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After digitizing libraries, translating knowledge?

Nine years ago I asked the question “Can we localize entire libraries?” In the wake of the National Library of Norway‘s (Nasjonalbiblioteket) digitization of its holdings and collaboration with Nigeria on materials in the latter’s languages, it seems like time to review what mass digitization could mean for translation of knowledge into diverse languages.

My original question in 2008 came from looking at trends in digitizing books – notably Google Books – and machine translation (MT). It elicited some interesting responses, including Kirtee Vashi’s mention of an Asia Online project planning to link these two trends.

Book scanners. Source: TecheBlog.com

In the ensuing years, Google Books’ digitization program – the biggest and most promising book digitization effort – ran into controversy over rights to reproduce copyrighted materials beginning in 2008. This ultimately has put their entire vision of digital access to a vast library of works in doubt. And the unrelated Asia Online project, which used statistical MT to translate 3.5 million pages of the English Wikipedia into Thai, was stopped in mid-2011 in the aftermath of a changed political situation in Thailand and funding issues.  (Asia Online has since become Omniscien Technologies)

So while the technologies for digitization and for MT – the two pieces in localizing libraries of information – are established and improving, each has encountered some combination of legal, political , or funding issues limiting their use individually for mass expansion of access to knowledge, as well as their potential use in tandem.

However, could the Norwegian program, announced in 2013, and the project it has with Nigeria, announced earlier this year, introduce a new dynamic, at least for mass digitization? Could and should large national libraries take the lead in this area?

The idea of digitizing libraries has generally been advocated in terms of access to knowledge, without particular reference to the languages in which publications are written. But languages are critical not only for access to knowledge, but also for facilitating scholarship and the interfacing of ways of knowing. Hence the need to associate mass digitization and MT.

There is at least one proposed project mentioning the potential for translation of books that have been digitized – Internet Archive’s initiative to digitize 4 million books (a semifinalist in the MacArthur Foundation’s 100 & Change grant competition).

Any such digital data produced by the Nasjonalbiblioteket, Google Books, Internet Archive, or any other organization could be translated with MT into other languages, with a few caveats (quality of optical character recognition [OCR]; how well resourced a particular language is; and of course the accuracy of the MT). This means that potentially any mass digitization could be mass translated into a large number of languages, given legal cover and sufficient funding.

What about the accuracy of MT, and how useful could mass MT of mass digitization be if there are inaccuracies? These are critical questions for any project to use MT to translate digitizations. Responses could reference, for instance, domain-specific MT, which is generally more accurate than general MT, provided of course that the material matches the domain used. Or perhaps some system for post-editing could be devised.

This is an exciting area that needs more attention and policy support. Books and other production in print can be digitized on a mass scale, making the knowledge in them more widely available. Digitized text can be machine translated into other languages, and the quality of that can be made high enough for use by speakers of the target languages. As much as the printing press revolutionized access to knowledge of that age, so too the potential to digitize and translate what is in print promises another revolution benefiting more people directly.

 

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CFP: Language and Development 2017

The 12th biennial Language and Development Conference (LDC) will be held in Dakar, Senegal, on 27-29 November 2017. The call for participation (CFP) deadline has been extended to 31 May (apologies as I’m just catching up on this).

The theme of this edition of the conference is: Language and the Sustainable Development Goals.

From the concept note for the conference (emphasis in original):
“Sustainable development is increasingly viewed not only from an economic perspective, but also from social and environmental perspectives. All three dimensions are important to ensure that human beings can fulfil their potential in dignity and equality. As language and communication are crucial to how societies grow, work together and become more inclusive, the conference will seek to explore the role of language in a range of interlinking aspects of development. It will do this by focussing on three of the goals:

  • SDG 4: Ensure inclusive and quality education for all and promote lifelong learning
  • SDG 8: Promote inclusive and sustainable economic growth, employment and decent work for all
  • SDG 16: Promote just, peaceful and inclusive societies”

“The conference programme will also take into consideration other cross cutting goals, notably SDG 5: Achieve gender equality and empower all women and girls; and SDG 10: Reduce inequality within and among countries.”

The conference has 3 sub-themes:

  • Multilingualism for Quality, Equitable and Inclusive Education
  • Language, Skills and Sustainable Economic Growth
  • Communication, Peace and Justice

The British Council is hosting this conference (it apparently has been involved in almost all the previous ones), in partnership with le Ministère de l’Enseignement Supérieur et de la Recherche et le Ministère de l’Education Nationale du Sénégal, the Council for the Development of Social Science Research in Africa (CODESRIA), UNESCO, the School of Oriental and Africa Studies (SOAS) and SIL Africa, along with others.

For additional information, see the website of the LDC series, and a posting on this blog about the 2015 LDC.

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A hidden class of “bike-to-workers”?

On the eve of the annual “Bike to Work Day” – which this year is observed nationally in the US on 19 May 2017 (with some exceptions like Chicago and Colorado) – here’s a quick look at a couple of aspects of bicycling that don’t seem to get much attention:

  • people who bike to work regularly, largely out of necessity; and
  • unavailability of bike racks, which seems to affect the latter most.

The question is whether there is a kind of second class of bicyclists overlooked both by events such as Bike to Work and by longer-term planning and installation of infrastructure for bicycling.

First, about Bike to Work Day – it is part of the League of American Bicyclists‘ initiative for National Bike Month and National Bike to Work Week (15-19 May this year), all of which apparently go back to 1956, as ways of promoting bicycling. In the Washington, DC area, which concerns me in this case, Bike to Work Day is a popular and well supported event, with volunteers working “pit-stops” on bike routes, food and drink available, and giveaways. Altogether a great way to encourage commuters to at least try bicycling, or to get back on their bikes as weather warms up.

But what about people who bike to work pretty much every day on less frequented routes?.

Lycra or jeans

Several years ago I observed in Vienna, a northern Virginia suburb of Washington, how there seemed to be two separate groups of bicyclists – one with better bikes perhaps in lycra, often seen on the bicycle-friendly W&OD Trail, and another with less expensive bikes, perhaps in jeans, more likely seen along the commercial main street. The latter group apparently included people commuting to lower paying jobs in shops and restaurants. Although I was not able to verify this through any systematic research, one did notice here and there bikes locked up behind or near shops. Also, research done by others has noted use of bicycles by “day laborers” in northern Virginia (a situation perhaps similar to what one finds in other areas like Los Angeles).

Where are the bike racks?

What brings me back to this topic is noting recently – or really taking the time to notice – bikes locked to trees in a couple of shopping centers near another northern Virginia suburb, Falls Church. This is actually not that uncommon, but often easy to miss (as for instance the photo below on right, where the bikes were in an area screened from the shops).

In Seven Corners area east of Falls Church (l.) & at The Shops at West Falls Church (r.)

It is my impression that there are relatively few shopping areas that have bike racks or installed stands for parking bicycles. The same was true of Vienna – where I recall personally having to lock up my bike against sign posts or railings when going into a store – and of Falls Church, especially outside of the downtown area (where some bike stands have been installed by the city).

Bike stands at different locations along Broad Street, Falls Church City, VA

One can easily get the impression that bike racks are a priority only in certain higher traffic areas and/or with certain types of cyclists in mind. Or at best that their locations are not thought through too thoroughly. Running an errand one Sunday midday along the main street of Falls Church, I noted several empty bike stands (two of which pictured above), but then a little farther away, two bikes and a one-wheel trailer locked up against an awning support in front of an eatery.

Bicycles & trailer locked to awning support, Broadale VIllage Shopping Center, Falls Church City

So there are really two levels of discussion on bike racks:

  1. Which areas do get them, and which simply don’t.
  2. Within the areas that do, how well placed they are for people to use.

On both levels, there are decisions about either public expenditure on racks or ordinances requiring residential or commercial properties to include provision for bicycle parking and locking. Within the city of Falls Church, there is a bicycle master plan that considers placement of racks. Outside, it is apparently another matter (both of the locations with bikes locked to trees happened to be just over the boundary in Fairfax County, a huge jurisdiction).

Who counts in planning for bicycles?

It’s not a coincidence that the pattern of provision for bicycle parking – racks or stands – facilitates certain kinds of use of bicycles more than others. The higher level regional planning for bicycle infrastructure, processes of input into policy, and the local decisions about what is installed where for bicycles all seem to happen without input from people who ride bikes to a local job where they have to lock them to trees or fences or whatever. Those same people are also the ones for which just about every day is a bike to work day.

Admittedly, part of the issue is numbers. If only a couple of people ride bikes to each small shopping area, it is not likely that they’ll get a rack for parking. On the other hand, a couple of bikes each day represents a steady traffic, perhaps enough to justify putting in some kind of rack. Still, it would probably take shoppers and restaurant clients biking in some numbers and complaining about lack of places to lock their bicycles for there to be a change. It shouldn’t have to be that way.

Another perspective is that adding bike racks in places where one sees bicycles locked to trees and whatnot would in addition to helping those less visible cyclists, also facilitate more people biking to those locations.

Maybe that’s something to think about on Bike to Work Day…

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Earth Day 2017: Let’s stop industrial-scale burning of wood for energy

EarthDay2017This year’s Earth Day (22 April 2017) has as its theme “Environmental & Climate Literacy.” In that spirit, I’d like to suggest that environmental and climate literacy require attention to the impact of industrial scale burning of forests, and the question of whether it makes sense as an investment in reducing carbon emissions.

Yesterday there were articles in the press celebrating Britain’s first full day of energy without burning coal since 1882. You have to dig in some articles (not all) to find out that they’re still doing a lot of burning to produce energy, including of imported pelletized wood, which comes mainly from a combination of waste wood (which is limited in quantity) and cutting forests in the southeast United States.

The rationale for cutting, processing, transporting, and burning massive amounts of wood to generate electricity is that it is “carbon neutral.” That is, the carbon released in burning the wood can be accounted as part of a cycle with growing trees (which captures carbon, as part of the natural plant growth process).

But is burning wood on this scale really carbon neutral? And are other externalities, such as environmental impact at points of harvest, adequately taken into account? Should industrialized countries, which otherwise have been pretty good about managing forests – and have been preaching to developing nations about forest conservation and management – be exploiting its forest resources as “nature’s powerhouse” (in the terms of FAO‘s unfortunate slogan for International Day of Forests last month)?

In a recent article entitled “Can We Have Our Forests and Burn Them Too?,” former CIFOR director-general Frances Seymour questions the rush to use wood for power generation based on the current approach to carbon accounting. and points out that the carbon cycle for trees is a very long one. A study by Chatham House, “The Impacts of the Demand for Woody Biomass for Power and Heat on Climate and Forests,” analyzes the accounting issues in more detail, concluding among other things, that “a proportion of the emissions from biomass may never be accounted for.” Similar issues are summarized in a paper on the Friends of the Earth-UK site entitled “Burning Wood for Power Generation The Key Issues Explained.”

The push to burn wood to generate energy, in short, is policy-driven (the science of the matter being read in a way favorable to certain outcomes), and may actually be worse in total impact than cleaner fossil fuels.

Big plants, big impact, small energy?

Among the big biomass/wood burning energy plants in Britain are Drax and Steven’s Croft. (BiofuelWatch has a map of all plants). Taken together, they seem to be having a big impact on forests and the “biomass market” (see for instance this EU press release about the potential impact of Drax), but surprisingly not accounting for that big a proportion of Britain’s overall energy – only 6.7% on the coal-free day, according to the UK Electricity National Control Centre (thanks to Steve Patterson for the pointer):

And the conversion of facilities from coal-burning to wood-burning was expensive (again regarding Drax, see this critical opinion piece). Might it not have made more sense to convert to gas and/or invest in other non-burning renewables?

“Transgenic” forests in the future?

As bad as the pelletizing of forests for electricity generation is today, it could get worse. Research on genetically engineered trees aims to enhance growth and change wood characteristics, with one of the main aims being production for energy (pellets but also biofuel). The continued use of wood to generate power on an industrial scale will generate funds and interest in further developing and planting these organisms, unfortunately probably without regard to impacts on the environment.  (Two older pieces give some perspectives – in The Guardian, 2012, and Earth Island via Salon, 2013.)

Missing the “sweet spot” for wood energy

I have some small experience with wood energy, and my perspective on the larger issues comes in part from two sources. The first began with work on forestry projects in Mali and Guinea which had as part of their purpose, helping rural people grow trees for firewood to use in cooking, rather than cutting natural growth. I’ve maintained an interest and awareness of the problems involved in this source of energy, and various programs and proposals to ameliorate environmental, health, and other problems associated with it. The second is installing and using a fireplace insert in our home, which uses purchased local firewood (coming from cleared and fallen trees in the region), as well as smaller branches and in a couple of instances fallen trees near our residence.

Five key concepts are involved here (I discussed four of these – not transfer – in more detail in the post, “Biofuels reconsidered“):

  1. local;
  2. small scale;
  3. minimal processing;
  4. more direct transfer of heat energy; and
  5. use of waste – that is wood that would otherwise go into a landfill, I am told.

When you get these five together, that’s what I’d consider the “sweet spot” for wood energy, the optimal position for energy efficiency and environmentally sustainable wood use. Sometimes it is hard to stay in that spot, or next to impossible, such as in communities in West Africa I have known – so small scale plantations, and medium-distance transport of wood becomes necessary. Or in the US, the market drives producing wood for fireplaces and firepits (those small mesh-packaged batches of split wood for sale outside supermarkets).

On the scale of, say, Drax and its suppliers, however, they’re off on all counts, pretty much by design: long distance between supply and use; very large scale; medium processing (not as bad as wood to liquid biofuel); indirect transfer (the heat released from burning only indirectly produces electricity, so there is energy loss); and due to the scale of demand, live trees are harvested and plantations made, with all kinds of externalities. Industrial scale burning of wood for energy in advanced economies, in other words, misses all the five criteria for optimal energy efficiency and environmental sustainability. So, if the “carbon neutrality” of this practice is also contested, why are we doing this?

Decoupling forests and energy

Which brings me back to the FAO’s disheartening – from the point of a former (re)forester and lifelong environmentalist – slogan for International Day of Forests (IDoF) on March 21: “The forest: nature’s powerhouse.” Their effort to link the small-scale household use of firewood (which for many is a simple necessity, not a preference) with industrial scale power generation from pelletized forests was misguided, in my opinion (and I believe that of many others). Their attempt to point to a long-term role of forests in energy generation and need for policy support to that end seems shortsighted. Do we really expect to devote a significant percentage of our dwindling forest lands to inefficient energy generation? (I annotated their infographic, which is included at the end of this post.)

Wood energy is a reality for many today, but it is not a vision for long-term development. It is time to plan for the gradual split between energy – the technology for which is “ephemeralizing” away from burning and combustion – and forests – which have critically important climatic roles in addition to supplying wood and other forest products for our use.

Of course, we will always like to sit by a wood fire on a cold night or at a campsite, or to grill over charcoal, but that kind of use should be as close to the “sweet spot” of optimization as possible.

Ms. Seymour in her article cited above had a memorable summation of the arguments she made (it’s not a long read, and highly recommended): “Whether temperate or tropical, we can’t have our forests and burn them too.” Hopefully FAO and other major agencies and organizations concerned with the future of forests and/or energy will take that assessment to heart.

Comments on FAO infographic “Forests and Energy” from IDoF 2017.

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AI & the self-driving resume: 3. How AI might work for job seekers & recruiters

This is the third of 3 posts on “AI & the self-driving resume”

The previous two posts1 have looked at the current state of the job market and why applications of artificial intelligence (AI) could make it function better for job seekers and recruiters. They have also mentioned aspects of how AI in this context might work, with the ultimate vision being of intelligent agents (IAs),2 working on behalf of job seekers and recruiters, and able to interact with each other.

The beauty of bringing AI more completely into the job market is not just in saving time and facilitating good matches, but the potential to generate new dynamics, from job matches that never would have been found with current methods, to benefits for career planning, staffing strategies, and potentially other areas of the economy.

What are the forms that AI (or specifically, the IAs) will take in the job market, and how (quickly) will they will evolve? At this early stage, there are many possibilities, but as some are developed and others not, the process becomes path dependent, meaning that your future options are conditioned by your current direction. Presently, there is attention to developing AI for recruiters and the intermediary job boards, but nothing I am aware of that would work for the job seeker, who is still using basically late 20th century tools to find jobs, write resumes, and apply for positions. The longer that disparity remains, the more it is likely to grow, and we could have a job market where AI works for hiring organizations, employment agencies, and job boards, but for job seekers only through them.

This post therefore focuses mainly on how AI for the job seeker might be developed, all within the understanding that the eventual system will involve a distribution of AI tools among all actors in the job market.

Self-driving resume, Mark 0.1

The “self-driving resume” in the title of this series comes from the notion that a computer program could write a resume, find a job opening, and deliver the resume (and job application) to the hiring agent. When you get into it, the steps are not so simple, and the technology has not yet been applied in this way, but the basic idea begins with two proven concepts: a web crawler (incorporating AI advances) and an intelligent program that can author documents (with some human participation, but less than required by the online template-based “resume generator” programs). As the process evolves, and AI can conduct much of the job search and writing tasks unassisted, the self-driving metaphor becomes a bit more apt.

Self-driving resume, Mark 0.1

With current technology (as I understand it), separate programs would probably be necessary for each of the two main processes – searching and writing. For the first, web crawlers are not new, although there are naturally efforts to make their searches more intelligent. For the second, programs that write human language are much newer, yet have had some interesting successes in specific authoring scenarios (from emails to novellas). For automating a job search, both would be designed as learning programs, and each would probably have to be “pre-learned” about the situations they would encounter (perhaps with AI chips?).

In the scenario sketched above (dubbed “Self-driving resume, Mark 0.1”), the crawler (1) searches job sites and organization employment pages according to parameters input by its owner, the job seeker. Those criteria would be types or titles of jobs, particular companies, or perhaps an industry in a region. Basically, the kinds of considerations that an individual searching the web would have in mind when they look for a job are the ones that have to be made clear for the crawler to work on. Assuming the crawler finds a match (2), for example a particular job listing, it would need to find and extract relevant data concerning the job description, qualifications, deadlines, contact names, and relevant details about the organization.

In this scenario, the writing program then enters the picture. The crawler would have as part of its function forwarding the data extracted from the job site to the writer in a form the latter can use (3). Perhaps the trickiest part of the scenario is this exchange of information between what are conceived here as two separate though allied processes.

The writing program, for its part, would have a number of tasks. It is possible that we might actually be talking about a suite of programs that would function in tandem, with each one specializing on writing different things – though for purposes of this example I’ll assume it’s a single program. One fundamental function of the writer, unconnected with the functioning of the crawler, would be authoring of the resume. The resume will be written by the writer based on input from its owner and then some kind of iterative process of review involving the owner to arrive at a satisfactory product that can be updated by the writer. In this scenario we also assume that the writing program has at its disposal a range of best practices, templates, keywords and other important input devices to use in construction of a quality resume. This resume then is a resource that then can be updated or are tweaked for particular employment opportunities.

Another set of responsibilities of the writing program, would be to analyze the data (including text) communicated to it by the crawler which, as described above, has just found a job match, and then to compose a draft cover letter. That cover letter probably would be based on some kind of template that has been pre-loaded, perhaps tweaked earlier by the owner earlier to conform with their style. Its draft letter would then be forwarded to the owner (4) with whatever details on the job it has received from the crawler. The owner then can review that letter, edit it, and as necessary consult the company site him or herself. The finalized letter and approval to go ahead are then returned by the owner to the writing program (5), which then can go to the companies job page, log in for the owner, fill out the forms for the job application(6), and attach the approved cover letter and a copy of the resume (7).

The filling out of online forms is a function relating to the writer’s work on the resume and the cover letter, drawing from the same text, to appropriately respond to the usual range of questions that appear on such forms.

All of the above assumes there would be no problems with the web crawler scraping the organizations job pages, or with the writer logging in (some sites block one, the other or both). It also assumes that the organization listing the job is not itself already using smart programs. What happens when job seekers and recruiters are working with intelligent automation?

From self-driving resume to quantum resume?

Moving beyond the rudimentary but still unprecedented Mark 0.1, AI will be enlisted on the part of individual job seekers and each recruiting organization, rather than centrally organized along the lines James Cooke Brown envisaged, or by major intermediary companies. The operant concept is the IA – independent learning programs that will act on behalf of their owners, though what operates for the job seeker and for the recruiter will naturally differ.

On one side, the job seeker’s crawling and writing programs discussed above would be united in a single “self-driving” IA – basically an autonomous learning program, able to complete the full range of tasks involved in searching and applying for a position, including preparing application materials, notably the resume, but also capable of scouting out potential positions at organizations in industries of interest (perhaps by monitoring contracts and investments to know which companies might be hiring). Once we have introduced AI into the job search process, it would be a short step to using the IA as a tool to help career planning.

An entirely new dimension in this phase would be the potential for interacting with other IAs – of recruiters and companies, obviously, but also with IAs of other people in the job market or just out there in case. Imagine sharing information with other applicants (minus name and personal details) regarding comparative qualifications for a job of interest. Or information on companies – hiring practices, workplace issues, salary levels & offers – from each one’s experience. All of a sudden a range of data becomes available from the IA – a significant benefit beyond automating repetitive tasks and extending searches beyond what is humanly possible.

On the other side, as it were, recruiters will also be working with some configuration of IAs (perhaps in a tiered system, partially for security reasons?) to handle job applications, communication with applicants, inquiries outside of job listings, vetting, etc., as well as IAs to seek out potential applicants (more or less reversing the contact and response processes).

The idea is not a more sophisticated information dump from one side and more sophisticated management and analysis process on the other, but rather IAs that can query, respond in kind, and exchange information, and that are capable of learning from the interactions in ways that both improve their function and produce and organize usable data for their owners. All that said, it is important to note that the use of IAs would not eliminate person to person contacts, serving instead to get us to where those contacts are most productive, and indeed giving us more time for them rather than repetitive tasks.

To the extent we begin talking about IAs interacting on their own – albeit with direction from and “ground truthing” with their owners – the dynamics become hard to predict. However there are some things that can be expected:

  • As IAs of job seekers and recruiters, or job seekers with other job seekers, communicate directly with each other, this will not take take place in human language (if we take recent experiments as an indication of what is to come).
  • The resume will no longer have a fixed form, except when needed for human reading, and then will change according to the context and the IA’s learning from experience – almost a quantum phenomenon. The database of professional information and job history that goes into the resume will originate from the owner, and be tweaked as appropriate, but the specific selection and organization of information transmitted in each circumstance – or generated into a printed document in the appropriate human language – would likely differ.
  • There will no longer be a need for companies to tell you they’ll “keep your resume on file,” since recruiters could page your IA (or the IA of anyone, or theoretically everyone) for resumes when they need them.
  • There will have to be protocols and standards for communication among IAs, including ways to translate their communication to forms we recognize for checking and analysis.

One interesting question is what will be the virtual space in which this interaction of IAs takes place? Would this happen simply over the Internet, or on the servers of particular companies or job boards, or some dedicated “agora” run by an intermediary non-profit organization?

Next steps, first steps

At this point there are three areas to get the process moving towards a Mark 0.1 stage and beyond:

  1. Setting up an experimental crawler that can find jobs and download (“scrape”) relevant information. The idea would be something that can be easily tasked (what to look for) and tweaked (to improve results). There are crawler programs available, but thinking here of something purpose-designed and friendly to non-expert users – something one could run from a desk-top or perhaps a smart phone.
  2. Setting up a program to author resumes based on information given it, perhaps in the form of an existing resume. However, this is an intelligent program, not a fillable template program, so it would be expected to produce a document with minimal input and understand when it needs more for a complete document, and where it can trim information for succinctness (and space limitations). A next step would be to be able to adjust the resume contents in function of input of a job description and requirements. The step after that would be authoring a cover letter in function of the resume material and input of job information. Here too, a priority is user friendliness.
  3. Development of a plan for how to link the two programs. The next step on this, as I see it, would be how to unify them into a single IA.

I am interested in the possibility of this being approached as an open-source project (though am unfortunately not at the level of being able to contribute to the actual development).

A meta-requirement is elaborating the vision of how IAs of job seekers and recruiters would interact. This would be more on the cutting edge of AI development as I understand it.


1. 1. The future of the job market & 2. The resume & the market
2. The concept of “intelligent agent” is applied variously, depending on the domain of activity and the specific need. Some useful discussions online (from short to long) illustrate the range: Business Dictionary; Webopedia; TechTargetTechopediaWikipediaConsortium on Cognitive Science Instruction; and Michael Wooldridge’s  chapter on Intelligent Agents in the first edition of Multiagent Systems (now in its second edition).

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AI & the self-driving resume: 2. The resume & the market

This is the second of 3 posts on “AI & the self-driving resume”

In a sense, the job market is really a market in resumes, even though a real person – someone seeking a job, a potential employee – is behind each one. Resumes stock databases, they are digitally searched, they may be passed around and printed and looked over. The distribution of resumes engages intermediary organizations, and advice about how to write and share resumes has become an economic activity. And of course, each resume itself represents an investment of time and resources.

Reign of the resume

HotR The resume, as a statement of one’s experience and education, and nowadays often cast as a marketing tool, is perhaps the central element in the current employment system. While some want to trace the history of the resume back to Leonardo da Vinci (see the diagram at right1), it really only became an established part of job applications well into the industrial age. Despite some changes in standard content over the years (e.g., bio info no longer appropriate, the objective statement now “out of style“), and in availability of much better tools for producing  and disseminating resumes on the one hand and processing them on the other, the basic concept hasn’t changed at all.

The ability to submit resumes has always run into the problem of whether and how they will be read on the receiving end. I still recall a talk back in the late 1970s in which the speaker suggested a red ribbon as a device to make a resume stand out in the very physical pile of paper before a recruiter hiring for entry level positions. Current availability of advice and tools to improve the content and look of a resume are more sophisticated, but still reflect the same concerns. In place of artifices like a red ribbon, job seekers of all levels these days are advised (1) to layer keywords into their resume or CV (one site lists 155) in order to make them findable in the digital pile, and (2) on the chance the resume is seen by human eyes, to format the content for the few fleeting seconds of attention they may get. For more detail, read the 300-page manual on resume writing offered by The Ladders.

Another factor is the disparity between those fleeting seconds of attention, and the hours spent putting together, updating, and tailoring a resume. It naturally takes much longer to write something worthwhile than it does to read it, but it seems that resumes are no longer even read. One quick estimate is that time spent glancing over a resume may be only 0.0003% to 0.0014% of the time taken to prepare it.

Meanwhile, the same digital technologies that have facilitated producing and editing resumes on the one hand, and online job listings on the other, have fostered a kind of trade in resumes. It is possible to post a resume on a website where, if one is to believe the advertising, it will be “exposed” to hundreds of thousands of employers. ResumeRabbit.com, for instance, claims over 1.5 million (via reposting to 89 other sites). It will however be one of probably millions vying for attention.

Even on a more specialized level, the numbers are incredible: DevelopmentAid.org for example, offers a “CV broadcast” service reaching 30,205 organizations (as of April 2017 – the number varies). In short, the system is flooded with ever more resumes.

Where is all this going?

One could argue that the humble resume is in effect being asked to do much more than it was ever intended to do. No surprise therefore to see radical-sounding prognoses such as the “death”  of the resume (for example in 2013, the previously cited 2015 list, and of course Nelson Wang’s 2012 book, The Resume is Dead), perhaps along with its recent counterpart, the applicant tracking system (ATS) .

Discussions of the “death of the resume” propose alternatives such as bios, videos, graphics, or the amorphous online personal brand. One blog post by Charles Handler several years ago suggested that “various elements of a resume are being teased apart and presented in a different format that is based more on profiles and portfolios.” But all these tend to end up with resume-surrogates that still have to be created by job seekers, and then sorted through and processed by recruiters, who for their part resort to the assistance of specialized software.2

In fact, the resume was never “alive” to begin with, given that it is static information on a page(s). Social media presence may be more current, and other resume-surrogates may have their appeal (some of them are quite creative in what they can convey), but in the end, these are all passive presentations of information, regardless of how well-crafted they may be.

Hence one of the questions here: Could artificial intelligence (AI) bring the resume to life, able to interact with a human or machine reader, bring forth relevant information, learn from the interaction, and inform the owner of the resume?

Mass market & upmarket

There is arguably an inherent dysfunctionality to this system, of which resumes are central, as the volume of applicants and positions grows. In the massifying job market, more people can send applications – relatively more easily thanks to technology – to more organizations for more positions. So, hiring departments adapt with more automated ways to screen out digital documents and reduced time for eyes-on review. But even if job seekers  “load” their resumes with keywords to get through the automated screening, the software is also “raising the bar,” to screen in more sophisticated ways. An extreme outcome of this “keyword arms race” is the oft-cited case where one company screened 29,000 applications for a single engineering position with not one of them found qualified.

Suggestions per Nelson Wang and others that job seekers take unorthodox strategies – the new “red ribbon” – are only an advantage when a few are using them. How much does anyone stand out if everyone is standing out, and how can recruiters with limited time sort through the cacophony? There are human limits. Hence on the recruiter side, automation and discussion of “AI” to deal with applicant data – which in turn reduce the applicant, or actually their resume, to data.

This massification of the job market is exactly the kind of situation – large and increasing number of actors, large but varying numbers and types of openings, complex quantitative and qualitative data, and waste – where a more intelligent, if not interactive, program or automation could do much better for everyone.

As one moves up the scale to more specialized and executive positions that are relatively fewer in number and higher in pay, the dynamic changes in some respects. Recruiters, sometimes from executive search services (headhunters) or firms specializing in particular professional fields, may reach out to contact prospective hires. Personal contacts developed in earlier work, may prove useful as sources for information and/or references – and indeed important as many positions on higher levels are not advertised (this is another issue that I’ll come back to). But there are still many people chasing mid to higher level positions in the same job market, relying on basically the same methods and advice.

Imperfect information & intermediaries

The job market is one of imperfect information – in several ways. A job seeker can’t know all available positions they qualify for, and even the best research on a potential employer goes only so far. Recruiters must judge candidates based only on resumes first (usually) and whatever other information comes in the application or via referrals, and they often – especially in some industries – are dealing with a subset of potential candidates they’ll never have the chance to consider.

So, another part of the system includes intermediary entities designed in one or another way to help link employers with job seekers in that uncertain environment:

  • Recruitment firms (“recruiters” may work for specific employers or for one of these firms) and headhunters;
  • Employment or staffing agencies;
  • Job or career coaches;
  • Online job boards;
  • Resume/CV writing services; and
  • Software companies.

All of the above (except for job agencies run by government or non-profits) are businesses, basically selling services to employers, job seekers, or sometimes both. Like any business, they seek to maximize income, minimize expenses, and provide services that attract (and in some cases, retain) customers.  Significant amounts of money and attention to diverse revenue streams (often from both recruiters and job seekers) are involved.

On the job seeker side, for instance, resume writing services run about $100, while job coaches cost clients hundreds or thousands of dollars, with one upper end service costing about $10k (and all of that without any guarantee of results). No figures on what recruiters pay for services in this subsector (in tandem with their in-house recruitment capabilities), but it likely is a lot.

Enter AI

So, we have a system that has grown in all respects, has costly inefficiencies, and is – along with all of us – in the midst of technological change including what we call AI. It is inevitable that some aspects of AI will be brought into the job market more fully, even as other aspects of AI are applied to automate various jobs. We already see the early stages of this, as mentioned in the previous post in this series. The next post will focus on AI, with particular attention to the less often considered subject of what it might do for the job search side of the system, and how it could change interaction between job seekers and recruiters.


1. Image source: Undercover Recruiter. Image credit: Rezscore.com. Click on image to open full size. (Note the 400-year gap in the “500 year evolution.”)
2. Since traditional resume-based approaches on the entry level may miss talent, some employers try to get away from the resume, even using social events or computer games to evaluate applicants. It is not entirely new to have such live-performance evaluations of candidates – the US Department of State has long used an observed role-playing exercise as part of the selection process for Foreign Service Officers.

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