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.
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Chess Metaphors

Chess Metaphors

This is a nice book review written by Garry Kasparov, insightful in part because of the major role he played in the area. Some interesting snippets below.

On human-computer team play:

The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.

The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

On the methodology of chess agents:

Like so much else in our technology-rich and innovation-poor modern world, chess computing has fallen prey to incrementalism and the demands of the market. Brute-force programs play the best chess, so why bother with anything else? Why waste time and money experimenting with new and innovative ideas when we already know what works? Such thinking should horrify anyone worthy of the name of scientist, but it seems, tragically, to be the norm.

 

Lighthill on AI, and some questions that remain open!

As an AI researcher, and a faculty member at Edinburgh where some of the most interesting episodes in the history of AI were enacted, I find the Lighthill report to be quite fascinating and I have occasionally come back to reading it when thinking about the big questions.

One interesting point is that many of the things he criticised so strongly, e.g., machine translation, have turned out to be the areas where in subsequent decades AI has made the most progress. Some of his other critiques, such as his scepticism about data-driven approaches (spoken like the applied mathematician he was), have turned out exactly the other way – as hallmarks of a methodology that has come to define our age.

There is one observation, however, that he rightly makes, that continues to remain a blind spot for the research community:

We must remember, rather, that the intelligent problem solving and eye-hand co-ordination and scene analysis capabilities that are much studied in category B represent only a small part of the features of the human CNS that give the human race its uniqueness. It is a truism that human beings who are very strong intellectually but weak in emotional drives and emotional relationships are singularly ineffective in the world at large. Valuable results flow from the integration of intellectual ability with the capacity to feel and to relate to other people; until this integration happens problem solving is no good because there is no way of seeing which are the right problems. These remarks have been included to make clear that the over-optimistic category-B-centred view of AI not only fails to take the first fence but ignores the rest of the steeplechase altogether.

He is right, and we still don’t pay nearly enough attention to this. Perhaps it is time, especially given the remarkable new opportunities created by new advances in allied areas ranging from experimental neurosciences to cognitive psychology?

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…

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.

Innovation at Bell Labs

This is a nice article on how Bell Labs achieved so much: http://www.nytimes.com/2012/02/26/opinion/sunday/innovation-and-the-bell-labs-miracle.html

A lot of the observations are perhaps not that new to people in the know. However, the point I take away from the article is this:

Despite the apparent replication of the Bell Labs atmosphere in the labs of the newer technology companies (e.g., MS Research, Google/Yahoo to a small extent but also outside the US, e.g., say, SAP or Daimler in Germany), is there any such place in the world today where we expect a Shannon or a Shockley to be working, seamlessly bridging the gap between world class theory and cutting edge practical application? I suspect not, but why and what does that say about how the whole research enterprise is perceived at all levels of the hierarchy?