and cognitive scientist David Chalmers, Skype co-founder Jaan Tallinn, as well as computer scientists Stuart Russell and Bart Selman. The discussion was led by MIT cosmologist Max Tegmark.

The group touched on a number of topics about the future benefits and risks of coming artificial superintelligence, with everyone generally agreeing that its only a matter of time before AI becomes ubiquitous in our lives. Eventually, AI will surpass human intelligence, with the risks and transformations that such a seismic event would entail.

Elon Musk has been a positive voice for AI, a stance not surprising for someone leading the charge to make automated cars our daily reality. He sees the AI future as inevitable, with dangers to be mitigated through government regulation, as much as he doesnt like the idea of them being a bit of a buzzkill.

He also brings up an interesting perspective that our fears of the technological changes the future will bring are largely irrelevant. According to Musk, we are already cyborgs by utilizing machine extensions of ourselves like phones and computers.

By far you have more power, more capability, than the President of the United States had 30 years ago. If you have an Internet link you have an article of wisdom, you can communicate to millions of people, you can communicate to the rest of Earth instantly. I mean, these are magical powers that didnt exist, not that long ago. So everyone is already superhuman, and a cyborg, says Musk [at 33:56].

He sees humans as information-processing machines that pale in comparison to the powers of a computer. What is necessary, according to Musk, is to create a greater integration between man and machine, specifically altering our brains with technology to make them more computer-like.

I think the two things that are needed for a future that we would look at and conclude is good, most likely, is, we have to solve that bandwidth constraint with a direct neural interface. I think a high bandwidth interface to the cortex, so that we can have a digital tertiary layer thats more fully symbiotic with the rest of us. Weve got the cortex and the limbic system, which seem to work together pretty well - theyve got good bandwidth, whereas the bandwidth to additional tertiary layer is weak, explained Musk [at 35:05]

Once we solve that issue, AI will spread everywhere. Its important to do so because, according to Musk, if only a smaller group would have such capabilities, they would become dictators with dominion over Earth.

What would a world filled with such cyborgs look like? Visions of Star Treks Borg come to mind.

Musk thinks it will be a society full of equals:

And if we do those things, then it will be tied to our consciousness, tied to our will, tied to the sum of individual human will, and everyone would have it so it would be sort of still a relatively even playing field, in fact, it would be probably more egalitarian than today, points out Musk [at 36:38].

The whole conference is immensely fascinating and worth watching in full. Check it out here:

Read the rest here:

Elon Musk's Surprising Reason Why Everyone Will Be Equal in the ... - Big Think

It's the stuff of many a sci-fi book or movie - could robots one day become smart enough to overthrow us? Well, a group of the world's most eminent artificial intelligence experts have worked together to try and make sure that doesn't happen.

They've put together a set of 23 principles to guide future research into AI, which have since been endorsed by hundreds more professionals, including Stephen Hawking and SpaceX CEO Elon Musk.

Called the Asilomar AI Principles (after the beach in California, where they were thought up), the guidelines cover research issues, ethics and values, and longer-term issues - everything from how scientists should work with governments to how lethal weapons should be handled.

On that point: "An arms race in lethal autonomous weapons should be avoided," says principle 18. You can read the full listbelow.

"We hope that these principles will provide material for vigorous discussion and also aspirational goals for how the power of AI can be used to improve everyone's lives in coming years," write the organisers of the Beneficial AI 2017 conference, where the principles were worked out.

For a principle to be included, at least 90 percent of the 100+ conference attendees had to agree to it. Experts at the event included academics, engineers, and representatives from tech companies, including Google co-founder Larry Page.

Perhaps the most telling guideline is principle 23, entitled 'Common Good': "Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organisation."

Other principles in the list suggest that any AI allowed to self-improve must be strictly monitored, and that developments in the tech should be "shared broadly" and "benefit all of humanity".

"To think AI merely automates human decisions is like thinking electricity is just a replacement for candles," conference attendee Patrick Lin, from California Polytechnic State University, told George Dvorsky at Gizmodo.

"Given the massive potential for disruption and harm, as well as benefits, it makes sense to establish some norms early on to help steer research in a good direction, as opposed to letting a market economy that's fixated mostly on efficiency and profit... shape AI."

Meanwhile the principles also call for scientists to work closely with governments and lawmakers to make sure our society keeps pace with the development of AI.

All of which sounds very good to us - let's just hope the robots are listening.

The guidelines also rely on a certain amount of consensus about specific terms - such as what's beneficial to humankind and what isn't - but for the experts behind the list it's a question of getting something recorded at this early stage of AI research.

With artificial intelligence systems now beating us at poker and getting smart enough to spot skin cancers, there's a definite need to have guidelines and limits in place that researchers can work to.

And then we also need to decide what rights super-smart robots have when they're living among us.

For now the guidelines should give us some helpful pointers for the future.

"No current AI system is going to 'go rogue' and be dangerous, and it's important that people know that," conference attendee Anthony Aguirre, from the University of California, Santa Cruz, told Gizmodo.

"At the same time, if we envision a time when AIs exist that are as capable or more so than the smartest humans, it would be utterly naive to believe that the world will not fundamentally change."

"So how seriously we take AI's opportunities and risks has to scale with how capable it is, and having clear assessments and forecasts - without the press, industry or research hype that often accompanies advances - would be a good starting point."

The principles have been published by the Future Of Life Institute.

You can see them in full and add your support over on their site.

Research issues

1. Research Goal:The goal of AI research should be to create not undirected intelligence, but beneficial intelligence.

2. Research Funding:Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:

3. Science-Policy Link:There should be constructive and healthy exchange between AI researchers and policy-makers.

4. Research Culture:A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI.

5. Race Avoidance:Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards.

Ethics and values

6. Safety:AI systems should be safe and secure throughout their operational lifetime, and verifiably so where applicable and feasible.

7. Failure Transparency:If an AI system causes harm, it should be possible to ascertain why.

8. Judicial Transparency:Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.

9. Responsibility:Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.

10. Value Alignment:Highly autonomous AI systems should be designed so that their goals and behaviours can be assured to align with human values throughout their operation.

11. Human Values:AI systems should be designed and operated so as to be compatible with ideals of human dignity, rights, freedoms, and cultural diversity.

12. Personal Privacy:People should have the right to access, manage and control the data they generate, given AI systems power to analyse and utilise that data.

13. Liberty and Privacy:The application of AI to personal data must not unreasonably curtail peoples real or perceived liberty.

14. Shared Benefit:AI technologies should benefit and empower as many people as possible.

15. Shared Prosperity:The economic prosperity created by AI should be shared broadly, to benefit all of humanity.

16. Human Control:Humans should choose how and whether to delegate decisions to AI systems, to accomplish human-chosen objectives.

17. Non-subversion:The power conferred by control of highly advanced AI systems should respect and improve, rather than subvert, the social and civic processes on which the health of society depends.

18. AI Arms Race:An arms race in lethal autonomous weapons should be avoided.

Longer term issues

19. Capability Caution:There being no consensus, we should avoid strong assumptions regarding upper limits on future AI capabilities.

20. Importance:Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources.

21. Risks:Risks posed by AI systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with their expected impact.

22. Recursive Self-Improvement:AI systems designed to recursively self-improve or self-replicate in a manner that could lead to rapidly increasing quality or quantity must be subject to strict safety and control measures.

23. Common Good:Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organisation.

Original post:

Experts have come up with 23 guidelines to avoid an AI apocalypse ... - ScienceAlert

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As Artificial Intelligence (AI) evolves by becoming smarter and more creative, will machines ever outthink us? How this question is answered may determine whether society will ultimately accept the further evolution of AI, or demand that it is stopped an outcome not dissimilar to the banning of human cloning. Many have argued about the inevitability of artificial superinteligence, by suggesting that once a machine becomes capable of self-learning and setting its own goals it will teleologically surpass the capability of any human brain. If this argument is true then we ought to be fearful of AI ever reaching that level, for who knows what will happen after that. Perhaps superintelligent AIs will decide the extermination of humans. But should we trust this argument for AI superiniteligence to be true?

George Zarkadakis

There is a fundamental philosophical assumption made by those who believe that artificial superintelligence is inevitable, and we need to examine it carefully. As I have argued in my book In Our Own Image: the history and future of Artificial Intelligence, to believe that a machine can be intelligent - in the same way that a human is intelligent - means that you take for granted that intelligence is something independent of the physicality of the brain. That bits and atoms are two different worlds; and that intelligence is all about bits and not about atoms. This is an idea that has roots in the philosophy of Plato. Plato believed that physical forms (e.g. a brain) are projections of non-physical ideals; and that those non-physical, immaterial, ideals are what constitute the ultimate truth. Applying this Platonic idea to Artificial Intelligence is what mainstream AI researchers do. They believe that it is possible to decode intelligence by studying the human brain; and then transfer the decoded pattern (the ideal, the bits, the master algorithm) to any other physicality they wish, for instance the hardware of a computational machine. For them the decoded pattern of intelligence can be discovered, given enough intellect and investment, just like mathematics is discovered according to Platonists because it always pre-exists. This worldview of intelligence is the foundation of the so-called computational theory of mind. If this theory is true, then it is indeed possible to create superintelligent machines, or rather superintelligent programs that can run on any general-purpose computational architecture.

The challenge to the computational theory of mind comes from an Aristotelian, or empirical, view of the world. In this worldview the form is the physical; there is no external, ideal, non-materialistic world. For Aristotelians the question whether mathematics is invented or discovered is answered emphatically as invented. Numbers do not pre-exist. It is the action of enumerating physical objects that requires the invention of numbers. If one takes the empiricist view then intelligence is a biological phenomenon, and not a mathematical phenomenon. It can be simulated in a computer but it cannot be replicated. To replicate intelligence in a computer would be similar to replicating say metabolism, or reproduction, which are also biological phenomena.

As I argued in my book, I am resolutely siding with the Aristotelians, however a minority they may be in the debate over the future, and nature, of Artificial Intelligence. I do so not because of some deep-seated materialistic conviction, but because it seems to me that when we speak about intelligence we often miss, or purposely ignore, how inseparable this concept is to consciousness. By intelligence I mean how competent an organism is in finding a novel solution to a new problem; and by consciousness its level of self-awareness or comprehension of its actions and internal states. But let me explain more why I am an Aristotelian when it comes to AI.

When I look at the natural world I see intelligence and consciousness as one, manifesting in varying degrees over the wide spectrum of life that begins with unicellular organisms and ends with more complex creatures such as dolphins, whales, octopuses, and primates. What I see in that spectrum is how awareness emerges out of biological automation. The level of awareness seems dependent on the number and sophistication of feedback loops inside a biological organism. Nature allows the evolution towards increasing levels of self-awareness because, for some species, higher levels of self-awareness provide significant survival advantages. We humans are not the only species with self-awareness, although we seem to be the species with the highest level of self-awareness. Perhaps the reason why we have this biological function so developed is because it is necessary in order to create civilizations, which in turn allow for more degrees of freedom for inventing strategies and technology for survival.

If my side of the argument is true, then it is impossible to decode biological intelligence in an artificial artefact. At best, one can only simulate some aspect of intelligence but never the whole thing. To have the whole thing, or something greater, you need biology and evolution. You need atoms and molecules. Maths and algorithms would never be enough. Nevertheless, as Turing demonstrated, you can have competence without comprehension. Intelligent machines will probably outthink us in nearly everything, except comprehension of what is that they are being competent of; for which consciousness is sine qua non.

Join me in debating Will Machines Ever Outthink Us? at Mathscon, at Imperial College, London on February 11, 2017.

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Will Machines Ever Outthink Us? - Huffington Post