I’ve just noticed that they’ve made a video of my talk available. I haven’t dared watch it (does anyone like watching videos of themselves?), but I figured I should share it anyway!
This week I went up to Newcastle for Thinking Digital.
It was the seventh Thinking Digital, but my first.
I’d seen a bunch of references to it being the UK’s answer to TED, the tickets aren’t cheap, videos from previous years look slick and professional, it’s held in The Sage which is a hugely impressive venue, they manage to get a great line-up of speakers, and the logistics in the run-up to the event were more organised than any event I’ve been to before.
So… I was expecting a cool and geeky, if faceless, serious, formal, and intimidating event.
I’d read it completely wrong. It’s absolutely a professionally run event. And there was no shortage of cool geekiness. But, more than that, the organizer, Herb Kim, has created a real sense of community in it. There’s a feeling of almost familial warmth amongst attendees who come year after year after year.
And they do it without being too cliquey. Everyone I spoke to was very friendly and welcoming, which made the few days a lot easier for an introvert like me. A few days being surrounded by and trying to talk to and socialise with several hundred smart brilliant people is the kind of thing I normally find hugely draining and more than a little daunting. But the crowd at TDC make it easier than most.
They value their time there, too. More than one person told me they’d paid for their own ticket and expenses to attend. I’m used to corporate-run conferences where everyone is paid for by their employer, or barcamps where people moan about being asked for a five pound deposit, so this surprised me.
The talks made for a fascinating and thought-provoking couple of days. I can’t do them justice here (when videos of the talks are available I’ll embed/link them here instead) but I want to give an idea of what the programme was like.
Mariana Mazzucato – University of Sussex
Argued that the image of the private sector as entrepreneurial and public sector as meddling and restrictive is an unhelpful myth and made the case for a bolder, entrepreneurial state.
Peter Gregson – Cellist
Gave a representation of the genome work that Jennifer had described. Instead of a data visualisation, it was a sonification. Using a cello.
Dale Lane – IBM
And I did a Watson talk. I really didn’t want it to seem like a sales pitch, so I tried to put it in a bigger context of being a step forwards in changing how we use computers. I talked about why I work on Watson, what motivates and inspires me about it, and why I think what we’re doing is difficult but hopefully valuable. And I walked through a short demo to explain the value I see in where we are even now. Annoying technical issues (Keynote + clicker + multiple screens = fail) aside, it went okay. It was a lot to try and fit into 20 minutes, so I talked fast.
It was a fantastic event, and one I’d wholeheartedly recommend.
If you can get to a future Thinking Digital, you absolutely should.
It’s one of the most thought-provoking and interesting couple of days I’ve had in a long time.
Full-diclosure: As a speaker, I didn’t have to pay for a ticket to attend this event. My travel and accommodation costs were paid for by IBM.
In this post, I want to explain how to create a text analytics application in BlueMix using UIMA, and share sample code to show how to get started.
First, some background if you’re unfamiliar with the jargon.
What is UIMA?
UIMA (Unstructured Information Management Architecture) is an Apache framework for building analytics applications for unstructured information and the OASIS standard for content analytics.
It’s perhaps better known for providing the architecture for the question answering system IBM Watson.
What is BlueMix?
It’s in open beta at the moment, so you can sign up and have a play.
I’ve never used BlueMix before, or Cloud Foundry for that matter, so this was a chance for me to write my first app for it.
A UIMA “Hello World” for BlueMix
I’ve written a small sample to show how UIMA and BlueMix can work together. It provides a REST API that you can submit text to, and get back a JSON response with some attributes found in the text (long words, capitalised words, and strings that look like email addresses).
The “analytics” that the app is doing is trivial at best, but this is just a Hello World. For now my aim isn’t to produce a useful analytics solution, but to walk through the configuration needed to define a UIMA analytics pipeline, wrap it in a REST API using Wink, and deploy it as a BlueMix application.
When I get a chance, I’ll write a follow-up post on making something more useful.
You can try out the sample on BlueMix as it’s deployed to bluemix.net
The source is on GitHub at github.com/dalelane/bluemixuima.
In the rest of this post, I’ll walk through some of the implementation details.
Runtimes and services
Creating an application in BlueMix is already well documented so I won’t reiterate those steps, other than to say that as Apache UIMA is a Java SDK and framework, I use the Liberty for Java runtime.
I’m not using any of the services in this simple sample.
The app is bundled up in a war file, which is what we deploy. This is specified in manifest.yml.
I’m deploying from eclipse, too, using the Cloud Foundry plugins for eclipse.
The type system is defined in an XML descriptor file and specifies the different annotations that can be created by this pipeline, and the attributes that they have.
Running JCasGen in eclipse on that descriptor generates Java classes representing those types.
The pipeline is also defined in XML descriptors: one overall aggregate descriptor which imports three primitive descriptors for each of the three annotators in my sample pipeline : one to find email addresses, one to find capitalised words and one to find long words.
Note that the imports in the aggregate descriptor need to be relative so that they keep working once you deploy to BlueMix.
These XML descriptor files are all added to the war file by being included in the build.xml with a fileset include.
Each of the primitive descriptor files specifies the fully qualified class name for the Java implementation of the annotator.
There are three annotators in this sample. (XML files with names starting “primitiveAeDescriptor”).
Each uses a regular expression to find things to annotate in the text. This isn’t intended to be an indication that this is how things should be done, just that it makes for a simple and stateless demonstration without any additional dependencies.
The UIMA pipeline is defined in a single Java class.
It creates a CAS pool to make it easier to handle multiple concurrent requests, and avoid the overhead of creating a CAS for every request.
Once the CAS has passed through the pipeline, the annotations are immediately copied out of the CAS into a POJO, so that the CAS can be returned to the pool.
The war file deployed to BlueMix contains a web.xml which specifies the servlet that implements the REST API.
The list of API endpoints is a list of classes that Wink uses. There is only one API endpoint, so only one class listed.
Everything is defined using annotations, and Wink handles turning the response into a JSON payload.
I think that’s pretty much it.
It’s live at uimahelloworld.mybluemix.net.
Like I said, it’s very simple. The Java itself isn’t particularly complex. My reason for sharing it was to provide a boilerplate config for defining a UIMA analytics pipeline, wrapping it in a REST API, and deploying it to BlueMix.
Once you’ve got that working, you can do text analytics in BlueMix as complex as whatever you can dream up for your annotators.
When I get time, I’ll write a follow-up post sharing what that could look like.
After Monkigras 2013, I was really looking forward to Monkigras 2014. The great talks about developer culture and creating usable software, the amazing buzz and friendliness of the event, the wonderful lack of choice over which talks to go to (there’s just one track!!), and (of course) the catering:
The talks at Monkigras 2014
The talks were pretty much all great so I’m just going to mention the talks that were particularly relevant to me.
Rafe Colburn from Etsy talked about how to motivate developers to fix bugs (IBMers, read ‘defects’) when there’s a big backlog of bugs to fix. They’d tried many strategies, including bug rotation, but none worked. The answer, they found, was to ask their support team to help prioritise the bugs based on the problems that users actually cared about. That way, the developers fixing the bugs weren’t overwhelmed by the sheer numbers to choose from. Also, when they’d done a fix, the developers could feel that they’d made a difference to the user experience of the software.
While I’m not responsible for motivating developers to fix bugs, my job does involve persuading developers to write articles or sample code for WASdev.net. So I figure I could learn a few tricks.
A couple of talks that were directly applicable to me were Steve Pousty‘s talk on how to be a developer evangelist and Dawn Foster‘s on taking lessons on community from science fiction. The latter was a quick look through various science fiction themes and novels applied to developer communities, which was a neat idea though I wished I’d read more of the novels she cited. I was particularly interested in Steve’s talk because I’d seen him speak last year about how his PhD in Ecology had helped him understand communities as ecosystems in which there are sometimes surprising dependencies. This year, he ran through a checklist of attributes to look for when hiring a developer evangelist. Although I’m not strictly a developer evangelist, there’s enough overlap with my role to make me pay attention and check myself against each one.
One of the risks of TED Talk-style talks is that if you don’t quite match up to the ‘right answers’ espoused by the speakers, you could come away from the event feeling inadequate. The friendly atmosphere of Monkigras, and the fact that some speakers directly contradicted each other, meant that this was unlikely to happen.
It was still refreshing, however, to listen to Theo Schlossnagle basically telling people to do what they find works in their context. Companies are different and different things work for different companies. Similarly, developers are people and people learn in different ways so developers learn in different ways. He focused on how to tell stories about your own failures to help people learn and to save them from having to make the same mistakes.
Again, this was refreshing to hear because speakers often tell you how you should do something and how it worked for them. They skim over the things that went wrong and end up convincing you that if only you immediately start doing things their way, you’ll have instant success. Or that inadequacy just kicks in like when you read certain people’s Facebook statuses. Theo’s point was that it’s far more useful from a learning perspective to hear about the things that went wrong for them. Not in a morbid, defeatist way (that way lies only self-pity and fear) but as a story in which things go wrong but are righted by the end. I liked that.
Ana Nelson (geek conference buddy and friend) also talked about storytelling. Her point was more about telling the right story well so that people believe it rather than believing lies, which are often much more intuitive and fun to believe. She impressively wove together an argument built on various fields of research including Psychology, Philosophy, and Statistics. In a nutshell, the kind of simplistic headlines newspapers often publish are much more intuitive and attractive because they fit in with our existing beliefs more easily than the usually more complicated story behind the headlines.
The Gentle Author spoke just before lunch about his daily blog in which he documents stories from local people. I was lucky enough to win one of his signed books, which is beautiful and engrossing. Here it is with my swagbag:
— Laura Cowen (@lauracowen) February 1, 2014
After his popular talk last year, Phil Gilbert of IBM returned to give an update on how things are going with Design@IBM. Theo’s point about context of a company being important is so relevant when trying to change the culture of such a large company. He introduced a new card game that you can use to help teach people what it’s like to be a designer working within the constraints of a real software project. I heard a fair amount of interest from non-IBMers who were keen for a copy of the cards to be made available outside IBM.
On the UX theme, I loved Leisa Reichelt‘s talk about introducing user research to the development teams at GDS. While all areas of UX can struggle to get taken seriously, user research (eg interviewing participants and usability testing) is often overlooked because it doesn’t produce visual designs or code. Leisa’s talk was wonderfully practical in how she related her experiences at GDS of proving the worth of user research to the extent that the number of user researchers has greatly increased.
And lastly I must mention Project Andiamo, which was born at Monkigras 2013 after watching a talk about laser scanning and 3D printing old railway trains. The project aims to produce medical orthotics, like splints and braces, by laser scanning the patient’s body and then 3D printing the part. This not only makes the whole process much quicker and more comfortable, it is at a fraction of the cost of the way that orthotics are currently made.
If you can help in any way, take a look at their website and get in touch with them. Samiya and Naveed’s talk was an amazing example of how a well-constructed story can get a powerful message across to its listeners:
"This is supposed to be a compliment, but your talk made me cry" – @monkigras
— Charlotte Spencer (@Charlotteis) January 31, 2014
After Monkigras 2014, I’m now really looking forward to Monkigras 2015.
— Paul Johnston (@PaulDJohnston) February 12, 2014
I attended ThingMonk 2013 conference partly because IBM’s doing a load of work around the Internet of Things (IoT). I figured it would be useful to find out what’s happening in the world of IoT at the moment. Also, I knew that, as a *Monk production, the food would be amazing.
What is the Internet of Things?
If you’re reading this, you’re familiar with using devices to access information, communicate, buy things, and so on over the Internet. The Internet of Things, at a superficial level, is just taking the humans out of the process. So, for example, if your washing machine were connected to the Internet, it could automatically book a service engineer if it detects a fault.
I say ‘at a superficial level’ because there are obviously still issues relevant to humans in an automated process. It matters that the automatically-scheduled appointment is convenient for the householder. And it matters that the householder trusts that the machine really is faulty when it says it is and that it’s not the manufacturer just calling out a service engineer to make money.
What is ThingMonk 2013?
ThingMonk 2013 was a fun two-day conference in London. On Monday was a hackday with spontaneous lightning talks and on Tuesday were the scheduled talks and the evening party. I wasn’t able to attend Monday’s hackday so you’ll have to read someone else’s write-up about that (you could try Josie Messa’s, for instance).
I bought my Arduino getting started kit (which I used for my Christmas lights energy project in 2010) from Tinker London so I was pleased to finally meet Tinker’s former-CEO, Alexandra Dechamps-Sonsino, at ThingMonk 2013. I’ve known her on Twitter for about 4 years but we’d never met in person. Alex is also founder of the Good Night Lamp, which I blogged about earlier this year. She talked at ThingMonk about “the past, present and future of the Internet of Things” from her position of being part of it.
I think it was probably Nick O’Leary who first introduced me to the Arduino, many moons ago over cups of tea at work. He spoke at ThingMonk about wiring the Internet of Things. This included a demo of his latest project, NodeRED, which he and IBM have recently open sourced on GitHub.
Sadly I missed the previous day when it seems Nick and colleagues, Dave C-J and Andy S-C, won over many of the hackday attendees to the view that IBM’s MQTT and NodeRED are the coolest things known to developerkind right now. So many people mentioned one or both of them throughout the day. One developer told me he didn’t know why he’d not tried MQTT 4 years ago. He also seemed interested in playing with NodeRED, just as soon as the shock that IBM produces cool things for developers had worn off.
Ian Skerrett from Eclipse talked about the role of Open Source in the Internet of Things. Eclipse has recently started the Paho project, which focuses on open source implementations of the standards and protocols used in IoT. The project includes IBM’s Really Small Message Broker and Roger Light’s Mosquitto.
There were a couple of talks about people’s experiences of startups producing physical objects compared with producing software. Tom Taylor talked about setting up Newspaper Club, which is a site where you can put together and get printed your own newspaper run. His presentation included this slide:
Patrick Bergel made the very good point in his talk that a lot of IoT projects, at the moment, are aimed at ‘non-problems’. While fun and useful for learning what we can do with IoT technologies, they don’t really address the needs of real people (ie people who aren’t “hackers, hipsters, or weirdos”). For instance, there are increasing numbers of older people who could benefit from things that address problems social isolation, dementia, blindness, and physical and cognitive impairments. His point was underscored throughout the day by examples of fun-but-not-entirely-useful-as-is projects, such as flying a drone with fruit. That’s not to say such projects are a waste of time in themselves but that we should get moving on projects that address real problems too.
The talk which chimed the most with me, though, was Claire Rowland‘s on the important user experience UX issues around IoT. She spoke about the importance of understanding how users (householders) make sense of automated things in their homes.
The food was, as expected, amazing. I’ve never had bacon and scrambled egg butties that melt in the mouth before. The steak and Guinness casserole for lunch was beyond words. The evening party was sustained with sushi and tasty curry.
Ten years ago.
I was a recent University graduate, arriving at IBM’s R&D site in Hursley for the first time. I remember arriving in Reception.
It was a Wednesday.
I’m still at the same company. I’m still at the same site. I still do the same drive to work, more or less.
For a *decade*.
How did that happen?
It was never The Plan. The Plan (as cynical as it sounds in hindsight) was that I’d stay for two or three years. I figured that would be long enough to get experience, and then I’d leave to work at a small nimble start-up which was where all the “cool” work was.
The Plan never happened. A few years passed, and then another few… I kept saying that I’d leave “later” and before I knew it a ten year milestone has kind of snuck up on me.
I think I’m more surprised than anyone. I’ve never been at any place this long. I was at Uni for five years. The longest I was at any school was four years.
It’s a serious commitment, and one I never realised that I had made. I’ve not even been married for as long as I’ve been with IBM.
So why? Why am I still here?
It’s been varied
I’ve spent ten years working for the same company, but I’ve had several jobs in this time.
I’ve been a software developer. I’ve been a test engineer. I’ve been a service engineer, fixing problems with customer systems. I’ve worked as a consultant, advising clients about technology through presentations and running workshops. I’ve done services work building prototypes and first-of-a-kind pilot systems for clients.
I’ve written code to run on tiny in-car embedded systems and apps that ran on mobile phones. I’ve worked as a System z Mainframe developer. I’ve written front-end UI code, and I’ve written heavy-duty server jobs that took hours to run (even when they weren’t supposed to).
It’s still challenging
I’ve worked on middleware technology, getting some of the biggest computer systems in the world to communicate with each other, reliably, securely and at scale. I’ve used analytics to get insight from massive amounts of data. I’ve worked on large-scale fingerprint and voiceprint systems. I’ve used natural language processing to build systems that attempt to interpret unstructured text. I’ve used machine learning to create systems that can be trained to perform work.
I’m still learning new stuff and still regularly have to figure out how to do stuff that I have no idea how to at the start.
I get to do more than just a “day job”
I do random stuff outside the day job. I’ve helped organise week long schools events to teach kids about science and technology. I’ve mentored teams of University students on summer-long residential innovation projects. I’ve prepared and delivered training courses to school kids, school teachers and charity leaders. I’ve written an academic paper and presented it at a peer-reviewed research conference. And lots more.
I’m a developer, but that doesn’t mean I’ve spent ten years churning out code 40 hours a week. There’s always something new and different.
I work on stuff that matters
Tim O’Reilly has been talking for years about the importance of working on stuff that matters.
“Work on something that matters to you more than money”
I can’t do his message justice here, but I just want to say that he describes a big part of how I feel very well. I want to work on stuff that I can be proud of. Not just technically proud of, although that’s important too. But the pride of doing something that will make a difference.
Working for a massive company gives me chances to do that. I’ve worked on projects for governments, and police forces, and Universities. I’ve done work that I can be proud of.
For the last couple of years, I’ve been working on Watson. It’s a very cool collection of technologies, and watching the demo of it competing on a US game show has a geeky thrill that doesn’t get old. But that’s not the most exciting bit. Watson could be a turning point. This could change how we do computing. If you look at what we’re trying to do with Watson in medicine, we’re trying to transform how we deliver healthcare. This stuff matters. It’s exciting to be a part of.
I like the lifestyle
Hursley is a campus-style site. It’s miles from the nearest town, and surrounded by fields and farms. It’s quiet and has loads of green open space.
My commute is a ten minute drive through a village and fields.
I don’t have to wear a suit, and I don’t stand out coming to work in a hoodie and combat trousers. Flexitime has been the norm for most of my ten years, and I am free to plan a work day that suits me. When I need to be out of the office by 3pm to get the kids from school, I can.
My kids are at a school half-way between home and the office, so I can do the school run on the way to work. As the school is only five minutes from work, I often nip out to see them do something in an assembly, or have lunch with them.
This is a nice aspect of the school – that parents are welcome to join their kids for lunch, and have a school dinner with them and their friends in the school canteen. But still… it’s pretty cool, and if I didn’t work just up the road from them, I wouldn’t be able to do it.
Once a month, I bring them to work in the morning before school starts for a cooked breakfast in the Clubhouse with the rest of my team.
All of this and a lot more tiny aspects like it add up to a lifestyle that I like.
I get to see the world
I enjoy travelling. I love seeing new places.
But I’d hate a job where I lived out of a suitcase and never saw the kids.
I’ve managed to find a nice balance. I travel, but usually on short trips and not too often.
In 2006, I worked at IBM’s La Gaude site near Nice. In 2007, Singapore, Malaysia, Philippines and Paris. In 2008, I worked in Copenhagen, Paris and Hamburg. In 2009, I worked in Munich many times, and Rotterdam. In 2010, Stockholm. In 2011, Tel Aviv and Haifa in Israel, Austin in Texas, Paris and Berlin. Last year, I worked in Zurich and Littleton, Massachusetts.
Plus working around the UK. It’s less glamorous, but it’s still interesting to go to new places. I’ve worked in loads of places, like Edinburgh, York, Swansea, Malvern, Warwick, Portsmouth, Cheshire, Northampton, Guildford… I occasionally have to work in London, although I tend to moan about it. And I spent a few months working in Farnborough. I think I moaned about that, too.
Travelling is a great opportunity. I couldn’t afford to have been to all the places that IBM has sent me if I had to pay for it myself.
The pay is amazing!
Will I be here for another ten years?
I’m trying to explain why I’m happy and enjoying what I do. I’m not saying I couldn’t get exactly the same or better somewhere else. Because I don’t know. Other than a year I spent as an intern at Motorola I’ve never worked anywhere else. For all I know, the grass might be greener somewhere.
Will I still be here in another ten years? I dunno… I do worry if that’s unambitious. I wonder if I should try somewhere else. I wonder if only ever working for one company is giving me an institutionalised and insular view of the world.
I keep getting emails from LinkedIn about all the people I know who have new jobs. There are a bunch of people I used to work with at IBM who have not only left to work at other companies, but have since left those companies and gone on to something even newer. While I’m still here.
Am I destined to be one of those IBMers who works at Hursley forever? That’s a scary thought.
For now, I’m enjoying what I do, so that’s good enough for me.
Happy 10th anniversary to me.
Unlike the 12 week NLP course last year which estimated 10 hours a week and turned out to be more like 15-20 hours a week, this course was much more realistic in estimation at 10 weeks of 8 hours. I think I more or less hit the mark on that point spending about 1 day every week for the past 10 weeks studying machine learning - so around half the time required for the NLP course.
The course was written and presented by Andrew Ng who seems to be rather prolific and somewhat of an academic star in his fields of machine learning and artificial intelligence. He is one of the co-founders of the coursera site which along with their main rival, Udacity, have brought about the popular rise of Massive Open Online Learning.
The Machine Learning Course followed the same format as the NLP course from last year which I can only assume is the standard coursera format, at least for technical courses anyway. Each week there were 1 or two main topic areas to study which were presented in a series of videos featuring Andrew talking through a set of slides on which he's able to hand write notes for demonstration purposes, just as if you're sitting in a real lecture hall at university. To check your understanding of the content of the videos there are questions which must be answered on each topic against which you're graded. The second main component each week is a programming exercise which for the Machine Learning Course must be completed in Octave - so yet another programming language to add to your list. Achieving a mark of 80% or above across all the questions and programming exercises results in a course pass. I appear to have done that with relative ease for this course.
The 18 topics covered were:
- Linear Regression with One Variable
- Linear Algebra Review
- Linear Regression with Multiple Variables
- Octave Tutorial
- Logistic Regression
- Neural Networks Representation
- Neural Networks Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
- Support Vector Machines
- Dimensionality Reduction
- Anomaly Detection
- Recommender Systems
- Large Scale Machine Learning
- Application Example Photo OCR
The major thought behind the course seems to be to teach as many different algorithms as possible. There really is a great range. Starting of simply with linear algorithms and progressing right up to the current state-of-the-art Neural Networks and the ever fashionable map-reduce stuff.
I didn't find the course terribly difficult, I'm no expert in any of the topics but have studied enough maths not to struggle with that side of things and don't struggle with programming either. I didn't need to use the forums or any of the other social elements offered during the course so I don't really have a feel for how others found the course. I can certainly imagine someone finding it a real struggle if they don't have a particularly deep background in either maths or programming.
There was, as far as I can think right now, one (or maybe two depending on how you count) omission from the course. Most of the programming exercises were heavily frameworked for you in advance, you just have to fill in the gaps. This is great for learning the various different algorithms presented during the course but does leave a couple of areas at the end of the course you're not so confident with (aside from not really having a wide grasp of the Octave programming language). The omission of which I speak is that of storing and bootstrapping the models you've trained with the algorithm. All the exercises concentrated on training a model, storing it in memory, using it and as the program terminates then so your model disappears. It would have been great to have another module on the best ways to persist models between program runs, and how to continue training (bootstrap) a model that you have already persisted. I'll feed that thought back to Andrew when the opportunity arises over the next couple of weeks.
The problem going forward wont so much be applying what has been offered here but working out what to apply it to. The range of problems that can be tackled with these techniques is mind-blowing, just look at the rise of analytics we're seeing in all areas of business and technology.
Overall then, a really nice introduction into the world of machine learning. Recommended!
Several presentations looked at how accessible the web is.
Web Accessibility Snapshot
In 2006, an audit was performed by Nomensa for the United Nations. They reviewed 100 popular websites for conformance to accessibility guidelines.
The results weren’t positive: 97% of sites didn’t meet WCAG level 1.
Obviously, conformance to guidelines doesn’t mean a site is accessible, but it’s an important factor. It’s not sufficient, but it is required. Conformance to guidelines can’t prove that a website is accessible, however there are some guidelines that we can be certain would break accessibility if not followed. So they are at least a useful starting point.
However, 2006 is a long time ago now, and the Internet has changed a lot since. One project, from colleagues of mine at IBM, is creating a more up to date picture of the state of the web. They analysed a thousand of the most popular websites (according to Alexa) as well as a random sampling of a thousand other sites.
(Interestingly, they found no statistically significant difference between conformance in the most popular websites and the randomly selected ones).
Their intention is to perform this regularly, creating a Web Accessibility Snapshot, with regular updates on the status of accessibility of the web. It looks like it could become a valuable source of information.
There was a lot of discussion about how to assess accessibility.
One paper argued there is an over-reliance on automated tools and a lack of awareness of the negative effects of this. They demonstrated a manual review of websites, comparing results with output from six popular tools. Their results showed how few accessibility problems automated tools discover.
Accurately assessing a website against accessibility guidelines doesn’t necessarily mean that you can prove a site is accessible or easy to use.
Some research presented suggests guidelines only cover a little over half of problems encountered by users. Usability studies suggest some websites that don’t meet guidelines may be easier to use than websites that do, as users may have effective coping strategies for (technically) non-compliant sites. This suggests we need a better way of assessing accessibility.
A better approach might be to observe users interact with a website and assess based on their experiences. One tool presented, WebTactics, showed an automated approach to assessing accessibility by observing a user and identifying behaviours they employ.
Given that most websites have some sort of accessibility problems, there was some talk about how this could be improved.
One project presented showed training that has been developed to raise awareness of how people with disabilities access the web, and the implications of the accessibility guidelines. It’s a practical course including hands-on assignments, and looks like it could be the sort of thing that could help web developers make a real difference.
Another project is using crowd-sourcing to improve web sites that already exist. Social Accessibility, another IBM project, enables volunteers to make web pages more accessible to the visually impaired.
It provides a mechanism for accessibility problems to be gathered directly from visually impaired users. Volunteers are then notified, and can respond using a tool that allows them to externally modify web pages to make them more accessible. It lets them publish metadata associated with the original web page. This can be applied to the web page for all visually impaired users who visit it in future using this tool, so that many users can benefit from the improvement.
Finally, a project called cloud4all is developing a roaming profile that stores your preferences in a way that multiple services can access. The focus is on accessibility – a user can store their accessibility needs in one place, and then interfaces can use this to adapt for them.
There were a few sessions presenting work done to improve understanding of how to better support people with dyslexia.
One interesting study investigated the effect of font size and line spacing on the readibility of wikipedia articles.
This was assessed in a variety of ways, some of which were based on the reader’s opinions, while others were based on measurements made of the reader during reading and of their understanding of the content after. The underlying question (can we make Wikipedia easier to read for dyslexics?) was compelling. It was also interesting to see this performed not on abstract passages of text, but in the context of using an actual website.
Accessibility isn’t just about the presentation but also the content itself. Another study looked at strategies for simplifying text that could make web pages more readable for dyslexic readers.
It compared the effectiveness of two strategies: firstly, providing synonyms on demand – giving a reader a way to be able to request an alternative for any word. The second was providing synonyms automatically – with complex words automatically substituted for simpler equivalents. Again, this was assessed in several ways, such as the speed of reading, the reader’s comprehension, on the reader’s opinion of easiness, on the effort it took (e.g. interpreting facial expression, etc.), on fixation duration measured using eye tracking, and so on.
On a more practical note, there were also tools presented that are being created to help support people with dyslexia.
Firefixia is a Firefox toolbar extension being created by colleagues of mine in IBM. It provides options for users to customise the web page they are looking at, offering modifications that have been demonstrated to make it easier for dyslexic users.
Dyseggxia is an impressive looking iPad game that aims to support children with dyslexia through fun word games.
Several of the projects that I saw showed glimpses of a possible future for screen readers.
I’ve written about screen readers before, and some of the challenges with using them.
One project interpreted pictures of charts or graphs and created a textual summary of the information shown in them.
I’m still amazed at this. It takes a picture of a graph, not the original raw data, and generates sensible summaries of what it shows.
For example, given this image:
It can generate:
This graphic is about United States. The graphic shows that United States at 35 thousand dollars is the third highest with respect to the dollar value of gross domestic product per capita 2001 among the countries listed. Luxembourg at 44.2 thousand dollars is the highest
The dollar value of gross domestic product per capita 2001 is 25 thousand dollars for Britain, which has the lowest dollar value of product per capita 2001. United States has 1.4 times more product per capita 2001 than Britain. The difference between the dollar value of gross domestic product per capita 2001 for United States and that for Britain is 10 thousand dollars.
Their focus is on the sort of graphics found in newspapers and magazines – informational, rather than scientific graphs. They want to be able to generate a high level summary, rather than a list of plot points that require the user to build a mental model in order to interpret.
The image shows a line graph. The line graph presents the number of Walmmartâ€™s sales of leather jackets. The line graph shows a trend that changes. The changing trend consists of a rising trend from 1997 to 1999 followed by a falling trend through 2006. The first segment is the rising trend. The rising trend is steep. The rising trend has a starting value of 1890. The rising trend has an ending value of 36840. The second segment is the falling trend. The falling trend has a starting value of 36840. The falling trend has an ending value of 12606.
The image shows a line graph. The line graph presents the number of people who started smoking under the age of 18 in the US. The line graph shows a trend that changes. The changing trend consists of a rising trend from 1962 to 1966 followed by a falling trend through 1980. The first segment is the rising trend. The rising trend is steep. The second segment is the falling trend.
It’s able to interpret an image and recognise trends, recognise how noisy or smooth it is, recognise if the trend changes, and more. Impressive.
Interpreting data in tables
Another project demonstrated restructuring data tables in web pages to make them easier to explore with a screenreader.
They have an interesting approach of analysing an HTML table and reorganising it to make it more accessible, abstracting out complex sections into a series of menus.
For example, given a table such as this:
it can produce navigable menus such as this:
Even quite complex tables, with row and column spans, which would otherwise be quite difficult to interpret if read row-by-row by a screenreader, is made much more accessible.
Capti web player
Another technology I saw demonstrated was the Capti web player.
This capability should be ideal for visually impaired users, but the tools themselves are still quite difficult to use and integrate poorly with assistive technologies. Someone described them as obviously “designed by sighted people for sighted people”.
Capti combines this capability with an accessible media player making it easy to navigate through an article, move through a list of articles, and so on. To a sighted user like me, it looked like they’ve mashed together instapaper with an audiobook-type media player. I often listen to podcasts while I go running, and am a heavy user of pocket and Safari’s reading list. So this looks ideal for me.
Multiple simultaneous audio streams
Finally, one fascinating project looked at how to make it quicker to scan large amounts of content with a screenreader to find a specific piece of information. I’ve written before that relying on a screenreader (which creates a sequential audio representation of the information on the page, starting at the beginning and going through the contents) can be tremendously time-consuming, and that it results in visually impaired users taking considerably more time to find information on the web.
This project investigated whether this could be improved by using multiple simultaneous sound sources.
It sounds mad, but they’re starting from observations such as the cocktail party effect – that in a noisy room with several conversations going on, we’re able to pick out a specific conversation that we want to listen for. Or that a student not paying attention in a lecture will hear if a lecturer says something like “this will be on the exam”.
They’re looking at a variety of approaches, such as separating the channels directionally, so one audio stream will sound like it’s coming from the left, while another is in front. Or having different voices, such as different genders, for the different streams. It’s an intriguing idea, and I’d love to see if it could be useful.