This is Circulating Ideas. I’m Steve Thomas. My guest today is David Weinberger. He’s a Senior Researcher at Harvard’s Berkman Klein Center for Internet and Society and is a former Co-Director of Harvard’s Library Innovation Lab. He’s the author of “Too Big To Know”, “Everything is Miscellaneous”, and a co-author of “The Cluetrain Manifesto.”
Circulating Ideas is brought to you with support from the University of South Carolina’s School of Library and Information Science and from listeners like you.
David, welcome to the show.
I wanted to start off, we’re going to get into a little more detail about how your work of sort of organizing information and everything kind of fits in with the mission of libraries as well and your work with the Harvard Innovation, Library Innovation Lab, but, what’s your earliest memory of visiting a library?
I have a pseudo made-up memory of going to a children’s room, but I, I suspect that it has nothing to do with reality.
I hope it was a good memory at least, even if it was made up, so.
A made-up one is great.
How is your work around organizing information evolved over the last few decades as the internet has kind of changed things and even, I guess, even more so in the current era of, I guess we’ll use the word fake news, but, even though I don’t like using it, so.
Ah, it’s a hard question as I’m not inherently, I’m not inherently interested. What you say is true, true enough for sure. But, I’m not inherently interested in the organization of information. I’m not somebody, somebody who, I don’t know what would be the cliché, child activities and, you know, baseball stats or, I’ve, I’m a slob. I’m very, very messy. My actual interests, my interests in organizing information is based on a prior and continuing interest in the messiness of meaning in the world which I think is a very, very good thing, an important thing. So I’m 66, I was graduated from college in 72, just to give some, basically I’m an old hippie, just to give some context to this, and back then that was the age of computers, which became the age of personal computers, but still, you know, computers were primarily ways of reducing information, both the amount and also having to come up with pretty, not entirely, but pretty inflexible ways of organizing it, or of modeling it.
And that’s not quite fair, but it, it’s fair enough. I’m, computers were famous when they first, hitting the culture in the 50s and in the 60s for sure as instruments of repression because they required making people conform to the very limited number of, of fields in a record, or, you know, holes on a punch card etc etc etc. So they, computers did tend to strip away differences that were considered inessential for whatever system, computing system you were using. Whether it was an HR system or whatever. You only had room to track the things that were common across all people, cause that’s how records worked. It’s not how linked data works, but it’s how records work. And computer memory and storage was so small that it was a tiny handful of information, the requisite information and computers, I don’t have to say something in favor of computers to you, do I? I mean, you know, they were awesome, but they did have that level of reducing things to their similarity and stripping out differences and, you know, I was part of that, I shared in that cultural perception and perhaps prejudice. But I actually think it’s, you now, it’s fairly well founded, just a limitation of computing. And for personal reasons in the 60s I was going through, nothing all that interesting, but a sort, typical what we call a identity crisis when I was an adolescent who got to college, where it seemed like meaning fell off of everything. Meaning just didn’t adhere to things and, which is very disturbing, and as a student I got interested in philosophy because of, or in some ways despite the philosophical tradition, but because of some philosophers who were interested in reinvesting or noticing, describing the world in extremely messy terms of meaning. As recognizing that meaning is rich and super saturated and changing and connected as is, the more connections, the more meaning.
So, as I became interested in technology in the 1980s, so rather late, and then fascinated with technology as it went from the computing age to the networked age, it was, in a large, especially in the networked age, it was to a large extent because the network can handle, in some ways designed for handling, massive, massive, massive amounts of extremely messy data, and that became liberating to me. I thought that was liberating to me personally, but also culturally. I hoped that it would be liberating. We wouldn’t have to, for the first time, we wouldn’t have to pare our information down to fit into our media, whether the media knowledge, whether they were books or, you know, sort of the old-world computers. That was, and, so that’s where my interest, I know it’s a very long answer, but that’s where my interest in organizing information comes from. It actually comes from the joy and richness of disorganizing information in useful ways. That we, now information becomes more useful, the more, in some sense, the more disorganized it is. That’s, that’s new in the world.
Right, and I was going to say, cause you, you talk about when computers were trying to sort of tap that down and construct that into a more manageable kind of way of dealing with it and more logical and not really what our minds may be, or need, but then the internet kind of blew out, blew that all back up again, made it, made it all messy again, so.
Yes. Not only, exactly that, but also made it messy in genuinely new ways.
On the planet, and I’ll say in two regards. One is that you get sort of this Galilean synthesis where not only does the internet enable unbounded messiness, but also enables it to be searched dynamically and organized locally. You can organize the whole thing locally and have many local organizations of it. You can do that across, many times in the day or within different applications. It’s capable of, it’s not simply disorganized, it is, it is rich with transient ways and sometimes highly meaningful ways of, of organizing it. And the second thing, of course, is that this is meaning that is aggregated in some sense, linked in the real sense globally in a way that we’ve never, ever had before. And so this, this is, this is the golden age of meaning as far as I can see.
Right, and some of that you kind of get into in your, in Everything Is Miscellaneous, cause it sort of that’s, everything is kind of everything and so everything is kind of, it’s hard to put things into clear boxes now, so.
It’s hard to keep them in clear boxes. Let me amend that, so it’s easier than ever to put things into as many different boxes as you want, and to that very fluidly we, we all do it extremely fluidly, you know, if you compare the internet to a library and how we manage, how we, everybody on the internet, that’s an over-statement, most people on the internet manage that richness compared to how a library has traditionally done it, we are doing this all the time every day managing messiness in very messy ways and that’s astounding that. So we get not, don’t just get the messiness, we also get messiness that we can organize extremely fluidly.
Right, and I think, I think librarians love that kind of thing because then its just, its more things for us to organize, that’s really what our, a lot of our mindset is I think, of wanting to do that, so.
And librarians are among the few people in the culture who are trained to think about how, how things go together. Right. There’s not a lot of people who are doing this full time. The population, all of us, who are not trained librarians need to get way, way, way better at thinking in meta ways, thinking about what we’re looking at because of fake news and other, other hardships. But also because you want to be able to use this stuff and we’re teaching ourselves, which is great, we are thinking in more meta ways, but we need to do it very very explicitly. And this is something that librarians, many librarians are deeply conversant in and some do full time.
Right. It’s obviously a huge issue and it’s hard to get our brains around that, of how we fight that, getting information literacy, how to fight fake, fake news either in the old sense of the word or the new sense of the word, however we want to talk about it, just. Alternative, alternative facts or whatever we’re saying now.
So the old sense of fake news is news that is fake and the new sense is fake news is basically anything that Trump says.
Well, okay. Just want to be clear.
So how, how do we, it’s, how do we fight that? How do we, I mean, obviously librarians, like you said, are trained to think that. How do we change broader people’s mindsets when there are so many people dedicated to both sense of the words of fake news. I mean there’s people who are intentionally putting, you know, the, those stories on Facebook just to get clicks and things like that. And then there’s also the Trump version of events, how do we, how do we help other people get to that point where we can see through all that?
Well, we can’t, never could, and never have. But we can get better at it. We are currently, it may be that we are currently on the upswing of getting better at it. We certainly have been on the downswing for a while. But, you know, we, since, for a couple of hundred years we have a history of journalism where large percentages of the population attempted to say the majority, but I don’t actually have any data, are reading tabloids that have a very low commitment to news, to factuality. You know, the, you know the sorts of tabloids I have in mind.
Those complete the weekly grocery shopping stuff, but when we had many more newspapers, some of them were pretty scurrilous. We had yellow journalism, we had newspapers that were getting, didn’t care about facts and were out promoting etc etc and that’s, you know, that’s institutionalized fake news. That’s always been the, the case, but always, again, an over-statement, at least in the last couple hundred years of, of the newspaper biz.
So I, in the one sense it’s, the Trump, the Trump administration’s active embrace of fake news and the use of bots by foreign powers to promulgate fake news is genuinely new and genuinely disturbing. And I don’t know how you, I have nothing to say that other people haven’t said better and thought of better about the techniques by which we can try to help identify fake news and flag it to readers. The problem is that we know there’s an appetite for it, that’s why it exists, that’s why it has existed for a long, long time. People, you know, it’s fake news tends to be really entertaining, tends to obviously to confirm one’s beliefs and they give one quote evidence for one’s beliefs and there are very strong economic and political reasons to keep pushing things that either are fake news or, or so close to it that it might as well be fake news. It’s just totally fabricated, it’s so blown up out of proportion. So there’s a real appetite for it which means that the, that the technological techniques that give choices to readers or try to inform them, I don’t know, yeah we should do it, absolutely, I just don’t have a lot of confidence that it’s going to work cause you, at the same time that, back in the days when you could pick a scurrilous tabloid off the rack or an actual newspaper that’s not doing fake news, a lot of people went for the tabloids right next to, you know, the New York Times or the whatever it was. So, giving people those choices is apparently historically not how you drive people away from, from fake news.
In the 80s basically, when I was teaching philosophy for five years every year, every semester, I taught a large, like 75 person course on logic at a school where there were basic, it was a literal handful, like 5 philosophy majors, it was not a school that was big on that sort of academics. But, my logic classes filled and I’ll, for a very good reason, because there are requirements and it seemed like an easy course for people to take to get their humanities requirement. So, I, because I knew that most of the students, very few of the students were going to go on to anything in philosophy, I tried to focus it on a logic course that would useful and I would teach, I taught, primarily informal logic or fallacies, how to spot fallacies, and, you know, so you send them home to find examples in the, in, on TV or in the magazines of the argument from authority and then you talk about that. And I think it was a good course, I don’t, you know, it was fine. It’s a good thing to do, but it was a very, very typical critical thinking course. I’m in favor of critical thinking, that’s fine. And so it seems to me what the obvious, what, part of what’s happened which is that back when there were only a handful of media sources, cause, you know, it was the old days before the internet and we had three TV stations when I was a kid and the city would have three or four newspapers, of course that’s now greatly reduced. Very limited media sources and so we believed, I believed with some correctness that what got published had gone through a process and had some degree of credibility simply because it was published. Now we know the tabloids, you know, that actually was not nearly the case.
Nevertheless, editorial process and decisions were made, you know, primarily a bunch of white guys made some decisions that got published. And, you know, that, that has the advantage of providing a relatively uniform picture of the world, very homogenous and, of course, biased by the privileges of the people who produce it. Nevertheless, so, in that environment the idea’s critical of teaching fallacies, of critical thinking, was, okay so we have this body of stuff that’s somewhat authoritative, some reliability, and then you have occasional eruptions of fallacious thinking by advertisers and politicians and so forth. And if you can teach a student how to spot those fallacies, she can then disregard those and fall back on the body of acceptable feted authoritative information and I hope, I hope I put that in enough parenthesis, that, you know. But there is a belief that there was an authoritative body. Now we don’t have that, at least not nearly so much because one of the glories of the internet is anybody can publish whatever they want. That means that being published just obviously does not mean, just simply by the fact that you’re published no longer means that it has been vetted or by anybody, that is a great, that is a feature not a bug, and it’s also, you know, in some ways a bug. So in this world, teaching critical thinking as a way of disbelieving, of spotting disbelief. In the old days when you did that, that was fine because students knew what to believe, they knew to believe CBS News and Walter Kronkite, you know, to their peril to some degree, but that was the idea. And now, if you only teach how to destroy arguments, to spot bad arguments, you’re not doing enough of a job because you also have to teach students how to believe, how to, how to come, not simply to disregard beliefs, but how to come to believe. This is something that librarians are superb at, this is something that librarians deeply, deeply understand. They, they are generally, in my experience, they are not there as cultural, primarily as cultural critics to keep telling their, their users what not to read.
They’re, well hopefully directing people to new and diverse heterogeneous sources.
That is, it, more, that is more necessary than ever. Simply teaching disbelief no longer is enough. It just turns students into cynics, turns citizens into cynics. Teaching how to build belief is very hard, it is not something in general that students are taught outside of maybe, well that’s an overstatement too, but they are taught that to some degree in science for sure, but it’s no longer enough simply to teach critical thinking.
Right, well, I wonder in other fields like in the sciences if they just kind of, if it’s thinking, you know, well I apply this to my field, but do they open their thinking and think that well I should apply this to the, to the entire, you know, everything I know about.
Yeah, and well it’s hard, and this is something I think that we need to do a far, far better job of teaching cause you, you learn how to do an experiment in biology, you know, typically in botany in school. You put three plants under different colored cellophane, you see which one grows better, or whatever, and that’s great, I mean that’s, you know, it’s really important, it’s teaching evidence, it’s teaching scientific method, it’s getting students interested and curious, totally in favor of that. But then if you ask how do I generalize that, the answer isn’t well I’m going to do critical experiments, I’m going to set up a control group, I want to know what to believe about, you know, issue of the day, healthcare insurance. The answer that for a citizen is not well let me set up a controlled study.
It’s a different set of, of, it’s a different methodology and learning that knowledge is dependent upon, is tied to the methodology by which it’s developed and that methodology varies from field to field, like the rules of evidence, well even the rules of conversation vary from field to field is a very subtle thing and so teaching a student one methodology is, is great because at least it gives them the idea that in, in this field you have to think about how you develop knowledge and in science that’s how students generally are taught, at least I assume that’s still the case. The next step is a very hard one which is to apply that to other fields, but not with the same methodology. But that’s exactly where we need to be, we, we need to be showing our evidence, understanding how evidence works, or what the rules of evidence are within a particular domain or for a particular topic and, and that’s, that’s really, really hard. That’s a never-ending conversation, but it’s the one that we need to be having and I worry we, our culture is not teaching that enough to our students, we’re still teaching them to the test frequently. And that’s not, that’s exactly backwards.
I want to talk about AI a little bit, artificial intelligence and how it can kind of help us with information organization, retrieval, things like that. But I want to start the conversation with wondering if you subscribe to the Elon Musk mindset about AI, that it’s dangerous? Or something that we should fear? Or is it something that we should be looking forward to and embracing? Or somewhere in the middle?
Well I have, I’ve mixed feelings about that because I do not think we need to fear it any more than we need to fear any other very advanced technology. I know, I don’t think that it’s, I don’t worry about Skynet, and on the other hand, the chances that I’m right about this and Elon Musk and Bill Gates are wrong, very very small. So, I don’t have a lot of faith in my beliefs about this.
Do you think artificial intelligence is something that can help us sort through that, an analogy I sometimes think about in terms of libraries, is that the internet, when people say that the internet is going to close down libraries because everybody can get their own information, all that’s done is it’s, it’s made a bigger haystack for us to find those needles in. Is artificial intelligences something that can help us find information better?
Oh, absolutely. Better than, than a person? Even a librarian? Yeah. I lost an argument in a public forum a few years ago where I was, where I was on a panel and I, I said that I would far rather have a librarian recommend a book to me than have Amazon do it, and the person I lost to sort of harrumphed, harrumphed in my face and slapped me down and basically said, contemptuously, so I, it, disagreed in. And if I had, if I were in a panel and I had actual time to, to discuss it with them I would have said, yeah, no basically you’re right, you’re not right now though.
And the reason why, even though Amazon knows more about my preferences than my local librarian does, cause I, I go, I go to a library infrequently, the local whatever, I’m in Brooklyn, we have a fantastic library system, just really, really wonderful. The town’s justly proud of it, but I, I don’t go in very often. I, you know, it means leaving my house, I don’t like to leave my house. And when I do, the librarians are always wonderful, but none of them know me. I’m not there enough. Amazon knows my reading as well as other habits, way better than anybody else on the planet, except maybe Google, I’m not sure. Yet, Amazon’s recommendations to me are pretty hit and miss. Often, I always look at them, often they’re, they’re really helpful and often they’re stupid enough that I say, well I know why, you know, but no I’m not really interested in this genre, it was for some special reason that it was, that I bought it.
The main problem that Amazon has in recommending is that it’s interests are not aligned with mine. Amazon wants, will always make the recommendation of least resistance. The thing that will most quickly get me to fork over some money, and very frequently I’ll be happy with the book that they recommend, ‘cause they’re good at that. But, it’s, a librarian would, would, if a librarian knew me, even if a library doesn’t know me that well, the librarian typically will offer, if I say I just read this book and it was, it was great. I just read Hornblower book, I’m re-reading them from my, from my childhood, from my adolescence, and really enjoying them.
So, I said, I just finished this Horn, I’m returning this Hornblower book, what do you recommend? And the librarian may well say well you know there’s another, I don’t know, 10 books in the series, and people seem to like this next one and that’s, that’s the recommendation of least resistance and it’s a good recommendation to make. But, she may also say, there is the, what is it called, Something and Commander series.
Master and Commander, yeah.
Master and Commander series, you might like that, same topic, sort of the same writing, or there’s the one about the, oh god, don’t get old, you can never remember any, the, the series of 19th century salt, British soldier guys, but may also recommend a, which is not quite least resistance, it’s expanding my world, cause the librarian wants me to experience more of the world. To get some contrary points of view, and, which, and who else, who else wants that for me? A, teachers do, and maybe my parents do if I’m, if I’m lucky. And, every good parent does like in general, but who does that for me? Except the librarian? And so she, she’ll, may recommend, say well you know there’s this really great history of 19th century, 19th century, there’s a biography of, of Admiral Nelson that’s wonderful, wonderful, non-fiction, it’s not Hornblower, but you might like it. That’s not least resistance, that’s moving me into a different area. Amazon has no interest in doing that. Amazon just wants to sell me the book I’m most likely to buy next. But that’s not the issue. Cause it, the AI that I’m going to, that is going to make better recommendations than a librarian will, at some point will have to be non-commercial cause otherwise it’s, you know, Amazon.
It doesn’t have my interests, not reflecting my interests, it’s reflecting Amazons. So, the sort of AI that’s most interesting to me, and I think that people are generally talking about when they talk about AI at this point would, which is machine learning or a version of machine learning called deep learning. And the thing that is sort of philosophically really interesting to me about those two, especially deep learning, is that machine learning systems make their own model of the world. Older computer systems, typical computer systems, their programmer makes a model and says that, here’s a model of the large objects in our solar system and the, the space probe that’s going to be flying near them and here’s, the model includes gravitational forces, those formulas and so tell me how to get it to Pluto. That’s a model that we construct out of what we know, or if it’s a business, you do it on a spreadsheet and you say here are the things that affect my business, it’s going to be expenses and etc etc etc, let’s make a spreadsheet that connects all the boxes and then put in the numbers and get an answer. That’s a human-made model and, you know, very handy obviously. But the, the awesome thing, the mind boggling thing about machine learning systems is that you feed them the data, you feed them the outcomes of the data so to speak and they make a model connecting the data points. They can have tens of thousands of variables that are just fed in and the model can connect those variables in, in probabilistic and multiple ways. A single node connecting to many others with probabilities, you know, of, of this node affecting another node.
This is particularly true in neural networks which are modeled on how the human brain works. The result is that you can have a, especially in deep learning you can have a model that works, that is it, it, with a good degree of accuracy predicts the outcomes, and not, for the weather, for example, right, you know that it, this model’s getting it better, this system’s getting the weather predictions better than human models, or humans, or old etc etc. So it works, but when you try to figure out how it knew that it was gonna rain for, in four minutes over there, the number of variables that it connects are so, is so immense that the human brain literally cannot follow them. Sometimes these variables are processed and reprocessed in the system so you’d have to do this multiple times and it, it’s simply, we are not, we can’t do it. We just literally can’t do it, so it cannot tell you how it came up with its answers, but you have evidence that it’s, it’s a pretty good model cause it’s working and predicting.
So, we will, I believe, at some point, and it’s probably won’t be too far off, have deep learning systems that know enough about our behavior or prior behavior or behavior of people who are quote like us, but maybe in very micro sorts of ways, that it’s able to make predictions that are, and I hope that we’ll tune these, not simply for recommendations that lowers resistance, but recommendations of the highest interest, of the highest and social good we can say that we, we want, it’s good to give people recommendations, to get them to read and experience things that are outside of their set of what they already know and experience and think about. As computer systems get more and more interconnected, these systems will have more and more information by which to make these predictions and I, I’m pretty convinced that they’re gonna be, if we ever choose to do this, that they will be immensely helpful.
Well that’s sort of what I was going to ask, who do you think, or what type of organization would be the kind that would build this because obviously it would take huge amounts of resources behind it, and we don’t want the Amazons, Google, Apple maybe necessarily to be the ones to do it and is it a government kind of project? I mean the.
Oh god no.
The current government obviously wouldn’t want anything to do with it, but is it, would any government be a good thing for that? Or no, we need to keep away from the government getting that information?
I don’t want the government making recommendations on what I should read in any case. I’m just a pretty standard issue Democrat, I’m not a paranoid conspiracy guy at all, but that’s not government’s business. I’d be happy to have them fund other groups to do so. So, you know, it, well I’m sorry to say it is likely to come out of, out of a Google or an Apple. It’s less likely to come reliably out of, out of Amazon because they’re interests, they’re too directly connected, their interests are too, you know, they want to sell books.
But it’s also, I’ve been, you know for years hoping that a library association, whether it’s, you know, the ALA or it’s some, you know, set of university libraries or in conjunction with public library, or whatever, would start to build the library graph. Well, so library graph is a set of, it’s a huge mad mess, why don’t I just say it. I’ll go back to the beginning, huge mess of information. Everything that libraries know about works, about the metadata of works, the content of works, the word usage patterns in works, plus everything they know about their users behavior, everything that we can hope is sufficiently anonymized or generalized, which is, you know obviously a big issue, but I think can be dealt with. Or put into a massive network of information with as many connections among the pieces as possible and then link that up with as many other such masses of information that might, that might matter. Everything from, you know, Wikipedia to whatever usage information we’re able to get out of vendors, or Facebook’s graph of social interactions. We’re graph, you know, users generally will understand, but to be clear, where graph is not a visual display of, you know, like a bar chart, but is the term for one of these networks of, of complex, complexly connected information.
And I, as you’re describing that, I’m almost thinking that’s a good project for the Digital Public Library of America maybe.
I, yes I agree. I’ve mentioned it to people there. It is, it’s also OCLC is doing something, I think they’re calling it the Orange Graph or Logic Graph, Library Graph, last time I heard which is a couple of years ago. Because they have vast, vast, vast amounts of information about books etc. So they’re in a good position to do it. My hope is that any such graph will be entirely open, you know, basically unlicensed for use and encourage contributions from as many different organizations of all sorts.
Well, the last thing that I wanted to ask you about was if you could tell the listeners what the Harvard Library Innovation Lab is, if they don’t, and what kind of the, you were the director there for a few years, co-director there for a few years. And then what’s sort of the most valuable lesson thing that you kind of took away from that experience.
So, I left, I think, over two years ago. It was founded, I was there for five years and it, pretty much at its founding. It was founded by John Palfrey who was head of the Harvard Law Library at the time. And his idea was, well okay let’s build a space, I mean literally in the basement of the Harvard Law Library, for a handful of developers to work on projects that would range from the serious and, and, you know, seriously useful to let them experiment, let them do things that are even whimsical. And that’s what the lab was, it was three or four developers and two co-directors, me and Kim Dulin, and we had a couple of large, I think very successful and exciting projects that everybody worked on and then we had a whole bunch of attempts. Some of which were frivolous, but most of which were innovative, they were [laughs]. So, it has changed its character a little bit now. It is doing really great work on projects, but the projects tend to come from outside of the development team. That’s not always the case, there are still projects the dev team does, but the big projects are, are, have faculty sponsors.
Still part of the Harvard Law Library, still in the basement along with the, the librarians. So, they’re pleased to be there. And it has a set of this awesome, awesome people. But the, the big project at the, the big project for the past couple of years has been perma dot CC. Perma.CC which comes from the head of, the new head of the law library, Jonathan Zittrain, who has, who is a deeply technological and advocate for open internet among other things, as well as a law scholar, a pretty amazing person. Perma.CC initially gave a permanent URL to any, any law, initially any law review, law journal article, so, because apparently 40% of the law review articles cited by the Supreme Court are broken links [laughs]. So, Perma.CC fixes that in a very robust and, and well-through way and it’s now opening up to other fields as well. Anyway. So that’s the sort of thing they do, among other things. A lot going on there.
And the broken, and the Berkman Center where you are now, obviously, does some work with libraries too, my professional Jason Griffey I know was a fellow there as well, and so there’s social connections with your work now with libraries as well, so.
Yes. So it’s actually now the Berkman Center.
It’s okay. Yeah, Jason is affiliated, he is, as you know and I hope everybody listening to this knows, is also is an awesome forest for good in the field and a wonderful person. The, yes, the Berkman Center has a, has expanded into the library space, at least to some extent, be recognizing that it’s such an important field that so much, you learn so much about the rest of, of the world, the world of information and how to organize information and how to keep it just messy enough. Yes, there’s so much to learn from librarians and so that’s been a really fruitful multi-sided partnership.
All right, well, David, thank you so much for talking to me for the show. If people want to learn more about you, or get in touch with you, how should they do that?
Go to Weinberger.org/ well that will, that will do it, and.
They can make their way from your name [laughs].
Yeah, there’s, yeah, and my email is up there if you want to get in touch with me. I am not interested in buying a timeshare, however, but other than that.
And it’s, it’s been a few years since you had a book out, do you have, are you working on another one?
Oh yes, I’m in, I’m in chapter five of a, of a book that’s due in in December.
Well, we’ll see about that.
Oh we’ll, we’ll see in 2018 if we get a new book from you, so [laughs].
Yeah, I hope so.
All right, thank you, David.
Thank you very much.
All right, bye, bye.
Circulating Ideas is produced by Steve Thomas in the suburbs of Atlanta with support from the University of South Carolina’s School of Library and Information Science and from listeners like you. Find out how you can help support the show by going to Circulatingideas.com and clicking on ‘Support’. You can subscribe to the show on Apple Podcasts, Google Play, Overcast, or your podcast app of choice. And help others find the show by leaving a rating or review. And follow the show on Twitter @circideas, or like the show’s Facebook page. Music is by Pamela Klicka. Thanks for listening, and keep circulating your ideas.