Moravec’s Paradox of Artificial Intelligence and a Possible Solution by Hiroshi Yamakawa with Interesting Ethical Implications

October 29, 2017

Have you heard of Moravec’s Paradox? This is a principle discovered by AI robotics expert Hans Moravec in the 1980s. He discovered that, contrary to traditional assumptions, high-level reasoning requires relatively little computation power, whereas low-level sensorimotor skills require enormous computational resources. The paradox is sometimes simplified by the phrase: Robots find the difficult things easy and the easy things difficult. Moravec’s Paradox explains why we can now create specialized AI, such as predictive coding software to help lawyers find evidence, or AI software that can beat the top human experts at complex games such as Chess, Jeopardy and Go, but we cannot create robots as smart as dogs, much less as smart as gifted two-year-olds like my granddaughter. Also see the possible economic, cultural implications of this paradox as described, for instance, by Robots will not lead to fewer jobs – but the hollowing out of the middle class (The Guardian, 8/20/17).

Hans Moravec is a legend in the world of AI. An immigrant from Austria, he is now serving as a research professor in the Robotics Institute of Carnegie Mellon University. His work includes attempts to develop a fully autonomous robot that is capable of navigating its environment without human intervention. Aside from his paradox discovery, he is well-known for a book he wrote in 1990, Mind Children: The Future of Robot and Human Intelligence. This book has become a classic, well-known and admired by most AI scientists. It is also fairly easy for non-experts to read and understand, which is a rarity in most fields.

Moravec is also a futurist with many of his publications and predictions focusing on transhumanism, including Robot: Mere Machine to Transcendent Mind (Oxford U. Press, 1998). In Robot he predicted that Machines will attain human levels of intelligence by the year 2040, and by 2050 will have far surpassed us. His prediction may still come true, especially if exponential acceleration of computational power following Moore’s Law continues. But for now, we still have a long was to go. The video below gives funny examples of this in a compilation of robots falling down during a DARPA competition.

But then just a few weeks after this blog was originally published, we are shown how far along robots have come. This November 16, 2017, video of the latest Boston Dynamics robot is a dramatic example of accelerating, exponential change.

Yamakawa on Moravec’s Paradox

A recent interview of Horoshi Yamakawa, a leading researcher in Japan working on Artificial General Intelligence (AGI), sheds light on the Moravec Paradox.  See the April 5, 2017 interview of Dr. Hiroshi Yamakawa, by a host of AI Experts, Eric Gastfriend, Jason Orlosky, Mamiko Matsumoto, Benjamin Peterson, and Kazue Evans. The interview is published by the Future of Life Institute where you will find the full transcript and more details about Yamakawa.

In his interview Horoshi explains the Moravec Paradox and the emerging best hope for its solution by deep learning.

The field of AI has traditionally progressed with symbolic logic as its center. It has been built with knowledge defined by developers and manifested as AI that has a particular ability. This looks like “adult” intelligence ability. From this, programming logic becomes possible, and the development of technologies like calculators has steadily increased. On the other hand, the way a child learns to recognize objects or move things during early development, which corresponds to “child” AI, is conversely very difficult to explain. Because of this, programming some child-like behaviors is very difficult, which has stalled progress. This is also called Moravec’s Paradox.

However, with the advent of deep learning, development of this kind of “child” AI has become possible by learning from large amounts of training data. Understanding the content of learning by deep learning networks has become an important technological hurdle today. Understanding our inability to explain exactly how “child” AI works is key to understanding why we have had to wait for the appearance of deep learning.

Horoshi Yamakawa calls his approach to deep learning the Whole Brain Architecture approach.

The whole brain architecture is an engineering-based research approach “To create a human-like artificial general intelligence (AGI) by learning from the architecture of the entire brain.”  … In short, the goal is brain-inspired AI, which is essentially AGI. Basically, this approach to building AGI is the integration of artificial neural networks and machine-learning modules while using the brain’s hard wiring as a reference. However, even though we are using the entire brain as a building reference, our goal is not to completely understand the intricacies of the brain. In this sense, we are not looking to perfectly emulate the structure of the brain but to continue development with it as a coarse reference.

Yamakawa sees at least two advantages to this approach.

The first is that since we are creating AI that resembles the human brain, we can develop AGI with an affinity for humans. Simply put, I think it will be easier to create an AI with the same behavior and sense of values as humans this way. Even if superintelligence exceeds human intelligence in the near future, it will be comparatively easy to communicate with AI designed to think like a human, and this will be useful as machines and humans continue to live and interact with each other. …

The second merit of this unique approach is that if we successfully control this whole brain architecture, our completed AGI will arise as an entity to be shared with all of humanity. In short, in conjunction with the development of neuroscience, we will increasingly be able to see the entire structure of the brain and build a corresponding software platform. Developers will then be able to collaboratively contribute to this platform. … Moreover, with collaborative development, it will likely be difficult for this to become “someone’s” thing or project. …

Act Now for AI Safety?

As part of the interview Yamakawa was asked whether he thinks it would be productive to start working on AI Safety now? As readers here know, one of the major points of the AI-Ethics.com organization I started is that we need to begin work know on such regulations. Fortunately, Yamakawa agrees. His promising Whole Brained Architecture approach to deep learning as a way to overcome Moravec’s Paradox thus will likley have a strong ethics component. Here is Horoshi Yamakawa full, very interesting answer to this question.

I do not think it is at all too early to act for safety, and I think we should progress forward quickly. Technological development is accelerating at a fast pace as predicted by Kurzweil. Though we may be in the midst of this exponential development, since the insight of humans is relatively linear, we may still not be close to the correct answer. In situations where humans are exposed to a number of fears or risks, something referred to as “normalcy bias” in psychology typically kicks in. People essentially think, “Since things have been OK up to now, they will probably continue to be OK.” Though this is often correct, in this case, we should subtract this bias.

If possible, we should have several methods to be able to calculate the existential risk brought about by AGI. First, we should take a look at the Fermi Paradox. This is a type of estimation process that proposes that we can estimate the time at which intelligent life will become extinct based on the fact that we have not yet met with alien life and on the probability that alien life exists. However, using this type of estimation would result in a rather gloomy conclusion, so it doesn’t really serve as a good guide as to what we should do. As I mentioned before, it probably makes sense for us to think of things from the perspective of increasing decision making bodies that have increasing power to bring about the destruction of humanity.

 


Six Sets of Draft Principles Are Now Listed at AI-Ethics.com

October 8, 2017

Arguably the most important information resource of AI-Ethics.com is the page with the collection of Draft Principles underway by other AI Ethics groups around the world. We added a new one that came to our attention this week from an ABA article, A ‘principled’ artificial intelligence could improve justice (ABA Legal Rebels, October 3, 2017). They listed six proposed principles from the talented Nicolas Economou, the CEO of electronic discovery search company, H5.

Although Nicolas Economou is an e-discovery search pioneer and past Sedona participant, I do not know him. I was, of course, familiar with H5’s work as one of the early TREC Legal Track pioneers, but I had no idea Economou was also involved with AI ethics. Interestingly, I recently learned that another legal search expert, Maura Grossman, whom I do know quite well, is also interested in AI ethics. She is even teaching a course on AI ethics at Waterloo. All three of us seem to have independently heard the Siren’s song.

With the addition of Economou’s draft Principles we now have six different sets of AI Ethics principles listed. Economou’s new list is added at the end of the page and reproduced below. It presents a decidedly e-discovery view with which all readers here are familiar.

Nicolas Economou, like many of us, is an alumni of The Sedona Conference. His sixth principle is based on what he calls thoughtful, inclusive dialogue with civil society. Sedona was the first legal group to try to incorporate the principles of dialogue into continuing legal education programs. That is what first attracted me to The Sedona Conference. AI-Ethics.com intends to incorporate dialogue principles in conferences that it will sponsor in the future. This is explained in the Mission Statement page of AI-Ethics.com.

The mission of AI-Ethics.com is threefold:

  1. Foster dialogue between the conflicting camps in the current AI ethics debate.
  2. Help articulate basic regulatory principles for government and industry groups.
  3. Inspire and educate everyone on the importance of artificial intelligence.

First Mission: Foster Dialogue Between Opposing Camps

The first, threshold mission of AI-Ethics.com is to go beyond argumentative debates, formal and informal, and move to dialogue between the competing camps. See eg. Bohm Dialogue, Martin Buber and The Sedona Conference. Then, once this conflict is resolved, we will all be in a much better position to attain the other two goals. We need experienced mediators, dialogue specialists and judges to help us with that first goal. Although we already have many lined up, we could always use more.

We hope to use skills in both dialogue and mediation to transcend the polarized bickering that now tends to dominate AI ethics discussions. See eg. AI Ethics Debate. We need to move from debate to dialogue, and we need to do so fast.

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Here is the new segment we added to the Draft Principles page.

6. Nicolas Economou

The latest attempt at articulating AI Ethics principles comes from Nicolas Economou, the CEO of electronic discovery search company, H5. Nicolas has a lot of experience with legal search using AI, as do several of us at AI-Ethics.com. In addition to his work with legal search and H5, Nicholas is involved in several AI ethics groups, including the AI Initiative of the Future Society at Harvard Kennedy School and the Law Committee of the IEEE’s Global Initiative for Ethical Considerations in AI.

Nicolas Economou has obviously been thinking about AI ethics for some time. He provides a solid scientific, legal perspective based on his many years of supporting lawyers and law firms with advanced legal search. Economou has developed six principles as reported in an ABA Legal Rebels article dated October 3, 2017, A ‘principled’ artificial intelligence could improve justice. (Some of the explanations have been edited out as indicated below. Readers are encouraged to consult the full article.) As you can see the explanations given here were written for consumption by lawyers and pertain to e-discovery. They show the application of the principles in legal search. See eg TARcourse.com. The principles have obvious applications in all aspects of society, not just the Law and predictive coding, so their value goes beyond the legal applications here mentioned.

Principle 1: AI should advance the well-being of humanity, its societies, and its natural environment. The pursuit of well-being may seem a self-evident aspiration, but it is a foundational principle of particular importance given the growing prevalence, power and risks of misuse of AI and hybrid intelligence systems. In rendering the central fact-finding mission of the legal process more effective and efficient, expertly designed and executed hybrid intelligence processes can reduce errors in the determination of guilt or innocence, accelerate the resolution of disputes, and provide access to justice to parties who would otherwise lack the financial wherewithal.

Principle 2: AI should be transparent. Transparency is the ability to trace cause and effect in the decision-making pathways of algorithms and, in hybrid intelligence systems, of their operators. In discovery, for example, this may extend to the choices made in the selection of data used to train predictive coding software, of the choice of experts retained to design and execute the automated review process, or of the quality-assurance protocols utilized to affirm accuracy. …

Principle 3: Manufacturers and operators of AI should be accountable. Accountability means the ability to assign responsibility for the effects caused by AI or its operators. Courts have the ability to take corrective action or to sanction parties that deliberately use AI in a way that defeats, or places at risk, the fact-finding mission it is supposed to serve.

Principle 4: AI’s effectiveness should be measurable in the real-world applications for which it is intended. Measurability means the ability for both expert users and the ordinary citizen to gauge concretely whether AI or hybrid intelligence systems are meeting their objectives. …

Principle 5: Operators of AI systems should have appropriate competencies. None of us will get hurt if Netflix’s algorithm recommends the wrong dramedy on a Saturday evening. But when our health, our rights, our lives or our liberty depend on hybrid intelligence, such systems should be designed, executed and measured by professionals with the requisite expertise. …

Principle 6: The norms of delegation of decisions to AI systems should be codified through thoughtful, inclusive dialogue with civil society. …  The societal dialogue relating to the use of AI in electronic discovery would benefit from being even more inclusive, with more forums seeking the active participation of political scientists, sociologists, philosophers and representative groups of ordinary citizens. Even so, the realm of electronic discovery sets a hopeful example of how an inclusive dialogue can lead to broad consensus in ensuring the beneficial use of AI systems in a vital societal function.

Nicolas Economou believes, as we do, that an interdisciplinary approach, which has been employed successfully in e-discovery, is also the way to go for AI ethics. Note his use of the word “dialogue” and mention in the article of The Sedona Conference, which pioneered the use of this technique in legal education. We also believe in the power of dialogue and have seen it in action in multiple fields. See eg. the work of physicist, David Bohm and philosopher, Martin Buber. That is one reason that we propose the use of dialogue in future conferences on AI ethics. See the AI-Ethics.com Mission Statement.

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New Draft Principles of AI Ethics Proposed by the Allen Institute for Artificial Intelligence and the Problem of Election Hijacking by Secret AIs Posing as Real People

September 17, 2017

One of the activities of AI-Ethics.com is to monitor and report on the work of all groups that are writing draft principles to govern the future legal regulation of Artificial Intelligence. Many have been proposed to date. Click here to go to the AI-Ethics Draft Principles page. If you know of a group that has articulated draft principles not reported on our page, please let me know. At this point all of the proposed principles are works in progress.

The latest draft principles come from Oren Etzioni, the CEO of the Allen Institute for Artificial Intelligence. This institute, called AI2, was founded by Paul G. Allen in 2014. The Mission of AI2 is to contribute to humanity through high-impact AI research and engineering. Paul Allen is the now billionaire who co-founded Microsoft with Bill Gates in 1975 instead of completing college. Paul and Bill have changed a lot since their early hacker days, but Paul is still  into computers and funding advanced research. Yes, that’s Paul and Bill below left in 1981. Believe it or not, Gates was 26 years old when the photo was taken. They recreated the photo in 2013 with the same computers. I wonder if today’s facial recognition AI could tell that these are the same people?

Oren Etzioni, who runs AI2, is also a professor of computer science. Oren is very practical minded (he is on the No-Fear side of the Superintelligent AI debate) and makes some good legal points in his proposed principles. Professor Etzioni also suggests three laws as a start to this work. He says he was inspired by Aismov, although his proposal bears no similarities to Aismov’s Laws. The AI-Ethics Draft Principles page begins with a discussion of Issac Aismov’s famous Three Laws of Robotics.

Below is the new material about the Allen Institute’s proposal that we added at the end of the AI-Ethics.com Draft Principles page.

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Oren Etzioni, a professor of Computer Science and CEO of the Allen Institute for Artificial Intelligence has created three draft principles of AI Ethics shown below. He first announced them in a New York Times Editorial, How to Regulate Artificial Intelligence (NYT, 9/1/17). See his TED Talk Artificial Intelligence will empower us, not exterminate us (TEDx Seattle; November 19, 2016). Etzioni says his proposed rules were inspired by Asimov’s three laws of robotics.

  1. An A.I. system must be subject to the full gamut of laws that apply to its human operator.
  2. An A.I. system must clearly disclose that it is not human.
  3. An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.

We would certainly like to hear more. As Oren said in the editorial, he introduces these three “as a starting point for discussion. … it is clear that A.I. is coming. Society needs to get ready.” That is exactly what we are saying too. AI Ethics Work Should Begin Now.

Oren’s editorial included a story to illustrate the second rule on duty to disclose. It involved a teacher at Georgia Tech named Jill Watson. She served as a teaching assistant in an online course on artificial intelligence. The engineering students were all supposedly fooled for the entire semester course into thinking that Watson was a human. She was not. She was an AI. It is kind of hard to believe that smart tech students wouldn’t know that a teacher named Watson, who no one had ever seen or heard of before, wasn’t a bot. After all, it was a course on AI.

This story was confirmed by a later reply to this editorial by the Ashok Goel, the Georgia Tech Professor who so fooled his students. Professor Goel, who supposedly is a real flesh and blood teacher, assures us that his engineering students were all very positive to have been tricked in this way. Ashok’s defensive Letter to Editor said:

Mr. Etzioni characterized our experiment as an effort to “fool” students. The point of the experiment was to determine whether an A.I. agent could be indistinguishable from human teaching assistants on a limited task in a constrained environment. (It was.)

When we did tell the students about Jill, their response was uniformly positive.

We were aware of the ethical issues and obtained approval of Georgia Tech’s Institutional Review Board, the office responsible for making sure that experiments with human subjects meet high ethical standards.

Etzioni’s proposed second rule states: An A.I. system must clearly disclose that it is not human. We suggest that the word “system” be deleted as not adding much and the rule be adopted immediately. It is urgently needed not just to protect student guinea pigs, but all humans, especially those using social media. Many humans are being fooled every day by bots posing as real people and creating fake news to manipulate real people. The democratic process is already under siege by dictators exploiting this regulation gap. Kupferschmidt, Social media ‘bots’ tried to influence the U.S. election. Germany may be next (Science, Sept. 13, 2017); Segarra, Facebook and Twitter Bots Are Starting to Influence Our Politics, a New Study Warns (Fortune, June 20, 2017); Wu, Please Prove You’re Not a Robot (NYT July 15, 2017); Samuel C. Woolley and Douglas R. Guilbeault, Computational Propaganda in the United States of America: Manufacturing Consensus Online (Oxford, UK: Project on Computational Propaganda).

In the concluding section to the 2017 scholarly paper Computational Propaganda by Woolley (shown here) and Guilbeault, The Rise of Bots: Implications for Politics, Policy, and Method, they state:

The results of our quantitative analysis confirm that bots reached positions of measurable influence during the 2016 US election. … Altogether, these results deepen our qualitative perspective on the political power bots can enact during major political processes of global significance. …
Most concerning is the fact that companies and campaigners continue to conveniently undersell the effects of bots. … Bots infiltrated the core of the political discussion over Twitter, where they were capable of disseminating propaganda at mass-scale. … Several independent analyses show that bots supported Trump much more than Clinton, enabling him to more effectively set the agenda. Our qualitative report provides strong reasons to believe that Twitter was critical for Trump’s success. Taken altogether, our mixed methods approach points to the possibility that bots were a key player in allowing social media activity to influence the election in Trump’s favour. Our qualitative analysis situates these results in their broader political context, where it is unknown exactly who is responsible for bot manipulation – Russian hackers, rogue campaigners, everyday citizens, or some complex conspiracy among these potential actors.
Despite growing evidence concerning bot manipulation, the Federal Election Commission in the US showed no signs of recognizing that bots existed during the election. There needs to be, as a minimum, a conversation about developing policy regulations for bots, especially since a major reason why bots are able to thrive is because of laissez-faire API access to websites like Twitter. …
The report exposes one of the possible reasons why we have not seen greater action taken towards bots on behalf of companies: it puts their bottom line at risk. Several company representatives fear that notifying users of bot threats will deter people from using their services, given the growing ubiquity of bot threats and the nuisance such alerts would cause. … We hope that the empirical evidence in this working paper – provided through both qualitative and quantitative investigation – can help to raise awareness and support the expanding body of evidence needed to begin managing political bots and the rising culture of computational propaganda.

This is a serious issue that requires immediate action, if not voluntarily by social media providers, such as Facebook and Twitter, then by law. We cannot afford to have another election hijacked by secret AIs posing as real people.

As Etzioni stated in his editorial:

My rule would ensure that people know when a bot is impersonating someone. We have already seen, for example, @DeepDrumpf — a bot that humorously impersonated Donald Trump on Twitter. A.I. systems don’t just produce fake tweets; they also produce fake news videos. Researchers at the University of Washington recently released a fake video of former President Barack Obama in which he convincingly appeared to be speaking words that had been grafted onto video of him talking about something entirely different.

See: Langston, Lip-syncing Obama: New tools turn audio clips into realistic video (UW News, July 11, 2017). Here is the University of Washington YouTube video demonstrating their dangerous new technology. Seeing is no longer believing. Fraud is a crime and must be enforced as such. If the government will not do so for some reason, then self- regulations and individual legal actions may be necessary.

In the long term Oren’s first point about the application of laws is probably the most important of his three proposed rules: An A.I. system must be subject to the full gamut of laws that apply to its human operator. As mostly lawyers around here at this point, we strongly agree with this legal point. We also agree with his recommendation in the NYT Editorial:

Our common law should be amended so that we can’t claim that our A.I. system did something that we couldn’t understand or anticipate. Simply put, “My A.I. did it” should not excuse illegal behavior.

We think liability law will develop accordingly. In fact, we think the common law already provides for such vicarious liability. No need to amend. Clarify would be a better word. We are not really terribly concerned about that. We are more concerned with technology governors and behavioral restrictions, although a liability stick will be very helpful. We have a team membership openings now for experienced products liability lawyers and regulators.


The Great Debate in AI Ethics Surfaces on Social Media: Elon Musk v. Mark Zuckerberg

August 6, 2017

I am a great admirer of both Mark Zuckerberg and Elon Musk. That is one reason why the social media debate last week between them concerning artificial intelligence, a subject also near and dear, caused such dissonance. How could they disagree on such an important subject? This blog will lay out the “great debate.”

It is far from a private argument between Elon and Mark.  It is a debate that percolates throughout scientific and technological communities concerned with AI. My sister AI-Ethics.com web begins with this debate. If you have not already visited this web, I hope you will do so after reading this blog. It begins by this same debate review. You will also see at AI-Ethics.com that I am seeking volunteers to help: (1) prepare a scholarly article on the AI Ethics Principles already created by other groups; and, (2) research the viability of sponsoring an interdisciplinary conference on AI Principles. For more background on these topics see the library of suggested videos found at AI-Ethics Videos. They provide interesting, easy to follow (for the most part), reliable information on artificial intelligence. This is something that everybody should know at least something about if they want to keep up with ever advancing technology. It is a key topic.

The Debate Centers on AI’s Potential for Superintelligence

The debate arises out of an underlying agreement that artificial intelligence has the potential to become smarter than we are, superintelligent. Most experts agree that super-evolved AI could become a great liberator of mankind that solves all problems, cures all diseases, extends life indefinitely and frees us from drudgery. Then out of that common ebullient hope arises a small group that also sees a potential dystopia. These utopia party-poopers fear that a super-evolved AI could doom us all to extinction, that is, unless we are not careful. So both sides of the future prediction scenarios agree that many good things are possible, but, one side insists that some very bad things are also possible, that the dark side risks even include extinction of the human species.

The doomsday scenarios are a concern to some of the smartest people alive today, including Stephen Hawking, Elon Musk and Bill Gates. They fear that superintelligent AIs could run amuck without appropriate safeguards. As stated, other very smart people strongly disagree with all doomsday fears, including Mark Zuckerberg.

Mark Zuckerberg’s company, Facebook, is a leading researcher in the field of general AI. In a backyard video that Zuckerberg made live on Facebook on July 24, 2017, with six million of his friends watching on, Mark responded to a question from one: “I watched a recent interview with Elon Musk and his largest fear for future was AI. What are your thoughts on AI and how it could affect the world?”

Zuckerberg responded by saying:

I have pretty strong opinions on this. I am optimistic. I think you can build things and the world gets better. But with AI especially, I am really optimistic. And I think people who are naysayers and try to drum up these doomsday scenarios — I just, I don’t understand it. It’s really negative and in some ways I actually think it is pretty irresponsible.

In the next five to 10 years, AI is going to deliver so many improvements in the quality of our lives.

Zuckerberg said AI is already helping diagnose diseases and that the AI in self-driving cars will be a dramatic improvement that saves many lives. Zuckerberg elaborated on his statement as to naysayers like Musk being irresponsible.

Whenever I hear people saying AI is going to hurt people in the future, I think yeah, you know, technology can generally always be used for good and bad, and you need to be careful about how you build it and you need to be careful about what you build and how it is going to be used.

But people who are arguing for slowing down the process of building AI, I just find that really questionable. I have a hard time wrapping my head around that.

Mark’s position is understandable when you consider his Hacker Way philosophy where Fast and Constant Improvements are fundamental ideas. He did, however, call Elon Musk “pretty irresponsible” for pushing AI regulations. That prompted a fast response from Elon the next day on Twitter. He responded to a question he received from one of his followers about Mark’s comment and said: I’ve talked to Mark about this. His understanding of the subject is limited. Elon Musk has been thinking and speaking up about this topic for many years. Elon also praises AI, but thinks that we need to be careful and consider regulations.

The Great AI Debate

In 2014 Elon Musk referred to developing general AI as summoning the demon. He is not alone in worrying about advanced AI. See eg. Open-AI.com and CSER.org. Steven Hawking, usually considered the greatest genius of our time, has also commented on the potential danger of AI on several occasions. In speech he gave in 2016 at Cambridge marking the opening of the Center for the Future of Intelligence, Hawking said: “In short, the rise of powerful AI will be either the best, or the worst thing, ever to happen to humanity. We do not yet know which.” Here is Hawking’s full five minute talk on video:

Elon Musk warned state governors on July 15, 2017 at the National Governors Association Conference about the dangers of unregulated Artificial Intelligence. Musk is very concerned about any advanced AI that does not have some kind of ethics programmed into its DNA. Musk said that “AI is a fundamental existential risk for human civilization, and I don’t think people fully appreciate that.” He went on to urge the governors to begin investigating AI regulation now: “AI is a rare case where we need to be proactive about regulation instead of reactive. Because I think by the time we are reactive in AI regulation, it’s too late.”

Bill Gates agrees. He said back in January 2015 that

I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don’t understand why some people are not concerned.

Elon Musk and Bill Gates spoke together on the Dangers of Artificial Intelligence at an event in China in 2015. Elon compared work on the AI to work on nuclear energy and said it was just as dangerous as nuclear weapons. He said the right emphasis should be on AI safety, that we should not be rushing into something that we don’t understand. Statements like that makes us wonder what Elon Musk knows that Mark Zuckerberg does not?

Bill Gates at the China event responded by agreeing with Musk. Bill also has some amusing, interesting statements about human wet-ware, our slow brain algorithms. He spoke of our unique human ability to take experience and turn it into knowledge. See: Examining the 12 Predictions Made in 2015 in “Information → Knowledge → Wisdom. Bill Gates thinks that as soon as machines gain this ability, they will almost immediately move beyond the human level of intelligence. They will read all the books and articles online, maybe also all social media and private mail. Bill has no patience for skeptics of the inherent danger of AI: How can they not see what a huge challenge this is?

Gates, Musk and Hawking are all concerned that a Super-AI using computer connections, including the Internet, could take actions of all kinds, both global and micro. Without proper standards and safeguards they could modify conditions and connections before we even knew what they were doing. We would not have time to react, nor the ability to react, unless certain basic protections are hardwired into the AI, both in silicon form and electronic algorithms. They all urge us to take action now, rather than wait and react.

To close out the argument for those who fear advanced AI and urge regulators to start thinking about how to restrain it now, consider the Ted Talk by Sam Harris on October 19, 2016, Can we build AI without losing control over it? Sam, a neuroscientist and writer, has some interesting ideas on this.

On the other side of the debate you will find most, but not all, mainstream AI researchers. You will also find many technology luminaries, such as Mark Zuckerberg and Ray Kurzweil. They think that the doomsday concerns are pretty irresponsible. Oren Etzioni, No, the Experts Don’t Think Superintelligent AI is a Threat to Humanity (MIT Technology Review, 9/20/16); Ben Sullivan, Elite Scientists Have Told the Pentagon That AI Won’t Threaten Humanity (Motherboard 1/19/17).

You also have famous AI scholars and researchers like Pedro Domingos who are skeptical of all superintelligence fears, even of AI ethics in general. Domingos stepped into the Zuckerberg v. Musk social media dispute by siding with Zuckerberg. He told Wired on July 17, 2017 that:

Many of us have tried to educate him (meaning Musk) and others like him about real vs. imaginary dangers of AI, but apparently none of it has made a dent.

Tom Simonite, Elon Musk’s Freak-Out Over Killer Robots Distracts from Our Real AI Problems, (Wired, 7/17/17).

Domingos also famously said in his book, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, a book which we recommend:

People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.

We can relate with that. On the question of AI ethics Professor Domingos said in a 2017 University of Washington faculty interview:

But Domingos says that when it comes to the ethics of artificial intelligence, it’s very simple. “Machines are not independent agents—a machine is an extension of its owner—therefore, whatever ethical rules of behavior I should follow as a human, the machine should do the same. If we keep this firmly in mind,” he says, “a lot of things become simplified and a lot of confusion goes away.” …

It’s only simple so far as the ethical spectrum remains incredibly complex, and, as Domingos will be first to admit, everybody doesn’t have the same ethics.

“One of the things that is starting to worry me today is that technologists like me are starting to think it’s their job to be programming ethics into computers, but I don’t think that’s our job, because there isn’t one ethics,” Domingos says. “My job isn’t to program my ethics into your computer; it’s to make it easy for you to program your ethics into your computer without being a programmer.”

We agree with that too. No one wants technologists alone to be deciding ethics for the world. This needs to be a group effort, involving all disciplines, all people. It requires full dialogue on social policy, ultimately leading to legal codifications.

The Wired article of Jul 17, 2017, also states Domingos thought it would be better not to focus on far-out superintelligence concerns, but instead:

America’s governmental chief executives would be better advised to consider the negative effects of today’s limited AI, such as how it is giving disproportionate market power to a few large tech companies.

The same Wired article states that Iyad Rahwan, who works on AI and society at MIT, doesn’t deny that Musk’s nightmare scenarios could eventually happen, but says attending to today’s AI challenges is the most pragmatic way to prepare. “By focusing on the short-term questions, we can scaffold a regulatory architecture that might help with the more unpredictable, super-intelligent AI scenarios.” We agree, but are also inclined to think we should at least try to do both at the same time. What if Musk, Gates and Hawking are right?

The Wired article also quotes, Ryan Callo, a Law Professor at the University of Washington, as saying in response to the Zuckerberg v Musk debate:

Artificial intelligence is something policy makers should pay attention to, but focusing on the existential threat is doubly distracting from it’s potential for good and the real-world problems it’s creating today and in the near term.

Simonite, Elon Musk’s Freak-Out Over Killer Robots Distracts from Our Real AI Problems, (Wired, 7/17/17).

But how far-out from the present is superintelligence? For a very pro-AI view, one this is not concerned with doomsday scenarios, consider the ideas of Ray Kurzweil, Google’s Director of Engineering. Kurzweil thinks that AI will attain human level intelligence by 2019, but will then mosey along and not attain super-intelligence, which he calls the Singularity, until 2045.

2029 is the consistent date I have predicted for when an AI will pass a valid Turing test and therefore achieve human levels of intelligence. I have set the date 2045 for the ‘Singularity’ which is when we will multiply our effective intelligence a billion fold by merging with the intelligence we have created.

Kurzweil is not worried about the impact of super-intelligent AI. To the contrary, he looks forward to the Singularity and urges us to get ready to merge with the super-AIs when this happens. He looks at AI super-intelligence as an opportunity for human augmentation and immortality. Here is a video interview in February 2017 where Kurzweil responds to fears by Hawking, Gates, and Musk about the rise of strong A.I.

Note Ray conceded the concerns are valid, but thinks they miss the point that AI will be us, not them, that humans will enhance themselves to super-intelligence level by integrating with AI – the Borg approach (our words, not his).

Getting back to the more mainstream defenses of super-intelligent AI, consider Oren Etzioni’s Ted Talk on this topic.

Oren Etzioni thinks AI has gotten a bad rap and is not an existential threat to the human race. As the video shows, however, even Etzioni is concerned about autonomous weapons and immediate economic impacts. He invited everyone to join him and advocate for the responsible use of AI.

Conclusion

The responsible use of AI is a common ground that we can all agree upon. We can build upon and explore that ground with others at many venues, including the new one I am trying to put together at AI-Ethics.com. Write me if you would like to be a part of that effort. Our first two projects are: (1) to research and prepare a scholarly paper of the many principles proposed for AI Ethics by other groups; and (2) put on a conference dedicated to dialogue on AI Ethics principles, not a debate. See AI-Ethics.com for more information on these two projects. Ultimately we hope to mediate model recommendations for consideration by other groups and regulatory bodies.

AI-Ethics.com is looking forward to working with non-lawyer technologists, scientists and others interested in AI ethics. We believe that success in this field depends on diversity. It has to be very interdisciplinary to succeed. Lawyers should be included in this work, but we should remain a minority. Diversity is key here. We will even allows AIs, but first they must pass a little test you may have heard of.  When it comes to something as important all this, all faces should be in the book, including all colors, races, sexes, nationalities, education, from all interested companies, institutions, foundations, governments, agencies, firms and teaching institutions around the globe. This is a human effort for a good AI future.

 

 


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