What do engineers assume about users’ ability to specify what they want?

I came across the following wonderful gems of tech history quotes in one of my recent readings:

The relational model is a particular suitable structure for the truly casual user (i.e. a non-technical person who merely wishes to interrogate the database, for example a housewife who wants to make enquiries about this week’s best buys at the supermarket).

In the not too distant future the majority of computer users will probably be at this level.

(Date and Codd 1975; 95)

Casual users, especially if they were managers might want to ask a database questions that had never been asked before and had not been foreseen by any programmer.

(Gugerli 2007)

As I was reading these, it occurred to me that many in my own area of robotics also often think in the same way. What would the proper abstractions look like in emerging areas such as robotics, mirroring the advances that have happened in the software space (e.g., contrast the above vision vs. Apple’s offerings today)? Our typical iPad-toting end user is going to speak neither SQL nor ROS, yet the goal of robotics is to let them flexibly and naturally program the machine – how can that actually be achieved?

References:

1. C.J. Date, E.F. Codd, The relational and network approaches: Comparison of the application programming interfaces, Proc. SIGFIDET (now SIGMOD) 1974.
2. D Gugerli, Die Welt als Datenbank. Zur Relation von Softwareentwicklung, Abfragetechnik und Deutungsautonomie, Daten, Zurich, Berlin: Diaphanes, 2007.

Smart watches are still pretty dumb

I have been looking into sensing technology (especially the wearable kind) with increasing interest. This is in part because of some current work I am involved with (e.g., our papers at IPSN ’13, and ACM TECS ’13, which were really initial forays), but more broadly because many of us are becoming convinced that persistent interaction between (wo)man and computational machines is a defining theme of the next decade or two of technology, and sensors are the mediating entities.

I have also felt for a while now that most sensors, even – especially? – when they are called ‘smart’ seem quite dumb actually. For instance, there is a wealth of papers that talk about smart this and that when what they really mean is just what an AI or robotics person would call reactive. In this context, I found this article in the MIT Technology Review to be interesting. As the author says,

After trying some smart watches, I’ve determined that a good one will need to be more than just reliable and simple to use—it will have to learn when and how to bother me. This means figuring out what I’m doing, and judging what bits of information among countless e-mails, app updates, and other alerts are most pressing. And, naturally, it must look good.

I think this is more than just a product design issue. This is fairly challenging to achieve properly even from a research point of view – requiring learning of models of human choice behaviour, etc. There is also the issue of designing good sensors that can be deployed, e.g., see this article. Lots to do, but it’s the kind of thing that’ll be fun to do!

Optimal vs Good enough – how far apart?

The Netflix Tech Blog has this very interesting piece which is very insightful: http://techblog.netflix.com/2011/01/how-we-determine-product-success.html.

 

In particular, this point is very important although easily and often very voluntarily ignored by so many researchers:

There is a big lesson we’ve learned here, which is that the ideal execution of an idea can be twice as effective as a prototype, or maybe even more. But the ideal implementation is never ten times better than an artful prototype. Polish won’t turn a negative signal into a positive one. Often, the ideal execution is barely better than a good prototype, from a measurement perspective.

Interaction is complicated….

… and the following figure underscores this point. This is the logic behind the home button in the iPhone.

Universal Robotic Gripper

I find this really awesome:

The underlying science behind this is very impressive too, see this PNAS paper. This makes one think carefully about the level at which one really wants to be anthropomorphic.

Lectureship in Design Informatics

My institute/school is advertising for an open position in ‘design-related informatics‘. The position seems to be defined in rather broad terms but it could be a great opportunity for interdisciplinary work.

Why do simple techniques work?

My past few posts have been driven by an underlying question that was pointedly raised by someone in a discussion group I follow on linkedin (if you’re curious, this is a Quant Finance group that I follow due to my interest in autonomous agent design and the question was posed by a hedge fund person with a Caltech PhD and a Wharton MBA):

I read Ernest Chan’s book on quantitative trading. He said that he tried a lot of complicated advanced quantitative tools, it turns out that he kept on losing money. He eventually found that the simplest things often generated best returns. From your experiences, what do not think about the value of advanced econometric or statistical tools in developing quantitative strategies. Are these advanced tools (say wavelet analysis, frequent domain analysis, state space model, stochastic volatility, GMM, GARCH and its variations, advanced time series modeling and so on) more like alchemy in the scientific camouflage, or they really have some value. Stochastic differential equation might have some value in trading vol. But I am talking about quantitative trading of futures, equities and currencies here. No, technical indicators, Kalman filter, cointegration, regression, PCA or factor analysis have been proven to be valuable in quantitative trading. I am not so sure about anything beyond these simple techniques.

This is not just a question about trading. The exact same question comes up over and over in the domain of robotics and I have tried to address it in my published work.

My take on this issue is that before one invokes a sophisticated inference algorithm, one has to have a sensible way to describe the essence of the problem – you can only learn what you can succinctly describe and represent! All too often, when advanced methods do not work, it is because they’re being used with very little understanding of what makes the problem hard.  Often, there is a fundamental disconnect in that the only people who truly understand the sophisticated tools are tools developers who are more interested in applying their favourite tool(s) to any given problem than in really understanding a problem and asking what is the simplest tool for it. Moreover, how many people out there have a genuine feel for Hilbert spaces and infinite-dimensional estimation while also having the practical skills to solve problems in constrained ‘real world’ settings? Anyone who has this rare combination would be ideally placed to solve the complex problems we are all interested in, whether using simple methods or more sophisticated ones (i.e., it is not just about tools but about knowing when to use what and why). But, such people are rare indeed.

On bottom-up/top-down development of concepts

For many years now, beginning with some questions that were part of my doctoral dissertation research, I have been curious about multi-level models that describe phenomena and strategies. A fundamental question that arises in this setting is regarding which direction (top-down/bottom-up) takes primacy.

A particular sense in which this directly touches upon my work is in the ability of unsupervised and semi-supervised learning methods to model “everything of interest” in a complex domain (e.g., robotics) so that any detailed analysis of the domain is rendered unnecessary. A claim that is often made is that the entire hierarchy will just emerge from the bottom-up. My own experience with difficult problems such as synthesizing complex humanoid robot behaviours makes me sceptical of the breadth of this claim. I find that, often, the easily available observables do not suffice and one needs to work hard to get the true description. However, I am equally sceptical of the chauvinistic view that the only way to solve problems is to model everything in the domain and dream up a clever strategy or the defeatist view that the only way to solve the problem is to look at pre-existing solutions somewhere else and copy them. Instead, in my own work, I have searched for a middle ground where one seeks general principles on both ends of the spectrum and tries to tie it together efficiently.

Recently, while searching google scholar for some technical papers on multi-level control and learning, I came across an interesting philosophical paper (R.C. Bishop and H. Atmanspacher, Contextual emergence in the description of properties, Foundations of Physics 36(12):1753-1777, 2006.) that makes the case that extracting deep organizational principles for a higher level from a purely bottom-up approach is, in a certain sense, a fundamentally ill-posed problem. Even in “basic” areas like theoretical physics one needs more context. Yet, all is not lost. What this really means is that there are some top-down contextual constraints (much weaker than arbitrary rigid prescriptions) that are necessary to make the two mesh together. You will probably have to at least skim the paper to get a better idea but I think this speaks to the same issue I raise above and says something quite insightful.

Braided Highways

Following a link from the Low Dimensional Topology blog, I found this really interesting tidbit of information about a highway interchange in the US that has nontrivial structure. This particular interchange combines the American clover leaf pattern resulting from the standard right turn along a loop with the ‘British pattern’ of left turns. And the two sets are combined together in an interesting braided structure!

I wonder if this is just an accident of incremental design or if all architects sit down and think up such elegant structures. In any case, it sounds like this structure is going to be eliminated due to an incremental change to improve efficiency of traffic flows. That’s a shame!