Are we clear?

Original post:  Sep 16, 2015

A colleague in Minnesota reached out to me via e-mail looking for some information. Instead of trying to convey complex information in long written missives, we scheduled a small conference call to discuss the issue.

Early on in the call, this colleague used a term I had never heard before. It sounded similar to something else that I remembered from the distant past. In order to avoid any misunderstandings, I asked the other party to explain what they meant when they used the term. "Well, maybe you're not the right person to help me" came the reply. After the initial shock wore off, I tried to calmly explain that I wasn't familiar with the term being discussed and if I could get that clarification, I might then provide the right response.

There are many occasions where something that is called "x" in Mounds View was called "y" in Mansfield. Unfortunately, there are very few people in Mansfield who can speak fluent SAP. It doesn't help when people looking for information aren't willing to explain what "x" means so that we can figure out what the correct "y" was. It's especially difficult when "x" and "y" aren't exactly the same things. Perhaps there might be a combination of several "y" parts to make up an "x" whole (or vice versa).

We eventually retrieved part of an answer. To be fully honest, we actually uncovered another layer of complexity that widened the conversation even further. As we continue forward, I have no doubt that this scene will replay itself out over and over again to a wider audience. I just hope we can remain patient with each other while we work through the transition.

Have you talked to your computer lately?

Original post:  Sep 3, 2015

Many new smartphones come equipped with a digital assistant. My wife is constantly talking to Siri. I don't have an Android, but I believe there is something similar with Cortana. I am sure there are others on the horizon. Each new improvement makes it easier for us to locate information that once required laborious searches through labyrinths of card catalogs and dusty shelves.

This article in Wired talks about how computers might actually be changing the way we describe the world about us: 

How Computers Are Changing the Way We Explain the World | WIRED

The example they give is in language translation. The first attempts at programming computers to perform this complex task involved a simple substitution. They would replace nouns with nouns and verbs with verbs (and so on). In the 1980's, a team working on an early version of IBM's Watson (the famous computer that defeated humans at Jeopardy!) came up with a new approach. They decided to use a statistical model:

They did this in a clever way. They got hold of a copy of the transcripts of the Canadian parliament from a collection known as Hansard. By Canadian law, Hansard is available in both English and French. They then used a computer to compare corresponding English and French text and spot relationships.

For instance, the computer might notice that sentences containing the French word bonjour tend to contain the English wordhello in about the same position in the sentence. The computer didn’t know anything about either word—it started without a conventional grammar or dictionary. But it didn’t need those. Instead, it could use pure brute force to spot the correspondence between bonjour and hello.

By making such comparisons, the program built up a statistical model of how French and English sentences correspond. That model matched words and phrases in French to words and phrases in English. More precisely, the computer used Hansard to estimate the probability that an English word or phrase will be in a sentence, given that a particular French word or phrase is in the corresponding translation. It also used Hansard to estimate probabilities for the way words and phrases are shuffled around within translated sentences.

Using this statistical model, the computer could take a new French sentence—one it had never seen before—and figure out the most likely corresponding English sentence. And that would be the program’s translation.

Their model proved successful. Google Translate uses this very method to perform its magic!

Language is not the only area where computers are pioneering:

Related stories are playing out across science, not just in linguistics. In mathematics, for example, it is becoming more and more common for problems to be settled using computer-generated proofs. An early example occurred in 1976, when Kenneth Appel and Wolfgang Haken proved the four-color theorem, the conjecture that every map can be colored using four colors in such a way that no two adjacent regions have the same color. Their computer proof was greeted with controversy. It was too long for a human being to check, much less understand in detail. Some mathematicians objected that the theorem couldn’t be considered truly proved until there was a proof that human beings could understand.

As computers grow more and more powerful, it will be fascinating to watch how they literally begin to change the way we experience the world around us. I am sure that healthcare will be no different than the world at large!