Filed under Technology

Predicting Human Intent

Readability is one of my favorite web services. Readability is well designed, carefully made, unobtrusive and they’re not trying to wring me of personal information to datamine and sell at every turn (at least I don’t think they are). Their web service gives you browser plugins that strip out everything but the content of a page and present it in a clean, crisp format. If you sign up for an account you can make use of their “Reading List” to store pages for later reading. Recently they released beautiful iOS and Android apps that sync with your reading list allowing you to stock up on things to read at your computer and read them on the move.

Though Readability is a great service and they have great supporting apps they have one flaw. However, it’s not entirely their fault: I think it’s a side-effect of the difficulty of predicting human intent. The problem has to do with Readability’s reading list. When you install the browser plugins you get three buttons: a “Read Now” button, a “Read Later” button and a “Send to Kindle” button. The “Send to Kindle” button formats and sends the web page you’re on to your Kindle (assuming you’ve set up and connected your Kindle properly). The “Read Later” button saves the page you’re on to your Reading List which can be synced to your iOS or Android devices. The “Read Now” button will do the Readability formatting on your current page and show you the cleaned up version right then for you to continue reading.

That’s all great. However, the “Read Now” button also drops the page you just converted into your Reading List. This is great if you start reading a long article and then have to leave your machine. You can come back to it later, on another browser or another device entirely. But what happens if you finish reading the article right there and then? The article still ends up in your reading list. That would be fine if the list was simply a history of things you’ve read. However the Reading List is also a list of things you’re going to read. So the Reading List now contains things I’m going to read, things I’ve read and things I might want to read again. I think this problem stems from the fact that Readability started as a formatter and added read-later functionality unlike services like Instapaper which are designed for savings articles for later.

How can we differentiate between all these types of articles? Readability provides the ability to “Archive” and “Favorite” articles. Once I’m done with reading an article I Favorite it if I’m going to read it again and Archive it otherwise. But could Readability do this for me? Could Readability somehow figure out what I want to happen to the article? The simplest solution would be to archive whatever I Read Now and only add to the Reading List ones that I mark to Read Later. However that means that if I start reading something and then have to leave it ends up in the Archive where I might never look at it again. Could Readability be a little smarter? One heuristic would be to check where I am in the article. By default an article is always added to the Reading List as it is now. But when I scroll to the bottom Readability takes that to mean that I’m done reading and moves it into the Archive. If I liked it and wanted to come back to it I manually mark it as a Favorite. (I don’t expect Readability to be that clever. Yet.)

Without having actually tested the solutions, I can’t say how well they would work. There are certainly edge cases: what if I scroll down to read a footnote and then scroll back up to read the rest of the article? What if I get to the end and then go back to re-read a particular section? What if I quickly skim through an article to get to the end and want to come back later to read it in more depth? I think there’s no clear answer because fundamentally we’re trying to have Readability “guess” what we’re trying to do without giving an unambiguous signal. Sure, all of them could be solved with a few manual interactions. But the whole point of having advanced software is so that I don’t have to tell my computer what to do in excruciating detail.

Like I said at the beginning I don’t think that Readability is necessarily at fault for how their service works. Any attempt to automatically manage the Reading List would require making some assumptions as to what it is the user wants to do. Even if those assumptions are right most of the time, there will almost certainly be times when they’re wrong. We are, after all, dealing with people here, and people aren’t perfectly predictable agents. If they were, human computer interaction and economics would both be very different fields.

Predicting human intent is a hard problem. Ultimately, some amount of direct intervention might be inevitable. While Readability is meant to be a product it would be interesting to see researchers using it (or similar services) for doing research with real users about how our software can make choices for us in a way that closely reflects what we would have done ourselves. Unlike some people I don’t want my software to do less and have fewer features. I want it to do more so that I can concentrate on more important things. Like saving the world.

Generation Flux

A few months ago Fast Company run a multipart piece on “Generation Flux”. The piece had two intertwined themes. The first is the idea that we’re living in age of constant (and perhaps accelerating) change and that to stay competitive businesses and institutes have to ride this wave of change and go with the flow. The second idea is the notion that the most successful people are those who are intimately familiar with this state of flux and can craft their lives to take advantage of it. As part of the piece they profiled several members of Generation Flux – technologists, businesspeople and researchers like danah boyd and DJ Patil.

Though the piece focused on the tech industry and business, I think the basic ideas apply to all fields including (especially?) academia. In fact I think that the best researchers and scientists have always been those who have been spread out over a number of areas. While focus and diligence are necessary for productive research, I’m starting to think that it’s important (if not fundamental) to have a wider halo of interests and knowledge surrounding your core area.

As a new graduate student this is a question of great personal interest: I have a limited amount of time and energy in grad school (and later) and it’s in my best interest to make the most of it. As with many important things there’s a dilemma: if I spend too much time and effort on one thing I’ll miss out on everything else and that can be very limiting. I already know this first hand: I know a good amount about programming languages, but I’ve been scrambling to teach myself about networks and know next to nothing about AI. But on the other hand if I don’t dig deep enough into one relatively narrow area I’ll never have the knowledge or the insight to know what the important problems and come up with appropriate solutions.

So what are the lessons of Generation Flux and how do they apply? Accepting and adapting to change is definitely a big part of it. As danah boyd tells us: “We all have to learn new skills. Being able to live on one set of skills over a career is not realistic. Change is going to happen, not all of it good, in serious ways.” But simply being able to ride the wave is not enough. And it’s certainly not advisable to jump ship to the next shiny thing at the first sign of trouble. DJ Patil has more personal advice to offer: “At the end of the day, you have two things: your energy and your intellectual curiosity. If you’re willing to apply them, try to add value to the world, the possibilities are so endless.”

Patil, boyd and the other Gen Fluxers seem to be able to strike a balance between change and constancy. In times of perpetual change the key to success seems to lie in two complementary values: first is the ability to live on the edge of chaos and move fluidly from one spot to another. But second (and just as important) seems to be the ability to be tenacious, diligent and sometimes downright stubborn. Patil for example taught himself mathematics and worked midnight to morning to get computer access. While he’s worked on amazing projects he’s also turned down lucrative offers because they didn’t fit his vision of what he wanted to do.

Viewed through the lense of graduate school the lessons become: Explore broadly and lightly across areas related to what you’re interested and then buckle down, dive deep and keep going until you get to something novel. Of course the timing is critical and to some extent they have to happen in parallel. As Matt Might puts it: going rogue too early or too late can be fatal. Luckily that’s what advisors, mentors and colleagues are for.

Personally I’m still in stage 1: I’m still taking classes and exploring the broad regions of computer science but I’m also making forays deep into some areas (particularly programming languages and datacenter networks). Looking further ahead I think it’s great that we’re going to be living in a time where being a member of Gen Flux is a good thing. Gen Flux is perhaps just a modern term for Renaissance Men (or Women) – people with a breadth of knowledge and skills but also with singular and far-reaching accomplishments in some of those fields. And that seems like a goal worthy of a lifetime worth of time and energy.

How much do environments matter?

The last week and a half has been really productive. I’ve written a lot of code, made progress on my research project and learned a lot of stuff in the process. Unfortunately it’s all been in one area, but that’s a matter for another post. But given how productive I’ve been one thing that I’ve been wondering is how important an environment really is to productive.

I’m usually of the opinion that environment (both physical and in terms of setup) is really important for any sort of creative or intellectual work. However I’m not quite so certain anymore. My current working setup is less than perfect. Though I have a nice DIY standing desk and a brightly lit office I also share the office with six other people and at times it can get pretty busy and crowded. I have a very powerful work machine but most of my recent work has been in a basic Ubuntu virtual machine with no customization other than my Bash and Emacs setups.

Despite the fact that my environment is not perfect the last week has probably been the most productive I’ve had all year. This begs the question: are environments really as important as I had thought they were? Or is it sufficient (and necessary) to have a project you’re really interested in? Of course, I understand that this is a personal question, so I’m just going to try it for myself.

What I’m starting to think is that the environment doesn’t need to be perfect, it just has to be “not painful”. There are some things that I just can’t stand: I can’t stand bad chairs, environments that are too noisy or too high of a room temperature. But once I have air conditioning, a standing desk and decent set of headphones I can quite easily tune out everything else. Similarly, once I have a command-line UNIX environment and a decent enough keyboard I care much less about what window manager I’m using, what size my monitor is or even what my language or toolchain is. Once I’m in the zone there’s very little that I care about.

I would say that environments matter, but only to some extent. After a point an interesting and exciting project can easily make up for any deficiencies in the environment. However, the opposite – a great environment but an uninspiring project – hardly makes you want to jump out of bed in the morning and get to work.

In addition to my Macbook and my work machine I have a small Eee PC lying around with a bare bones Arch Linux install on it. As a small experiment I want to see if I can be as productive on that machine as I am on my work machine. In addition to my research project I’m taking a programming languages class and TAing a functional programming class, so I regularly find myself in the mood for some OCaml hacking. Admittedly it won’t be a scientifically controlled and rigorous experiment, but it will be interesting to see how far an interesting project can compensate for a less then ideal environment.

Crystallized Archetypes

Manuel Simoni writes a very interesting (and brilliantly named) blog on programming languages entitled The Axis of Eval. For almost two years now he’s been delving deep and wide across the field of programming languages. He was interviewed by the State of Code blog a few months ago. I read something in that interview that has been lurking on and off in the back of my mind:

I think Lisp is the archetype, the crystallized form of all dynamically-typed scripting languages, and for that reason I like it. I also like C and Haskell, because they’re similarly crystallized languages in their respective areas.

As I study more about languages, and use a growing number of them on a regular basis, I can’t help but think: what are some of the other crystallized archetypes of languages out there?

Let’s start with Manuel’s list. Lisp stands out as the archetype for dynamically typed scripting languages. Other similar languages (Perl, Python, Ruby and the like) all emulate bits and pieces of Lisp (with varying success). Each new scripting language that becomes popular seems to be just a little more Lisp-y. Haskell is then the archetype for statically typed, functional programming languages. One could argue that this title belongs to OCaml or the various other MLs. From my experience working with them both occupy very strong points in the space of statically-typed functional languages. Which one you choose depends very much on what your particular needs are.

On another level entirely resides C. While both Lisp and Haskell strive to help you create elegant towers of abstraction, C will happily lay open the bare hardware for you. C is your archetype for a low-level systems programming language for a von Neumann machine. There’s certainly a new generation of systems programming languages waiting in the wings: D, Go and Rust to name a few. Though I have limited experience with any of them, from what I can tell they are all a few layers above the machine. While that’s certainly great for programmers (and I hope it picks up speed) if you want to get down into the guts of your computer you’d better reach for your C compiler. If you have more experience on this front, feel free to enlighten me.

While Java and C++ might have introduced much of the world to object-oriented programming, the title of archetype almost certainly goes to Smalltalk. While Lisp can play host to a powerful object system and OCaml goes far towards combining OO and functional programming, Smalltalk stands out as being object-oriented from the bottom up, so to speak. Of the modern popular languages, Ruby is probably closest to the Smalltalk way while still being a scripting language for Unix-like systems. I’ve only done a tiny bit of Smalltalk programming myself (and certainly haven’t made a large system) but it’s definitely a very interesting experience. I don’t know if there’s an ‘aha moment’ like there is with Lisp, but I highly recommend it if you write OO code regularly.

While those four cover most of the ground for popular programming paradigms, there are a handful of other interesting archetypes out there. For concatenative programming I’d recommend Factor. Similarly, Prolog stand for declarative, logic programming. I’m hesitant to name archetypes for web or distributed programming. Personally I think of JavaScript as more of a prototype-based OO language that happens to be in the browser than as the archetype for a web programming language. Somehow I feel like we can do better than the HTML, CSS and JS trifecta we have now.

As I keep learning about languages, I’m looking forward to exploring these archetypes in greater depth (and their particular instantiations). I might even try implementing a few of them. So much to learn, so much code to write, so little time.

Sunday Selection 2012-02-19

Around the Web

UNIX as IDE I have a love-hate relationship with IDEs. While I understand that IDEs are useful (if not essential) for languages like Java and C++, I personally can stay away from those languages and hence I use Emacs + command line tools as my IDE. Working primarily in Linux (and sometimes OS X) makes this very easy. This series (it’s a long read) is chock full of useful tips to better use Unix as your IDE.

Don’t Fall in Love with Your Technology Our job is not to use the best technology or write code. Our job is to solve problems.

Letter to a Young PL enthusiast This pretty topic-specific, but I’m a PL enthusiast so it’s relevant. If you’re interested in PL research (or just like exposing yourself to new ideas) this is a worthwhile. I don’t know half the things mentioned, but I try to learn a little more everyday. Additionally there seems to be a new PL-oriented mailing list called LL.next.

Fromt the Bookshelf

How to Steal like an Artist Ok, I’m jumping the gun on this one. It just came out and I pre-ordered on Amazon so I’m going to get it in a few days. But judging from the talk and blog post that started it all, it’s going to be awesome. Amazon has it for the cost of a venti something-or-the-other from Starbucks.

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