2016/09/07 “One World Anthropology | Tim Ingold | AIBR

Plenary presentation by Tim Ingold, at the 2nd @AIBR_ International Conference of Anthropology.
Plenaria Tim Ingold 1, AIBR Antropólogos Iberoamericanos en Red

The 2nd AIBR International Conference of Anthropology brings together anthropologists from different parts of the world under the theme “Identity: Bridges, Thresholds, and Barriers.”

[….]

[6:58] So what is the relation between the life of the soul and soul life, or to put it in more general terms, between the particular life and life itself. Is it a relation of part to whole? Now, I have nothing against the idea of life as a whole, so long as we do not think of this whole as a totality. Holism is one thing. Totalization is quite another, and it is vital to acknowledge the distinction. Totality to my ear at least implies addition and completion, but life itself is never complete, nor, as I have tried to show, can we approach it by any process of summation, whether addititive, additive or multiplicative. It is not a completion but the continual origination. Life as one elder from among the women she Cree of northern Canada told the ethnographer Colin Scott life is continuous birth. It is the generative potential of a world in becoming a world that is forever worlding.

[8:20] So is the particular life a part of life as a whole. Is the life of the soul apart of soul life? And, again. I have nothing against the idea of lives as parts but then we should think of these parts too, as ways of carrying on like the voices of a composition. And the analogy I have in mind is that of polyphonic music in which every voice, for every instrument, carries on along its own melodic line. In music the relation between parts and whole is not summative, it is neither additive nor multiplicative, but contrapuntal. Think of the tenor part in the chorus or the cello part in the symphony and I want to think of the life of every particular soul.

[9:19] Likewise, as a line of counterpoint, that even as it issues forth, is continually attentive and responsive to each and every other. Souls as we might say are answerable to one another, a condition that carries entailment of both responsiveness and responsibility. Precisely because souls go along together and because their continual regeneration is nourished and impelled by the memory of their association the composition formed by their contrapuntal movement cannot be decomposed without causing grief if not destruction to the lives of its parts.

[….]

[….]

[29:02] And I think it is to the oneworldness of this whole that anthropology must remain committed.

[29:11] As I stated at the outset the world is a conversation, it is not the object of our conversation. In this conversation lies ontogenesis, the becoming of being. And it is high time to restore ontogenesis to life. We will then see that every particular life is both an open-ended exploration of the possibilities of being that are one world affords, and a contribution to it its ongoing formation, that is to its worlding.

[29:49] It is in a sense a never-ending quest for an answer to the problem of what being human, or what living in this world actually means. But every answer is a response, and not a solution. Responding to the question, we respond to one another, that is, we correspond. And in this we do not so much look out from a position as long for one that is forever beyond our grasp

[30:22] Life is a question to which there is no answer but in this one world of ours we are all tasked with looking for it, and it is in the search that all life is lived. And it is just as well that there is no final solution for that indeed would put an end to us all.


Plenaria Tim Ingold 1 | September 2016 | AIBR Antropólogos Iberoamericanos en Red at https://www.youtube.com/watch?v=3TbG2Lo_9fk

Plenaria Tim Ingold 2 | September 2016 | AIBR Antropólogos Iberoamericanos en Red at https://www.youtube.com/watch?v=fn9DfiAteFQ

2nd AIBR International Conference on Anthropology program is at http://2016.aibr.org/en/programaen/search .

Tagged with:
Posted in Talk Video Streaming

Service Platforms, Living Labs, The Future of Open Innovation | Henry Chesbrough | Sept. 2016 | Berkeley Exec Ed (web video)

Responses @HenryChesbrough on service platforms, living labs, and the future of open innovation.  Since 2003, he has provided a precise definition, as the godfather of open innovation.

InFocus Podcast with Dr. Henry Chesbrough. Dr. Chesbrough is the Program Director for the UC Berkeley Executive Education program, Corporate Business Model Innovation.

[12:34] >>Interviewer: When you talk about service platforms and you need a good service platform to succeed, is that what you’re talking about, or can you explain to us what a successful platform is?

>>Dr. Chesbrough: The service platform resolves a fundamental tension in this idea of adding services to be more intimate with your customers to escape the commodity trap.
Basically to make things affordable, you want to standardize them so you can share and reuse as much as possible. The problem is we don’t all want the same thing. The best way to give us what we want is to have everything be fully customized, get exactly what you want, which may be different than what I want.

The tension between standardization and customization is where the service platform comes in. You embed in the platform the things that can be shared and reuse widely, and then you extend off of the platform to deliver the customized things that individuals want.

A quick example of this would be all the apps on our smart phones. The smart phones themselves and the app store that deliver them are the platforms, but none of us have the exact same suite of applications on our smart phones nor do we have to. We get the things that we want but we get them in a way that makes it very affordable for us as well.

So that’s the idea of a service platform.

[13:54] >>Interviewer: I’d love it if you could explain, too … I know you’ve been quoted talking about living labs. Obviously it must apply to open innovation. Can you give us a little background on what that is?

>Dr. Chesbrough: Living labs is something that’s really emerged out of Europe.

I had the great pleasure of spending nine months last year in Barcelona, which if you have to leave Berkeley, Barcelona’s a great place to spend some time.

One of the challenges in Europe, they’re very envious of us here in the US especially in Silicon Valley for the magic we have of turning all these great ideas and research into new ventures, new products, new businesses. In Europe they complain about what they call a innovation gap. They’ve got great science and great technology, but they don’t have the same vibrant startup environment, the same new culture toward entrepreneurship that we have in the US.

So living labs is a response to try to close the innovation gap.

When these research and technology projects are concluded in the university setting, can we find a place to put them to nurture them further and bring them to life? So it’s not a traditional academic research lab. It’s a much more practical place to work more on these technologies to really get them ready for use in industry.

One area where we’re seeing these living labs is in smart cities where municipalities like Barcelona or Amsterdam or London or Paris are trying to put technology to work to really reshape the urban environment in which these people live in ways that make citizens’ lives better, bring new sources of revenue to the city, and often save money or improve safety of these kinds of things using technology and experimenting through these labs.

[….]

[24:56]>>Interviewer: How about the future for open innovation? If you had a crystal ball, if you were looking down the road 10, 15, 20 years from now, where do you see it going?

Dr. Chesbrough: Let me give you a short-term and a long-term answer.

In the short-term, open innovation is moving from collaborating between individual firms or organizations to collaboration throughout an entire ecosystem of companies, developers, third parties, users, suppliers, a very rich, multifaceted thing. This is actually been labeled open innovation 2.0 by the European Commission which has really gotten behind this as part as their policy going forward. In the short-term, it’s easy to say because it’s already starting to happen. This is where open innovation is going.

In the longer-term, I think open innovation might follow a path like that of the quality revolution.

In the 1980s, the US companies woke up to the gap with Japan and how much more reliable many Japanese products were relative to US products, and so US companies really embraced quality as a strategic imperative. They had quality departments, quality organizations, and they embedded in the companies a real need to do this well from the very beginning of the design, not just inspecting at the end of the process.

Today, most of those quality organizations are gone. The thinking is there, but it’s now embedded in how the company does business so you no longer need the quality department overseeing all this. That’s a possible long-term future for open innovation. Openness is not going to go away, but it may become part of the fabric of the company. Instead of today having open innovation departments and people with titles of manager of open innovation, director of open innovation, and my personal favorite, vice president of open innovation, in 30 years those titles may be gone, those organizations may be gone, and this may just be part of how companies do business.

“InFocus Podcast with Dr. Henry Chesbrough | September 2016 | UC Berkeley Executive Education at https://www.youtube.com/watch?v=cxhrg_ndz9M

Tagged with: , ,
Posted in Talk Video Streaming

2016/05/11 “Apache Open Tech is fueling tomorrow’s game changing innovations” | Todd Moore | ApacheCon North American 2016 (web video)

History of open source at IBM from 1998 @tmmoore_1 @ApacheCon NA 2016, and current IBM Open Cloud Architecture.

Todd Moore, VP of Open Technology at IBM will share a retrospective of IBMs deep roots ASF and then follow with some crystal ball gazing on some key projects that are poised to become engines of new innovation both within and in conjunction with ASF projects.

[00m30s] [slide: IBM has a long view of open source]

[00m57s] There are 62,000 IBMers who are trained up in open source contribution and participation. There’s about 400,000 of us in total. I run that program across IBM. Many of our open source committers, main contributors to the project, are part of my organization. [….]

[01m40s] About ’98, we started looking into Linux …

[02m35s] … at the time, Robert LeBlanc was in charge of software strategy. Robert, in his quote, basically as part of that press release, said we expect this to foster skills, to build communities, and to build markets. And that is exactly what that has done. [….]

[03m18s] These were bets. We didn’t know if open source would grow. We knew that there would be roadblocks and things that we would have to overcome along the way, but we thought that it would have promise and would be the way of the future. It was a strategic move.

[03m38s] [slide: Apache’s steady growth]

[04m26s] We started to build the fences around open source. We then also showed clients and customers, and all of the rest who might come out and develop in it, that it was a safe place to do that. By the fact that we were in these organizations, and the other businesses were in there with us, we were making a commitment that this was a safe place to go and work. And I think that’s what really turned the corner. Because, now, with that backing, people could come in, evaluate, play with, see the quality of the code that was being done — great code, done and being out in the open — and feel comfortable about it. That’s been part of the engineer of growth that has fuelled our success at Apache.

[05m13s] [slide: Community plus strong believers and supporters] Just doing some surfing using e-mail addresses to figure out where contributions are coming to, and who some of the backers really are. This is looking over the last year, it’s not any kind of completeness. Just to hit some of the key folks who are in here, contributing into these projects being part of it. Obviously IBM, about a third of the work in relative investment of company size, Google, HP, Microsoft, others, Twitter, Red Hat, etc., making large contributions into the community. [….]

[05:50] The companies are behind it. We’ve got really deep support in here.

[06:00] [slide: The next bets … in the Cognitive, Cloud Era]
[dw Open projects, now incubating at Apache]
[- SystemML]
[- Toree (Spark Kernel)]
[- Quarks]
[Existing Apache projects]
[- Mesos]
[- Spark]
[- Kafka]
[- CouchDB]

IBM has 936 projects, I looked out on Github this morning. Kinda hard to figure out what’s really important, what’s not important. We started an effort last year to cull that down to make something that is understandable. It’s a work we call developerWorks Open.

[07m20s] Two-thirds of our activity in Apache right now is SystemML.

[10m50s] [slide: IBM’s Open Cloud Architecture]
[IoT: MQTT, Node-RED, Quarks]
[Web and Mobile: jQuery, HTML5, Apache Cordova, Loopback, Activity Streams, JSON]
[Runtimes: node.js, Java, Swift, Go]
[Data and Analytics: Spark, CouchDB, Redis, TinkerPop, Titan, Hadoop, MongoDB, data.gov]
[Security: OAuth2, OpenID]
[Operating Environment: Cloud Foundry, Docker, OpenStack, Cloud Native Computing Foundation, Open Container Project, OpenPower, Mesos, Linux, OpenVSwitch]
[DevOps: Ansible, Git, … Jenkins, Chef]

“Apache Open Tech is fueling tomorrow’s game changing innovations” | Todd Moore | May 11, 2016 | ApacheCon North American 2016 at https://www.youtube.com/watch?v=MG2iZBLz9g8&index=2&list=PLGeM09tlguZTvqV5g7KwFhxDlWi4njK6n

ApacheCon schedule archive at https://wiki.apache.org/apachecon/Past_Conference_Resources#ApacheCon_North_America_2016.2C_Vancouver.2C_BC.2C_Canada

Via “IBM’s Wager on Open Source Is Still Paying Off” | Ian Murphy | August 2, 2016 | Linux Foundation News at https://www.linux.com/news/ibms-wager-open-source-still-paying

Tagged with: ,
Posted in Talk Video Streaming

2012/03/13 Tim O’Reilly interviewed by Andrew McAfee, “Creating More Value Than You Capture”, SXSW Interactive

Digest of an interview of @timoreilly by @amcafee below, abstract from schedule.sxsw.com/2012/events/event_IAP100142:

Tim O'Reilly, SXSW 2012

One of the great failures of any company – for that matter of a capitalist economy – is ecosystem failure. Great companies build great ecosystems, one in which value is created not just for a single company or group of industry players, but for partners who didn’t even exist when the product or service was introduced. Many companies start out creating huge value. [….]  Since the cycle of capitalism depends on consumers as well as producers, and consumers are less and less able to find employment, at some point, we’re going to have to start thinking about how to put people to work, rather than how to put them out of work. At O’Reilly, we’ve always tried to live by the slogan “Create more value than you capture.” It’s a great way to build a sustainable business and a sustainable economy.

Andrew McAfee, SXSW 2012

Andrew McAfee, author of “Race Against the Machine,” will engage with Tim about these ideas, and about how rethinking the economy becomes even more urgent in the face of the trend he explores in his book, in which jobs are being outsourced not just to low-wage countries, but increasingly to machines.


[long introduction]

[… skipping ahead to focus on the ideas on policy  …]

[21:40 AM] A lot of what these people did was what you called the Clothesline Paradox.  Can you tell us more about that?

[21:42 TO] … It’s a paper I read in 1975 in Coevolution Quarterly …

[23:30 TO] … We kicked the can down the road …

[….]

[23:40 TO]  Back to this clothesline paradox.  Steve Baer had this insight.  He said, when somebody decides to hang their clothes on a clothesline, instead of putting them into a dryer, we don’t take that little energy savings, and move it from the fossil fuels column into the energy renewable column in our accounting.  It just disappears.

[24:05 AM] So it’s literally a shrinkage in the economy.

[24:07 TO] That’s right.

[24:08 AM] It looks small than it used to.

[24:09 TO] That’s right.  So you can look at a lot of issues — like in the SOPA — it’s a great metaphor for how we think about the Internet economy, when people entertain themselves by watching free Youtube videos, or interact with friends on Facebook, instead of watching Hollywood movies or buying copyrighted content.  The copyright industry says “look at the value that was destroyed, the free Internet is destroying value”.

[24:40 TO] But it’s quite clear.  It’s like the utilities people saying, those people hanging their clothes on clotheslines are destroying value.  They’re not using our product.

[24:52 AM] And by that definition, the open source software movement has shrunk the software industry.

[24:55 TO] Absolutely.

[24:56 AM] And therefore destroyed value.

[24:59 TO] Except, no.  That was exactly what Bob Young, who started Red Hat, said:  my goal is to shrink the share of the operating system market.  And you look at MySQL, shrunk the size of the database market.  But it didn’t really, of course, it actually grew it.

[25:17 TO] What we understand, now, is that when we now have these breakthroughs in generosity, you grow the market.  You grow value for society.

[25:28 AM] But you don’t grow the economy that we know how to measure, that we’re kind of pointing our measurement instruments at.

[25:35 TO] Often you grow it, we just don’t look at the instruments.

[…]  [The book, Race Against the Machine]

[28:35 TO] We really need to think about a new shape of the economy. …

[28:37 AM] And this is actually a great segue, because this is the next set of questions that I wanted to post to Tim.

[28:41 AM] So we have one set of challenges, which are pretty clear and substantial about measuring value in our economy.

[28:47 AM] We have another set of challenges around compensating people for contributing value in this economy.  Because, as Tim says, a lot of the people who are putting value out there, are doing it in ways that don’t immediately lead to recompense or compensation.

[29:02 AM] There’s another problem, which Eric and I dove in on, in the book, which is as technology just races ahead, and continues to demonstrate just weirdly powerful new capabilities and skills, the data are pretty clear that it’s leaving some people, and a larger number of people, behind, in our economy, over time.

[29:25 AM] And the super-shorthand way to talk about that is:  think about what happens when we hook up Siri to Watson, and let both of those technologies improve for a few years.  Cause if they follow the trajectory of Moore’s Law, and they’re going to follow them with at least that much acceleration, they’re going to be about 16 times better than they currently are, in 6 years.

[29:49 AM] Now I think that puts a lot of people who are doing what they are currently doing for a living right in the sights of the automation of the economy.

[… customer self-service …]

[… customers create jobs …]

[32:37 TO]  It’s a situation that’s been, first of all, framed by the race of our economy to take labour costs out.  What we’ve failed to do is to find a way to redistribute those gains.  We have them go disproportionately to a very small number of people.  I find it fairly inconscionable that companies are basically firing workers while paying hundreds of millions of dollars to a few top executives, because “we can’t afford …”  That’s just bullshit.

[33:09 TO] The fact is, we’ve made choices about who we’re going to reward, and they’re ultimately self-destructive choices for our society.

[33:17 AM Okay.

[33:18 TO] But now what we have is the race of technology, with more and more jobs being taken over by machines,

[33:35 AM] You and I had a fascinating conversation, a while back, because I was laying out the things I was saying.  And I found it really easy to find examples of encroaching automation in jobs under threat.  Tim did the best job of pointing me toward examples of job creation, not just among the data scientists and web designers of the world, which I was anticipating, but you’ve given great examples of people putting labour back into our economy.

[33:59 TO] Let me put it this way.  I’m looking for those examples, and I’m starting to find them.

[34:03 TO] The way my mind works, is I kind of have some notion, and then I start looking for some data to support that notion, or to disprove it.  In this case, the notion I came to was, oh, given what I said about if you don’t have any consumers, you don’t have any businesses, we’re going to have to put labour back into the economy.

[34:22 TO] We have to find a way to pay people. Or people will have to find a way to pay each other. Or we’ll have a very new shape to the economy.  That’s really what’s the heart of what I’m trying to talk about, here.

[… some green shoots, use of computers to add value to low-skilled jobs that we’ve been trying to ring out of society …]

[… The Apple Store …]

[… Walgreen … home health care IT people …]

[… Kickstarter, Etsy … examples of putting labour cost back into the economy]

[38:13 TO] Somebody basically took a commodity product, and lovingly added value to it, and then resold it.  I thought, that’s kind of an interesting data point.  I think we’ll be doing more of that added value for each other, in this future economy.

[ … Youtube economy, where artists are starting to make a living, based on an advertising economy]

[… P2P sharing economy … AirBnB …]

[39:25 TO] It seems to me that, when you see a sharing economy, it eventually does get monetized.  The early web, everybody was just equal, we were just doing things each other.  Then, this advertising economy grew up around it.

[39:46 TO] There’s still a huge distance ahead for the advertising economy.  The Internet average share of advertising is still a fraction of television, even though there’s more hours now spent on the Internet, entertaining each other, than there are spent on television.  So there’s a lot of money to come from industry into another.

[40:05 TO] So that kind of leads me to a policy recommendation.  Policy makers need to focus on protecting the future from the past, rather than protecting the past from the future.

[40:18 TO] Most of the policy that we see is oriented towards protecting incumbents, because of course they have the loudest voices …

[40:28 AM] … and the biggest chequebooks.

[40:30 TO] I had this interesting conversation with Nancy Pelosi about SOPA and PIPA.  It was eye-opening.  I was just explaining my experience as a publisher.  We’ve been publishing books DRM-free, and yes, some people steal them, particularly in countries where they weren’t going to pay us anyway.

[…]

[40:56 TO] It does not keep me up at night, because, in fact, our business is growing.  We were selling in markets we could never have sold in, before.  It’s a rapidly growing part of my business.

[41:08 TO] I’m trying to explain, and she says, we have to take into account the concerns of Hollywood.  I said, no you don’t.  You have to find the right answer for society.  Your job is to work for all of us.  It’s not to work for this interest group versus that interest group.

[… open for questions from the audience …]

[44:24 audience]  Do you have an axiom that you would consider for a startup founder who’s trying to make decision between where to create value for the investors, where to cleave the line and say that this should be something that goes into the ecosystem?  How you make that judgement call?

[44:47 TO] I think it should be scientific.  I remember having this argument with Richard Stallman about open source.  I said the difference between free software and open source is that open source should be science, not religion.  In other words, it should work.  The decision you’re making, if you’re looking over time, you should believe that it’s better for the investor, as well as for society.  Because, in fact, short-term thinking is not best for a long-term investor.  So that means, of course, that you also have to find an investor who is thinking in the long term.  Of course, great investors do think longer term.  They’re not looking for the quick exit.  They’re looking to build the great company that survives and grows and serves customers over the long term.  If you’re doing it right, you should, in fact, be looking at building a vibrant ecosystem around your company that creates value for a lot of other people.  You’ll find that’s actually better for your company.  So look fo rthat win-win .   […]  Although these days, win-win seems to mean we win twice for our team.

[more questions, audio ends at 1h01m01s

Audio replay available at “Create More Value Than You Capture” | SXSW Interactive | 2012 at  http://schedule.sxsw.com/2012/events/event_IAP100142

[download audio]

Subsequent blog post, Andrew McAfee, “Tim O’Reilly on Putting Labor Back Into the Economy” | March 2012 at http://andrewmcafee.org/2012/03/mcafee-sxsw-tim-oreilly-labor-automation-race-against-the-machine/

Tagged with: , , , , ,
Posted in Talk Audio Download, Talk Audio Streaming

Cognitive overload as business opportunity for IBM since 2005 | Ginni Rometty | June 1, 2016 | Code Conference (web video + audio)

Cognitive overload is a challenge IBM has worked since 2005, says @GinniRometty in @KaraSwisher interview @RecodeEvents. Thus, cognitive computing combining man and machine is more than artificial intelligence. Better decisions in open domains will lead to solving problems not solved before.

In T-shaped thinking, not just the jack-of-all-things of unstructured data + natural language + images, but the depth in domain knowledge to understand, reason and learn. If it’s digital, it will be cognitive. In education, reading and mechanical skills won’t be enough, will have to teach and learn data skills.

Blockchain could be even bigger than Watson, with opportunities in efficiencies in supply chains and capital flows. Open source will be important, the Hyperledger project gives transparency to regulators and institutions.

Ginni Rometty, IBM, at Code 2016

[The full video of 27m44s is online from recode.net.  A 4m19s excerpt is searchable on Youtube]

[5:00 Ginni Rometty] The reason we don’t call it artificial intelligence — which is much easier to say, by the way, and spell … I’ve heard this from everyone. It is a little bit of history. It’s really all about man and machine. And I know there are various views about this. And AI can have a loaded set of connotations with it.

[5:25 Kara Swisher]: About destroying the earth.

[5:26 Ginni Rometty]: More than that. Jobs, many other things that people bring up about that topic.

[5:31 Ginni Rometty]: The point was, it was man and machine. We got started on this, Kara, in 2005, when we started our work on this. We’ve been at this on for a good decade, now. And it started because we said, look, people are going to be overloaded. There’s so much information. It’s cognitive overload. You’ve got to do something about it. And that’s what started us down this path. And therefore, this really true belief that you will make better decisions, and you will in fact solve problems that haven’t been solved before.

[5:56 Ginni Rometty]: Which is why, good or bad, we picked the first thing with healthcare. It is the most … I would have to ask everyone … Does everyone think? There’s not even really a system around it, it’s so dysfunctional. There’s so much waste. 8 trillion, a third of it is waste.

[6:12 Kara Swisher] Let’s talk about the decision to go with AI. [….] How do you turn that into a business? [….] From being, ooh, isn’t it cool, to being able to beat the chess guy. Which is fun …

[6:51 Ginni Rometty] And then Jeopardy. Because chess is really such a long time ago. And chess has many more — yes, you were thinking Jeopardy — because chess has many more mathematically determined …. That’s just horsepower, when you’re doing chess. But that’s not true with Jeopardy. Jeopardy is open domain, so it’s infinite answers. It’s a very different set of issues that are there.

[7:08 Ginni Rometty] When you say why, and can it be a business? This goes back to thinking that it could be an everyday, impact. I would say that in 5 years, there is no doubt in my mind, that cognitive AI will impact every decision made. 5 years. In some way, in some sense. It can be everyday stuff. And when you say, to make it a business, just look at healthcare, education, I look at what we’ve done in financial services, and I can see what clients are doing. [….]

[7:58] One thing that is important for everything thinking in the future of AI … a couple things. It’s easy to think about, okay, you’ll have to deal with unstructured data. Mary talked about natural language. But it’s not just that, and it’s not just images. It’s going to have to be domain knowledge, and that this ability to understand, reason and learn. And if you can do that in a domain, you’re in a way different world. It’s not like being a jack-of-trades on a really thin thing.

[8:25] This is going down the path, if you’re an oncologist, how can you look at an EMR, look at x-rays, look at the knowledge that has been printed out there, form hypotheses, decide your level of confidence … What experts want — what you and I want, when we want to make a decision … You don’t want to be told the answer. You want to see, here’s the different reasons, here’s how I thought about it, here’s the evidence that proves it. When you’re being treated for cancer, you may not want your hair to fall out. That may mean a lot to you. So there is no right or wrong answer.

[8:54] So I see this world … This is where we’ll going to want to deal with the grey area. And that is really a different business. And I think most businesses are there.

[….]

[9:07 Kara Swisher] Right now, you put cognitive businesses at $4 billion, about 20% of your business.

[9:12 Ginni Rometty] Oh, no, no. [….] Analytics is $18 billion, and increasing. [….]

[9:25 Kara Swisher] You don’t break out how much the Watson is …

[9:30 Ginni Rometty] We don’t, for a reason. Anyone who’s building something … It’s going to be a silver thread …

[9:58 Ginni Rometty] If it’s digital, it’ll be cognitive. Anything you do digital, it will be cognitive. So, if you think that, you’re going to be a way that you really run your business. [….] We will solve things that haven’t been solved. [….]

[11:10] The other parts of the business, I say, they’re not growing, they’re declining. But, my, good, we run the railroads, the airlines, the banks of the world. Those are systems, by the way, that are to be changed. I hope we do do talk a little about systems like blockchain, which, I believe, as much as discussion as we have about AI, the blockchain will have as much as an influence on many different ways that businesses are run.

[ … story about Jill Watson ….]

[16:40 Kara Swisher] That is the friendly view of AI. […] Your thing is not so much doing the why, you’re showing the path of decision-making. But at some point, two things could go wrong. One is replacing jobs. Completely replacing jobs. [….] The other one is evil in taking over the planet.

[17:30 Ginni Rometty] I think there are a couple points that you should think about. When you think about … I’ll keep using the word cognitive, because AI is a subset. For me as an engineer, technically, AI is a subset of cognitive. There are many more statistical engines in here, and what it does. But what really matters is who teaches these things. Watson is taught. When you come in, it’s both the data you use to teach, and who does the teaching. So, as an example …. This is why verticals become really important. So, when you’re in healthcare, IBM has been taught by the world’s greatest oncologists right now. [….]

[18:20] This idea of knowing which data to use for what …. If you are going to diagnose someone, would you go to the journals, and all of the literature of medicine, or would you go to Twitter? [….]

[18:34] … if you were trying to predict the pandemic, you might also have to go there. But there’s the idea of knowing which way to go is really important. And who does the training is important. So when you’re in verticals, you will be trained by experts. [….] For one of the most important business decisions, who does the training is important.

[18:57] Your other point though, is what about job? I think that is is inevitable. Things that are repetitive, they will have a job impact. That is foolish to say it won’t. We’re doing the work on radiology today. [….]

[19:29] The radiologist can do what he really should be doing, and that’s what he’s going to be putting the premium on. But this will circle back …. The root of this is going to go back to education. Because, you aren’t going to stop it. Mary had that chart up on transportation. The trend is going to keep moving, right?

[19:45] The chart on transportation …. If you go back to farming, people had to read. The industrial age, then they had to teach mechanical skills. I think what ever we’ll end up calling this age, people will have had to learn all of these data skills, right? [….] I do think that there will be tons more of jobs that will open up. But there will be this discontinuity period. They don’t always line up. That’s the thing about transitions.

[….]

[20:25 Kara Swisher] Last question, blockchain. Why do you think it’s going to be important?

[20:30 Ginni Rometty] How many people are familiar with blockchain? [….] How many of you think it’s going to have a profound impact on some of the biggest business processes in the world? I would say, maybe 60-70%.

[20:45] We have about 200 projects going. But, we did something else. And ghis is probably the most instructive. Cause blockchain for me …. Anything that is a supply chain, you can improve its efficiency. It doesn’t matter whether it’s a people supply chain, numbers ….

[21:08] IBM Global Financing is $40 billion, one of the world’s largest. So we’ve put up our own ledger on a shadow blockchain already. Very interesting. I place a lot of commercial paper. I start to place my own commercial paper. Some people don’t fit in that flow. The flow goes down, money’s shortened. I can see it happening already. I have a big services business. There’s lots of supply chain. People parts moving around. You have tons of people on both sides that are reconciling. Blockchain addresses it. I have banks, reconciliation, stock exchanges, not very liquid assets, trading. Big shippers, to big retailers. So, I can see the the efficiency. This is another area. It will be efficiency. Efficiency of capital flows, and efficiency ….

[21:55] It’s a great opportunity. You need two things to do it. You need transaction flows, and cryptography. And I also believe, it’s going to be important to be out in open source. We actually put the fabric for it, in Hyperledger. Because you will need visibility in the world. The regulators, the federal banks we worked with, you have to have it. I think — and we didn’t talk about it at all — I think that the opportunities are super-great around this. I see tons of little companies popping up, taking all different applications. I think it’s almost limitless what the ideas will be, what the people will do with this. In the good way that you’ll need to have transparency with this, and we’ll go through some learning. Again, some of the vehicles out there are not transparent, they’re opaque, you won’t be able to do that with financial transactions.

[… audience questions and answer follow ….]

“Full video: IBM CEO Ginni Rometty at Code 2016” (27m44s) | Recode at http://www.recode.net/2016/6/8/11829636/ginni-rometty-ibm-full-video-code

Excerpt 4m19s “Using AI to combat cognitive overload | Ginni Rometty, CEO IBM | Code Conference 2016” at https://www.youtube.com/watch?v=uy3lZhQ5Cb8

Tagged with: ,
Posted in Talk Audio Download, Talk Video Streaming

“Conversation about Cybernetics” | Joi Ito, Paul Pangaro | MIT Media Lab | Mar 17, 2016 (web video)

Conversation at the MIT Media Lab about cybernetics with Paul Pangaro, Nathan Felde, Mike Bove, Iyad Rahwan, Edith Ackermann, Joi Ito and Lorrie LeJeune.

A few background posts:

jods.mitpress.mit.edu/pub/designandscience
dubberly.com/articles/cybernetics-and-counterculture.html

Chat posted live on Facebook Mentions at facebook.com/joiito/videos/961545600598042/

Conversation at the MIT Media Lab about cybernetics with Paul Pangaro | Joi Ito | March 18, 2016 at joi.ito.com/weblog/2016/03/18/conversation-at.html

[Warning: the audio recording level on Youtube is low. An audio amplifier will be helpful for listening]

[00:00 Introductions]

[….]

[09:45 As we started to think about the future of Media Lab … and the Media Lab has recently been getting into machine learning, synthetic biology, and other complex adaptive systems. If we go back to the roots of Media Lab, there were some roots of cybernetics there, but for a while a lot of our focus is on computer-human interface and things that were a little more objects. And then we started to get into networks, and systems. But now, we’re shifting into a lot of the hard sciences in self-adaptive complex systems.

[10:30] And then, as I looked at people like Kevin Esvelt who has been doing CRISPR gene drive, he thinks much more about how do we think about who should decide, and how should that enter the system, and less about what is the specific technology that is in the gene drive. And there he can see the design across scales, where at each scale you have a complex system that interacts with other systems across scales.

[10:55] As we start to put all of the science together, what we’re realizing is that the traditional disciplinary science rewards a very focused, single object, rather than systems connected. Now, there is systems thinking going on. But also multiple systems across scales. If you look at Ed Boyden’s lab, he’s got about 50 people working — at very interdisciplinary and antidisciplinary — we touch multiple systems, we create tools that look at multiple systems, and we perturb multiple systems.

[11:30] And then we’ve got people like Kevin Slavin who have come in, sort of talking about participatory design. You’re not stuck in traffic, you are traffic. You put that together. We had a conference last year called Knotty Objects, which is about design. We brought Paolo Antonelli and they talked about critical design. One of the things I realized is that there’s critical design, which was a lot about people not doing it, but critiquing it. And then we had the Media Lab kids who tend to do things, but not being as self-critical as you might want. And they thought more in objects, than systems.

[12:05] So, we were grappling with how do we think about what we were all doing, and how could science be more responsible. We launched this Journal of Design Science, which was to try to break design into science, which means thinking about all of the systems, thinking about things as iterative interventions in an unpredictable complex system, rather than how do I make those predictable things more efficient. Then, how can we change design so it’s less focused on the customer, and more on nature, or the system, across scales.

[12:35] So, as I was searching for the right word to describe what we were doing, I found I was repeating on second order cybernetics.

[12:50] We were having a discussion at the faculty meeting about being interdisciplinary / antidisciplinary. The Media Lab was where all disciplines were brought to work together. Well, there actually was a thing called cybernetics where all of the discipline had come together in the Macy Conference, where was this wonderful moment where it felt like we were going to go transdisciplinary. But, sort of in retrospect, somehow it disappeared.

[13:12] So, how did it disappear, where did it go? Stewart Brand would say “it got bored to death”. Some people said it got too academic, some people said the Macy Conferences ended and disappeared. Some people said the applications got so compelling that it ended up being applied. Theoretical. So there were a lot of reasons why it disappeared.

[13:40] So, I traced someone holding a torch. So now, what I want to explore is a couple things. One is, is there a way to connect cybernetics into what we’re doing in research? Now that I’m turning 50 this year, as an old-timer, I hear the crypto-currency people saying the same things that I was saying back in the 90’s, and making the same mistakes we made when we were building the Internet. I don’t like to repeat mistakes. I don’t like to rehash stuff that’s already been done. So, what can we can learn from cybernetics’ successes? But there are also learning from its failures. What could you or we have done better?

[14:15] We can look at the met-catalyst(?) movement in architecture, which was about bringing biology and architecture together. It was Tungay in Japan that did it. But it kind of died. I think it was because we didn’t have the tools to bring biology into architecture. Today, we do. There are a lot of things, like machine learning, and other things, where the technology has caught up to the theory, so we can apply it.

[14:40] So is it possible that maybe some of the things that we thought about in second-order cybernetics are more relevant and more possible now? [….]

[15:05] So one thing right now that is an argument — a disagreement — at the faculty level is about whether we should grab cybernetics, or not. Shouldn’t we just use the word “design”? Cybernetics comes with a lot of baggage. There a lot of people who are practitioners of cybernetics. When I wrote a little bit about cybernetics, I found some very enlightening comments coming in. But some people who had done stacks of work that seemed a little bit too … like the tools they were using to think about it, they weren’t using the new tools. I didn’t want to diminish what they were doing, and get on their turf, but I didn’t think I could grab the whole lot. [….]

[15:50] Since all of you are in touch with where we are with the Media Lab, or at least you know the DNA of the Media Lab, and you also know the history of cybernetics, I’d love to find — talk about the history of cybernetics, and talk about the forensics of it, and where it is today, and then maybe talk with about how you think it might apply to the future. [….]

[19:00 Paul Pangaro starts the discussion with “what does cybernetics offer”?]

[….]

[59:25] So where is this discussion, now, around, what is the terminology?

[59:30] This has been a useful conversation. We’re launching this idea around — we’re using the words “extended intelligence”, to be what we’re using instead of AI, to talk about this environment. We’re also going to be doing this meeting around AI and governance. And we’ll be launching what it means to put society into the loop.

[1:00:00] In terms of doing, we’re doing. I think a lot of the idea and the words you’re talking about resonate with what we’re thinking. We launched this Journal of Design Science, and are now working on how we describe it. We’re trying very hard — and Stewart has been helpful in thinking about this — to keep it a conversation, rather than be a series of peer-reviewed papers. The way we’re trying to do it is to have a conversation; have the online — we’re using a platform called PubPub that allows versioning and commenting — for the output to also be a reflection of the conversation. […]

[1:00:55] So, we’re moving forward. What would be interesting — talking about cybernetics — would be to learn from cybernetics, and also to see, if you and other people working in cybernetics can take some of the things that we’re working on — like say, machine learning, or evolutionary biology [….]

[1:01:50] The tricky part for me is, how do we have this conversation? I do think that kids don’t read books any more. It’s an ongoing conversation. The words are very fluid. Some of them mean different things to different people. [….]

[1:02:25] The world is much more global, now. Macy was kind of confined, in the cultural context. You talked about the influence of the Austrians. But now we have the impact of everyone. How do we have this conversation across languages?

[1:02:40] To me, that may be the harder thing.

[….]

[1:10:35] So that’s my personal meta place. What is the institution? I look at Bauhaus, I look at Black Mountain, I look at RLE, and all of the other institutions. And for some reason, the Media Lab has sustained over 30 years. I think a lot of it is the approach. [….]

[1:11:00] That could be what we learn about the forensics of cybernetics. [….] Some people had a tremendous amount of impact, but not sustainability. The Media Lab has a weird thing. We like orthogonality and disagreement, we build tools, we’re not obsessed about theory, although we have it, not as a primary output. And then, we’re happy to move along, as new tools come, and new technologies come.

[….]

[1:14:00] I feel like the best designer designs themselves, as the intervention. And, so, it’s me personally, and then the institution. The journal is for us, but I don’t want to create the church. So you have to make the membrane permeable, but humble in that I want to affect myself. In affecting myself, I may affect the environment, in a responsible way … but not to be evangelical about it.

[….]

Tagged with: ,
Posted in Talk Video Streaming

Ian Mitroff | “Dirty Rotten Strategies: How We Trick Ourselves and Others into Solving the Wrong Problems Precisely” | Feb. 24, 2010 | Commonwealth Club (web video, MP3 audio)

Don’t solve the wrong problems precisely.  Type 3 Errors and Type 4 Errors, by Ian Mitroff, extending the Design of Inquiring Systems.

Abstract, from http://www.commonwealthclub.org/events/2010-02-24/dirty-rotten-strategies-how-we-trick-ourselves-and-others-solving-wrong-problems-p

How can people or groups tell whether others are deliberately steering us down faulty paths? Mitroff delves into how organizations and interest groups lure us into solving the “wrong problems” with intricate but inaccurate solutions that are based on faulty and erroneous assumptions – and offers strategies and solutions.

Video of 9m57s (with slides) on “Book TV: Ian Mitroff & Abraham Silvers, Dirty Rotten Strategies” at https://www.youtube.com/watch?v=CjgJVp9f_1k

Video of 59m47s (with slides) on “Book Discussion on Dirty Rotten Strategies” at http://www.c-span.org/video/?292366-1/book-discussion-dirty-rotten-strategies

Audio podcast 1h41s downloadable at http://www.commonwealthclub.org/events/archive/podcast/ian-mitroff-dirty-rotten-strategies-how-we-trick-ourselves-and-others-solving

Preview of book at books.google.com/books?id=9Iol_cctGHkC&printsec=frontcover#v=onepage&q&f=false


[This digest started with the Youtube transcript, and therefore initially uses that time code to 09m57s.  The version on c-span.org has a 14 second header and then runs to 59m47s.]

[00:00] If I had to sum up the the book in a single statement, it would be:  don’t solve the wrong problems precisely, because if you do, it not only a waste of precious resources, time and energy, but it leads to cynicism and despair and puts off the true problem such that they build up into a crisis.

[00:19] Also, if I had to summarize in a in a single saying, it would be from the celebrated author Thomas Pynchon:   if they can get you asking the wrong questions, then they don’t have to worry about the answers.

[….]

[00:40] What’s worse, the wrong solution to the right problem, or, the right solution to the wrong problem?

[00:50] Well, the right solution to the wrong problem is worse, because if you get the “right solution to the wrong problem” you convince yourself that you’ve solved the right problem, and you don’t go back up to the start of the tunnel coming into all of the different branches where you can branch off.

[01:07] You say, I’ve gone down the right path.  But if you keep getting the wrong solution to the right problem you say ok, I’ve made an error and hopefully the error will be self-correcting or I will eventually come to the right solution.

[01:20] Why solve the wrong problem precisely, as I said, a waste of time?

[01:25] In every case, whether you solve the right or wrong problem, it’s due to a set of assumptions.   Solving the wrong problem precisely is due to faulty assumptions, which leads to having to know your assumptions.

[Ally Bank, “Pony”, see http://www.adweek.com/video/ally-bank-pony-121402]

[….]

[03:16] Let me give you an overview of the talk and what’s in the book.   So, we’re gonna talk about something called E3 and E4: Error of the Third Kind, Error of the Fourth Kind.  They’re central to solving the right or the wrong problem.

E3: Trick ourselves; E4: Trick Others

[slide:

E3: Trick Ourselves

E4: Trick Others

]

[….]

[03:54] Let me start with E3 and E4.

[03:56] If you take a course in statistics, just about every courses talks about two types of error, type 1 and type 2 error. Everybody whose taken a course knows about that.

[04:07] And the easiest way to understand it is: you’re a drugmaker.   You have a new drug, and an old drug.   And what you you do you is to go out and test on a sample and hopefully the new drug is better than your old drug.

[04:17] But there are two types of errors you can make.

[04:19] One error is to say the new drug is better than the old drug when it really isn’t.

[04:24] And vice versa, the old drug is better the new drug when it really isn’t.

[04:27] And those are type 1 and type 2 errors.

[04:29] And those have to do with the bell-shaped curve and when you got the right samples.

[04:36] E3 is very different.  Have I tested the right hypothesis to begin with?  Am I asking the right question?

[04:43] So whether it’s the cost or the efficacy of a drug or of health care, it’s how E3 has to do with how we define a problem in the first place.

[04:52] And so E3 is when we trick ourselves.  Not necessarily anybody else, but, we tricked ourselves.  Okay, we fall in love with your pet hypothesis.

[05:00] E4 is more deceptive and potentially more harmful

[05:04]  It’s when I try to convince you, that the formulation that I and my company, my organization or industry has come up [with] is the right formulation of the problem.  And that you ought to accept it.  And there is no other way to formulate the problem.

[05:18] So it’s large fundamentally with miseducation.

[05:22] … in the book …

[slide:

Starts with Mis-education

X + 6 = 11 is an exercise.

Exercises ≠ Problems

Problems ≠ Messes

]

[05:25]  I’m not a proponent of textbooks.  Most of us start learning things from textbooks.  So the first thing we learned was X + 6, for example, equals 11.  What’s X?  That’s not a problem.  It’s an exercise.  The reason why it’s not a problem:  it’s already preformulated  There’s one and only one right answer, but you can usually convert it into a problem.

[05:46] Billy has six dollars and needs eleven dollars to buy a video game.   But Billy is in a poor family.  He has to give his money to help his mother and father.  Then it becomes a problem.

[05:55] Because the context is all-important.  Exercises remove all the context, descriptions.

[06:01] Now the problem with exercises, you give students, you know, 20 or 12 years, whatever it is, and education with exercises.  You turn them into certainty junkies and they balk like mad if you give them a real problem where they have to formulate the problem.

[06:15] In real problems, they have more than one way to formulate.   There’s not just one formulation.

[06:19] So you get into problem negotiation.  But you don’t get that, as you go through typical education.  Exercises don’t equal problems.  And problems don’t equal messes.

[06:30] A mess is a whole system.  A set of problems that are dynamically interconnected and change all the time. This is Russ Ackoff, who died recently, one of my mentors.

[06:40] But managers don’t solve problems, they manage messes.

[06:44] And that’s what President Obama certainly has to do.  It’s not a single well-defined problem, but how all these things are interconnected so the health care problem is not separate from the financial recovery and jobs recovery and all the rest.

[06:57] In fact, if you have a mess, and I’ll show you an example, and you take any the elements or problems out of the mass that constitute it, you distort the problem.   You distort the mess, because you have to look at the interactions.  Problems are not separable.

[07:13]  Health care.  Let me give an example of how we get off and solve the wrong problem.

[slide:

Health Care

Technically, the US has the best Medical System.

But, Technology ≠ Best Health Care System.

US has a poor Sick Care System.

Solves which problem?

]

[07:17] Technically, the U.S. has the best medical system in the world.  No question about that, from a technical standpoint.

[07:24] But technology does not actually equal the best delivery of health care as we want it.

[07:29] They’re not the same.  So solving the medical problem is not the same as solving the health care problem.

[07:36] In fact, the U.S. has a poor sick care system …

[….]

[08:04]  The health care system — and we’ll talk about the current health care bill — is founded on three primary assumptions.  (1) Government is the problem.  (2) Healthcare is a business like any other business.  And (3) cost-cutting is the primary aim.

[slide:

Three Wrong Assumptions

1. Government is the problem

2. Health care is a business

3. Cost-cutting is the primary aim]

[….]

[09:29] Now, it’s not that you have to accept my formulation or my statements.  That’s not the point. But I put my new things, my assertions, strongly as possible, so you know what I’m saying.  If if you disagree, therefore you have hopefully a better clarity on what you agree.

[….]

[Switch to c-span.org timecode]

[10:40 slide] The Critical Role of Critical Assumptions

[10:42] Everything is dependent upon assumptions.

[….]

[10:55] What happens in a crisis in principally this:  It’s not, yteah, that people die, which they do, it costs a lot of money, the organization loses money.

[11:06] One the primary things that happens that most people aren’t aware of:  a crisis literally demolishes all, or nearly all, of the principal assumptions that we use to give meaning to our life, to our reality.  That’s why I give an existential definition to a crisis.

[….]

[11:40 slide

Fort Hood.

1. Enemy

1. Location

1. Mental health professionals

1. One of our own

]

[….]

[11:50] When I listen to crises, I take them in a different way, because I’ve been so tuned to crises for 25 years.  In virtually every case, a crisis  undermines a primary set of beliefs that we use to make sense of reality.  And that’s why they’re existential crises.

[….]

[13:00 slide

Unreality

What's real?

Infotainment

Twittering in Operations

Normalization of the bizarre

]

[…]

[14:40 slide

Knowledge

How can we determine if we are committing an E3 or E4 error?

Inquiry systems]

[….]

[14:50 slide

Way Out

1. Expert Consensus Most Common
2. Scientific Modeling Most Common
3. Multiple Models E3 Assessed
4. Conflict E3 Assessed
5. Systemic E3 Rare

]

[16:40] You can’t really determine whether you’re committing a Type 3 or Type 4 Error, if you’re only using models 1 and 2, the first two ways.  Because they typically only produce one view of a problem, what they take as a “correct one”.

[16:55] It’s only when you get to multiple ways of defining the problem that you can begin to get an handle on “what is truth” or “what is false”.  Otherwise you can’t do it.  Not that it’s perfect.  I’m not saying that.  It’s only when you get to, then, 4 or 5.

[17:10] When you get down to 5, it’s the rarest type of knowledge system of all.  We don’t train people how to think systemically.  And that’s really the only way out of these horrific problems we face.  They can’t be defined by one discipline, one profession.  In fact, when I hear people come up with — boom — one definition of any problem, I want to run like mad, because I know you’ll have to accept their assumption.  It’s very rarely that people make their assumptions clear.

[17:50 Slide

Religion

Solutions to the social problems of 2000-5000 years ago

Rational reasons for God

Not he wrong solution to the wrong problem

]

[….]

[Karen Armstrong, The Case for God; contrary to Richard Dawkins]

[….]

[18:50] In fact, one of the first books I did was, The Subjective Side of Science.  I studied the Apollo moon scientists, not the astronauts.  And if you think a scientist worth his or her salt is going to give up his or her pet hypothesis, particularly for the origin of the moon, just because the first round of rocks are  returned from the moon, you’ve got to be crazy.  They’re going to do everything they can in the world to defend it.  Ultimately, they’re going to give it up.  But only after they’ve defended it to the death.  When I interviewed 42 of the most prestigious scientists, they said that was rational, that a scientist shouldn’t give up his or her pet hypothesis, too soon, lest they give up something worth exploring.

[….]

[20:20 Slide

Way Out?

Messes cannot be managed by the mindsets that created them.

]

[A paraphrase of Albert Einstein]

[….]

[20:50] The fundamental purpose of a university, to me, is to teach critical thinking. Yes, teach technology, and theories, and all the rest of that.  Knowledge.  Of course, all of that is important. But the fundamental job is critical thinking.  And critical thinking involves knowledge of assumptions, to be able to criticize your assumptions, to be able to replace them, to think about alternate assumptions, and to be able to appreciate complex messes, not simple-minded problems, in their entirety.  And to bring to bear on them, multiple ways of looking at them, from multiple disciplines, from multiple points of view.  To say, by looking at the mess, maybe now I have a better idea of which parts of mess I want to concentrate on for the time being.  But in order to know that, I have to see the entire mess.

[21:40] Is there any way to definitely say that you understand the mess fully?  Of course not, it’s a starter.  [….]

[22:10 Slide modified from George Patton

In conclusion:

If everybody is thinking alike, then NOBODY IS thinking"

Mitroff & Silvers

]

[23:25 Questions]

[….]

[24:25 Los Angeles Police Department]

[25:20 Ford Firestone]

[26:10 Toyota]

[26:45 Bill Clinton]

[27:10] What I’ve said to my clients, the people I’ve consulted with, is that if you have only one thing to do in a crisis, my recommendation is: hire an ex-investigative reporter to dig around all of the dirt of the corporation, and make a mock newspaper or a mock tv interview, to show your corporation in the worst light.  Because I can guarantee you that’s what will happen.  Now why doesn’t that happen?  Denial is so powerful.

[28:00 Defence mechanisms]

[29:00 We don’t have learning organizations]

[30:00 Environmental organization.  False choices, that lead to false policies]

[31:00 Five inquiry methods are abstract.  Have turned them into planning methods.  An example: Myers-Briggs Type Indicator. Put all of the people of one personality type into the same group.  When you do that, it intensifies the way of looking.  Ask them to define the problem.  List the major stakeholders that affect or are affected by the solutions, and what assumptions they make.  A systematic way to get a constructive debate]

[32:30  If you can’t get a Myers-Briggs, here’s another way to do it.  One group to argue status quo, whether they believe it or not.  Put people who are in moderate opposition, then more, then radical.  Then list the major stakeholders.]

[33:20 The biggest problem on which I’ve worked.  The U.S. Census Bureau, 1980, 1990.  Undercount.  We set up a week-long debate.]

[34:30 If you have a small organization, could be hard.  Professional management.]

[35:10 More systemic methods.  Where are we cultivating?]

[35:45 Book, in chapter on religion.  Ken Wilbur.  The power of human development.  Once an idea is unleashed, it gains currency, and can take off]

[36:50]  That’s one of the Type 3 errors that I talk about on the chapter on religion.  Here it is: confusing one state of development for the lack thereof in another state.  And that’s one of our principles.  We try to solve problems at one level, by a level one or two steps down, and they can’t be solved.  That’s the whole point.  And that’s why I’m talking about systemic.  Because the problems that we have cannot really be solved, unless, there’s a systemic perspective.  [….]

[37:40] I have to thank my editor at Stanford University Press for taking a radical manuscript, like this, with this kind of a message, to say: the ability to challenge our assumptions, to rethink our assumptions, to think expansively, to think beyond the confines of a single narrow discipline or profession.  That’s the way out.  If we’re mired in one set of assumptions, or one organization, we can’t do it.  [….]

[38:20 15% of organizations can thing proactively, systemically.  85% can’t.]

[38:50 Global warming]

[39:15 Comment. Set up 20% of the time up front, defining the problem.  Then we can work on solving the problem.]

[40.00 Agree.  John Dewey said problems don’t start in disinterest, they start in moral outrage. Wellpoint.]

[40:50 Four steps of scientific problem solving.  1.  Defining the problem.  Conceptual model of the problems.  Broad variables.  Single explanation, no!  Each profession will define differently.  Advanced medical students, psychological students.

[41:50 2.  Build a scientific model.  The first stage is semantic.  The second stage is syntactic].

[42:00 The third stage is to derive a solution, not to the solution, but to the model.]

[42:10 The fourth stage is pragmatic.  Take the solution and see if it solves the problem.]

[42:20 The Type 3, Type 4 errors primarily happen in the first stage, defining the problem wrongly.  If don’t see all of the stages, have defined the problem incorrectly.  Different people focus on different branches.  They don’t see the scientific problem systemically].

[43:20 Initially, doing dialectic doubles the time.  In reality, the more you do it, it doesn’t double the time.  You can’t define one, without the other].

[44:10 Government, messes everywhere.]

[44:25 Broader than that.  Type 3, Type 4 in many areas.  Governments.  Corporations.  Point of strategic planning is not thinking about isolated problems, and to anticipate problems.  Don’t see one as better than others.  General Motors bureaucracy rivals federal government.]

[46:30 Los Angeles Police Department]

[46:40 Salt Lake Winter Olympics.  Problem was Russian ice dancers downgraded.  Didn’t think of all of the crises that could hurt the Olympic committee.  Could show families and grouping of crises.  Not the case that there are not good organizations to learn from.]

[48:30 Ken Wilbur.  Ability to think more complexly.  Challenges are greater.  Hope.]

[49:00 International news 15 minutes every evening, now 24 hour news.  Exacerbates problems?]

[49:40 More is not better.  May not lead to more insightful.  PBS 6 or 10 minutes more insightful.  Facebook and social media hasn’t led to better coverage.  Can now manipulate and merge images.  People can’t tell the difference, don’t care about the difference.  Ally Bank commercial]

[51:10 Michael Vick, football player.  Moral devaluation.  Someone auctioned off notes.  Expect more from the human society.]

[52:30 Media, merging images, real and non-real.  Young people hooked on texting while driving, a problem.  Technologically advanced is not the same as socially advanced.  An engineer, but not solely an engineer.]

[53:50 Tiger Woods.  Got so big, the rules didn’t pertain to him.  The first billion dollar athlete.  Shows had rapidly an icon can crash.  No secrets.  Horrible stitched together videos.  That’s what will happen.  Who will hire someone who will put them in the worst light. Primary thing in crisis:  you don’t own the clock.  The only way to gain control is to fess up, and hopefully the American public will accept it.]

[56:40 Toyota.]

[57:00 Betrayal]

[57:20 Crisis.  You can’t solve the right problem.  Assumptions have a half life, and decay over time.  As circumstances change, your assumptions have to change.   As Ackoff said, plan or be planned for.  Have a real learning organization and a real learning society].

 

Tagged with: , ,
Posted in Talk Audio Download, Talk Video Streaming
Beyond this media queue
This content is syndicated to Twitter. For professional perspectives, look to Coevolving Innovations; for a photoblog, look to Reflections, Distractions.
  • 2016/12 Moments December 2016
    Helsinki; Finland; London, England; Dublin, Ireland; Toronto, Ontario; Fairfield, Iowa
  • 2016/11 Moments November 2016
    Markham, Ontario; Toronto, Ontario; Scarborough, Ontario; London, Ontario; Montreal, Quebec; Brussels, Belgium; Woluwe Saint Pierre, Belgium; Amersfoort, Netherlands; Hameenlinna, Finland
  • 2016/10 Moments October 2016
    Toronto, Ontario; Richmond Hill, Ontario; Don Mills, Ontario; San Francisco, California; Oakland, California; Berkeley, California; San Jose, California; Mountain View, California; Las Vegas, Nevada; Los Gatos, California
  • 2016/09 Moments September 2016
    Toronto, Ontario (all images within bicycling distance of home)
  • 2016/08 Moments August 2016
    Washington, Iowa; Fairfield, Iowa; Cedar Rapids, Iowa; Toronto, Ontario
  • 2016/07 Moments July 2016
    Toronto, Ontario; Gravenhurst, Ontario; Denver, Colorado; Boulder, Colorado; Moline, Illinois; Coralville, Iowa
Contact
I welcome your e-mail. If you don't have my address, here's a contact page.