A Crisis in Discoverability and how we can move towards fixing it

Lacking a single central repository that collects information about scholarly papers from each discipline, it is somewhat hard to estimate the exact number of journals and papers that are published each year. A conservative estimate was generated by Lutz Bornmann and Ruediger Mutz in their 2014 paper Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references, where they track all material — papers, books, datasets, and even websites — cited between 1980 and 2012. From this, they plotted the data and found that the rate of scientific output increases by 8–9% every year, meaning there is a doubling of total output every nine years. (The dip in recent years can plausibly be chalked up to more recent papers simply not having had enough time to be cited)

Admittedly, this is an imperfect measure because it ignores all those sources that were never cited, as well as those simply no longer cited. Still, there is at least a prima facie case that there is a dramatic increase in the amount of research currently created.

And even this might be understating the actual amount of potentially valuable work produced. One academic estimates that every year 10,000 papers gets written within his discipline, which compete for around 2,000 spaces. Those whose papers are rejected don’t just give up, but keep trying to publish in other reputable sources, leading to a backlog which spikes rejection rates to 94%. Since it seems quite plausible that a substantial chunk of those not-published papers might actually be valuable and only missed out because of a lack of space, he advocates for “creating a lot more journal space (maybe 3 times as much as we have now) for the additional papers to be published”.

And this isn’t even taking into consideration the effect of the Open Access movement and the trend of sharing results directly on social media and the web, and how the lack of traditional gatekeepers will almost certainly increase how much content gets produced.

What these discussions mean for publishers is that there is going to be an increasing need for efficiently sifting through large quantities of research output, because if relevant work can be located, then it is immaterial how much more unrelated material is added. In other words, discoverability is going to become an increasingly pressing issue.

I speculate that two kinds of tech changes will be necessary if we are going to deal with this issue. The first is an increasingly fine-grained tagging of content that will permit researchers to conduct incredibly precise searches for the topic they’re interested in. This might mean, for example, that instead of settling for a handful of keywords along with the title and author information, books will have to offer chapter-level tagging to provide more metadata as well as more precise metadata.

But as the metadata requirements get more demanding, it will also become increasingly onerous for the traditional manual generation of relevant metadata. This will call for machine learning approaches to rapidly scan content and generate the relevant kinds of metadata, which can then simply be approved by a human counterpart. This isn’t going to be a simple requirement, because different kinds of data (photos, paragraphs, etc) will have quite different technical approaches, with some involving the clever manipulation of language rules, and others looking to image identification techniques. And different academic fields might require very different metadata, indicating that tech will have to pay close attention to the variety of demands instead of simply producing a generic, high-level solution.

The increase in scholarly output might seem intimidating, but I prefer to look at it more optimistically since it suggests that we have the good fortune to be living in a time where we are producing more knowledge than we know how to handle. With some clever technical fixes, we should be able to harness this increase in productivity across the board, and effortlessly navigate through these changing times.

The State of Automation — Part 2

Two weeks ago, we published the first blog in our The State of Automation series, which looked at what the experts are saying about automation and how it is likely to impact the job market, specifically putting the creative industries under the spotlight. This week we delve into how automation might affect those who work in the publishing industry, asking key questions such as: which roles could be most under threat? In what ways will automation likely help us or hinder us? And will it replace certain functions and tasks?

Many high-profile sources have proclaimed that the creative industries are among the safest sectors when it comes to the very real threat posed by automation. But that does not necessarily mean that we all have a hall pass and our jobs will be secure for life. The big differentiator here is that a “creative industries worker” is not the same thing as a “creative type”. While the latter will be far less likely to be replaced by bots, by contrast the former is just as likely as the next person to see their role affected by automation in the future in some shape or form.

Automation in publishing

The truth is we don’t know exactly how and when automation will transform certain aspects of publishing. In some areas it already has, such as the increased usage of Content Management Systems which provide simple formatting and publication. We can gaze into the crystal ball and speculate all we like, but technology evolves and accelerates at its own, often astounding, speed, and it can also be reined in and regulated in equal measure. However, what we do know is how innovations like machine learning are currently starting to be applied, and which kind of functions it is starting to assist and benefit on one hand, but supersede, replace and render superfluous, on the other.

Like any other industry, the work that goes on behind the production of a book, magazine, newspaper or journal involves a huge range of different types of people. The publishing ecosystem is made up of legal professionals, accountants, HR directors, marketing personnel, sales people, production and editorial staff, and C-level execs, in addition to those who originate the product (authors) and those who sell it (retailers). While publishing sets itself apart from many other industries in being very social and reliant on human-to-human dynamics and interactions, on the face of it, we are still looking at organisations like any in any other domain. So, let’s analyse how automation might impact key positions within a publishing house:

C-level and upper management: It might be easy to think that those at the top of the tree will remain largely unscathed by automation — these are the decision-makers whose leadership we rely on to run a company, after all. However, a report in the Harvard Business Review in 2016 stated that managers spend 54 per cent of their time on administrative tasks. Many of the managers surveyed welcomed AI as a means of reducing their administrative workload in return for more time spent on “judgement work”, strategic thinking and building deep social skills and networks. Although automation is likely to help managers cut out daily tasks considered below their pay grades, it may also lead to the consolidation of managerial roles, for example an organisation may not consider it necessary to continue employing COOs, COIs, CFOS, SVPs and MDs if the CEO is able to take a more active role.

HR: If there is one department within an organisational structure where the human element reigns supreme, it’s human resources. Jobs in HR will be hard to automate, yet it’s predicted that technological developments, particularly around AI, will end up benefiting the profession a great deal in the long run. With tech giants such as Slack already developing HR-dedicated Siri-esque chatbots to handle many of the more mundane daily employee queries, platforms such as Job Market Maker and Entelo providing ever more sophisticated ways of managing talent acquisition, and training and development increasingly moving into the digital sphere, the HR role will undoubtedly be changed for the better by AI…which will give them more time to focus on any organisational fallout generated by automation.

Legal/rights: Technology has long been eating into what were once considered core legal tasks. Interestingly, a study by Duke Law and Stanford Law School recently found that AI software was able to deliver a 94 per cent accuracy rate when reviewing legal documents, compared to 85 per cent by human lawyers. AI techniques such as natural language processing have already started to provide a great deal of assistance to those in the profession and increasingly AI contracting software is being used to help process more routine contracts. As due diligence and contract work becomes more automated, legal professionals are having to focus more on assessing risk and providing counsel, areas which are yet to be impacted by automation. Another development worth watching, particularly for rights professionals, is Microsoft’s new rights and royalties blockchain platform, EY, which, when it rolls out later this year, is rumoured to be a game-changer for managing complex digital rights and royalties transactions. Whether this becomes a force for good in publishing, a job threat, or both, remains to be seen.

Financial: While the financial industry itself is consistently earmarked among the top three sectors to be impacted by automation, finance jobs within publishing are less likely to be affected for the foreseeable future. Research by Bloomberg concluded that financial managers and advisors are among the lowest risk group in the sector. Meanwhile it is expected that roles in accountancy and bookkeeping will become enhanced and will evolve to incorporate aspects of automation which make the role less open to human error.

In our next The State of Automation post we will analyse how automation may affect other roles within the publishing arena, including editorial, production, sales and marketing positions. Watch this space!

Why Publishers today can’t do without Version Control: A Primer

Philosopher Daniel Dennett once wrote that “There is no such thing as philosophy-free science, just science that has been conducted without any consideration of its underlying philosophical assumptions.” Something analogous is true for version control systems and any collaborative work place—the question isn’t whether you use one, it’s about how considered and efficient it is.

This might seem like an odd claim, but consider that a version control system is simply the process through which changes to documents are managed. So if you manage your files through imaginative names like “Meeting Report Draft”, “Meeting Report Final”, “Meeting Report Final FINAL”, you are already employing crude version control.

Of course, there are downsides to an informal system like this. Over time you are bound to mix up files, and will have to manually trawl through various folders trying to locate what you are looking for, hoping your past self didn’t use something obscure for the document title. Systematic version control stores all files in a single, easily accessible repository and timestamps each file for easy searches. The use of Word2vec can make this even easier, helping embed timestamped information in image metadata, letting you track down a version any time.

With the use of each file assiduously tracked, collaboration becomes easy because it can be verified that a certain file has been worked on by whoever is in the prior stage of the workflow. The ability to lock a file in use and grant control over when the file becomes editable for someone else ensures that no one starts working on a file before all the work that needs to get done actually gets done. Of course, system administrators can override these locks in case the person who locked the file suddenly gets otherwise occupied. Importantly, the lock controls who can edit the file, but it can still be viewed and downloaded at any time.

Past versions don’t get discarded, but are all stored in the server. This way, they can be referred to in case there are issues or questions, and in case work on a particular version renders it unfixable, previous versions can quickly be switched to and treated as the latest version. This creates a sandbox to try out new ideas without being forced to abide by a tentative choice that was being tried out.

Finally, the fact that multiple versions are stored on the server allows for an incredibly fine-grained catalogue of track changes that documents what the changes are, who made the changes, and when. Additionally, comments about why, when relevant, can be added. This is particularly useful for production processes that are many-layered, because linking comments to a certain set of edits goes a long way in making the system intelligible across the board.

For many publishers, their production process tends to be both intimate and informal, with many of their standard processes having been shaped by the culmination of decisions made over years, sometimes decades. These decisions often reflect the vast amount of experience they gather in the industry, and made with certain real issues in mind. This means sometimes the way they work may feel personalized, like tradition.

The flip side to this is that sometimes there might be some inertia against switching to new systems and new ways or working, because why change something that isn’t broken? Still, I think it’s good to step back once in a while, and ask whether the way you are working is actually logical and whether a simple fix could make things much easier. I think incorporating an official system for version control is one of those things that offer huge pay-outs in productivity and efficiency, while changing little about the way people actually work.

Digital Clutter: How Does Machine Learning Make Things Easier?

Today, in an effort to avoid physical clutter and piles of paper, we tend to scan in old documents, do everything by email, and store everything in the cloud.  But, we are setting ourselves up for a different kind of problem.  With a stack of paper, you could easily see immediately what each file is and discard those that are no longer important.  With digital files, you often don’t even take note of how much data is piling up—files, photographs, apps—until it is too late.

The Dangers of Digital Clutter

Lack of organization:  With digital clutter, there is no physical pile of papers to signal a time to go through them, so computer files and apps tend to pile up, forcing users to spend hours upon hours sifting through folders and files to find what they’re looking for.  According to a McKinsey report in 2012, workers spend on average 1.8 hours every day searching for information and it has only gotten worse in the last 6 years.

Security breaches:  Not knowing which apps or documents have personal information on them can open users up to hacking or possible misuse of sensitive information.  As the Facebook information breach shows, users are open to having their personal data analyzed and used at all times, without even knowing it. 

System slowdowns:  Having a lot of memory eaten up by unwanted files can cause a slowdown on your system, making opening and closing of files, web pages, and processes take much longer.

How Workflow Solutions and Machine Learning Can Help

Keeping You on Track and Eliminating Unnecessary Files:  Some workflow systems, including our product suite, allow for helpful reminders to bring users back to a file that needs attention, plus eliminates unnecessary files by having all team members working off one version of a document.  That reduces the number of versions on each team member’s computer and, thus, eliminates unwanted documents.

Organize and categorize photos: Google’s Cloud Vision API is among those tools using machine learning to analyze photos, categorize them, and organize them, making finding images easier both for work or for personal use. 

Erase Personal Data on Apps:  While users may want to retain personal information on their phones and computers, there are services, such as CompleteWipe that can go through and systematically delete all personal information.

Automatically File and Delete Unwanted Emails:  Though there doesn’t appear to be a system yet that deletes unwanted files after they become useless, there are plenty of systems that help reduce unwanted apps and emails.   For emails, ActiveInbox, helps turn each email into an action item with a due date and then puts each in a folder.  Though it requires a more active way of addressing email, it does pay off in time saved.

As we build up more and more digital clutter, the use of machine learning tools can quickly and easily help us manage this tsunami of data that will only continue to grow and overwhelm us.    

The Games of Alan Turing

Are we asking the wrong questions about AI?

There's no lack of discussion about whether machines can be conscious and whether they can undertake all that is distinctly human. But these tend to centre around the relatively narrow question of their computational capabilities, obscuring important aspects of how we think about consciousness.

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Let's begin with Alan Turing's seminal paper, Computing Machinery and Intelligence, where he proposes we replace the more abstract question of "Can machines think?" with a clever thought experiment called The Imitation Game, now more popularly known as the Turing Test. According to this, an interrogator is allowed to ask questions to someone in another room using only a typewriter. The interrogator is allowed to ask whatever question he wants, and he receives responses from the person in the other room. According to Turing, instead of wondering in the abstract whether machines are capable of thought, a sufficient condition for a machine being able to think would be a digital computer's possession of the ability to answer the interrogator's questions well enough to fool the interrogator into thinking it is human.

Turing gives examples of how exchanges in this game could occur:

Q: Please write me a sonnet on the subject of the Forth Bridge.
A : Count me out on this one. I never could write poetry.
Q: Add 34957 to 70764.
A: (Pause about 30 seconds and then give as answer) 105621.
Q: Do you play chess?
A: Yes.
Q: I have K at my K1, and no other pieces. You have only K at K6 and R at R1. It is your move. What do you play?
A: (After a pause of 15 seconds) R-R8 mate.

He argues that this set-up is valuable because it is "suitable for introducing almost any of the fields of human endeavour that we wish to include".
This aspect of the test is important to note because the stringency of the requirement is often not taken too seriously. For example, the recent unveiling of Google Duplex, Google Assistant's newest feature that automatically sets up appointments for its users, was met with excited headlines like Did Google's Duplex AI Demo Just Pass the Turing Test?. While the system certainly seems competent with respect to its narrow goal, it does not come close to capturing the massive variability and depth of human communication, and so obviously fails the Turing Test.

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Turing's paper came out in 1950, and he hoped that within a century, it would be commonsensical that machines could think:

I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted

While the century hasn't run out just yet, this transformation in the way we think hasn't quite come to pass. One reason for this are a class of arguments Turning termed "Arguments from Various Disabilities", which argue that even if certain human capabilities could be carried out by machines, it takes more than that to actually think or be conscious. There will always be certain things they wouldn't be able to do, including:

Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humour, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make some one fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behaviour as a man, do something really new.

Turing's own response to these was that they were a result of faulty scientific induction. According to him, people had just been exposed to a small range of machines with limited capabilities, and had made sweeping and unwarranted assumptions about the limitations of all machines based on these. This is almost certainly right, but here Turing fails to develop a line of inquiry which I believe is vital to understanding the force of this objection.

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Turing was sensitive to the fact that an adult mind doesn't leap into existence from nowhere. He points out that along with the mind at birth, there is much that is taught and experienced which eventually shapes how the adult mind functiona. But here Turing doesn't go far enough in seeing how dependent consciousness is on other people. After all, his way of talking about learning and experiencing treats machines as purely cerebral and solipsistic.

As Abeba Birhane explains in a recent Aeon article titled Descartes was wrong: 'a person is a person through other persons', there are aspects of human identity and being which are irreducibly relational. The presence of others and paying attention to their perspectives (both actual and imagined) play crucial roles in how humans develop a sense of self and function in the world.

I'm not suggesting Turing necessarily missed this, after all a key reason for his development of the Imitation Game was to produce a stripped-down test which would not require much background information. But by not exploring questions about the nature of the self in his paper, Turing inadvertently kicked off a research programme which centered questions about the capabilities of machines in isolation, and to this day this colours the way we think about AI. To move past this, we'll have to face head-on those possibilities where machines develop their capabilities over time, through interactions with humans and each other, all while being able to run computations much faster than we ever could.

I suspect that doing this will force us to confront the very plausible scenario of our oncoming obsolescence. It's tempting to pretend this isn't a serious issue, that it can never come about, but to echo Turing, I think "consolation would be more appropriate".

The State of Automation - Part 1

Automation and its impact on the job market, our livelihood and our way of life has been a hot topic for several years now. Seemingly every management consultancy, recruitment firm, IT company, think tank and government body in the world has at some point weighed in and released a study or white paper projecting the future impact of automation and all the doom and gloom that comes with it. 

We’ve seen research from leading IT analysts Gartner and Forrester, consultancies and auditors such as McKinsey and PwC, as well as renowned global economic organisations such as the OECD and the World Economic Forum (WEF) - all throwing their sizeable hats into the automation ring.

What the experts say

Each study has attempted to paint a picture of what the short and long-term future will look like; from analysing which social groups are most at risk to highlighting which jobs are most likely to become obsolete, from calculating how many of us will suffer to capturing the general public’s fears when it comes to automation.

Much of the research seems to conclude that certain jobs will become more at risk than others, highlighting those in the financial and manufacturing sectors as the most under threat. And it would appear that low-skilled workers and young people with entry level roles are the most at risk from automation, validating Martin Ford’s theory that those whose jobs “are on some level routine, repetitive and predictable” will likely feel the pinch. 

The OECD goes as far as predicting that automation will create more divisions in society between the educated classes and working classes, the high skilled and low skilled worker and the rich and the poor.

To believe or not to believe, that is not the question

Varying wildly in their prognoses on a scale of conservative to devastating, barely any of the research we’ve seen to date can be corroborated or supported by parallel studies, which points to a rather confusing landscape. Do we actually know how AI, robotics and other forms of automation will affect us in five,10 or 20 years? Apparently not, is the one main takeaway to be gleaned from all of this. 

But that is not to say that we should just dismiss all this heavyweight research as tedious scaremongering. After all, the fact that the research is being conducted in the first place speaks volumes. What we do know, is that to some extent and at some point, within the years to come, automation will touch our lives, and this could be in a positive or negative way depending on a variety of geographical and socio-economic factors. It’s now up to us to speculate as to how our roles might evolve over time and how we choose to be prepared for the possible, probable or inevitable.

Impact on the creative industries

Those who work in the creative industries are often cited as one of the low risk groups, who, alongside healthcare and science professionals, are less likely to see their roles disrupted or destroyed by automation. 

In 2015, Hasan Bakshi from UK non-profit Nesta claimed that “creativity is one of the three classic bottlenecks to automating work” and that “tasks which involve a high degree of human manipulation and human perception – subtle tasks – other things being equal will be more difficult to automate.

Within the creative industries, including publishing, these kinds of hypotheses have triggered the common and widespread view that we are all somehow exempt from automation, and that the craft and humanistic qualities of our work will shield us from the dangerous and entangling tentacles of automation.  

But this couldn’t be further from the truth. 

Over the next few weeks in this State of Automation series, we will examine how automation, particularly AI, will likely affect the publishing industry. We will look at the roles which are most and least at risk and discuss how the industry could potentially evolve to be better equipped to embrace forthcoming innovation.

Why Publishers need an industry-specific CMS

As publishers realize that using a Content Management System (CMS) is not just good organizational practice, but increasingly indispensable to remain robust and competitive, an increasingly common question to consider is what sort of CMS to acquire. While it might be tempting to simply use free services like Dropbox or Google Drive, I’ve found that there are four reasons why a more specialized system that is specifically designed for publishing makes a lot more sense.

The virtues of Book Folders

While it is a truism that every book is unique, this doesn’t mean that certain trends tend to repeat. Recognizing that most books are split into chapters with different teams working on art, editorial, design, proofreading, etc., a CMS built for publishing automatically creates a comprehensive folder structure for each book. A sample screenshot from PageMajik is provided as an example:

For a production team where art, editorial, design, and proofreading are handled by different people or at different stages, distinct folders are provided to store their files.

While it certainly is convenient to not have different teams constantly ensuring that they know which files are theirs and having to adopt intricate naming conventions, the folder structure enforces version throughout by making it possible to store each version of every file, as well as detailed metadata on each of those files. The presence of older versions lets users open any previous version to compare and contrast newer ones, and if the latest version proves unsatisfactory, a previous one can be reverted to.

As figure 1 shows, the metadata associated with each file that can be stored includes who created and updated it, when it was updated, and how many previous versions of that file are stored. This bird’s eye view of all content lets you monitor, search, and retrieve any information required, granting unprecedented control over the publishing process.

You get a Workflow, you get a Workflow, everybody gets a Workflow!

Your CMS doesn’t necessarily have to be a cluttered space where everyone has access to every file. You can specify instructions in advance regarding who is allowed to access what, letting you tailor the system to your particular needs. This doesn’t just keep files safe, it also removes the need to remember onerous instructions about who you should inform when you finish your work on the file or who to send your file to. Now the pre-set instructions will ensure that everything that has to be done at a certain stage is completed, and that once all the tasks are finished and signed-off on, the system will automatically trigger the next stage of production and everyone with permissions will be informed about this change.

This minimizes errors by not having to depend on just human supervision to ensure all the work gets done. In addition, it simply makes it more convenient for everyone involved, because they can focus on their work without having to deal with the hassles of the larger process itself.

 

 

The AI Wars

How Does Publishing Compare to Other Industries?

A report published last year noted that by 2023 the artificial intelligence market will be a $14.2 billion industry, up from $525 million in 2015, with most of the growth taking place in North America. “The reason behind the positive growth of AI markets in this region is the wide-scale adoption of AI technologies in various end-use industries such as manufacturing, media and e-commerce,” the report noted.

But how is AI currently being integrated into our lives? And can publishing learn anything from these other industries?

Retail

In online shopping, we see AI play a role in recommendations based on previous purchases, programmatic advertising based on behavior, and with chatbots helping to answer simple questions during a shopping experience. Today, almost every retail site features these tools, with Amazon and Apple’s iTunes leading on development in this field.

Media

In the media space for example, machine learning is already being employed on both the editorial and advertising side of operations. In a previous blog post, we noted how The Washington Post used bots to help with their Olympic reporting. In addition, Associated Press partnered with Automated Insights to use AI technology to automate quarterly reports. Content producers from every segment of the media are beginning to use AI software to improve the speed and efficiency of their workflow, the production process, and their ability to organize and categorize content.

Advertising

As with retail sites, advertisers are exploring a variety of ways to tailor messaging based on reader/user behavior. In addition, as mentioned in this AdWeek article, advertising agencies are using AI to discover new consumer targets and to customize information based on region or interests of individual users. What’s more, McCann Erickson Japan even hired an AI Creative Director to direct commercial design.

Entertainment

For music, film, and TV, today’s users require and expect curation and personalization. Netflix’s 104 million global users and Spotify’s 140 million global users go to each streaming site to be recommended films, television, and music that they will want to see. AI helps in creating that.

Music

Though technology was to blame for the demise of the music industry a decade ago, AI seems to be helping to bring it back. AI-generated music can help reduce time and cost, saving record labels significant amounts of money in the process, while also allowing musicians who may not be able to afford a band to play behind them to create the music they want with Garageband and other programs. According to a Goldman Sachs report, streaming services, such as Spotify, will generate over $34 billion in revenue in the music industry by 2030. As noted in this Forbes article, user behavior and interests that come from using streaming services can help the music industry better understand the market, what types of music and artists to invest in, and how quickly to roll out new music.

Film

In 2016 for the film “Morgan,” 20th Century Fox partnered with IBM Research to create the first ever cognitive movie trailer. As noted in IBM’s Think blog, “Traditionally, creating a movie trailer is a labor-intensive, completely manual process. Teams have to sort through hours of footage and manually select each and every potential candidate moment. This process is expensive and time consuming –taking anywhere between 10 and 30 days to complete. From a 90-minute movie, our system provided our filmmaker a total of six minutes of footage. From the moment our system watched ‘Morgan’ for the first time, to the moment our filmmaker finished the final editing, the entire process took about 24 hours.” It is streamlining these time-consuming processes throughout the industry where AI can be of best service.

Publishing

For publishing, there are a lot of possibilities for where to use AI, but the need and use so far has outweighed the development. For example, technology and better direct connection to readers has provided publishers with an extraordinary amount of granular information about customers and products in the marketplace. Unfortunately, although they have this information, there is simply no way for a human to go through and easily process this information and develop ways to use it.

As previously mentioned in retail, recommendations on bookselling sites is probably the most prominent use of AI in the industry at this moment in time.

For academic publishers, AI can measure a student’s understanding of concepts and tailor a specific framework for that student’s learning.

For the PageMajik product suite, we are using AI to help speed up the workflow from author to the marketplace in order to save the publisher time and money. We hope to eliminate some of the redundant and time-consuming tasks throughout the publishing process by automating significant portions with AI.

Is Technology Fatigue Holding Publishers Back?

If the CEO Roundtable at BookExpo is any indicator, publishers are still focusing on traditional channels in which to reach readers. As Shelf Awareness reported, “[Macmillan CEO John] Sargent agreed that the ‘long-term health of the industry’ was good, but said he thought that in the coming years publishers will face ‘some serious issues’ pertaining to ‘changing consumer buying behaviors.’ As consumers shop more and more online, it will be harder for them to discover books; Sargent argued that what publishers need to protect is ‘lots and lots of shelf space’ in which customers can browse and discover books.”

Music, film, and television have embraced the discovery tools and companies like Spotify, Netflix, and Hulu have helped them find both tried-and-true and new audiences using AI discovery tools. Books and readers have yet to embrace that technology. Other than subscription models and the Amazon algorithm, there have been few ways that the publishing industry is really exploring discovery via AI.

Is this due to a lack of understanding of the changing marketplace? Or an unwillingness to give up on existing channels and modes of discovery? Or is it something to do with how readers discover books?

Traditionally, discovery has been about browsing a bookshop, as Sargent noted; seeing an enticing cover, reading the flyleaf, scanning the first page. Today, that isn’t the speed at which the world works and traffic to bookstores isn’t what it once was. We need new discovery tools and a way to connect to readers where they are—on their computers, smartphones, and tablets.

Discovery isn’t the only place in which publishers continue to follow traditional channels. Back-end systems for workflow and rights management continue to be maintained in older methods. AI can help speed up time-consuming processes and provide better record-keeping, but what is slowing publishers down is something else that is going on—technology fatigue.

For the past 11 years since the Kindle turned the world on its ear, the centuries old industry of the printed word has been trying to play catch-up to the ever-changing consumer. Every year, there are new tools, new channels, new ways of consuming content, and new perspectives on the industry. Are publishers just exhausted by the ideas and want to revert to old ways?

Perhaps.

At April’s Book Industry Study Group annual meeting, Maureen McMahon, president and publisher of Kaplan Publishing, and BISG chair discussed the challenges the book industry is facing as technology continues to impact it. When blockchain came up, she joked, “I’m not ready to think about it.”

And yet, as much as some of these sales channels and discovery tools and systems still work, publishing can be doing better if they just embrace some tools that can make jobs simpler and connect to readers more directly.

Our customers who have taken a chance on our product suite have seen a 40% increase in efficiency in the publishing process. Buying back that time in the day, freeing up staff to work on other projects, and speeding books and journals to the marketplace to meet growing demand, can help a publisher increase revenue dramatically. So, while the ever-changing technological landscape can sometimes be daunting and exhausting, it is worth the struggle for publishers to embrace these changes, adapt, and take control of their own future.

The Mona Lisa and Machines

A psychological theory for why we don’t take AI as seriously as we should

The artistic machines are coming. Artificial intelligence is already starting to upend deep assumptions about the indispensability of human input in the diverse areas like journalism, archaeology, writing, and even musical composition. Although a lot of this technology is still in its infancy, there doesn’t seem to be any real limitation in principle to the extent to which machines could take over in these domains, at least in the long run.

This awareness of our possible looming obsolescence should be a source of anxiety, but to be honest I just don’t feel it. At a visceral level, I still have a persistent gut-feeling that the richness of human art and creativity simply cannot be replicated by non-human machines, and this is unshaken by the accumulating evidence suggesting otherwise. A theory by Yale psychologist Paul Bloom explains why.

In a 2005 piece for the Atlantic, Bloom summarizes a fascinating theory of two distinct ways humans categorize objects in the world:

A distinction between the physical and the psychological is fundamental to human thought. Purely physical things, such as rocks and trees, are subject to the pitiless laws of Newton. Throw a rock, and it will fly through space on a certain path; if you put a branch on the ground, it will not disappear, scamper away, or fly into space. Psychological things, such as people, possess minds, intentions, beliefs, goals, and desires. They move unexpectedly, according to volition and whim; they can chase or run away.

From this difference arises two distinct domains of objects—the physical and the social—with their own interior logic and expectations. While both these domains are descriptions of the same world, they operate in non-overlapping ways:

We perceive the world of objects as essentially separate from the world of minds. This separateness of these two mechanisms, one for understanding the physical world and one for understanding the social world, gives rise to a duality of experience. We experience the world of material things as separate from the world of goals and desires.

While “physical” and “social” might be distinguished easily enough conceptually, in the real world the same object can have both a physical aspect and a social aspect. Consider, for example, the Mona Lisa. Of course, a big part of what makes this so valuable is how it looks—the way the light blends, the use of perspective, the enigmatic smile. But notice that these physical aspects (after all, just a specific placement of pigments) are replicable given the technology today. A 3D printer can probably generate a fake so similar to the original that even experts would be unable to tell the difference. But even if such a fake were produced, the value of the original would be undiminished and the fake would not suddenly be valued in the millions.

This indicates that a necessary part of what makes the Mona Lisa so valuable are the social aspects of the original painting—its particular history, including the fact that it was painted by Leonardo da Vinci in the 16th century using certain experimental techniques. Machine-made art lacks social aspects since we don’t impute intentions or goals to their makers, and these social aspects are necessary to make sense of why art in general is held to be valuable at all. 

So while it is amusing to consider the abstract possibility that a monkey hitting a typewriter for an infinite amount of time would almost surely type out the entire corpus of Shakespeare, for all intents and purposes, the social aspects of human art—the fact that a particular human being, with particular intentions, goals, and purposes—remain essential to our identification of and valuation of art. For now, the social aspects are considered necessary for art, but it isn’t implausible at all to think that this might change.

For instance, if you can’t tell human-made artifacts from machine-made, the social origins would simply matter less in any marketplace where the merits of the physical aspects is an independent metric of its value. After all, given time, AI might even start composing music that exceeds that which has human creators. At that point, the dominance of the social aspects in gate-keeping what is considered art will wither away slowly, as more and more people realize that their hangup over origins is keeping them away from superior art.

This isn’t to say that machine-made artifacts would necessarily be embraced rapidly or by everyone, but it has to be conceded that the distinction between the physical and social we currently rely on tacitly in privileging human-made art, and the consequent dismissal of the possibility of machines making inroads into the human world of creativity, is far shakier than we might think.

To come back to where we started, I still have a visceral sense that the richness of human art and creativity simply cannot be replicated by non-human machines, it is just hard-wired into our brains. But I’ve come to realize that this feeling shouldn’t be counted on.

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