Matt Warren

Author: Matt

  • Embracing Obstacles

    This book is currently on sale for just $3 and it’s a hell of a deal.  I picked this up after hearing it referenced from several different groups of people in the span of a week, enough to make me curious.

    “The obstacle is the way” presents an argument that much of what prevents us from achieving our goals in life is the perspective we apply to the objective reality of any given situation.  A challenging business negotiation can be seen as a stressful make or break deal, or a chance to further refine your negotiation skills.  The reality is that if you have one business negotiation it’s likely that you’ll have more in the future, thus taking the positive perspective of the situation will position you better for the next one regardless of the outcome.

    When each obstacle you encounter can be re-framed into a learning experience, then everything compounds into more and more experience and skill.

    The common approach that people take to a challenge is to give in – we say it can’t be done or we get angry when we try and fail.  There may be some learned helplessness, or emotional reactions that prevent clear thinking in these situations.  But stopping to get an external view can sometimes shead a lot of light.  We find it easy to suggest fixes to other people’s problems but often have a hard time thinking objectively about our own.

    The book is a quick read, and worth checking out.

  • Agile Methodoligy For Achieving Goals

    At the beginning of the year I, like many other people, set some goals for the year.  I also took the step of putting a reminder in my calendar to revisit my goals list every month to assess my progress.

    After a couple of cycles of that I recognized the pattern, I had inadvertently applied a piece of the agile development method to my goals.

    In typical agile we set a small set of attainable near term goals and work over the course of 1-2 weeks to accomplish them.  Then at the end of that sprint we pause to assess and examine 3 questions:

    1. what went well?
    2. what didn’t go well?
    3. what can be improved for the next sprint?

    Small stretches of effort separated by pauses to assess and repoint are a fantastic way to reach a goal whether it is sailing across the ocean, building a software project or losing 10lbs.

    The time to revisit and refocus allows you to keep all your goals front of mind and gives you a chance to re-evaluate if they were reasonable goals at all to begin with.

    One of my goals was to be able to do 50 pull-ups.  After several weeks of exercise and research on training plans I’ve come to the conclusion that 50 is just an insanely unrealistic number.  So in light of this new perspective I’m re-targetting 20 reps.

  • Fun With Chat

    Chat is becoming the next platform. Interacting with computers through channels that you already use to talk to people (Facebook Messenger, SMS, Slack etc) in a way that feels natural is a powerful way to accomplish many things for which web pages, mobile apps, desktop applications, or commandline scripts are cumbersome.

    Chat is not a panacea for all user experience problems but there are many day to day things that might be able to transition to this way of interacting with computers or new ways that as yet haven’t been able to work.

    In some ways this is evidence of more encroachment of technology into taking ever more jobs from humans.

    Facebook has a bot they run on Messenger (available limited to Silicon Valley area) which can answer ‘any’ question and perform any number of activities on your behalf. Right now it is operating on a hybrid model. There is an ever growing list of queries for which it can automatically handle, and anything which the AI is not yet able to handle is directed back to an actual person to do. “what is a good pizza place nearby?” can be sourced from Yelp reviews. “Can you call and reserve a table for this evening” -> maybe can’t be handled automatically and will go to an actual person to do. As development continues the features that can be handled will be incrementally expanded.

    I have a service running to maintain my healthy habits. Persistence is an app that messages me to do things when it can tell that I haven’t. It’s like a dependable friend that holds me accountable to my goals. There’s no reason why all this can’t happen through a communicative medium like chat. There’s no reason why this couldn’t feel like chatting to an actual friend.

    After playing with the Slack and Facebook APIs around chat, it’s proving to be both more difficult and more interesting than I imagined. Difficult because in these early days creating interfaces that process natural language and conversation threads is missing a lot of tooling. Interesting because this is all so new that you really get a chance to build something that no one has really thought of yet. This is a new platform and there is still much to learn.

  • Writing a Bot

    Facebook recently announced and opened an API for building applications that can have conversations through Facebook Messenger.  This is a huge thing.  Messenger has 900,000 active users and it presents a huge business opportunity for new ways at interacting with people.

    Many people are calling 2016 the year that conversational interfaces will take off, and a huge amount of capital investment is going into figuring out how to make txting with computer programs easier and more intuitive.

    One of my old projects (which I still use) could really benefit from a message interface.  Persistence is a webapp I built to monitor my activities and nag me to maintain good habits.  One of the problems I have run into with it is that my email nag messages get filtered by gmail as not important enough for a push notification, and I miss them.

    Chat provides a more direct messaging platform which doesn’t (yet?) have a way to filter things out, and thus would be more reliable at getting my attention.  It also offers a way to interact with the system without installing an app or visiting a webpage.  Perhaps I could just message back ‘go to sleep’ to silence things for a short period.

    So I started to build it out, and it’s been pretty fun.

  • Fear Of The Unfamiliar

    A number of things have come up in the last few weeks for a project I’m involved in that has shaken my beliefs and shifted my perspective.

    The team I work with have all developed a deep level of experience within their domain of expertise.  This is part of the competitive advantage we use to win clients.  Smart people with a tightly focused specialization can work faster and produce better work than a generalist that hops to the new hotness on every other project.

    The problem is that these specializations prevent individual developers from breaking out of their circle of perceived experience without great effort.  Specialization codifies itself into the how the team functions, who can work on what and who cannot work on what.

    When a problem that demands some technology or tools which are outside of the normal comes up it can throw a wrench in things. Sometimes there are justifiable questions – can we support a solution written in Lua if the original developer leaves?  Other times it can devolve into a rather insulting “‘We’ don’t know how to do that” for something that can be learned in a few hours of reading or working through a tutorial.

    The perceived difficulty and risk of something new can prompt only the most senior developers to get assigned to work on new things.

    In the past at various different jobs we did one of two things.  We noticed that the existing technology stack was no longer meeting our needs so we evaluated some alternatives and then everyone was given some insight into the decision, how it was made and then either:

    1. Everyone was given training to get up to the same level of proficiency at the same time
    2. A hard cut over to the new project on a new stack and forced everyone to pick it up quickly on their own

    Most good developers have no problem picking up a new language quickly. A significant portion of the knowlege you have as a Computer Science or Software Engineer is not tied to the syntax of a particular language.

    Part of a good education in Computer Science is experience with a wide range of types of applications.  I did AI algorithms, wrote a real-time operating system, OpenGL and ray-tracing,  web applications along with the basics of algorithms and data structures.  Part of being a great developer is having the breadth of experience to know when to apply certain technologies over others.

    A perceived specialization can negate all that past experience, and hinder an individual’s opportunity to tackle new challenges.  It demands a balance that must weigh company goals and efficiency won from deep expertise with the desires of each individual developer to work on interesting things and continue to learn.

     

  • Writing Algorithms

    Ah algorithms, that fundamental part of computer science education (and job interviews) which seem to very rarely come up in real world, day to day software development.

    Sometimes I have a  real yearning to tackle a technical problem that involves writing an implementation of a red-black tree or a bucket sort, or hell, just write a function that uses recursion.  These opportunities come up so infrequently that when they do it’s a real pleasure to be savored.

    It’s worth making those moments happen if they don’t come up very often at work.  Go out of your way to find projects that create the challenges you want to take on.  It’ll really remind you about what you love about writing software.

  • Chat First Design

    They say that the market for Mobile Apps is now mature. The stores are topped by the big companies – Facebook, Google, Microsoft and the design languages are now more or less stable for mobile platforms.

    The next hype’d interface is chat. For a generation of kids txting is the most comfortable way to communicate and having services that can tap into that already popular mode may have a good chance of sticking.

    Text bots are apps that interact with users almost exclusively through chat applications – Facebook Messenger, Google Hangouts, WeChat, SMS, iMessage etc.  But even though these platforms are relatively new, text bots certainly go back a long way.

    I can recall one particularly smart grade 9 kid in school wrote a computer chat application back in 1995.  Not that it was very sophisticated.. more or less just 1000’s of hard-coded text responses to your questions.  By the time that IRC started to become popular there were plenty of hackers writing bots that would try and pass off as human, or just do useful things.  It’s hardly a new idea to have text conversations with computers over chat.

    Yet here we are in  2016 and the new hotness in UX and interactive design is to go back to text chat. But there’s one thing that is being overlooked.

    Despite it’s long history, it’s still really hard to do a good job.  There are few if any frameworks for building applications like this.  Sure, you can pull out some NLTK code and parse sentences, but writing something to reliably handle arbitrary strings of text from users is still very difficult.

    I had gotten my hopes up, with the growth of Slack and the buzz around chat, only to be dashed when it came time to implement an app idea I have.  Suddenly I’m finding myself inventing algorithms for maintaining the context of a conversation.  There are still hard problems to solve before chat interfaces will be something that are easy to build.

    And until they are easy, they won’t be prolific.

    What the world needs is a Ruby On Rails for chat.  A conventional approach for building complex chat applications that will allow any developer to take their ideas to production.

    It’s something I wish I had time to work myself, but am instead asking the wider community to pick up the torch and help us all out.

  • Opportunity Costs

    We all only have so much time and attention, and with constantly being pulled by work, friends, family in various directions we are only able to give our own causes and ambitions a percentage of ourselves.

    Opportunity cost is what we lose out on for taking one path over another.  In a way opportunity cost is infinite.  We could at any time take any of thousands of possible actions which could, in aggregate, lead to very different futures.

    In the present though, opportunity cost is speculative.  We can’t know for certain how much taking one action over another will be better or worse than another action.  Although we usually have enough information to help make informed choices.

    Making the optimal choice is non-trivial.  And so we apply tools to help us make smart decisions.  We go to school to understand how things work, study history to learn how things have been done in the past, and use frameworks to help guide us to use best practices.

    Going against these best practices are countless other factors.  Emotional impulses, reaction to external factors, others asking things of you, habits, and restraints. They act like friction preventing us from making better decisions. Some of these things we can easily do things about, recognize and counteract. While other things are very difficult to ignore or fix.

     

  • Best way to learn Machine Learning

    I’m going deeper in my learning about how to successfully implement machine learning algorithms this year by initally doing a survey of all the resources out there for learning this stuff.

    It is a fast moving area of expertise and as such newer techniques and tools wont be covered in older books or tutorials.

    MOOCs are now a great way to get up to speed on the Deep Learning approaches to Machine Learning.  And while there are some good quality general books out there about ML, most are currently in pre-order.

    The most appealing to me right now is the course on Udacity, presented by Google which uses Tensorflow in iPython notebooks to teach how to build and apply ML.  The best thing is that it’s

    1. in Python
    2. uses the latest ML library TensorFlow (developed at Google)
    3. Is free

    As with all learning, the best way to learn is by doing it yourself and practicing enough to make it stick.

    This is not the first resource I’ve used to learn about topics in Machine Learning and it won’t be the last.  Taking multiple courses, reading multiple books and tackling multple problems on your own is the best way to ensure you have no gaps and a well rounded deep understanding of the concepts.

    Actually mastering a new skill is hard and there are no shortcuts.  Accept that and jump into the challenge.

  • Slowly Delving into AI

    This year seems to be a big year for AI development. Deep Learning approaches are going to be applied to more areas and I expect most of the big name tech companies will continue to expand their research in the area.

    The encouraging thing for the rest of us developers is going to be the opening up of core technologies.  The algorithms themselves are not significantly complicated. And the true value comes from the data used to train these models.  So there is some incentive for companies like Google to open-source their AI tooling.  It will enable more developers the chance to push the boundaries of AI techniques, while the companies themselves maintain ownership of the critical training data used to get the best results from these models.

    What that means is that this year there will be more than a few new start-ups trying to turn these AIs into web services, or sell trained libraries as tools you can use in your own code.

    Take for instance, something like sentiment analysis.  There are already quite a few APIs you can easily tie into to get this sort of analysis added to your own projects.

    This year I expect this will expand into a large variety of areas.

    Spell checking is prime for disruption.  For too long spell checking has relied on simple dictionary lookups and Levenshtein distance to guess at correct spelling.  These are relatively crude compared to the ability to understand context within a sentence and give much more probable corrections.

    Google has open-sourced TensorFlow, and it has already gotten some significant attention from the developer community.  As more developers learn how to use these tools this year, you’ll see a lot of very interesting developments.