Lessons Learned from Establishing an Internal Knowledge Base

If you’re unfamiliar with the concept, a knowledge base is a collection of easily-searchable and well-organized documents to which anyone in the company can contribute. The purpose is to document institutional knowledge in a centralized place to which everyone has access.

At companies I’ve previously worked at, the internal wiki has been immensely useful for me. It was the first place I went to if I ran into any issues, technical or non-technical. If an answer didn’t exist in the wiki, I’d write a page myself. I would also create pages for myself and add my daily notes in the hope that it might help me or someone else someday. And it often did.

At Agolo, I advocated to start a knowledge base for our company. After a lot of investigation and deliberation, we decided to choose DokuWiki. I’m learning a lot from the process.

This blog post is an attempt to organize and distill the options we looked at before settling on DokuWiki. Hopefully, it helps to guide you through a similar process at your organization. In the next post, I will write about the challenges and lessons learned from using our DokuWiki.

The following are all the options we considered when picking a knowledge base software for our startup.

1. Wiki

I heavily advocated for this tool because of my previous experience with it.

We considered a number of alternative wiki offerings. First, we looked for a hosted solution that was preferably free. We looked at Wikia, but it didn’t offer private, internal wikis.

We looked at Gollum, and possibly creating an empty GitHub repository just for its wiki, but decided against it. It doesn’t have a large enough community or a library of plugins to choose from. Also, it doesn’t have the ability to tag pages as far as I could tell. But it was a very strong contender.

We also considered Confluence, but decided it was too complex and too expensive. We don’t use JIRA or any other Atlassian products, so we felt we wouldn’t be making full use of it for the amount of money it costs. If not for the cost, Confluence has everything we were looking for.

How.dy’s Slack Wiki looks ideal from what we could tell. It uses Markdown, it has a simple and modern UI, and it’s free. It has Slack integration and uses Slack for authentication. It also uses flat text files as its storage engine. It might not have search, so that was one drawback. The main deterrent to using it, however, was that it is not available to the public. The blog post says that it will be opened up, but I could not find a follow-up post that announces its availability.

And last but not least, we investigated using MediaWiki, which is what Wikipedia is built on. This is a very full-featured wiki software. It’s tried-and-true in its usage at Wikipedia. Most people are very familiar with its UI. The reason we didn’t choose it is because it is too powerful for our needs. We wanted our wiki to be lightweight, easy to install, and not too overwhelming. In addition, we wanted to avoid using a full-fledged database for the storage engine. So, MediaWiki was out.

2. Gitbook

One of my coworkers recently used Gitbook to write a book, and he was very enthusiastic about it. So, we considered having our knowledge base in Gitbook format as well. The idea was each page of the knowledge base would be its own chapter, or sub-chapter, or sub-sub-chapter, depending on where it fits in a global hierarchy of documents.

The advantages would be that it uses Github-flavored Markdown, which all of us are already pretty familiar with. It has a modern look-and-feel, which most wiki offerings do not. It also forces us to think in a hierarchical structure when creating pages.

However, I felt that it had too many drawbacks. I’d say that the forced hierarchical nature is a drawback in itself. Knowledge bases should have as little friction as possible for page creation. If creating a page meant thinking about where it fits in the global structure of the knowledge base, page creation would become less frequent.

In addition, moving from read mode to write/edit mode feels very sluggish — this transition has clearly not been optimized. When writing a book, an author doesn’t often switch between editing and reading. So, this makes sense for that use case. A knowledge base should be much more permissive of switching from reading to editing. This would encourage more participation.

Another disadvantage is that in my mind, a knowledge base should be littered with links to other pages in the knowledge base. In Gitbooks, linking to another chapter is not a frequent use case. This is because the paradigm it’s based on is books, which are read linearly from one chapter to the next.

Another big reason why I advocated against Gitbooks is because it is not scalable. Having one chapter per document might work in the first few months. However, as our company grows, so will our knowledge base. Having 500 chapters would become cumbersome if every new chapter had to fit into an existing hierarchy. Also, the list of chapters would become totally useless.

And finally, having a private Gitbook costs $7 per month.

So, we decided not to use Gitbook for our company knowledge base.

3. Evernote

Some of us are already heavy users of Evernote. So, we considered just creating a notebook where all of us would keep adding notes.

We already use Evernote as a collaborative tool for some specific purposes. For example, we store meeting notes in Evernote notebooks. This is made especially easy because some of us use the Scannable app to take a photo of a hand-written page of notes and make it searchable in Evernote. This is a huge advantage.

Another advantage of using Evernote is the extremely low friction of creating and editing notes. There is no notion of edit mode vs. read-only mode, so users will be encouraged to edit whichever page they’re reading. This is a highly desirable effect in a knowledge base. Also, because it is an application, it is extremely mobile-friendly in addition to being highly performant on our laptops.

However, I felt there is an inherent lack of structure when it comes to dumping everything into Evernote. This is on the opposite end of the spectrum from Gitbook. To me, it would cause more problems by making it too easy to create new pages — it would become more difficult to find old pages.

In addition, having every document in the knowledge base live under the same notebook also seems problematic. If it were possible to have a shareable hierarchy of notebooks, Evernote would be more viable in my eyes. Unfortunately, Stacks are not shareable. It would be easier to organize the knowledge base into smaller categories.

So, while Evernote is a strong contender, we decided against it.

4. Pivotal Bookbinder

We weren’t too familiar with the principles behind Bookbinder, and the documentation doesn’t seem to be detailed enough for us to get acquainted. In addition, the setup seemed like a barrier to entry to us.

In addition, we could not tell if this software supported the tagging and categorization of pages.

5. Sharepoint

In addition to being pricey, Sharepoint is also too heavyweight for our needs. We aren’t extensive users of the MS ecosystem. In addition, it seems too complex for our needs with a steep learning curve.

While Sharepoint also has an option to create a Wiki, it does not support Markdown. So, we did not want to make the commitment to using Sharepoint.

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Tay, Twitter Bots, and the Value Alignment Problem

Recently, Microsoft launched a bot on Twitter that learns to speak from anyone who speaks to it. The results were disastrous on multiple levels:

Trolls turned Tay, Microsoft’s fun millennial AI bot, into a genocidal maniac

First, let’s look at some of the many reasons why it’s a bad thing this happened:

  • On a purely business level, this is a PR disaster for Microsoft. In the present-day culture of instant outrage, this was the perfect news story. The headline “Microsoft Builds Racist Robot” is a guaranteed clickthrough. It makes Microsoft look either evil, negligent, or incompetent.
  • On a user experience level, this bot makes wide swaths of the population feel excluded and attacked. That’s simply bad UX.
  • On an infosec level, it has wide-open attack vectors. The most obvious one is you can get it to tweet anything if you use the following phrase: “repeat after me.” It’s the most obvious injection attack vector possible.
  • On a social level, hate speech already has enough of a platform. This bot was turned into an amplifier for the most deplorable parts of humanity.
    • Even if it were just some pranksters from 4Chan messing with the bot as a joke, it has the unintended side-effect of making visible hateful, fringe viewpoints beyond their proportional representation in society.
    • It was promoted by Microsoft, one of the largest corporate presences in the world by any measure.
    • It was on Twitter — a platform not only with millions of users, but one that’s closely watched by every news agency and blog to be amplified to audiences worldwide.

As bots and other self-sustaining agents become more prevalent in day-to-day life, they absolutely need to deal with these issues.

Why did this happen? How can we avoid this?

For something more clear-cut, let’s take a look at a similar snafu that happened with Google Photos last summer:

Google Photos Mistakenly Labels Black People ‘Gorillas’

This algorithm did not have the knowledge of context and history and racial issues that a human would have. It was simply working with a collection of training data and statistical models. In essence, it was matching new input with its knowledge of old input and producing the most probable output. As with all statistical modeling, it has some error rate, and just like sometimes, it would mislabel a chair as a stool, it mislabeled this input as well.

It’s tempting to say that algorithms are neutral.

They are not.

Machine learning algorithms, by definition, are biased. They have to be. If they were neutral, they would have no better results than flipping a coin. They have to have bias built into them. What builds this bias into the statistical models? Training data and those who design the algorithm. And, as much as we in the software industry would like to believe otherwise, both of those things have complicated relationships with the real world.

Data is not a perfect representation of the real world. A dataset is highly dependent on the choices, conscious and unconscious, made by those who collected the data. If your training data seems comprehensive (e.g. every photo indexed by Google Image Search), that’s when you have to be careful.

How do you know you have enough photos of dark-skinned people to be able to distinguish them from animals that they’ve been historically associated with as a means to oppression and dehumanization? If your test data is equally biased, you can’t, until it blows up out in the real world when a real-life dark-skinned person tries to use it. This is especially true if your team of computer scientists, data scientists, and software engineers are full of people who don’t have experience with these issues socially. That brings us back to Tay.

Here is a breakdown of Tay’s failures in the context of a larger culture where these issues are generally not visible:

The Ongoing Lessons of Tay

Particularly, take a look at this quote:

A long time ago, I observed that there are hundreds of NLP papers on sentiment classification, and less than a dozen on automatically identifying online harassment. This is how the NLP community has chosen to prioritize its goals. I believe we are all complicit in this, and I am embarrassed and ashamed.

This is a consequence of the free market. There is a business demand for sentiment analysis tools (to classify customer reviews of products as positive or negative, for example), but no demand for anti-harassment technology. Research with an immediate business impact is prioritized over research with long-term social and business (PR) consequences. The skeptical response is: “Why is this bad in the long run? Why not let the free market take care of it? If ethical algorithm design becomes a priority, it will automatically become prioritized.”

I’m not convinced this is true.

This line of thinking follows the ideology of utilitarian ethics, which has many problems of its own. For example, take a look at this article. You can justify a lot of morally unsound behavior and decisions with utilitarianism.

Another reason we should not always let market forces rule public goods (like society’s body of research and publically available algorithms) is because it is a short-sighted force of nature. As humans, we should have more of an interest in our long-term survival. Here are some situations where the free market has, is, and will fail us:

  • environmental concerns
  • sustainable energy usage for the long term
  • market bubbles and crashes, ruining individual lives
  • child labor
  • investment in space travel for us to become a multi-planetary species to reduce the chances of annihilation

The free market has worked mostly well for us until now. However, the lack of focus on the long term is troubling especially now that we live in such an abstract, accelerating world. Each individual has far-reaching powers unimaginable to anyone even half a century ago. We are inching ever-closer to creating algorithms that have significant impact on our day-to-day lives. This brings us to the Value Alignment Problem.

Here is the Arbital page for Value Alignment Problem. In essence here is what it is: How do we design systems (particularly self-sufficient software systems such as AGI) that has motivation to do its best to help humanity? How do we align its values with values possessed by the best of our species (for a well-thought-out definition of “best”)?

In the far (but not too far) future, this issue will suddenly become an emergency if not dealt with now. The Machine Intelligence Research Institute (MIRI) is starting to tackle some of these problems, but the free market is not.

The free market is not set up to deal with issues like the Value Alignment Problem. It needs to be solved by forces outside the market. Government is the most obvious candidate, but a government run by the governed often has trouble solving large, abstract problems. Maybe we need more organizations like MIRI. Maybe we need more individuals willing to get involved in civic hacking even as just a hobby. I don’t know what the solution is but I do know the market will have nothing to do with it until it’s too late.

Let’s get back to Tay. What should the Tay team have done differently?

Tay is a relatively simple Twitter bot. Twitter already has a tight-knit, conscientious community of botmakers, all of whom already deal with ethical questions pretty well. The easiest thing in the world for Microsoft to do would have been looking into prior art before creating a Twitter bot. Here is an article containing interviews with some of the more prominent botmakers:

How to Make a Bot That Isn’t Racist

Microsoft’s engineers failed to do their due diligence before launching Tay, and this failing points to much larger issues that we are all about to face.

 

Data-Driven Product Development

Data Driven Products Now!

Here’s a video of this talk. In this blog post, I will be using some screenshots from the above presentation.

In this presentation deck, former Etsy developer Dan McKinley outlines two very important ideas:

  1. Data-driven strategies of project management
  2. Data-driven tactics of product management

First, the Agile-esque process of iterative development using prototyping and A/B testing at key milestones is an interesting approach. This is difficult to do in both small and large companies due to different reasons. In small companies, the resources required for the discipline of this kind of development is too big. At large companies, a single developer or product manager would not have enough control to apply this process in a realistic way, especially given constraints from other teams, QA, designers, and management.

Screenshot 2015-12-17 00.11.13.png

 

This is a great ideal to shoot for. But personally, I don’t know how plausible it is. It requires buy-in from everyone involved. McKinley even briefly mentions this when he talks about discussions with his designer, in which he promises to polish up the prototype in the second phase (“Refinement”) of development.

The second great concept in this talk is the tactical use of simple arithmetic and statistics to make decisions about products and features. While the previous idea’s concern is the quality of a particular product in development, this idea pertains to the nitty-gritty of picking which products to develop in the first place.

Screenshot 2015-12-17 00.15.25.png

This back-of-the-envelope calculation, he outlines, has saved him from spending weeks or months in design, development, testing, deployment, and analysis. This idea seems fundamental and possibly obvious to experienced product managers. However, it is tempting to bypass the rigor this level of analysis requires when everyone involved is swept up in the excitement of a cool new feature.

While these two ideas a vital enough in themselves, this presentation gives us this diagram as icing on the cake:

Screenshot 2015-12-17 00.21.54.png

These charts demonstrate the change in priorities that must happen as a company grows. There are two reasons for this necessity:

  1. Risk mitigation – reducing the number of moonshot ideas being implemented
  2. The absolute importance of using data to make product decisions

And, as McKinley mentions at the beginning of the talk, a dangerous pitfall is to mis-categorize these three by forming an opinion and then finding some pieces of data to back it up. The approach should have more scientific rigor: it should start with a dispassionate hypothesis based on prior data, and through the process of prototyping and A/B testing, the hypothesis should either stand and be refined, or easily discarded after a falsification.

The 25 Meanings behind Favoriting on Twitter

I found an interesting paper describing the 25 possible motivations for clicking the “Favorite” button on someone’s tweet:

More than Liking and Bookmarking? Towards Understanding Twitter Favouriting Behaviour

The team that came up with the fav button probably did not anticipate all of these uses. I’m guessing they probably thought of 3 or 4 at most. This is interesting because it stresses the importance of studying user behavior and motivation. Changes to the behavior of the fav button will affect many of these motivations in unpredictable ways.

I wonder if Facebook’s “Like” button has similar connotations. I’d like to see a study comparing similar functionality across multiple social media sites.

I found this paper when reading this post on Medium, which is interesting in itself:

What’s Wrong with Twitter’s Latest Experiment with Broadcasting Favorites: It Steps over Social Signals While Looking for Technical Solutions

The <picture> tag is coming

I found this article on HackerNews:

How a new HTML element will make the Web faster

For more context on the <picture> tag, here’s the website dedicated to responsive images:

ResponsiveImages.org

And here’s an HTML snippet:

<picture>
    <source media="(min-width: 40em)" srcset="big.jpg 1x, big-hd.jpg 2x">
    <source srcset="small.jpg 1x, small-hd.jpg 2x">
    <img src="fallback.jpg" alt="">
</picture>

 

It looks like one of my biggest issues with responsive design is being addressed.

However, my other issue with it is that it’s not applicable for most of the interesting web apps and websites out there. It’s mostly for content display, which means it’s useful for sites like blogs and newspapers. However, for more complicated sites, where content display is just one component of many, responsive design doesn’t give you that much of an advantage. Your design will still need to address different screen properties (size, aspect ratio, touch capability) in ways different enough that it falls outside the scope of responsive.

Nevertheless, I’m excited to see this tag in the wild. Maybe I won’t burn through the data limit on my phone plan as quickly anymore.