WHY I LOVE PREDICTIVE CODING: Making Document Review Fun Again with Mr. EDR and Predictive Coding 4.0

December 3, 2017

Many lawyers and technologists like predictive coding and recommend it to their colleagues. They have good reasons. It has worked for them. It has allowed them to do e-discovery reviews in an effective, cost efficient manner, especially the big projects. That is true for me too, but that is not why I love predictive coding. My feelings come from the excitement, fun, and amazement that often arise from seeing it in action, first hand. I love watching the predictive coding features in my software find documents that I could never have found on my own. I love the way the AI in the software helps me to do the impossible. I really love how it makes me far smarter and skilled than I really am.

I have been getting those kinds of positive feelings consistently by using the latest Predictive Coding 4.0 methodology (shown right) and KrolLDiscovery’s latest eDiscovery.com Review software (“EDR”). So too have my e-Discovery Team members who helped me to participate in TREC 2015 and 2016 (the great science experiment for the latest text search techniques sponsored by the National Institute of Standards and Technology). During our grueling forty-five days of experiments in 2015, and again for sixty days in 2016, we came to admire the intelligence of the new EDR software so much that we decided to personalize the AI as a robot. We named him Mr. EDR out of respect. He even has his own website now, MrEDR.com, where he explains how he helped my e-Discovery Team in the 2015 and 2015 TREC Total Recall Track experiments.

Bottom line for us from this research was to prove and improve our methods. Our latest version 4.0 of Predictive Coding, Hybrid Multimodal IST Method is the result. We have even open-sourced this method, well most of it, and teach it in a free seventeen-class online program: TARcourse.com. Aside from testing and improving our methods, another, perhaps even more important result of TREC for us was our rediscovery that with good teamwork, and good software like Mr. EDR at your side, document review need never be boring again. The documents themselves may well be boring as hell, that’s another matter, but the search for them need not be.

How and Why Predictive Coding is Fun

Steps Four, Five and Six of the standard eight-step workflow for Predictive Coding 4.0 is where we work with the active machine-learning features of Mr. EDR. These are its predictive coding features, a type of artificial intelligence. We train the computer on our conception of relevance by showing it relevant and irrelevant documents that we have found. The software is designed to then go out and find all other relevant documents in the total dataset. One of the skills we learn is when we have taught enough and can stop the training and complete the document review. At TREC we call that the Stop decision. It is important to keep down the costs of document review.

We use a multimodal approach to find training documents, meaning we use all of the other search features of Mr. EDR to find relevant ESI, such as keyword searches, similarity and concept. We iterate the training by sample documents, both relevant and irrelevant, until the computer starts to understand the scope of relevance we have in mind. It is a training exercise to make our AI smart, to get it to understand the basic ideas of relevance for that case. It usually takes multiple rounds of training for Mr. EDR to understand what we have in mind. But he is a fast learner, and by using the latest hybrid multimodal IST (“intelligently spaced learning“) techniques, we can usually complete his training in a few days. At TREC, where we were moving fast after hours with the Ã-Team, we completed some of the training experiments in just a few hours.

After a while Mr. EDR starts to “get it,” he starts to really understand what we are after, what we think is relevant in the case. That is when a happy shock and awe type moment can happen. That is when Mr. EDR’s intelligence and search abilities start to exceed our own. Yes. It happens. The pupil then starts to evolve beyond his teachers. The smart algorithms start to see patterns and find evidence invisible to us. At that point we sometimes even let him train himself by automatically accepting his top-ranked predicted relevant documents without even looking at them. Our main role then is to determine a good range for the automatic acceptance and do some spot-checking. We are, in effect, allowing Mr. EDR to take over the review. Oh what a feeling to then watch what happens, to see him keep finding new relevant documents and keep getting smarter and smarter by his own self-programming. That is the special AI-high that makes it so much fun to work with Predictive Coding 4.0 and Mr. EDR.

It does not happen in every project, but with the new Predictive Coding 4.0 methods and the latest Mr. EDR, we are seeing this kind of transformation happen more and more often. It is a tipping point in the review when we see Mr. EDR go beyond us. He starts to unearth relevant documents that my team would never even have thought to look for. The relevant documents he finds are sometimes completely dissimilar to any others we found before. They do not have the same keywords, or even the same known concepts. Still, Mr. EDR sees patterns in these documents that we do not. He can find the hidden gems of relevance, even outliers and black swans, if they exist. When he starts to train himself, that is the point in the review when we think of Mr. EDR as going into superhero mode. At least, that is the way my young e-Discovery Team members likes to talk about him.

By the end of many projects the algorithmic functions of Mr. EDR have attained a higher intelligence and skill level than our own (at least on the task of finding the relevant evidence in the document collection). He is always lighting fast and inexhaustible, even untrained, but by the end of his training, he becomes a search genius. Watching Mr. EDR in that kind of superhero mode is what makes Predictive Coding 4.0 a pleasure.

The Empowerment of AI Augmented Search

It is hard to describe the combination of pride and excitement you feel when Mr. EDR, your student, takes your training and then goes beyond you. More than that, the super-AI you created then empowers you to do things that would have been impossible before, absurd even. That feels pretty good too. You may not be Iron Man, or look like Robert Downey, but you will be capable of remarkable feats of legal search strength.

For instance, using Mr. EDR as our Iron Man-like suits, my e-discovery Ã-Team of three attorneys was able to do thirty different review projects and classify 17,014,085 documents in 45 days. See 2015 TREC experiment summary at Mr. EDR. We did these projects mostly at nights, and on weekends, while holding down our regular jobs. What makes this crazy impossible, is that we were able to accomplish this by only personally reviewing 32,916 documents. That is less than 0.2% of the total collection. That means we relied on predictive coding to do 99.8% of our review work. Incredible, but true.

Using traditional linear review methods it would have taken us 45 years to review that many documents! Instead, we did it in 45 days. Plus our recall and precision rates were insanely good. We even scored 100% precision and 100% recall in one TREC project in 2015 and two more in 2016. You read that right. Perfection. Many of our other projects attained scores in the high and mid nineties. We are not saying you will get results like that. Every project is different, and some are much more difficult than others. But we are saying that this kind of AI-enhanced review is not only fast and efficient, it is effective.

Yes, it’s pretty cool when your little AI creation does all the work for you and makes you look good. Still, no robot could do this without your training and supervision. We are a team, which is why we call it hybrid multimodal, man and machine.

Having Fun with Scientific Research at TREC 2015 and 2016

During the 2015 TREC Total Recall Track experiments my team would sometimes get totally lost on a few of the really hard Topics. We were not given legal issues to search, as usual. They were arcane technical hacker issues, political issues, or local news stories. Not only were we in new fields, the scope of relevance of the thirty Topics was never really explained. (We were given one to three word explanations in 2015, in 2016 we got a whole sentence!) We had to figure out intended relevance during the project based on feedback from the automated TREC document adjudication system. We would have some limited understanding of relevance based on our suppositions of the initial keyword hints, and so we could begin to train Mr. EDR with that. But, in several Topics, we never had any real understanding of exactly what TREC thought was relevant.

This was a very frustrating situation at first, but, and here is the cool thing, even though we did not know, Mr. EDR knew. That’s right. He saw the TREC patterns of relevance hidden to us mere mortals. In many of the thirty Topics we would just sit back and let him do all of the driving, like a Google car. We would often just cheer him on (and each other) as the TREC systems kept saying Mr. EDR was right, the documents he selected were relevant. The truth is, during much of the 45 days of TREC we were like kids in a candy store having a great time. That is when we decided to give Mr. EDR a cape and superhero status. He never let us down. It is a great feeling to create an AI with greater intelligence than your own and then see it augment and improve your legal work. It is truly a hybrid human-machine partnership at its best.

I hope you get the opportunity to experience this for yourself someday. The TREC experiments in 2015 and 2016 on recall in predictive coding are over, but the search for truth and justice goes on in lawsuits across the country. Try it on your next document review project.

Do What You Love and Love What You Do

Mr. EDR, and other good predictive coding software like it, can augment our own abilities and make us incredibly productive. This is why I love predictive coding and would not trade it for any other legal activity I have ever done (although I have had similar highs from oral arguments that went great, or the rush that comes from winning a big case).

The excitement of predictive coding comes through clearly when Mr. EDR is fully trained and able to carry on without you. It is a kind of Kurzweilian mini-singularity event. It usually happens near the end of the project, but can happen earlier when your computer catches on to what you want and starts to find the hidden gems you missed. I suggest you give Predictive Coding 4.0 and Mr. EDR a try. To make it easier I open-sourced our latest method and created an online course. TARcourse.com. It will teach anyone our method, if they have the right software. Learn the method, get the software and then you too can have fun with evidence search. You too can love what you do. Document review need never be boring again.

Caution

One note of caution: most e-discovery vendors, including the largest, do not have active machine learning features built into their document review software. Even the few that have active machine learning do not necessarily follow the Hybrid Multimodal IST Predictive Coding 4.0 approach that we used to attain these results. They instead rely entirely on machine-selected documents for training, or even worse, rely entirely on random selected documents to train the software, or have elaborate unnecessary secret control sets.

The algorithms used by some vendors who say they have “predictive coding” or “artificial intelligence” are not very good. Scientists tell me that some are only dressed-up concept search or unsupervised document clustering. Only bona fide active machine learning algorithms create the kind of AI experience that I am talking about. Software for document review that does not have any active machine learning features may be cheap, and may be popular, but they lack the power that I love. Without active machine learning, which is fundamentally different from just “analytics,” it is not possible to boost your intelligence with AI. So beware of software that just says it has advanced analytics. Ask if it has “active machine learning“?

It is impossible to do the things described in this essay unless the software you are using has active machine learning features.  This is clearly the way of the future. It is what makes document review enjoyable and why I love to do big projects. It turns scary to fun.

So, if you tried “predictive coding” or “advanced analytics” before, and it did not work for you, it could well be the software’s fault, not yours. Or it could be the poor method you were following. The method that we developed in Da Silva Moore, where my firm represented the defense, was a version 1.0 method. Da Silva Moore v. Publicis Groupe, 287 F.R.D. 182, 183 (S.D.N.Y. 2012). We have come a long way since then. We have eliminated unnecessary random control sets and gone to continuous training, instead of train then review. This is spelled out in the TARcourse.com that teaches our latest version 4.0 techniques.

The new 4.0 methods are not hard to follow. The TARcourse.com puts our methods online and even teaches the theory and practice. And the 4.0 methods certainly will work. We have proven that at TREC, but only if you have good software. With just a little training, and some help at first from consultants (most vendors with bona fide active machine learning features will have good ones to help), you can have the kind of success and excitement that I am talking about.

Do not give up if it does not work for you the first time, especially in a complex project. Try another vendor instead, one that may have better software and better consultants. Also, be sure that your consultants are Predictive Coding 4.0 experts, and that you follow their advice. Finally, remember that the cheapest software is almost never the best, and, in the long run will cost you a small fortune in wasted time and frustration.

Conclusion

Love what you do. It is a great feeling and sure fire way to job satisfaction and success. With these new predictive coding technologies it is easier than ever to love e-discovery. Try them out. Treat yourself to the AI high that comes from using smart machine learning software and fast computers. There is nothing else like it. If you switch to the 4.0 methods and software, you too can know that thrill. You can watch an advanced intelligence, which you helped create, exceed your own abilities, exceed anyone’s abilities. You can sit back and watch Mr. EDR complete your search for you. You can watch him do so in record time and with record results. It is amazing to see good software find documents that you know you would never have found on your own.

Predictive coding AI in superhero mode can be exciting to watch. Why deprive yourself of that? Who says document review has to be slow and boring? Start making the practice of law fun again.

 

 


Proportionality Φ and Making It Easy To Play “e-Discovery: Small, Medium or Large?” in Your Own Group or Class

November 26, 2017

Every judge who has ever struggled with discovery issues wishes that the lawyers involved had a better understanding of proportionality, that they had spent more time really thinking about how it applies to the requisites of their case. So too does every lawyer who, like me, specializes in electronic discovery. As Chief Justice Roberts explained in his 2015 Year-End Report on the Federal Judiciary on the new rules on proportionality:

The amended rule states, as a fundamental principle, that lawyers must size and shape their discovery requests to the requisites of a case. Specifically, the pretrial process must provide parties with efficient access to what is needed to prove a claim or defense, but eliminate unnecessary or wasteful discovery. The key here is careful and realistic assessment of actual need.

Proportionality and reasonableness arise from conscious efforts to realistically assess actual need. What is the right balance in a particular situation? What are the actual benefits and burdens involved? How can you size and shape your discovery requests to the requisites of a case?

There is more to proportionality than knowing the rules and case law, although they are a good place to start. Proportionality is a deep subject and deserves more than black letter law treatment. 2015 e-Discovery Rule Amendments: Dawning of the “Goldilocks Era” (e-discoveryteam.com, 11/11/15) (wherein I discuss proportionality, the Golden Ratio or perfect proportionality, aka Φ, which is shown in this graphic and much more, including the spooky “coincidence” at a CLE with Judge Facciola and the audience vote). Also see: Giulio Tononi, Phi Φ, a Voyage from the Brain to the Soul (Pantheon Books, 2012) (book I’m rereading now on consciousness and integrated information theory, another take on Phi Φ).

We want everyone in the field to think about proportionality. To be conscious of it, not just have information about it. What does proportionality really mean? How does it apply to the e-discovery tasks that you carry out every day? How much is enough? Too much? Too burdensome? Too little? Not enough? Why?

What is a reasonable effort? How do you know? Is there perfect proportionality? One that expresses itself in varying ways according to the facts and circumstances? Does Law follow Art? Is Law an Art? Or is it a Science? Is there Beauty in Law? In Reason? There is more to proportionality than meets the eye. Or is there?

Getting people to think about proportionality is one of the reasons I created the Hive Mind game that I announced in my blog last week: “e-Discovery: Small, Medium of Large?”

This week’s blog continues that intention of getting lawyers to think about proportionality and the requisites of their case. It concludes with a word document designed to make it easier to play along with your own group, class or CLE event. What discovery activities required in a Big Case are not necessary in a Small Case, or even a Medium Sized case? That is what requires thought and is the basis of the game.

Rules of Federal Procedure

Proportionality is key to all discovery, to knowing the appropriate size and shape of discovery requests in order to fit the requisites of a case. Reading the rules that embody the doctrine of proportionality is a good start, but just a start.  The primary rule to understand is how proportionality effects the scope of relevance as set forth in Rule 26(b)(1), FRCP:

Parties may obtain discovery regarding any nonprivileged matter that is relevant to any party’s claim or defense and proportional to the needs of the case, considering the importance of the issues at stake in the action, the amount in controversy, the parties’ relative access to relevant information, the parties’ resources, the importance of the discovery in resolving the issues, and whether the burden or expense of the proposed discovery outweighs its likely benefit.

But you also need to understand how it impacts a lawyer’s overall duty to supervise a discovery request and response as set forth in Rule 26(g). See Rule 26(g)(1)(B)(iii), FRCP:

neither unreasonable nor unduly burdensome or expensive, considering the needs of the case, prior discovery in the case, the amount in controversy, and the importance of the issues at stake in the action.

Many other rules have concepts of proportionality either expressly or implicitly built in, including Rule 26(b)(2)(B) (not reasonably accessible); Rule 26(b)(2)(C)(i) (cumulative); Rule 1 (just, speedy and inexpensive), Rule 34, Rule 37(e), Rule 45.

Case Law

Reading the key cases is also a help, indispensable really, but reading what the judges say is not enough either. Still you need to keep up with the fast growing case law on proportionality. See for instance the fine collection by K&L Gates at: https://www.ediscoverylaw.com/?s=proportionality and the must-read, The Sedona Conference Commentary on Proportionality_May 2017. Here a few of my favorites cases:

  • In re Bard IVC Filters Prods. Liab. Litig., D. Ariz., No. MDL 15-02641-PHX DGC, 2016 U.S. Dist. LEXIS 126448 (D. Ariz. Sept. 16, 2016). In this must-read opinion District Judge David G. Campbell, who was the chair of the Rules Committee when the 2015 amendments were passed, takes both lawyers and judges to task for not following the new rules on proportionality. He then lays it all out in a definitive manner.
  • In re Takata Airbag Prods. Liab. Litig., No. 15-02599-CIV-Moreno, MDL No. 5-2599 (S.D. Fla. Mar. 1, 2016). Judge Moreno quotes Chief Justice Roberts’ comments in the 2015 Year-End Report that the newly amended Fed.R.Civ.Pro. 26 “crystalizes the concept of reasonable limits in discovery through increased reliance on the common-sense concept of proportionality.” 2015 Year-End Report on the Federal Judiciary.
  • Hyles v. New York City, No. 10 Civ. 3119 (AT)(AJP), 2016 WL 4077114 (S.D.N.Y. Aug. 1, 2016) (Judge Peck: “While Hyles may well be correct that production using keywords may not be as complete as it would be if TAR were used, the standard is not perfection, or using the “best” tool, but whether the search results are reasonable and proportional. Cf. Fed. R. Civ. P. 26(g)(1)(B)”)
  • Johnson v Serenity TransportationCase No. 15-cv-02004-JSC (N.D. Cal. October 28, 2016) (“… a defendant does not have discretion to decide to withhold relevant, responsive documents absent some showing that producing the document is not proportional to the needs of the case.”)
  • Apple Inc. v. Samsung Elecs. Co., No. 12-CV-0630-LHK (PSG), 2013 WL 4426512, 2013 U.S. Dist. LEXIS 116493 (N.D. Cal. Aug. 14, 2013) (“But there is an additional, more persuasive reason to limit Apple’s production — the court is required to limit discovery if “the burden or expense of the proposed discovery outweighs its likely benefit.” This is the essence of proportionality — an all-to-often ignored discovery principle. Because the parties have already submitted their expert damages reports, the financial documents would be of limited value to Samsung at this point. Although counsel was not able to shed light on exactly what was done, Samsung’s experts were clearly somehow able to apportion the worldwide, product line inclusive data to estimate U.S. and product-specific damages. It seems, well, senseless to require Apple to go to great lengths to produce data that Samsung is able to do without. This the court will not do.)
  • PTSI, Inc. v. Haley, 2013 WL 2285109 (Pa. Super. Ct. May 24, 2013) (“… it is unreasonable to expect parties to take every conceivable step to preserve all potentially relevant data.”)
  •  Kleen Products, LLC, et al. v. Packaging Corp. of Amer., et al.Case: 1:10-cv-05711, Document #412 (ND, Ill., Sept. 28, 2012).

Also see: The Top Twenty-Two Most Interesting e-Discovery Opinions of 2016 (e-discoveryteam.com, 1/2/17) (the following top ranked cases concerned proportionality: 20, 18, 17, 15, 14, 11, 6, 4, 3, 2, 1); and, Good, Better, Best: a Tale of Three Proportionality Cases – Part Two (e-discoveryteam.com 4/8/12) (includes collection of earlier case law).

Sedona Commentary

The Sedona Conference Commentary on Proportionality_May 2017 is more than a collection of case law. It includes commentary hashed out between competing camps over many years. The latest 2017 version includes Six Principles that are worthy of study. They can certainly help you in your own analysis of proportionality. The cited case law in the Commentary is structured around these six principles.

THE SEDONA CONFERENCE PRINCIPLES OF PROPORTIONALITY

Principle 1: The burdens and costs of preserving relevant electronically stored information should be weighed against the potential value and uniqueness of the information when determining the appropriate scope of preservation.

Principle 2: Discovery should focus on the needs of the case and generally be obtained from the most convenient, least burdensome, and least expensive sources.

Principle 3: Undue burden, expense, or delay resulting from a party’s action or inaction should be weighed against that party.

Principle 4: The application of proportionality should be based on information rather than speculation.

Principle 5: Nonmonetary factors should be considered in the proportionality analysis.

Principle 6: Technologies to reduce cost and burden should be considered in the proportionality analysis.

Conclusion

Proportionality is one of those deep subjects where you should think for yourself, but also be open and listen to others. It is possible to do both, although not easy. It is one of those human tricks that will make us hard to replace by smart machines. The game I have created will help you with that. Try out the Small, Medium or Large? proportionality game by filling out the online polls I created.

But, you can do more. You can lead discussions at your law firm, company, class or CLE on the subject. You can become an e-discovery proportionality Game-Master. You can find out the consensus opinion of any group. You can observe and create statistics of how the initial opinions change when the other game players hear each others opinions. That kind of group interaction can create the so-called hive-effect. People often change their mind until a consensus emerges.

What is the small, medium or large proportionality consensus of your group? Even if you just determine majority opinion, and do not go through an interactive exercise, you are learning something of interest. Plus, and here is the key thing, you are giving game players a chance to exercise their analytical skills.

To help you to play this game on your own, and lead groups to play it, I created a Word Document that you are welcome to use.

Game-Master-Hive-Mind_e-Discovery_Proportionality_GAME

 

 



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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