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.

Scientists for EU Letter

If you are in the UK, you know about the intense ongoing debate regarding the referendum on whether to leave the EU. Scientists for EU has organised a letter that represents the view of many in the UK scientific community, which you might consider adding your support to: http://scientistsforeu.uk/sign-save-science/.

The core argument being put forth is as follows:

Scientific advance and innovation are critically dependent on collaboration. To remain a world-leading science nation, we must be team players.

The EU leads the world in science output, is beating the US in science growth – and is rapidly increasing investment in research. The EU is a science superpower. Our place in this team has boosted our science networking, access to talent, shared infrastructure and UK science policy impact. The economy of scale streamlines bureaucracy and brings huge added value for all. International collaborations have 40% more impact than domestic-only research.

Strong science is key for our economy and quality of life. It creates a virtuous cycle, leveraging investment from industry, raising productivity and creating high-value jobs for our future. In fact, 20% of UK jobs currently rely on some science knowledge. Science brings better medicines, cleaner energy, public health protections, a safer environment, new technologies and solutions to global challenges.

If we leave the EU, the UK will lose its driving seat in this world-leading team. Free-flow of talent and easy collaboration would likely be replaced by uncertainty, capital flight, market barriers and costly domestic red-tape. This would stifle our science, innovation and jobs.

It is no surprise that a recent survey showed 93% of research scientists and engineers saying the EU is a “major benefit” to UK research. The surprise is that many voters are still unaware that UK science and its benefits would be demoted by a vote to leave.

We, the undersigned, urge you to seriously consider the implications for UK science when you vote in the referendum on UK membership of the EU.

 

Ockham’s razor in different fields

Someone participating in an ongoing exchange within a public newsgroup made the following observation which I believe is very pertinent to areas I work in as well:

Ockham’s razor was a terrible heuristic for planetary science for about 1500 years, not with respect to replicating the position of the planets in the night sky (big data ‘prediction’ problem as it would currently be called now) but instead as a model to actually understand planetary motion, which became the foundation for mechanics and modern physics.

It would perhaps be considered unfashionable to knock Ockham in today’s age of data-driven science, but I think that reaction only arises from an underestimation of how valuable a good theory is. Throughout graduate school I was just in awe of concepts like the variational formulation of mechanics which were not only the very foundations of physics but also slowly crept under to provide the foundation for the most efficient methods for data-driven inference in graphical models and so on! It would be a real shame if researchers interested in intelligence, be it AI or neuroscience, gave up on the search for these principles…

On disciplinary boundaries

A poet once said, “The whole universe is in a glass of wine.” We will probably never know in what sense he meant that, for poets do not write to be understood… How vivid is the claret, pressing its existence into the consciousness that watches it! If our small minds, for some convenience, divide this glass of wine, this universe, into parts– physics, biology, geology, astronomy, psychology, and so on– remember that nature does not know it! So let us put it all back together, not forgetting ultimately what it is for. Let it give us one more final pleasure: drink it and forget it all!

-Richard Feynman, from Six Easy Pieces

How do innovations spread?

Atul Gawande raises a very interesting question in his recent piece in The New Yorker.

The question is, why do some innovations spread so swiftly and others so slowly? He compares two innovations that arose at similar times, surgical anaesthesia and antiseptics. They both were invented in the mid 1800s, in established centres of medical practice. Anaesthesia spread rapidly and quickly became the default practice, even though there were all manner of people who objected to it, ranging from clerics to old surgeons. On the other hand, antiseptics took much longer to be accepted. Although the basic ideas were already published in the Lancet in 1867, even after the 1880s, it wasn’t common practice – not even in the big hospitals! Why?

A key difference, as Gawande points out, is that both innovations helped the patient and saved lives, but one of them required a lot more from the surgeon (proper antiseptic treatment was hard and required some discomfort for the surgeon, who had to put up with carbolic acid and so on). Equally importantly, one of these – the antiseptics – was a hidden cause and effect phenomenon so that it was hard for everyone involved to appreciate the significance of each little step when the phenomenon isn’t so clearly visible.

Apparently, a lot of the same issues prevent effective solutions to some very common but pressing worldwide health issues. Gawande talks about neonatal care in places like India. Many many simple solutions, such as swaddling a baby along with the mother to let the mother’s body temperature regulate the baby’s, aren’t systematically implemented while too much faith is placed on technology-driven solutions such as baby warmers which may not get used due to lack of electricity or spare parts. How does one get these solutions implemented? One could use punishments, or maybe incentives. Neither seem to actually work on their own, and are too hard to ensure correctness of. What one wants is for these things to become norms, and norms get created by an expensive process of teaching cohorts of people who go out and train others, and so on. So, “diffusion is essentially a social process through which people talking to people spread an innovation,”

The rest of the article, which is a fascinating read, goes on to talk about the story of oral rehydration and cholera. The gem of the idea here is that, “coaxing villagers to make the solution with their own hands and explain the messages in their own words, while a trainer observed and guided them, achieved far more than any public-service ad or instructional video could have done.”

I think these lessons are much more broadly applicable beyond public health. Within my own research community, robotics, sometimes we seem to be cooking up more and more elaborate justifications for complex technologies that seem (with my private citizen’s hat on) only suited to showing that we researchers are smart enough to build really complicated systems. We are also very much susceptible to rewarding only the obvious solutions, like anaesthetics, and almost never rewarding the hidden drivers, like antiseptics. If we are serious about one day having some real impact on the world at a large, we also need to heed the rest of the message in Gawande’s story and think about exactly which bits are real bottlenecks to be targeted and how our technology is a solution to some meaningful question out there in the world.

What constrains your research?

Matt Welsh, a former Harvard professor who moved to Google, and who writes the Volatile and Decentralized blog, has some interesting observations on this topic: http://matt-welsh.blogspot.co.uk/2013/04/the-other-side-of-academic-freedom.html.

As he points out, there are at least four constraints that must be simultaneously satisfied:

  • What you can get funding to do;
  • What you can publish (good) papers about;
  • What you can get students to help you with;
  • What you can do better than anyone else in the field.

Wise words of advice that should be heeded, especially by early career researchers! I found the discussion in the comments as interesting as the main post.