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.


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.


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.


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?


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.


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.


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.


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.


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.


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.


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?


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.

The Ripple Effects of Blockchain Investment

This week US-based cryptocurrency start-up Ripple, announced the launch of its University Blockchain Research Initiative (UBRI) by committing over $50m to 17 global universities in order to support and accelerate education and technical development around blockchain.

This significant move by one of the most widely talked about crypto firms will see Ripple form close collaborations with institutions and offer technical resources and expertise, in addition to funding. Projects being undertaken by universities as part of the programme include research by Princeton University into the global policy impact of blockchain and a blockchain research program being built at the University of Luxembourg. Other prestigious schools involved in the UBRI include the University of North Carolina, MIT, and the University of Pennsylvania.

While the UBRI is likely to focus predominantly on digital payments in the financial sector, Ripple’s main business interest, the initiative is just one example of many buoyant investment drives taking place all over the globe, which aim to nurture a new generation of blockchainers and help blockchain realize its massive potential.

Worldwide bragging rights

Although most of the major blockchain success stories to date undoubtedly come from Silicon Valley and some of the main tech hubs in Europe, China is also becoming increasingly active and ambitious at encouraging blockchain innovation. In April, a new Blockchain Industrial Park opened in Hangzhou, home of Alibaba, which is designed to act as an incubation centre for blockchain start-ups.

Concurrently, a fund of $1.6bn was made available to help support some of the country’s most promising blockchain projects. The Xiong’An Global Blockchain Innovation Fund is partially funded by the city government and will be managed by Li Xiaolai, a renowned blockchain investor and bitcoin tycoon.

On an international level, governments are extremely eager to be in the blockchain game, and venture capitalists are fully aware of the benefits which come with investing in this phenomenal growth industry. There is a lot of money being made available and a huge appetite to drive blockchain innovation across multiple industries, across multiple usage scenarios.

Plugging the skills gap

But there is a problem. As was the case with all the major tech booms of yesteryear — the Cloud being the most referred to predecessor — demand and hunger for innovation far outweigh supply.

A report by freelance employment website Upwork stated that blockchain technologists have become one of the most sought-after, hottest commodities on the job market, second only to those working in robotics. Meanwhile LinkedIn reported that last year there were 4,500 job openings posted containing the term “blockchain” in their description, a threefold increase on the previous year. But, unfortunately, many of these positions will not be filled. A TechCrunch article from earlier this year claimed that there are now 14 job openings for every single blockchain developer/engineer.

The demand for technical expertise in developing blockchain-based technologies is through the roof, yet in reality very few technology professionals possess the skills or knowledge required to satisfy this growth in demand.

If we want to address this global shortfall and challenge, the Ripple approach may be the best way forward. Investing in grassroots level education and training so that the next wave of graduates become blockchain-savvy is a sure-fire way of bringing blockchain supply closer to blockchain demand. And whether you work in law, accountancy, or sales, in industries as broad as healthcare, government, and publishing, the impact of these types of long term investments will be felt in years to come as the blockchain gathers pace and becomes an integral part of our everyday life.

Archaeology and…machine learning?

Studying 2600-year old artifacts with algorithmic techniques

With the increase in use of machine learning and artificial intelligence in every domain, it is now commonplace to find reports about how humans are likely to become increasingly otiose in the coming world. A more clear-eyed analysis of how technology is being used, however, reveals that these pronouncements are still very much premature, and that an alternate (and more plausible) outcome is one where technology doesn’t replace but supplements human labour in complex ways.

An excellent example of this kind of work is documented in a 2016 Proceedings of the National Academy of Sciences paper by a team from Tel Aviv University. A central question in biblical scholarship concerns when exactly the various parts of the Bible were written, which is made particularly complex because we have so little background knowledge information about life 2,500 years ago. Some traction on this was made through the innovative use of machine learning algorithms to try to determine the level of literacy in the community, giving us an idea of whether people in that community would be capable of producing a work of enormous complexity such as the Bible. The project considered 16 inscriptions found in the area of the desert fort of Arad.

Each of these was an ostracon or a piece of broken pottery used to write on, like in the figure above. Notice how it is chipped, meaning that traditionally only brief excepts are present. In addition, over time the writing can fade, making reading it difficult, let alone comparing and contrasting different pieces. That’s where the tech comes in.

After restoring the script as much as possible, the researchers used machine learning software to identify individual characters and then compare the same letter on different Ostracons on a range of metrics like overall shape, the angles between strokes, the character’s center of gravity, as well as their horizontal and vertical projections. Allowing for some range in handwriting variability, the programme would identify distinct authors through letters which exceeded a threshold of difference. Through this method, the authors concluded that there were a minimum of six authors for the artifacts they had.

This was clearly a case of machine learning performing tasks that humans cannot even dream of doing with their naked eye, and someone who wanted to push the narrative of a coming apocalypse of job losses for human beings can treat this research as confirming their world view. But a closer examination of the variety of methods indicates a slightly more complex story.

Although the programme did identify at least six authors, this fact by itself says very little about the extent of the literate population—after all it could have been the case that only six people in the area had been literate or it could have been that a lot more people were. To make inroads with regard to this question, the results of the application were analyzed by human researchers and a model of the hierarchical relationships between the authors and intended recipients of each message was constructed:

Since there appeared to be people from every sociopolitical strata represented, the authors concluded that it was likely literacy was widespread among the inhabitants of the area in the kingdom of Judah near Fort Arad in 600 BCE.

For the wider audience, the lesson from this study is that we shouldn’t be too certain that machine learning and AI will mean the end of jobs, since there is still the possibility of modifying older ways of working that incorporate technology while still relying substantially on human minds and hands. The effects of the coming machine learning revolution should not be prophesied about in general terms, but instead we should engage in nuanced studies and projections of individual fields and sub-fields.

The future is neither completely opaque nor transparent, and what we can glean about it is almost definitely going to be fragmentary, tentative, and context-dependent, instead of a single grand narrative.


Preview of Society for Scholarly Publishing and BookExpo

Next week in the US, two big annual events will take place—The Society for Scholarly Publishing Annual Meeting in Chicago and BookExpo in New York.

The focus of the Society for Scholarly Publishing’s Annual Meeting is “Scholarly Publishing at the Crossroads: What’s working, what’s holding us back, where do we go from here?” and, as they celebrate the organization’s 40th anniversary, the meeting will focus on past and future practices, technology, establishing and reaching new markets, and how publishers keep up with the changing needs of researchers and academics as both authors and users.

This year’s BookExpo is “Reimagined,” according to parent company Reed Exhibitions, BookExpo will become the “first end-to-end business solution for the global publishing industry,” with attendees experiencing “how content creation, rights trading, retail strategy and consumer behavior will increase profit and give you the tools to succeed in today’s shifting marketplace.”

The two events highlight how far scholarly and STM publishing have come in embracing technology in the workflow and address user needs as they are in today’s world, whereas trade publishing continues to focus on print vs. digital, metadata, and predominantly on adapting an existing system rather than creating something entirely new.

Below are our highlights from both events’ programs, a selection of the events which will help publishers improve their business structure.

SSP Annual Meeting

Wednesday, May 30th

8:30–11:30 Pre-Meeting Seminar: Humans, AI, and Decision Making: How Do We Make Use of Data, Text Mining and Machine Learning for Better Decision Making

AI represents a suite of technologies that are already supporting and assisting human decision-making in a whole host of settings. In this seminar, we’ll discuss some of the ways in which publishers and institutions are using big data, semantics and analytics to make smarter strategic decisions.

Thursday, May 31st

10:30–12:00 pm Artificial Intelligence: How Publishers will Benefit from Artificial Intelligence?

Smart publishers are beginning to embrace AI and are weaving it into the core of their business—to source new content, to inform and improve content and for new product development. Publishers are also using AI to reduce costs in their editorial processes.

3:30–4:30 pm Strange Bedfellows: Integrating Editorial and Sales to Maximize Success

The scholarly communications landscape is increasing in complexity. Publishers can no longer afford to allow departments to operate in silos. Sales colleagues at a publishing house need to understand the goals and objectives of their Editorial colleagues—and vice versa—in order to make the most of market conditions and partner effectively.

Friday, June 1st

11:00–12:30pm New Tools and Trends in Discovery Technologies

With over 2.5 million scholarly articles published each year—more than 8,000 each day—the glut of available scholarly content poses challenges to researchers, authors, publishers, and libraries. For authors and publishers, getting their work discovered and read, and ultimately cited, can be a career-defining challenge. Libraries compete with the open web by providing enhanced discovery services which they hope will be valued by their users. No single solution has emerged to satisfy all of these needs.


Thursday, May 31st

9:45am Leadership Round Table: Publishers on Publishing

This roundtable will feature CEOs from top publishing houses, including Markus Dohle, CEO of Penguin Random House; Carolyn Reidy, President and CEO of Simon & Schuster; and John Sargent, CEO of Macmillan in a powerhouse presentation that will surely be a highlight of BookExpo. Together, these leaders will reflect on industry trends, market highlights, and the power and responsibilities of publishers as global, corporate citizens. Maria A. Pallante, Association of American Publishers President and CEO, will moderate.

11:00am The Content Liberation Movement

Even well into the digital age, publishers have persisted in maintaining processes that confine their businesses to a specific format (usually, the book) and to a single business model. Forward-thinking editors today demand freedom to reuse and repurpose content in innovative, high value ways, especially on mobile devices. Content management systems, though, aren’t fast enough at identifying assets and don’t go far enough when assembling new products.

1:00 pm The State of the Publishing Industry Today

Join Jonathan Stolper, the President of NPD Books, as he breaks down the latest outlook for the US book market. Drawing on data from NPD’s BookScan, PubTrack, and Books & Consumer platforms, this presentation will deliver essential insights into the latest trends from book publishing’s most authoritative source of industry information, including:
 • A recap of key industry performance in 2017/2018
 • The significant trends in content and platform
 • The outlook for digital versus print in the next few years
 • The opportunities (and risks) for publishers and retailers in 2018 and beyond

Friday, June 1st

12:00 pm KeywordsEnhance Discoverability and Increase Sales on Amazon

Hear from technology experts & publishers how they are using the latest machine learning and AI technology tools to increase discoverability, drive sales and help make effective marketing decisions.



Stalking the Muse with Kanye West

A technological response to the question of the origins of creativity

Human beings have always had a close affinity to art. Our humanoid ancestors etched shells hundreds of thousands of years ago, and we have continued to make and celebrate artistic achievement in an unbroken line since then. But this importance placed on art inevitably raises a question—where does creativity come from?

In Plato’s Ion, Socrates faces the rhapsode Ion, a performer of epic poetry, and argues that while his talents were indeed impressive, they were not the application of any skill. Rather, it was divine inspiration coursing through his mind:

Many are the noble words in which poets speak concerning the actions of men; but like yourself when speaking about Homer, they do not speak of them by any rules of art: they are simply inspired to utter that to which the Muse impels them…for not by art does the poet sing, but by power divine. The poets are only the interpreters of the Gods by whom they are severally possessed.

We might reject this as quaint, but what it gets right is that creativity is not generated by an insular process cut off from others and the past, but rather through the interaction of the artist with something outside of the artist. But what Plato wholly attributed this “something” to the work of the Gods, we now partially attribute to prior art itself.

As a culture, we note that often creative work is part inspiration and part adaptation, with artists drawing on earlier work that may have influenced their novels, plays, or films. For example, when the smash-hit musical “Hamilton” first appeared on the scene, multiple mainstream sources Slate, Vulture, The Guardian, The New York Times traced the influences that inspired Lin-Manuel Miranda to create such a groundbreaking work.

Some artists very clearly outline their influences, such as beloved children’s book writer and illustrator Maurice Sendak. He made no secret of his antecedents and sources, and instead wore them on his sleeve. When reading a biography on William Blake, with a rare honesty, he stated:

I read Blake because I want to schlep something from him that I can eat raw, have…Why am I clinging to every word Blake says in this book? I’m trying to suck all his strength out.

And it wasn’t just Blake he was drawing from. It was his standard modus operandi, a part of his creative process:

The muse does not come pay visits, so you go out stalking, hoping that something will catch you. Where do I steal from?

While these might suggest that Sendak was simply borrowing other people’s ideas, the real story is far more complicated. Sendak’s “stealing” was not merely appropriation, but a transmutation of prior work into something unseen. We can note the influences, but no one who has read Where the Wild Things Are or In the Night Kitchen can deny that these were Sendak originals, unquestionably terrific and original works of art.

Sendak shows that even if we draw heavily on past works for inspiration, our art can be wholly our own and new

Or to put a modern spin on it:

This idea of inspiration sparked PageMajik’s newest idea an AI engine that analyzes scenes and points out similar contexts and ideas in the works of great authors. For example, if a dramatic scene involving a dysfunctional family was being written, you might be shown brief excerpts from A Long Day’s Journey into Night or August: Osage County.

Why would this be useful for publishers?

With the threat of plagiarism or reusing material that has come up in the last few years, for those self-publishing their work or even for bestselling writers, it looks at new submissions to make sure they don’t match previously published work.

Why would this be useful to writers?

In a way of enabling Sendak-style inspiration, it can provide authors with an opportunity to boost their creative ideas by highlighting excerpts in similar work that might help them figure out a plot point or a way to interpret the scene in a new and interesting way.

By making overt some of these influences, this system can ensure that what’s being written really does vary from earlier texts and isn’t just an accidental copy.

As someone who firmly believes we can’t know how good a tech idea is until multiple people use it independently over a decent period of time, I can’t wait to see how this works out.

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