Do TAR the Right Way with “Hybrid Multimodal Predictive Coding 4.0”

October 8, 2018

The term “TAR” – Technology Assisted Review – as we use it means document review enhanced by active machine learning. Active machine learning is an important tool of specialized Artificial Intelligence. It is now widely used in many industries, including Law. The method of AI-enhanced document review we developed is called Hybrid Multimodal Predictive Coding 4.0. Interestingly, reading these words in the new Sans Forgetica font will help you to remember them.

We have developed an online instructional program to teach our TAR methods and AI infused concepts to all kinds of legal professionals. We use words, studies, case-law, science, diagrams, math, statistics, scientific studies, test results and appeals to reason to teach the methods. To balance that out, we also make extensive use of photos and videos. We use right brain tools of all kinds, even subliminals, special fonts, hypnotic images and loads of hyperlinks. We use emotion as another teaching tool. Logic and Emotion. Sorry Spock, but this multimodal, holistic approach is more effective with humans than an all-text, reason-only approach of Vulcan law schools.

We even try to use humor and promote student creativity with our homework assignments. Please remember, however, this is not an accredited law school class, so do not expect professorial interaction. Did we mention the TAR Course is free?

By the end of study of the TAR Course you will know and remember exactly what Hybrid Multimodal means. You will understand the importance of using all varieties of legal search, for instance: keywords, similarity searches, concept searches and AI driven probable relevance document ranking. That is the Multimodal part. We use all of the search tools that our KL Discovery document review software provides.

 

The Hybrid part refers to the partnership with technology, the reliance of the searcher on the advanced algorithmic tools. It is important than Man and Machine work together, but that Man remain in charge of justice. The predictive coding algorithms and software are used to enhance the lawyers, paralegals and law tech’s abilities, not replace them.

By the end of the TAR Course you will also know what IST means, literally Intelligently Spaced Training. It is our specialty technique of AI training where you keep training the Machine until first pass relevance review is completed. This is a type of Continuous Active Learning, or as Grossman and Cormack call it, CAL. By the end of the TAR Course you should also know what a Stop Decision is. It is a critical point of the document review process. When do you stop the active machine teaching process? When is enough review enough? This involves legal proportionality issues, to be sure, but it also involves technological processes, procedures and measurements. What is good enough Recall under the circumstances with the data at hand? When should you stop the machine training?

We can teach you the concepts, but this kind of deep knowledge of timing requires substantial experience. In fact, refining the Stop Decision was one of the main tasks we set for ourself for the  e-Discovery Team experiments in the Total Recall Track of the National Institute of Standards and Technology Text Retrieval Conference in 2015 and 2016. We learned a lot in our two years. I do not think anyone has spent more time studying this in both scientific and commercial projects than we have. Kudos again to KL Discovery for helping to sponsor this kind of important research  by the e-Discovery Team.

 

 

Working with AI like this for evidence gathering is a newly emerging art. Take the TAR Course and learn the latest methods. We divide the Predictive Coding work flow into eight-steps. Master these steps and related concepts to do TAR the right way.

 

Pop Quiz: What is one of the most important considerations on when to train again?

One Possible Correct Answer: The schedule of the humans involved. Logistics and project planning is always important for efficiency. Flexibility is easy to attain with the IST method. You can easily accommodate schedule changes and make it as easy as possible for humans and “robots” to work together. We do not literally mean robots, but rather refer to the advanced software and the AI that arises from the machine training as an imiginary robot.

 

 

 

 

 

 

 

 

 

1 Comment | Lawyers Duties, Related Legal Webs, Review, Search | Tagged: , , , , , , , , , | Permalink
Posted by Ralph Losey

Play the New e-Discovery “Hive Mind” Game: Small, Medium or Large?

November 19, 2017

Playing games is a great way to learn. That’s one reason I’ve devised a game concerning the interesting and fairly complex issues involved in trying to determine what e-discovery activities are proportional and appropriate in various sized cases. Specifically, what should you do to prepare for federal court 26(f) conferences in small and medium sized cases, versus large, complicated cases? Small, Medium or Large? is kind of a Goldilocks game of proportionality.


I have had to give these proportionality questions a lot of thought as part of my practice as a lawyer supervising hundreds of e-discovery projects at a time, projects of all different sizes. I could simply give you my answer, but after five books, I’ve already been there and done that. So I thought I’d try something new and make this learning into a game where you consider and vote on what activities you think are appropriate for a Small, Medium or Large case.

The format of this proportionality game is loosely based on the ideas of Hive Mind, a pretty cool new insight into collective intelligence, which, as Wikipedia puts it, is a “shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making.” See: Is Collective Intelligence a safer form of A.I?

The Hive Mind is different from Crowdsourcing, but related. To do properly, a Hive Mind requires Swarming, which we cannot really do properly in this game using polls. Louis Rosenberg, Super-Intelligence and the virtues of a “Hive Mind” (Singularity, 2/10/16). One Silicon Valley startup Unanimous A.I., is developing technologies that enable sophisticated online human swarming and thus better collective intelligence. I might try their free product for social research, UNO, if I further investigate the power of the Hive Mind. Maybe at NY LegalTech? (Email me if you’re interested.) In the meantime we are going to use simple polling for the e-Discovery Hive Mind Game: Small, Medium or Large?

Let’s play the game and see what collective intelligence emerges from many individuals giving their opinion about proportionality and e-discovery. I am playing this game now with all of the litigation associates and paralegals in my law firm, which is a pretty large swarm by itself. Your responses will join in the swarm, the collective intelligence.

In January I’ll share the results of all the polling and opine away as to how well the Hive Mind performed. You can win the game in one of two ways, either by matching the most popular Hive Mind responses or by matching my responses. (I assume there will be a difference because not enough experts will be playing, but who knows, maybe not. In theory, with enough experts swarming, the group, the Hive Mind, will always have the best answer.)

Background To Play the Game

In order to play the Small, Medium or Large? game, you first need to be familiar with the checklist of the Southern District Court of Florida of all of the things that you should do to prepare for e-discovery in a large case. It is a pretty good list and I have written about it before. Good New 33-Point e-Discovery Checklist From Miami (e-Discovery Team, October 1, 2017) (a must read to fully prepare for this game). My article contains comments and explanations about all checklist items, which is the beginning of a kind of swarm interaction, that is, if you take time to ponder the signals. The Court’s checklist incorporates the new provisions in the rules on relevance and proportionality (Rule 26(b)(1)) and on specific objections (Rule 34(b)(2)).

It is not a perfect list, but it is the best one now out there with a court pedigree. It is not too long and complex, like the older lists of some courts that are very detailed, and not too short, like the easy-peasy list that Bill Hamilton and I created for the Middle District Court of Florida many years ago. (Attorneys still complained about how burdensome it was!) In sum, the 33-Point Checklist out of Miami is a good list for legal practitioners all over the country to use to prepare for e-discovery, which means it is a good basis for our Small, Medium or Large? Hive Mind Game. Come play along.

Rules of the Game

The goal of our Hive Mind Game is to determine which of the thirty-three points on the checklist are applicable to big cases only, which are applicable to medium size cases and which to small cases. You are to assume that all thirty-three points apply to large cases, but that they are not all applicable to medium and small size cases. The Hive Mind voting is used to allow the swarm – that’s you – to identify which of the thirty-three only apply to small cases, and which only apply to medium size cases. If a checklist item applies to a medium size case, it automatically also applies to a small size case.

In other words, the game is to sort the thirty-three into three piles, Small, Medium or Large? Simple, eh? Well, maybe not. This is a matter of opinion and things are pretty vague. For instance, I’m not going to define the difference between large, small and medium size case. That is part of the Hive Mind.

The game is important because proportionality in the law is important. You do not prepare for a big case the same way you prepare for a small case. You just don’t. You could, but it would be a waste of your clients money to do so. So the real trick in e-discovery, like in all other aspects of the litigation, is to determine what you should do in any size case to prepare, including even the small cases. For instance, outside of e-discovery, most people would agree that you should take the parties depositions as a minimum to prepare for a trial of any size case, including small ones.

What are the equivalent items in the 33-point checklist? Which of them should be applied to all cases, even the small ones? Which of them are too complicated and expensive to apply in a small case, but not a medium sized case? Which too complicated and expensive to apply in a small or medium sized case, but not a large one? That is where the real skill and knowledge come in. That is the essence of the game.

Assume All 33 Items Apply to Big Cases

This Small, Medium or Large? Hive Game requires you to assume that all thirty-three items on the Court’s checklist apply to big cases, but not to all cases, that there are certain checklist items that only apply to medium size cases, and others, a smaller list, that only apply to small cases. You may question the reality of this assumption. After all, the Court does not say that. It does not say, here’s a checklist we made to guide your e-discovery, but you can ignore many of the items on this list if you have a small case, or even a medium size case. Still, that’s what they mean, but they do not go on to say what’s what. They know that the Bar will figure it out themselves in due time, meaning the next several years. And right they are. But why wait? Let’s figure it out ourselves now with this Small, Medium or Large? Hive Game, and condense years to weeks.

The Hive Game will allow us to fill in the blanks of what the Court did not say. But first, lets focus again on what the Court did say. It said that the checklist may be used by members of the Bar to guide the Rule 26(f) e-discovery conferences and Case Management Reports. It did not say shall be used. It is a suggestion, not a requirement. Still, as every long-term member of the court knows, what they mean is that you damn well should follow the checklist in a big case!  Woe unto the lawyers who come before the judges in a big case with an e-discovery issue where they never even bothered to go through the checklist. If your issue is on that list, and chances are it will be, then dear slacker, prepare for a Miami style bench-slap. Ouch! It is going to hurt. Not only you and your reputation, but also your client.

I feel confident in making the game assumption that all thirty-three checklist items apply to big cases. I am also confident they do not all apply to medium and small size cases and that is the real reason for the court’s use of may instead of shall.

With that background we are almost ready to start playing the game and opine away as to which of the 33 are small and medium size only. But, there is still one more thing I have found very helpful when you try to really dig into the checklist and play the game, you need to have the numbers 1-33 added to the list. The one mistake the Court made on this list was in using headings and bullet points instead of numbers. I fix that in the list that follows, so that you can, if nothing else, more easily play the game.

 Preservation

  1. The ranges of creation or receipt dates for any ESI to be preserved.
  2. The description of ESI from sources that are not reasonably accessible because of undue burden or cost and that will not be reviewed for responsiveness or produced, but that will be preserved in accordance with Federal Rule of Civil Procedure 26(b)(2)(B).
  3. The description of ESI from sources that: (a) the party believes could contain relevant information; but (b) has determined, under the proportionality factors, is not discoverable and should not be preserved.
  4. Whether to continue any interdiction of any document-destruction program, such as ongoing erasures of e-mails, voicemails, and other electronically recorded material.
  5. The number and names or general job titles or descriptions of custodians for whom ESI will be preserved (e.g., “HR head,” “scientist,” “marketing manager”).
  6. The list of systems, if any, that contain ESI not associated with individual custodians and that will be preserved, such as enterprise databases.
  7. Any disputes related to scope or manner of preservation.

Liaison

  1. The identity of each party’s e-discovery liaison, who will be knowledgeable about and responsible for each party’s ESI.

Informal Discovery About Location and Types of Systems

  1. Identification of systems from which discovery will be prioritized (e.g., e-mail, finance, HR systems).
  2. Descriptions and location of systems in which potentially discoverable information is Stored.
  3. How potentially discoverable information is stored.
  4. How discoverable information can be collected from systems and media in which it is stored.

Proportionality and Costs

  1. The amount and nature of the claims being made by either party.
  2. The nature and scope of burdens associated with the proposed preservation and discovery of ESI.
  3. The likely benefit of the proposed discovery.
  4. Costs that the parties will share to reduce overall discovery expenses, such as the use of a common electronic-discovery vendor or a shared document repository, or other costsaving measures.
  5. Limits on the scope of preservation or other cost-saving measures.
  6. Whether there is relevant ESI that will not be preserved in accordance with Federal Rule of Civil Procedure 26(b)(1), requiring discovery to be proportionate to the needs of the case.

Search

  1. The search method(s), including specific words or phrases or other methodology, that will be used to identify discoverable ESI and filter out ESI that is not subject to discovery.
  2. The quality-control method(s) the producing party will use to evaluate whether a production is missing relevant ESI or contains substantial amounts of irrelevant ESI.

Phasing

  1. Whether it is appropriate to conduct discovery of ESI in phases.
  2. Sources of ESI most likely to contain discoverable information and that will be included in the first phases of Federal Rule of Civil Procedure 34 document discovery.
  3. Sources of ESI less likely to contain discoverable information from which discovery will be postponed or not reviewed.
  4. Custodians (by name or role) most likely to have discoverable information and whose ESI will be included in the first phases of document discovery.
  5. Custodians (by name or role) less likely to have discoverable information from whom
    discovery of ESI will be postponed or avoided.
  6. The time period during which discoverable information was most likely to have been
    created or received.

Production

  1. The formats in which structured ESI (database, collaboration sites, etc.) will be produced.
  2. The formats in which unstructured ESI (e-mail, presentations, word processing, etc.) will be produced.
  3. The extent, if any, to which metadata will be produced and the fields of metadata to be produced.
  4. The production format(s) that ensure(s) that any inherent searchability of ESI is not degraded when produced.

Privilege

  1. How any production of privileged or work-product protected information will be handled.
  2. Whether the parties can agree on alternative ways to identify documents withheld on the grounds of privilege or work product to reduce the burdens of such identification.
  3. Whether the parties will enter into a Federal Rule of Evidence 502(d) stipulation and order that addresses inadvertent or agreed production.

One Example Before the Games Begin

We are almost ready to play the e-Discovery Small, Medium or Large? Hive Mind Game. We will do so with thirty-two polls that are presented to the player in the same order as the Court’s checklist. To make sure the rules are clear (this is, after all, a game for lawyers, not kids) we start with an example, the first of the thirty-three items on the checklist. The court’s first item is to suggest that you Determine the range of creation or receipt dates for any ESI to be preserved.

The “right answer” to this first item is that this should be done in every case, even the small ones. You should always determine the date range of data to be preserved. In most cases that is very easy to do, and, as every lawyer should know, when in doubt, when it comes to preservation, always err on the side of inclusion. That means you should check the Small Case answer as shown in the “dummy poll” graphic below.

Checklist 1

We have set these polls up so that you cannot see the results, but you can leave private comments. We may do this again later and experiment with what happens when you can see the results. We will share the results (and some comments) when the game ends on January 1, 2019.

Now for the live polls and game proper. Note that several of the checklist items, including number two and three, which are the first two polls shown below, are so long that we had to paraphrase and shorten them to fit in the space allocated in the polling software. To see the original of all thirty-three items on the checklist, go to my prior blog explaining the list (highly recommended) or the court’s page.

Let the Games Begin!

We are now ready to begin playing the e-Discovery Hive Mind Game. So get ready to plug-in. Select an answer to each of the thirty-two polls that follow. After you vote, you also have a chance to leave a private comment to each poll, but that is optional and will not impact your score.

Checklist 2


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Checklist 3

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Checklist 4


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Checklist 12

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Checklist 14

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Checklist 15

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Checklist 18

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Checklist 19

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Checklist 20

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Checklist 21

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Checklist 29

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Checklist 30

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Checklist 31

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Checklist 32

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Checklist 33

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Congratulations! You have finished the Game and made your contribution to the e-Discovery Hive Mind. Look for results sometime in early 2018. You can then determine how your answers compared with the collective Hive Mind.

I will also let you know how the Hive Mind answers compared with my own. So you will have two chances to win. Anyone who matches all of my answers wins a free lunch with me in Orlando. Other prizes have yet to be determined. Vendors care to contribute some goodies? Perhaps Elon will donate a free trip to Mars, where I for one hope we don’t run into any Borg cubes, I don’t care how good their Hive Mind is.

In the meantime, please encourage your e-discovery friends and colleagues to join in the game. Teachers and Partners are invited to require their students and associates, paralegals to play too. Resistance is futile! Digging deep into this checklist is a great way to expand your knowledge and expertise of electronic discovery law and practice.

 

 

 

 

 

 

2 Comments | informaton, knowledge, Lawyers Duties, Metadata, New Rules, Related Legal Webs, Review, Search, Security, Spoliation/Sanctions, Technology, VENDORS, wisdom | Tagged: , , , , , , , , , , , | Permalink
Posted by Ralph Losey

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.

 

 

3 Comments | AI Ethics, informaton, knowledge, Lawyers Duties, Related Legal Webs, Technology, wisdom | Tagged: , , , , , , , , , , , | Permalink
Posted by Ralph Losey

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.

 

6 Comments | Evidence, informaton, knowledge, Lawyers Duties, New Rules, Related Legal Webs, Review, Search, Technology, VENDORS, wisdom | Tagged: , , , , , , , , , , , , , , , , , , , | Permalink
Posted by Ralph Losey

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