Artificial intelligence and machine learning are two subjects in which I’ve long had interest, especially as they relate to organizational change, people’s behaviours and communication, and the future of work. This keen interest is a prime reason why I joined IBM earlier this year, and has intensified as I’ve got to know a great deal about cognitive computing and, of course, IBM Watson.

They are both areas where I have some clear views on what this means for organizational communication (including public relations), eg, automation in the workplace. So I was thrilled to be invited by Chip Griffin to join him in conversation in his latest episode of the “Chats with Chip” podcast to discuss such topics.

I’ve known Chip since 2005, from when his company CustomScoop became the first sponsor of the FIR podcast I co-presented with Shel Holtz for a decade.

In “Chats with Chip,” we talk for a little over 30 minutes about the roles artificial intelligence and machine learning will have in the public relations industry. We talk about everything, from the automated creation of news stories by computers to the role that big data plays in communications, to the crucial role organizations must play in softening the social cost of such technological change, and a great deal more.

Take a listen:

If you prefer to read, here’s a transcript of our conversation.

*** UNVERIFIED TRANSCRIPT ***
Please review the audio before quoting to confirm accuracy of this unverified transcript.

Chip Griffin: Hi, this is Chip Griffin, and welcome to another episode of “Chats with Chip.” I am very pleased to have as my guest today, Neville Hobson. Neville, of course for our long time listeners is the co-founder of the FIR-podcast network along with Shel Holtz, and now he’s left the routine podcasting world behind and simply appears as a guest, but he’s also working for IBM, so welcome Neville and why don’t you tell us a little about what you are doing with IBM.

Neville Hobson: Yes, I will Chip. Thanks very much, indeed a pleasure to be chatting with you on this podcast. I joined IBM in January 2016 that’s about 6 months ago from as we’re talking today. A bit of a pivot actually. It’s not much to do with organizational communication in the sense of what I was doing before. It’s a lot to do with business transformation and a lot of corporate words like that that clients of IBM go through, so I tend to have conversations with people looking at the social elements of all of that in terms of sentiment analysis; in terms of how that enables people to make better decisions I suppose, and that’s very close to a topic that I am very keenly interested in, one of the reasons I went to IBM, which is this whole huge area of artificial intelligence machine learning and so forth and so on, epitomized in IBM Watson, and so that’s basically where I am at. A real career pivot I would add, so it’s a big change.

CG: Well it sounds very interesting, and I think it gives us a lot of fodder for things to talk about on this show because artificial intelligence machine learning, obviously Watson is at the pinnacle of that I think when people think of, you know, smart machines, but, you know, as we look at the communications industry, you know, whether you are on the PR side or the media side or marketing side there’s a lot of change that’s going to be happening I think in the coming years and really already has a little bit because of the rise of the machines as it were, and what it can do for you, and you know, one of the things I was struck by was a blog post you wrote earlier this year, and it had a prediction from [Gartner 00:02:07] where it says that, “20 percent of business content will be authored by machines, within the next 2 years,” and you know, we’ve certainly seen some stories.

There’s a company that produces for the Associated Press, automated financial report stories and now just recently came out said that they were going to automatically generate stories about Minor League Baseball games based on statistics that they were given in box scores and those sorts of things. First of all do you agree with that prediction? Do you think really that much of business content is going to come from machines in that sort of period of time?

NH: I’m not sure about most. I would say that the trend is quite clear and if we look at what machines, for want of a better word, are doing in this area, the AP is a very good example with the automated, some people are calling robo-journalism, where computer algorithms basically create the content and if you look at what exactly are they creating? It tends to be content that doesn’t require reasoning that doesn’t require, for want of a better word, deep cognition in terms of looking at things from different angles and presenting scenarios, they are reports largely and so you see things like the AP on sports reporting that other you mentioned recently about, I think it was baseball wasn’t it Chip?

CG: Yes.

NH: That Major League, not major league, the other league of baseball that this sort of reporting that recounts a factual event, you know, this happened, it started so forth; he did that ; he did that and so forth, yet it’s adding some interesting elements to it, which are suppositious, almost philosophical opinions, but this is still relatively simple for that technology to handle, and if you look at that kind of content, we have tons of it in the communication business ranging from press releases through to white papers, and that kind of factual content that is simply stating facts in a certain format and so that lends itself, I believe, very nicely to clever technology, to take that burden away from people and be able to churn out this kind of content far more rapidly, far more quickly.

Now that’s a dead symbol [inaudible 00:04:25]. They are all sorts of issues surrounding all of this as most people would agree, and that’s just looking at the simple level, wait until we get to talk about the real cognitive aspects that are coming down the line that I think will have a dramatic on society at large, much of which we are seeing already people discussing. Often in rather scary kind of terms, you know, “Robots are coming, is going to get rid of all our jobs and we won’t have work,” etc etc. It’s a huge topic as you noted Chip, so lots to talk about without any question.

CG: Yeah, you know look, I mean I think, and I am a huge fan of AI machine learning technology in general, I’m a complete geek, you know, but I guess I look at it and say, you know, I think the real value of it lies in informing your decisions in making your jobs easier, but you know, I’m not a huge fan of the robo-journalism approach. I mean, to me what they are doing is they are taking data that doesn’t even necessarily need to be in paragraph form and turning it into a paragraph, right? It’s almost just adding useless texts to call it a story, and so I’m curious whether that’s actually beneficial or whether that just sort of contributes to the damning down of the media industry that I think has been occurring now for some time.

NH: Well, you know, that’s an interesting observation, I haven’t looked at it like that. I don’t necessarily agree that it is damning it down because if you read some of the content, and again I go back to the AP because they are now an interesting use-case, if you will, in the sense that they started doing what they were doing i. E. an algorithm writing the sports report. They’ve been doing it for about a year before they told people they were doing it, and nobody really noticed, and I saw lots of people commenting on like going back to old reports and dissecting it, saying you know, “We couldn’t tell that a machine had written this,” It was a common comment I saw.

I’m not sure it’s damning it down, it’s taking on a responsibility to do something that previously had been done by a human being and now cause therein lies the issue. For instance, yesterday I saw a news story here in the UK about Sky News, the satellite TV company, and Sky Sports, which is a huge sports broadcaster and a news broadcaster that they are eliminating 50 job roles in the UK and replacing, these are cameramen, replacing them with robotic cameras, so automated cameras, so these are the things you see already notably on the BBC News here because they tend to show you this kind of sweeping views of the news room and there’s the news reader sitting at this really large table [inaudible 00:07:01] attractive set-ups surrounded by machines that are moving of their own volition, these are the cameras, so that’s what’s happening.

You think, “Wow, here is an example of how automation is replacing human beings,” yet the other part of the story is equally interesting in that that change has also resulted in 30 new jobs that wouldn’t have existed if they hadn’t made this change, so the net loss is still, you know, 20 or so people, but I think that’s an inevitability that there are going to be situations where the technology is more efficient and more cost effective, all those words that are not human friendly to fulfill a certain function meaning that the human beings who did that are going to have to be redeployed, find something else, retrain or not work there anymore, and that’s as blunt as I can put it that in my view is part of the new reality that is coming.

It will be, to a certain extent, disruptive in our society. It will have a social cost, but look at any change over the last couple of hundred years, starting with the industrial revolution that led to this that’s not to say therefore, it’s all OK, far from it, but the reality is that these sorts of changes in democracy certainly which are not, you know, market forces’ control things if you will are going to happen. It’s up to others, not to the government, to try and see how the social cost of this is as minimal as possible with whatever they need to do to participate in these changes, so that’s a bigger picture view of what’s coming I think Chip.

CG: As you know, this is not something new, it is something that every time technology has advanced there have been these switches. I mean, you know, there used to be more farmers than there were more factory workers, they used to be most postal workers, now there are more people working on email, you know, there is a constant evolutionary process that’s going on, you know, within the working economy, and I think that, you know, yes is it going to be painful for some individual people who are out of jobs?

No doubt it, and I certainly feel for, you know, the camera people who have lost their jobs with the BBC, but at the same time, as you know, it’s created new job opportunities and it’s also, you know, probably, you know, increased some of the capabilities of, not just the BBC, but other TV production companies to have these things and make it … For example a lot of people who appear on, you know, CNN here in the States actually have a robotic camera in their homes, if they are regular guest and so, you know, they no longer have to go to a studio in order to appear live on TV, and so that’s a huge benefit both to the network, as far as the quality of content they can get, but also to the guests, so that they are not constantly travelling back and forth to a studio.

You know, I mean look, it certainly can create pain, but I think the amount of opportunity that technology is creating, you know, far surpasses us.

NH: Yeah, I agree, and in fact if we kind of bring the focus to the communicator, I tend to see it, you noticed what I wrote in my blog about Gartner’s prediction and how this is coming at a pace. I agree it is coming at a pace, but it’s not uniform. This is not like suddenly tomorrow everything will change and we are all under threat of losing our jobs, it’s not quite like that. I see significant opportunities all around us, for communicators, in whatever branch of communication you happen to be working. I would add though that if your hundred percent job is writing press releases that’s probably a good place to start in evolving your career and do something that is not going to be the target of robotic journalism, for want of a better way of putting it, because that to me is a classic example.

What I see happening is the computer algorithms, the technology behind it all, taking on the job, if you will, of taking data that it is able to repurpose in a sense, to extract insights in a way that we can also do that, but it takes us a very long time. It’s at the whim of human behaviors, and all that stuff that you could think of, and here instead you have a machine doing this stuff, but then presents us with that analyzed data that let’s us then write something real smart or again putting it in a simple sense, the algorithm will write the first draft for you, and it stops. It then lets you focus on adding your own reasoning, adding your own insight, your own take on a particular story.

I would argue that’s not really any different to how things have been done like that for a long time with research assistants and other people. Now the question comes as, because I have been asked, “Does that mean they are going to lose their jobs?” “I don’t know.” I would imagine the focus on job loss and serious changes in circumstances of human beings working assumes that robots come and nothing else changes. I certainly don’t see it like that at all. I could see this as part of the broader evolution in business structures, in the workplace, in how people see their jobs, in the sort of things that people want to do that they can do that they are encouraged to do that they are unable to do, and that’s not to say that therefore this is a nice rosy utopia we are heading towards.

I think it will be tough, and there will be a high social cost with these changes, yet is it inevitable? I believe it is. Equally, I know lots of people who argue very strongly that that is not an inevitability because of the social cost, so this is a kind of an ongoing discussion that there isn’t an easy, simple answer. Reality, in the communication business, I believe that before too long, it will be common place that things like white paper, things like the kind of documentation that we create for our clients or our employers ranging from white papers, I mentioned through the press releases other content like that will be better done by computer algorithms that leave us to concentrate on more valuable activity on the one hand or use that to computer generated content, to enhance our own cognition if you will.

It kind of reinforces us. It helps us be more effective in what we do and how we identify topics that we want to write about or get zero in on the important facts in the document, the machine can do that more effectively and quicker than we can, so I think of it as a very useful research assistant on the one hand, so there are a lot of people with different views in all of this Chip, but in this profession I see that as one example of the beneficial changes that’s coming our way very soon.

CG: Yeah, I think you used a key word there, and that word is “helps,” you know, I don’t think ultimately that, you know, AI, machine learnings, smart machines, whatever you want to call it, replaces the jobs that communicators do, and, you know, I’m a huge data nerd, you know, I founded a company in Custom Scoop that is, you know, really focused on mining data and providing insights, you know, nowadays I would call it media intelligence rather than simply media monitoring, but it is, you know, at the end of the day even though I talk about, what I would call data driven communications, it’s really, you know, data informed or data assisted communications because ultimately we all need to bring to bare our own expertise our own experiences, and our own judgment, and so I think even in the case of a white paper press release, I can’t imagine a day where you would have that go out automatically without a human review.

You know, computers would do a very good job of a first draft, but it’s still, to me, as a first draft, and you will still want to go through it and, you know, make sure that the computer didn’t make a misjudgment anywhere because ultimately, computers are just a reflection of the people who created them, and the intelligence that goes into them is based on the judgments that the coders made and the business analysts made, you know, at the start of the process.

NH: Yeah, broadly I’d agree. I’d also add that since I joined IBM I’ve been playing around a lot with what Watson can do, and that’s, in my opinion, that’s way beyond anything I have currently seen elsewhere, out in the public domain. I’m not thinking of other companies like Google and Microsoft and even Apple for that matter, their own efforts with developing artificial intelligence, but in the case of Watson it is truly [streets 00:15:30] ahead of anything else that I have seen; in that, it gives me, certainly, confidence with some of the little experiments I’ve played with, of that tool creating something for me that I don’t need to check that I’m confident after 4 or 5 goes at it that, “Yeah, OK, I can trust it,” that it isn’t suddenly going to insert a random sentence that is completely off the wall or signifies it had a headache or something like that and BOOM! There’s a blip, a paragraph that makes little sense in the middle of the text.

I have not seen that, so it’s already quite clever, yet I would add, as well, that this is a bit like, you know, automated driving, in cars or autonomous cars and what happened with Tesla recently, where this is definitely not perfect yet. This is still very much , “We’re trying to figure this out,” territory, and not everything is going at an equal pace. Look at the huge interest right now in chat bots. Look at, recently, the Microsoft experiment with Ta, and how wrong that went.

I would argue very much that that wasn’t about the tech that was about the human beings, both decoders and how they approached it, plus, you know, the reality of people’s behavior on the social network like Twitter, so that reflected a dark side of human behavior they had anticipated and that’s nothing to do with the technology in my view, so we are at the beginning of all this, and so I see, literally, anything I see going on is still, “We are trying to figure this out.”

It’s something that’s very good, but generally I wouldn’t disagree with you at all Chip that If I were, you know, in a real world situation relying on my cognitive assistant, let’s call it, to generate content for me, how comfortable would I really be if I were presenting reports and papers to someone else in our employer or a client for instance that I would be OK with that without checking it. Well, I’d probably apply the 80-20 rule, in that I’d be comfortable most of the time, but now and again I’d probably want to check it, and indeed I definitely would to do that if it was something I felt was seriously critical. Until such time, as it evolves in to something that I can feel comfortable in and have trust in that it isn’t going to screw it up, a bit like that autonomous car that isn’t going to crash or misread a situation, either cause an accident or itself be in one. We are still at the early days of all this I think.

CG: Absolutely, and look I think it will change too, you know, as more and more people start using automated technology to create content, right? Because, you know, one of the challenges you have today is that, you know, you probably wouldn’t go out and broadcast, “Hey, I didn’t actually write this white paper, it was done by computer,” you know, maybe I would be [M-wood 00:18:12] right? Because it’s beneficial to, you know, to the company to do that, but I mean certainly if I’m creating a white paper I am going to pretend as if it’s human-generated because right now people value that more than computers, and so, you know, that puts you in the awkward position of, if there is a mistake, having to not only confess to the mistake, but also confess to how it was made.

Whereas, you know, in 3 or 4 or 5 or 10 ten years time, whenever it is, if a lot of people are doing it then it becomes less significant, and look I think, you know, it goes to the automated driving example, you know, self-driving cars, which I think is a great development because personally I hate driving. If something can get me from point A to point B without, you know, my engagement that’s fantastic, but, you know, today, you know, if a Tesla car gets in a single fatal accident that makes national headlines.

NH: Right.

CG: You know, but over time as more cars are being driven by computers, you still will have access, but they won’t be as newsworthy because it’s just, you know, it’s sort of a common behavior at that point.

NH: Yeah, it is and in fact let’s look at one other thing too that things like automated presser or whatever it is that people tend to focus one is just one element of all these, and to me it’s not the most important element. I see way more value activity coming from automation of repetitive tasks in the workplace, and again looking at it from the communications point of view that we look at the amount of information we have to sift through, the amount of content we have to read and absorb in order to make judgments nor to come to decisions on the next stage we take that to, in that particular bit of research, we often tend to call it research.

We read documents, we read reports, we look at the newspapers, the radio, the TV, social media, all that stuff and we take key elements of all of that to make our own decisions, so much of that is still utterly manual. Yeah, we might use some, you know, propriety services we subscribe to which gives us the filtered information, all that kind of stuff, yet that’s still not good enough. I’ve seen some fabulous statistics recently Chip about the amount of data that’s coming, you know, we are in a stage now where we’ve got, you know, whatever the word is, petabytes or zeta-bytes, I’m not sure, but I saw a great slide the other day that talked about in 4 years time, 2020 we are expecting to be exposed to to something like 45 zeta-bytes of data, every single day.

You think, “That sounds fab. What on earth does it mean? How much is that?” The creator of that translated it into a form that we can understand. It’s equal to 600 volumes of the complete Harry Porter series everyday; that’s 6000 billion, sorry I missed off the important number. 600 billion copies of Harry Porter, every single day that’s what’s out there, and the reality is we cannot hope to grab even, you know, a no point nor one percent of what we ought to be looking at and that’s where machines can help us, machines I am using that in the accepted terms people do, computer algorithms or whatever it might be that will analyze that structured and unstructured data and there’s something I have learnt recently that unstructured data is the key one, and that’s data you cannot anticipate what it is.

News reports for instance, events even, geo-spacial data, weather data, social media, all those things, you cannot predict what is going to be said in 3 days, lets say, where a structured data are things like the records you keep, predictive modelling that you might conduct, your expertise and thought leadership in your organization. You have got to record all of that. You know that data, and it’s in the data base somewhere, so you’ve got all that stuff, but all that other stuff you don’t have, and then look at data that’s coming in this scenario model that I mentioned about what’s coming, 2020, the so called “Internet that thinks.”

Everyone agrees, this is big data at large, but no one agrees exactly what it looks like. You’ve got so many different views. Something is clear though, this is coming, whether it’s 2020 or 2025 or whatever, it is on the way. We’ve got images, the growth of video and all this stuff that is hard to search that’s changing. You have tools, indeed Watson has some of these capabilities of analyzing videos through really [ansanted 00:22:28] way of describing it Chip, but I call it “Number crunching on a mega scale.” It looks at pixels, it looks at light and darkness densities and a whole ton of things to come up with. “This picture is X or this picture shows this person in this situation,” and we can’t do that right now, as human beings.

I like it a bit, although the scale is hugely different to the dawn of social media when things started going, ranging from blogging through to podcasting and then Facebook, and then social networks started developing, where we had tools like radian6 that’s the one that comes readily to mind because I used to use it that was how we would analyze this and draw insights from what the analyzed data was telling us, but that now is not really capable of doing what we really need to do, where you do need, something that we currently call artificial intelligence.

I think a more appropriate way is a cognitive enhancement I suppose that this information enables us to perform our jobs far more effectively and I see this as something that we should not fear at all, we should look at this as a hugely beneficial evolution, but tempered with the reality that there is a social cost to this. I believe very clearly that there would be a social cost and we have to plan for that in organizations, and figure out, you know, “What do we do if we decide to go down this route? That brings in this technology that means that we can have a computer algorithm performing the tasks that we have 10 people doing before, what happens to those 10 people? How do we evolve their roles to take advantage of this?” These are huge challenges for the HR folks in every organization-ship I reckon.

CG: You know, you very smartly note the difference between structured and unstructured data, and, you know, the huge potential in dealing with unstructured data and of course, you know, with Custom Scoop that’s basically what we’ve dealt with for 16 years and, you know, we don’t do it in an artificial intelligence kind of way, we simply try to impose some degree of structure on unstructured content and that’s, you know, beneficial, but, you know, clearly artificial intelligence and machine learning will help us do those kinds of things more effectively, but frankly even on the structured data side there is so much of it that I think machines can do a better job of helping us to understand it like, I think even something simple that almost every communicators familiar with to one degree or another and that’s Google Analytics.

NH: Right.

CG: You know, Google Analytics shouldn’t go in and slice and dice the data in a lot different ways and, you know, but if you read someone like Christopher Penn who I think, you know, probably knows Google Analytics better perhaps than the creators of Google Analytics, and does all sorts of tremendous blog posts explaining how to get the most out of it, but even he’s, sort of, it’s almost a game of Twister, where he’s you know, you have got to bend around backwards and guess and do all these things in order to get the best insights possible out of it. I mean, if we had, you know, really smart computers that could simply look at that and say, “OK, here’s the important nugget for you,” and it’s not necessarily which piece of content was the top on your list or, you know, may not be the, you know, even the page that converted best, but “Here’s something that we [inaudible 00:25:44] from this data that, you know, is actionable intelligence for you,”
When computers are able to start doing that on a routine basis for people, I mean, that would be huge for, not just for communicators, but for anybody.

NH: Utterly agree with you, and therein I think lies the reality of what we are going to expect to see that will happen on a very uneven scale, where some organizations will be at it, others won’t, but that’s just the nature of human society, I think. We’re not all the same. Our needs are very different, and we will see the early the adopters and the leaders doing things we can learn from, and I think, you know, those organizations who will be at the vanguard, will be out of the from there, would be wonderful if they were able to openly share their learnings all these things.

We are in, I think, a time in society, generally speaking, where sharing is a common activity these days, and I could see that fitting in with the other shifts we’re seeing in organizations. The very structure of organizations, what I intend to call, I’ve not invented this phrase by the way, the, “Gig Economy,” where people are taking a temporary relationships and building bridges with people inside and outside the organization, so those organizational borders are becoming ever more porous, so we are able to connect with others in ways that we couldn’t imagine doing, 10 years ago, never mind a generation ago, so a lot is fluid.

A huge amount is changing, it’s shifting sands in front of our very eyes. We need maps to navigate this and a lot of it we need to figure out ourselves, luckily we have the internet to help us, but I see organizations needed to do a lot more to help us navigate these things, by helping us create those maps, so there’s an opportunity and I say, communicators have that opportunity as much as anyone else does.

CG: Mm-hmm (affirmative). We’ve only go a couple of minutes left, but something you touched on a few times in this podcast that I would like you to just circle back to, as our final bit, is you’ve talked about the social cost and the need for both organizations and government and others to really be mindful of this. I mean, do you have particular ideas of things that we should be all thinking about as far as, you know, how to address the social cost of these advances, or is it really just sort of an evolution of the same kinds of things that organizations have had to look at, you know, in the past, you know, 20, 30, 50, 80 years? Job training, you know, employee education, all those kinds of things … What do you have in mind?

NH: It’s all of those things Chip without any question because not everyone works at, you know, internet [speedless 00:28:18], call it that. Not everyone is comfortable with this online world in every society; in fact they need hand holding, they need help, so there is a great opportunity for social advancements in all these, in the original sense of the word social. I don’t see it in political terms, by any means. I think governments have a duty, a responsibility to enable things to happen, and by that I don’t necessarily mean funding or whatever, it’s just making things available, opening up doors, lessening barriers, getting rid of all the red tape that we have all around us all the time, but I think organizations need to set their stall out there in terms of what they are going to do with all of these.

We could see the evolution of more collaborative activities between organizations including governments, and we have seen in the past. I have a glass half-full approach to all this Chip. I’m very optimistic and I believe we will see things like that happening. I don’t believe it will be uniform. I think there will be some parts of governments in particular, that have you dragged kicking and screaming into this, so we need strong voices. People who are confident, people we look up to now, finding, if you will, heroes in our environments that we can look up to, who can take the lead in helping us understand this and navigate the changes that are coming.

Otherwise I think, the pain will be great, so in a sense it’s minimizing the pain on the one hand, but it is enabling us to be confident in these changes and not be too fearful, understanding, yes, putting in the place of retraining and new job opportunities totally enabling companies to make all that sort of thing happen, so it’s an interesting time coming. I think it presents communicators, as well as many others for that matter, but let me focus on communicators; with great opportunities to help communicators for understanding, and also to find out, you know, what the issues are in organizations that need to focus on, where communicators play a key role in this.

It’s another interesting time we are about to embark on, I believe.

CG: It certainly is and the opportunities are great, and there are tons more things we could talk about on this topic. Unfortunately, we have reached the end of our allotted time, but hopefully our listeners are sitting here, hoping that we could talk more because that’s always the way I like to leave people, wanting more as opposed to saying, “You’re still going,” but in any case Neville I really appreciate you joining me today. Perhaps you could tell listeners where they can find you online.

NH: Sure, yes my pleasure Chip. It’s been great fun having this conversation, so I write a blog, NevilleHobson.com, I’m on Twitter @jangles, and indeed I am all over the social web, but Twitter is the primary place where you can find me.

CG: Fantastic. Thanks Neville and thank you all for listening.

NH: My pleasure.

(Keyboard mage at top via Wikipedia.)

• Related: In this conversation with Chip, big data was a topic we touched on. It’s a topic I discuss with Thomas Stoekle and Sam Knowles in the new Small Data Forum podcast published monthly by LexisNexis Business Insight Solutions. We recorded episode two recently, talking about the role of data in the UK EU referendum communication. Check it out.