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.

 

 


E-DISCOVERY IS OVER: The big problems of e-discovery have now all been solved. Crises Averted. The Law now has bigger fish to fry.

July 30, 2017

Congratulations!

We did it. We survived the technology tsunami. The time of great danger to Law and Justice from  e-Discovery challenges is now over. Whew! A toast of congratulations to one and all.

From here on it is just a matter of tweaking the principles and procedures that we have already created, plus never-ending education, a good thing, and politics, not good, but inevitable. The team approach of lawyers and engineers (vendors) working together has been proven effective, so have the new Rules and case law, and so too have the latest methods of legal search and document review.

I realize that many will be tempted to compare my view to that of a famous physicist in 1894 who declared:

There is nothing new to be discovered in physics now. All that remains is more and more precise measurement.

Lord Kelvin (1824-1907)

Then along came Einstein. Many attribute this humorously mistaken assertion to Lord Kelvin aka William Thomson, 1st Baron Kelvin. According to Quora, scholarship shows that it was probably said by the American physicist, Albert Michelson, behind the famous Michelson–Morley experiment on the speed of light.

Still, even mindful of the dangers of boasting, I still think that most of the really tough problems in electronic discovery have now been solved.

The time of great unknowns in e-discovery are past. The rules, principles, case law, procedures, software, methods, quality controls vendor services are now well-developed. All that remains is more and more precise measurement.

The Wild West days are way gone. Certainly new problems will arise and experiments will continue, but they will not be on the same level or intensity as before. They will be minor problems. They will likely be very similar to issues we have already addressed, just with exponential magnification or new twist and turns typical of the common law.

This is a tremendous accomplishment. The crises we all saw coming around the corner at the turn of the century has been averted. Remember how the entire legal profession was abuzz in emergency mode in 2005 because of the greats dangers and burdens of e-discovery?  Yes, thanks to the hard work and creativity of many people, the big problems have now been solved, especially the biggest problem of them all, finding the needles of relevance in cosmic-sized haystacks of irrelevant noise. TARcourse.com. We now know what is required to do e-discovery correctly. EDBP.com. We have the software and attorney methods needed to find the relevant evidence we need, no matter what the volume of information we are dealing with.

We have invented, implemented and perfected procedures than can be enhanced and altered as needed to accommodate the ever growing complexity and exponential growth. We expect that. There is no data too big to handle. If fact, the more data we have, the better our active machine learning systems get, like, for instance, predictive coding. What an incredible difference from the world we faced in e-discovery just five years ago.

This success was a team effort by thousands of people around the world, including a small core group who devoted their professional lives to solving these problems. My readers have been a part of this and you can pat yourself on the back too. The paradigm shift has been made. Maybe it was the Sedona vortexes?

Now that the tough parts of e-discovery are over, the rest of the ride is downhill. Some of my readers have already moved on. I will not retire, not just yet. I will keep up the work of e-discovery, even as I watch it transition to just teaching and politics. These activities have there own unique challenges too, even if they are not really all that impact-full in the big scheme of things. Plus, I find politics disgusting. You will see tons of dirty pool in our field soon. I cannot talk about it now. We have some renegades with authority who never solved an e-discovery problem in their life. Posers with power.

But what is that new turbulence I hear in the distance? It is a bizarre new sound with vibrations never experienced before. It lies far outside of well trodden paths and sounds both discordant and harmonious, sirens-like at the same time. It lies on the outer, cutting edges of law, science and technology. It sounds like a new, more profound Technology and Law challenge has emerged. It is the splashing of bigger fish to fry. I am hearing the eerie smarts sounds of AI. A music of both exuberance and fear, utopia or extinction.

The Biggest Challenge Today is the Ethics of Artificial Intelligence.

Following my own advice of the Hacker Way approach I have given this considerable thought lately. I have found an area that has far more serious challenges and dangers than e-discovery – the challenges of AI Ethics.

I think that my past hacks, my past experiences with law and technology, have prepared me to step-up to this last, really big hack, the creation of a code of ethics for AI. A code that will save humanity from a litany of possible ills arising out of AI’s inevitable leap to super-intelligence.  I have come to see that my work in the new area of AI Ethics could have a far greater impact than my current work with active machine learning and the discovery of evidence in legal proceedings. AI Ethics is the biggest problem that I see right now where I have some hand-on skills to contribute. AI Ethics is concerned with artificial intelligence, both special and general, and the need for ethical guidelines, including best practices, principles, laws and regulations.

This new direction has led to my latest hack, AI-Ethics.com. Here you will find 3,866 words, many of them quotes; 19 graphics, including a photo of Richard Braman; and 9 videos with several hours worth of content. You will find quotes and videos on AI Ethics from the top minds in the world, including:

  • Steven Hawking
  • Elon Musk
  • Bill Gates
  • Ray Kurzweil
  • Mark Zuckerberg
  • Sam Harris
  • Nick Bostrom
  • Oren Etzioni
  • 2017 Asilomar conference
  • Sam Altman
  • Susumu Hirano
  • Wendell Wallach

Please come visit at AI-Ethics.com. The next big thing. Lawyers are needed, as the web explains. I look forward to any recommendations you may have.

I have done the basic research for AI Ethics, at least the beginning big-picture research of the subject. The AI-Ethics.com website shares the information that had biggest impact for me personally. The web I hacked together also provides numerous links to resources where you can continue and customize your study.

I have been continuously improving the content since this started just over a week ago. This will continue as my study continues.

As you will see, a proposal has already emerged to have an International Conference in Florida on AI Ethics as early as 2018. We would assemble some of the top experts and concerned citizens from all walks of life. I hope especially to get Elon Musk to attend and will time the event to correspond with one of SpaceX’es many launches here. My vision for the conference is to facilitate dialogue with high-tech variations appropriate for the AI environment.

The Singularity of superintelligent AIs may come soon. We may live long enough to see it. When it does, we want a positive future to emerge, not a dystopia. Taking action now on AI ethics can help a positive future come to pass.

Here is one of many great videos on the subject of AI in general. This technology is really interesting. Kevin Kelly, the co-founder of Wired, does a good job of laying out some of its characteristics. Kelly takes an old-school approach and does not speak about superintelligence in an exponential sense.

 


What Chaos Theory Tell Us About e-Discovery and the Projected ‘Information → Knowledge → Wisdom’ Transition

May 20, 2016
Ralph and Gleick

Gleick & Losey meeting sometime in the future

This article assumes a general, non-technical familiarity with the scientific theory of Chaos. See James Gleick’s book, Chaos: making a new science (1987). This field of study is not usually discussed in the context of “The Law,” although there is a small body of literature outside of e-discovery. See: Chen, Jim, Complexity Theory in Legal Scholarship (Jurisdymanics 2006).

The article begins with a brief, personal recapitulation of the basic scientific theories of Chaos. I buttress my own synopsis with several good instructional videos. My explanation of the Mandelbrot Set and Complex numbers is a little long, I know, but you can skip over that and still understand all of the legal aspects. In this article I also explore the application of the Chaos theories to two areas of my current work:

  1. The search for needles of relevant evidence in large, chaotic, electronic storage systems, such as email servers and email archives, in order to find the truth, the whole truth, and nothing but the truth needed to resolve competing claims of what happened – the facts – in the context of civil and criminal law suits and investigations.
  2. The articulation of a coherent social theory that makes sense of modern technological life, a theory that I summarize with the words/symbols: Information → Knowledge → Wisdom. See Information → Knowledge → Wisdom: Progression of Society in the Age of Computers and the more recent, How The 12 Predictions Are Doing That We Made In “Information → Knowledge → Wisdom.”

Introduction to the Science of Chaos

Gleick’s book on Chaos provides a good introduction to the science of chaos and, even though written in 1987, is still a must read. For those who have read this long ago, like me, here is a good, short, 3:53, refresher video James Gleick on Chaos: Making a New Science (Open Road Media, 2011) below:

mandelbrot_youngA key leader in the Chaos Theory field is the late great French mathematician, Benoit Mandelbrot (1924-2010) (shown right). Benoit, a math genius who never learned the alphabet, spent most of his adult life employed by IBM. He discovered and named the natural phenomena of fractals. He discovered that there is a hidden order to any complex, seemingly chaotic system, including economics and the price of cotton. He also learned that this order was not causal and could not be predicted. He arrived at these insights by study of geometry, specifically the rough geometric shapes found everywhere in nature and mathematics, which he called fractals. The penultimate fractal he discovered now bears his name, The Mandelbrot Fractalshown in the computer photo below, and explained further in the video that follows.

Mandelbrot set

Look here for thousands of additional videos of fractals with zoom magnifications. You will see the recursive nature of self-similarity over varying scales of magnitude. The patterns repeat with slight variations. The complex patterns at the rough edges continue infinitely without repetition, much like Pi. They show the unpredictable element and the importance of initial conditions played out over time. The scale of the in-between dimensions can be measured. Metadata remains important in all investigations, legal or otherwise.

mandelbrot_equation

The Mandelbrot is based on a simple mathematical formula involving feedback and Complex Numbers: z ⇔ z2 + c. The ‘c’ in the formula stands for any Complex Number. Unlike all other numbers, such as the natural numbers one through nine – 1.2.3.4.5.6.7.8.9, the Complex Numbers do not exist on a horizontal number line. They exist only on an x-y coordinate time plane where regular numbers on the horizontal grid combine with so-called Imaginary Numbers on the vertical grid. A complex number is shown as c= a + bi, where a and b are real numbers and i is the imaginary number. Complex_number_illustration

A complex number can be visually represented as a pair of numbers (a, b) forming a vector on a diagram called an Argand diagram, representing the complex plane. “Re” is the real axis, “Im” is the imaginary axis, and i is the imaginary number. And that is all there is too it. Mandelbrot calls the formula embarrassingly simple. That is the Occam’s razor beauty of it.

To understand the full dynamics of all of this remember what Imaginary Numbers are. They are a special class of numbers where a negative times a negative creates a negative, not a positive, like is the rule with all other numbers. In other words, with imaginary numbers -2 times -2 = -4, not +4. Imaginary numbers are formally defined as i2 = −1.

Thus, the formula z ⇔ z2 + c, can be restated as z ⇔ z2 + (a + bi).

The Complex Numbers when iterated according to this simple formula – subject to constant feedback – produce the Mandelbrot set.

mandelbrot

Mandelbrot_formulaThe value for z in the iteration always starts with zero. The ⇔ symbol stands for iteration, meaning the formula is repeated in a feedback loop. The end result of the last calculation becomes the beginning constant of the next: z² + c becomes the z in the next repetition. Z begins with zero and starts with different values for c. When you repeat the simple multiplication and addition formula millions of times, and plot it on a Cartesian grid, the Mandelbrot shape is revealed.

When iteration of a squaring process is applied to non-complex numbers the results are always known and predictable. For instance when any non-complex number greater than one is repeatedly squared, it quickly approaches infinity: 1.1 * 1.1 = 1.21 * 1.21 = 1.4641 * 1.4641 = 2.14358 and after ten iterations the number created is 2.43… * 10 which written out is 2,430,000,000,000,000,000,000,000,000,000,000,000,000,000. A number so large as to dwarf even the national debt. Mathematicians say of this size number that it is approaching infinity.

The same is true for any non-complex number which is less than one, but in reverse; it quickly goes to the infinitely small, the zero. For example with .9: .9.9=.81; .81.81=.6561; .6561.6561=.43046 and after only ten iterations it becomes 1.39…10 which written out is .0000000000000000000000000000000000000000000000139…, a very small number indeed.

With non-complex numbers, such as real, rational or natural numbers, the squaring iteration must always go to infinity unless the starting number is one. No matter how many times you square one, it will still equal one. But just the slightest bit more or less than one and the iteration of squaring will attract it to the infinitely large or small. The same behavior holds true for complex numbers: numbers just outside of the circle z = 1 on the complex plane will jump off into the infinitely large, complex numbers just inside z = 1 will quickly square into zero.

The magic comes by adding the constant c (a complex number) to the squaring process and starting from z at zero: z ⇔ z² + c. Then stable iterations – a set attracted to neither the infinitely small or infinitely large – become possible. The potentially stable Complex numbers lie both outside and inside of the circle of z = 1; specifically on the complex plane they lie between -2.4 and .8 on the real number line, the horizontal x grid, and between -1.2 and +1.2 on the imaginary line, the vertical y grid. These numbers are contained within the black of the Mandelbrot fractal.

Mandelbrot_grid

In the Mandelbrot formula z ⇔ z² + c, where you always start the iterative process with z equals zero, and c equaling any complex number, an endless series of seemingly random or chaotic numbers are produced. Like the weather, the stock market and other chaotic systems, negligible changes in quantities, coupled with feedback, can produce unexpected chaotic effects. The behavior of the complex numbers thus mirrors the behavior of the real world where Chaos is obvious or lurks behind the most ordered of systems.

With some values of ‘c’ the iterative process immediately begins to exponentially increase or fall into infinity. These numbers are completely outside of the Mandelbrot set. With other values of ‘c’ the iterative process is stable for a number of repetitions, and only later in the dynamic process are they attracted to infinity. These are the unstable strange attractor numbers just on the outside edge of the Mandelbrot set. They are shown on computer graphics with colors or shades of grey according to the number of stable iterations. The values of ‘c’ which remain stable, repeating as a finite number forever, never attracted to infinity, and thus within the Mandelbrot set, are plotted as black.

Mandel_Diagram

Some iterations of complex numbers like 1 -1i run off into infinity from the start, just like all of the real numbers. Other complex numbers are always stable like -1 +0i. Other complex numbers stay stable for many iterations, and then only further into the process do they unpredictably begin to start to increase or decrease exponentially (for example, .37 +4i stays stable for 12 iterations). These are the numbers on the edge of inclusion of the stable numbers shown in black.

Chaos enters into the iteration because out of the potentially infinite number of complex numbers in the window of -2.4 to .8 along the horizontal real number axis, and -1.2 to 1.2 along the vertical imaginary number axis. There are an infinite subset of such numbers on the edge, and they cannot be predicted in advance. All that we know about these edge numbers is that if the z produced by any iteration lies outside of a circle with a radius of 2 on the complex plane, then the subsequent z values will go to infinity, and there is no need to continue the iteration process.

By using a computer you can escape the normal limitations of human time. You can try a very large number of different complex numbers and iterate them to see what kind they may be, finite or infinite. Under the Mandelbrot formula you start with z equals zero and then try different values for c. When a particular value of c is attracted to infinity – produces a value for z greater than 2 – then you stop that iteration, go back to z equals zero again, and try another c, and so on, over and over again, millions and millions of times as only a computer can do.

Mandel_zoom_08_satellite_antennaMandelbrot was the first to discover that by using zero as the base z for each iteration, and trying a large number of the possible complex numbers with a computer on a trial and error basis, that he could define the set of stable complex numbers graphically by plotting their location on the complex plane. This is exactly what the Mandelbrot figure is. Along with this discovery came the surprise realization of the beauty and fractal recursive nature of these numbers when displayed graphically.

The following Numberphile video by Holly Krieger, an NSF postdoctoral fellow and instructor at MIT, gives a fairly accessible, almost cutesy, yet still technically correct explanation to the Mandelbrot set.

Fractals and the Mandelbrot set are key parts of the Chaos theories, but there is much more to it than that. Chaos Theory impacts our basic Newtonian, cause-effect, linear world view of reality as a machine. For a refresher on the big picture of the Chaos insights and how the old linear, Newtonian, machine view of reality is wrong, look at this short summary: Chaos Theory (4:48)

Anther Chaos Theory instructional applying the insights to psychology is worth your view. The Science and Psychology of the Chaos Theory (8:59, 2008). It suggests the importance of spontaneous actions in the moment, the so-called flow state.

Also see High Anxieties – The Mathematics of Chaos (59:00, BBC 2008) concerning Chaos Theories, Economics and the Environment, and Order and Chaos (50:36, New Atlantis, 2015).

Application of Chaos Theories to e-Discovery

The use of feedback, iteration and algorithmic processes are central to work in electronic discovery. For instance, my search methods to find relevant evidence in chaotic systems follow iterative processes, including continuous, interactive, machine learning methods. I use these methods to find hidden patterns in the otherwise chaotic data. An overview of the methods I use in legal search is summarized in the following chart. As you can see, steps four, five and six iterate. These are the steps where human computer interactions take place. 
predictive_coding_3.0

My methods place heavy reliance on these steps and on human-computer interaction, which I call a Hybrid process. Like Maura Grossman and Gordon Cormack, I rely heavily on high-ranking documents in this Hybrid process. The primary difference in our methods is that I do not begin to place a heavy reliance on high-ranking documents until after completing several rounds of other training methods. I call this four cylinder multimodal training. This is all part of the sixth step in the 8-step workflow chart above. The four cylinders search engines are: (1) high ranking, (2) midlevel ranking or uncertain, (3) random, and (4) multimodal (including all types of search, such as keyword) directed by humans.

Analogous Application of Similar Mandelbrot Formula For Purposes of Expressing the Importance of the Creative Human Component in Hybrid 

4-5-6-only_predictive_coding_3.0

Recall Mandelbrot’s formula: z ⇔ z² + c, which is the same as z ⇔ z2 + (a + bi). I have something like that going on in my steps four, five and six. If you plugged the numbers of the steps into the Mandelbrot formula it would read something like this: 4 ⇔ 4² + (5+6i). The fourth step is the key AI Predictive Ranking step, where the algorithm ranks the probable relevance of all documents. The fourth step of computer ranking is the whole point of the formula, so AI Ranking here I will call ‘z‘ and represents the left side of the formula. The fifth step is where humans read documents to determine relevance, let’s call that ‘r‘ and the sixth step is where human’s train the computer, ‘t‘. This is the Hybrid Active Training step where the four cylinder multimodal training methods are used to select documents to train the whole set. The documents in steps five and six, r and t are added together for relevance feedback, (r + ti).

Thus, z ⇔ z² + c, which is the same as z ⇔ z2 + (a + bi), becomes under my system z ⇔ z + (r + ti). (Note: I took out the squaring, z², because there is no such exponential function in legal search; it’s all addition.) What, you might ask, is the i in my version of the formula? This is the critical part in my formula, just as it is in Mandelbrot’s. The imaginary number – i – in my formula version represents the creativity of the human conducting the training.

The Hybrid Active Training step is not fully automated in my system. I do not simply use the highest ranking documents to train, especially in the early rounds of training, as do some others. I use a variety of methods in my discretion, especially the multimodal search methods such a keywords, concept search, and the like. In text retrieval science this use of human discretion, human creativity and judgment, is called an ad hoc search. It contrasts with fully automated search, where the text retrieval experts try to eliminate the human element. See Mr EDR for more detail on 2016 TREC Total Recall Track that had both ad hoc and fully automated sections.

My work with legal search engines, especially predictive coding, has shown that new technologies do not work with the old methods and processes, such as linear review or keyword alone. New processes are required that employ new ways of thinking. The new methods that link creative human judgments (i) and the computer’s amazing abilities at text reading speed, consistency, analysis, learning and ranking (z).

A rather Fat Cat. My latest processes, Predictive Coding  3.0, are variations of Continuous Active Training (CAT) where steps four, five and six iterate until the project is concluded. Grossman & Cormack call this Continuous Active Learning or CAL, and they claim Trademark rights to CAL. I respect their right to do so (no doubt they grow weary of vendor rip-offs) and will try to avoid the acronym henceforth. My use of the acronym CAT essentially takes the view of the other side, the human side that trains, not the machine side that learns. In both Continuous Active Learning and CAT the machine keeps learning with every document that a human codes. Continuous Active Learning or Training, makes the linear seed-set method obsolete, along with the control set and random training documents. See Losey, Predictive Coding 3.0.

In my typical implementation of Continuous Active Training I do not automatically include every document coded as a training document. This is the sixth training step (‘t‘ in the prior formula). Instead of automatically using every document to train that has been coded relevant or irrelevant, I select particular documents that I decide to use to train. This, in addition to multimodal search in step six, Hybrid Active, is another way in which the equivalent of Imaginary Numbers come into my formula, the uniquely human element (ti). I typically use most every relevant document coded in step five, the ‘r‘ in the formula, as a training document, but not all. z ⇔ z + (r + ti)

I exercise my human judgment and experience to withhold certain training documents. (Note, I never withhold hot trainers (highly relevant documents)). I do this if my experience (I am tempted to say ‘my imagination‘) suggests that including them as training documents will likely slow down or confuse the algorithm, even if temporarily. I have found that this improves efficiency and effectiveness. It is one of the techniques I used to win document review contests.

robot-friendThis kind of intimate machine communication is possible because I carefully observe the impact of each set of training documents on the classifying algorithm, and carryover lessons – iterate – from one project to the next. I call this keeping a human in the loop and the attorney in charge of relevance scope adjudications. See Losey, Why the ‘Google Car’ Has No Place in Legal Search. We humans provide experienced observation, new feedback, different approaches, empathy, play and emotion. We also add a whole lot of other things too. The AI-Robot is the Knowledge fountain. We are the Wisdom fountain.That it is why we should strive to progress into and through the Knowledge stage as soon as possible. We will thrive in the end-goal Wisdom state.

Application of Chaos Theory to Information→Knowledge→Wisdom

mininformation_arrowsThe first Information stage of the post-computer society in which we live is obviously chaotic. It is like the disconnected numbers that lie completely outside of the Mandelbrot set. It is pure information with only haphazard meaning. It is often just misinformation. Just exponential. There is an overwhelming deluge of such raw information, raw data, that spirals off into an infinity of dead-ends. It leads no where and is disconnected. The information is useless. You may be informed, but to no end. That is modern life in the post-PC era.

The next stage of society we seek, a Knowledge based culture, is geometrically similar to the large black blogs that unite most of the figure. This is the finite set of numbers that provide all connectivity in the Mandelbrot set. Analogously, this will be a time when many loose-ends will be discarded, false theories abandoned, and consensus arise.

In the next stage we will not only be informed, we will be knowledgable. The information we all be processed. The future Knowledge Society will be static, responsible, serious and well fed. People will be brought together by common knowledge. There will be large scale agreements on most subjects. A tremendous amount of diversity will likely be lost.

After a while a knowledgable world will become boring. Ask any professor or academic.  The danger of the next stage will be stagnation, complacency, self-satisfaction. The smug complacency of a know-it-all world. This may be just as dangerous as the pure-chaos Information world in which we now live.

If society is to continue to evolve after that, we will need to move beyond mere Knowledge. We will need to challenge ourselves to attain new, creative applications of Knowledge. We will need to move beyond Knowledge into Wisdom.

benoit-mandelbrot-seahorse-valleyI am inclined to think that if we ever do progress to a Wisdom-based society, we will be a place and time much like the unpredictable fractal edges of the Mandelbrot. Stable to a point, but ultimately unpredictable, constantly changing, evolving. The basic patterns of our truth will remain the same, but they will constantly evolve and be refined. The deeper we dig, the more complex and beautiful it will be. The dry sameness of a Knowledgable based world will be replaced by an ever-changing flow, by more and more diversity and individuality. Our social cohesivity will arise from recursivity and similarity, not sameness and conformity. A Wisdom based society will be filled with fractal beauty. It will live ever zigzagging between the edge of the known and unknown. It will also necessarily have to be a time when people learn to get along together and share in prosperity and health, both physical and mental. It will be a time when people are accustomed to ambiguities and comfortable with them.

In Wisdom World knowledge itself will be plentiful, but will be held very lightly. It will be subject to constant reevaluation. Living in Wisdom will be like living on the rough edge of the Mandelbrot. It will be a culture that knows infinity firsthand. An open, peaceful, ecumenical culture that knows everything and nothing at the same time. A culture where most of the people, or at least a strong minority, have attained a certain level of personal Wisdom.

Conclusion

Back to our times, where we are just now discovering what machine learning can do, we are just beginning to pattern our investigations, our search for truth, in the Law and elsewhere, on new information gleaned from the Chaos theories. Active machine learning, Predictive Coding, is a natural outgrowth of Chaos Theory and the Mandelbrot Set. The insights of hidden fractal order that can only be seen by repetitive computer processes are prevalent in computer based culture. These iterative, computer assisted processes have been the driving force behind thousands of fact investigations that I have conducted since 1980.

I have been using computers to help me in legal investigations since 1980. The reliance on computers at first increased slowly, but steadily. Then from about 2006 to 2013 the increase accelerated and peaked in late 2013. The shift is beginning to level off. We are still heavily dependent on computers, but now we understand that human methods are just as important as software. Software is limited in its capacities without human additive, especially in legal search. Hybrid, Man and Machine, that is the solution. But remember that the focus should be on us, human lawyers and search experts. The AIs we are creating and training should be used to Augment and Enhance our abilities, not replace them. They should complement and complete us.

butterfly_effectThe converse realization of Chaos Theory, that disorder underlies all apparent order, that if you look closely enough, you will find it, also informs our truth-seeking investigatory work. There are no smooth edges. It is all rough. If you look close enough the border of any coastline is infinite.

The same is true of the complexity of any investigation. As every experienced lawyer knows, there is no black and white, no straight line. It always depends on so many things. Complexity and ambiguity are everywhere. There is always a mess, always rough edges. That is what makes the pursuit of truth so interesting. Just when you think you have it, the turbulent echo of another butterfly’s wings knock you about.

The various zigs and zags of e-discovery, and other investigative, truth-seeking activities, are what make them fascinating. Each case is different, unique, yet the same patterns are seen again and again with recursive similarity. Often you begin a search only to have it quickly burn out. No problem, try again. Go back to square one, back to zero, and try another complex number, another clue. Pursue a new idea, a new connection. You chase down all reasonable leads, understanding that many of them will lead nowhere. Even failed searches rule out negatives and so help in the investigation. Lawyers often try to prove a negative.

The fractal story that emerges from Hybrid Multimodal search is often unexpected. As the search matures you see a bigger story, a previously hidden truth. A continuity emerges that connects previously unrelated facts. You literally connect the dots. The unknown complex numbers – (a + bi) – the ones that do not spiral off into the infinite large or small, do in fact touch each other when you look closely enough at the spaces.

z ⇔ z2 + (a + bi)

SherlockI am no Sherlock, but I know how to find ESI using computer processes. It requires an iterative sorting processes, a hybrid multimodal process, using the latest computers and software. This process allows you to harness the infinite patience, analytics and speed of a machine to enhance your own intelligence ……. to augment your own abilities. You let the computer do the boring bits, the drudgery, while you do the creative parts.

The strength comes from the hybrid synergy. It comes from exploring the rough edges of what you think you know about the evidence. It does not come from linear review, nor simple keyword cause-effect. Evidence is always complex, always derived from chaotic systems. A full multimodal selection of search tools is needed to find this kind of dark data.

The truth is out there, but sometimes you have to look very carefully to find it. You have to dig deep and keep on looking to find the missing pieces, to move from Information → Knowledge → Wisdom.

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blue zoom Mandelbrot fractal animation of looking deeper into the details

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Why the ‘Google Car’ Has No Place in Legal Search

February 24, 2016

Google_Car_HybridHybrid Multimodal is the preferred legal search method of the e-Discovery Team. The Hybrid part of our method means that our Computer Assisted Review, our CAR, uses active machine learning (predictive coding), but still has a human driver. They work together. Our review method is thus like the Tesla’s Model S car with full autopilot capabilities. It is designed to be driven by both Man and Machine. Our CAR is unlike the Google car, which can only be driven by a machine. When it comes to legal document review, we oppose fully autonomous driving. In our view there is no place for a Google car in legal search.

Google cars have no steering wheel, no brakes, no gas pedal, no way for a human to drive it at all. It is fully autonomous. A human driver cannot take over, even if they wanted to. In Google’s view, allowing humans to take over makes driverless cars less safe. Google thinks passengers could try to assert themselves in ways that could lead to a crash, so it is safer to be autonomous.

Tesla_autopilotWe have no opinion about the driverless automobile debate, and only like the analogy up to a point. Our opinion is limited to computer assisted review CARs that search for relevant evidence in law suits. For purposes of Law, we want our CARs to be like a Tesla. You can let the car drive and go hands free, if and when you want to. The Tesla AI will then drive the car for you. But you can still drive the car yourself. The second you grab the wheel, the Tesla senses that and turns the Autopilot off. Full control is instantly passed back to you. It is your car, and you are the driver, but you can ask your car to help you drive, when, in your judgment, that is appropriate. For instance, it has excellent fully autonomous parallel parking features, and you can even summon it to come pick you up from out of a nearby parking lot, a truly cool valet service. It is also good in slow commuter traffic and highways, much like cruise control.

When it comes to law, and legal review, we want an attorney’s hands on, or at least near the wheel at all times. Our Hybrid Multimodal approach includes an autopilot mode using active machine learning, but our attorneys are always responsible. They may allow the programmed AI to take over in some situations, and go hands free, much like autonomous parallel parking or highway driving, but they always control the journey.

Defining the Terms

The e-Discovery Team’s Hybrid Multimodal method of document review is based on a flexible blend of human and machine skills, where a lawyer may often delegate, but always retains control. Before we explore this further, a quick definition of terms is in order. Multimodal means that we use all kinds of search methods, and not just one type. For example, we do not just use active machine learning, a/k/a Predictive Coding, to find relevant documents. We do not just use keyword search, or concept search. We use every kind of search we can. This is shown in the search pyramid below, which does not purport to be complete, but catches the main types of document search used today. Using our car analogy, this means that when a human drives, they have a stick shift, and can run in many gears, use many search engines. They can also let go of the wheel, when they want to, and use AI-enhanced search.Search_pyramid

man_robotWe call this a Hybrid method because of the manner in which we use one particular kind of search, predictive coding. To us predictive coding means active machine learning. See eg. Legal Search Science. It is a Man-Machine process, a hybrid process, where we work together with our machine, our robot, whom we call Mr. EDR. In other words, we use the artificial intelligence generated by active machine learning, but we keep lawyers in the loop. We stay involved, hands on or near the wheel.

Augmentation, Not Automation

iron_manThe e-Discovery Team’s Hybrid approach enhances what lawyers do in document review. It improves our ability to make relevance assessments of complex legal issues. The hybrid approach thus leads to augmentation, where lawyers can do more, faster and better. It does not lead to automation, where lawyers are replaced by machines.

The Hybrid Multimodal approach is designed to improve a lawyer’s ability to find evidence. It is not designed to fully automate the tasks. It is not designed to replace lawyers with robots. Still, since one lawyer with our methods can now do the work of hundreds, some lawyers will inevitably be out of a job. They will be replaced by other, more tech savvy lawyers that can work with the robots, that can control them and be empowered by them at the same time. This development in turn creates new jobs for the experts who design and care for the robots, and for lawyers who find new ways to use them.

robots_newspaperWe think that empowering lawyers, and keeping them in the loop, hands near the wheel, is a good thing. We believe that lawyers bring an instinct and a moral sense that is way beyond the grasp of all automation. Moreover, at least today, lawyers know the law, and robots do not. The active machine learning process – predictive coding – begins with a blank slate. Our robots only know what we teach them about relevance. This may change soon, but we are not there yet. See PreSuit.com. Another advantage that we currently have, again one that may someday be replaced, is legal analysis. Humans are capable of legal reasoning, at least after years of schooling and years of legal practice. Right now no machine in the world is even close. But again, we concede this may someday be automated, but we suspect this is at least ten years away.

Robot_with_HeartThe one thing we do not think can ever be automated is the human moral sense of right and wrong, our ethics, our empathy, our humor, our instinct for justice, and our capacity for creativity and imagination, for molding novel remedies to attain fair results in new fact scenarios. This means that, at the present time at least, only lawyers have an instinct for the probative value of documents and their ability to persuade. Even if legal knowledge and legal analysis are some day programmed into a machine, we contend that the unique human qualities of ethics, fairness, empathy, humor, imagination, creativity, flexibility, etc., will always keep trained lawyers in the loop. When it comes to questions of law and justice, humans will always be needed to train and supervise the machines. Not everyone agrees with us.

There is a struggle going on about this right now, one that is largely under the radar. The clash became apparent to the e-Discovery Team during our venture into the world of science and academia at TREC 2015. Some argue that lawyers should be replaced, not enhanced. They favor fully automated methods for a variety of reasons, including cost, a point with which we agree, but also including the alleged inherent unreliability and dishonesty of humans, especially lawyers, a point with which we strenuously disagree. Some scientists and technologists do not appreciate the unique capabilities that humans bring to legal search. More than that, some even think that lawyers should not to be trusted to find evidence, especially documents that could hurt their client’s case. They doubt our ability to be honest in an adversarial system of justice. They see the cold hard logic of machines as the best answer to human subjectivity and deceitfulness. They see machines as the impartial counter-point to human fallibility. They would rather trust a machine than a lawyer. They see fully automated processes as a way to overcome the base elements of man. We do not. This is an important Roboethics issue that has ramifications far beyond legal search.

con manAlthough we have faced our fair share of dishonest lawyers, we still contend they are the rare exception, not the rule. Lawyers can be trusted to do the right thing. The few bad actors can be policed. The existence of a few unethical lawyers should not dictate the processes used for legal search. That is the tail wagging the dog. It makes no sense and, frankly, is insulting. Just because there are a few bad drivers on the road, does not mean that everyone should be forced into a Google car. Plus, please remember the obvious, these same bad actors could also program their robots to do evil for them. Asimov’s laws are a fiction. Not only that, think of the hacking exposure. No. Turning it all over to supposedly infallible and honest machines is not the answer. A hybrid relationship with Man in control is the answer. Trust, but verify.

JusticeThe e-Discovery Team members have been searching for evidence, both good and bad, all of our careers. We do not put our thumb on the scale of justice. Neither do the vast majority of attorneys. We do, however, routinely look for ways to show bad evidence in a good light; that is what lawyers are supposed to do. Making silk purses out of sow’s ears is Trial Law 101. But we never hide the ears. We argue the law, and application of the law to the facts. We also argue what the facts may be, what a document may mean for instance, but we do not hide facts that should be disclosed. We do not destroy or alter evidence. Explaining is fine, but hiding is not.

Many laypersons outside of the law do not understand the clear line. The same misunderstanding applies to some novice lawyers too, especially the ones that have only heard of trials. Hiding and destroying evidence are things that criminals do, not lawyers. If we catch opposing counsel hiding the ball, we respond accordingly. We do not give up and look for ways to turn our system of justice over to cold machines.

Conclusion

Robot_CAR_driverWe should not take away everyone’s license just because a few cannot drive straight. A Computer Assisted Review guided solely by AI alone has no place in the law. AI guidance is fine, we encourage that, that is what Hybrid means, but the CARs should always have a steering wheel and brake. Lawyers should always participate. It is total delegation to AI that we oppose, fully automated search. Legal robots can and should be our friends, but they should never be our masters.

Robot_handshakeHaving said that, we do concede that the balance between Man and Machine is slowly shifting. The e-Discovery Team is gradually placing more and more reliance on the Machine. We learned many lessons on that in our participation in the TREC experiments in 2015. The fully automated methods that the academic teams used did surprisingly well, at least in relatively simple searches requiring limited legal analysis. We expect to put greater and greater reliance on AI in years to come as the software improves, but we will always keep our hands near the wheel.

Mr_EDRWe believe in a collaborative Man-Machine process, but insist that Man, here Lawyers, be the leaders. The buck must stop with the attorney of record, not a robot, even a superior AI like our Mr. EDR. Man must be responsible. Artificial intelligence can enhance our own intelligence, but should never replace it. Back to the AI car analogy, we can and should let the robot drive from time to time, they are, for instance, great a parallel parking, but we should never discard the steering wheel. Law is not a logic machine, nor should it be. It is an exercise in ethics, in fairness, justice and empathy. We should never forget the priority of the human spirit. We should never put too much faith in inhuman automation.

For more on these issues, the hybrid multimodal method, competition with fully automated methods, and much more, please see the e-Discovery Team’s final report of its participation in the 2015 TREC, Total Recall Track, found on NIST’s web at: http://trec.nist.gov/pubs/trec24/papers/eDiscoveryTeam-TR.pdf. It was just published last week. At 116 pages, it should help you to fall asleep for many nights, but hopefully, not while you are driving like the bozos in the hands-free driving video below.

 


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