Detaining Tsarnaev but not your Grandmother: From Automation to Assistance

By David MurgatroydRecent reports exposed an embarrassing missed opportunity with Boston Marathon Bomber Tamerlan Tsarnaev. Although he was on a watch list, he was not detained on his return from Dagestan because the list used a different spelling of his name — “Tsarnayev”.

Obviously we need technology to be more robust to simple spelling variations. But then again, if tolerance for misspellings is blindly increased, how many more stories of wrongly detained grandmothers will appear? Your name may be just a typo or two away from one of the hundreds of thousands on government watch lists.

Unfortunately, because the current system is classified we can’t prescribe how to fix it. But we can learn from the mistakes of an even more ambitious system that also made headlines.

That system is IBM’s Watson. When it famously won on the TV game show Jeopardy! it had access to 200 million pages of content via 90 powerful servers. But Watson made its own embarrassing mistake on a question in the category of “U.S. Cities”: “Its largest airport is named for a World War II hero; its second largest, for a World War II battle.”

Watson responded with “What is Toronto?????”, which isn’t just the wrong answer, it’s in the wrong category! If only it had known Toronto is not a U.S. city it could have given the next answer down its list, the right one, Chicago.

Why did this prove to be such a challenge? An IBM blog post explained: “This is just one of those situations that’s a snap for a reasonably knowledgeable human but a true brain teaser for the machine.”

First, there’s the variety and volume of data that Watson ingested. To Watson, “Toronto” is ambiguous. It had read about many places around the world called Toronto, including several towns in the Midwest. Second, modern artificial intelligence systems “think” about this data often by using fuzzy connections among strings of text. This could cause Watson to conclude that, because Toronto’s professional baseball team is in the American League, then Toronto itself is in the U.S.

When we move from game shows to missions like stopping Tamerlan Tsarnaev, curious errors can become critical failures. So what’s the remedy? It revolves around people.

Technology is moving from a half century of automating human tasks to the next half century of assisting humans in those tasks. Watson is a pinnacle of the era of automation. But as the Toronto example makes clear, Watson needs a partner. Imagine if Watson collaborated with Jeopardy! champion Ken Jennings, or just with you or me. We could establish a virtuous cycle where our correction of simple errors would result in subtle improvements down the line. This is the next frontier for what is called human language technology: intelligence built upon human-computer collaboration and it’s already becoming available to defense and intelligence agencies.

Let’s consider an agent at JFK airport the day Tamerlan Tsaernaev came through. Let’s pretend she had a system that handled more spelling variation, she would have seen the “Tsaernayev” match but also some spurious “grandmother” results. She would have eliminated these spurious matches by diving deeper into the person’s itinerary or history on the watch list.

Every time she interacts with the data – honing in on people of interest and discarding the misfires – the computer understands this and presents the next batch of results with greater awareness of what is being sought. All of this is conducted in a very natural way, much like two people working together, side-by-side.

As the collaboration between the person and the computer becomes richer, each will want the ability to communicate their confidence about certain judgments to the other. You can see a hint of this in the question marks in Watson’s “What is Toronto?????”. It’s almost as if it was asking for help. Those question marks would be a strong clue to a human collaborator to focus on that answer and its alternatives.

Confidence can also avoid problems when the system’s default settings filter out either too much or too little. When U.S. officials are planning for a future event, like a visiting Head of State, and need to assess the security risk, they want the deepest possible information on potential threats and bad actors. In this case, because they have the time, analysts will want to investigate even items of low confidence that might point to a security threat.

However, when responding to a time sensitive event, such as after the Boston bombing, officials only have time to pay attention to the highest quality results, so they can filter on results with no less than 90 percent confidence. Yet the remaining results of less than 90 percent confidence will be available later, when they can conduct deeper inquiry and analysis.

Even with a collaborative interface between the human and computer that allows the expression of confidence, if the computer is stuck thinking about string of texts then the user may have a hard time feeding it useful information. Telling Watson that “Toronto” is a wrong answer to the question above will only help if it has some way of internalizing that the most popular real-world city by that name is not in the U.S.

Historically, national security operatives and analysts have attempted to overcome computers’ focus on strings of text by tediously curating complex search queries, creating long queries of keywords, joined by ANDs, ORs, and NOTs to try to capture every possible string of relevant text. Unfortunately this method fails at finding new variations, misspellings, or terms. Instead, analysts should be able to issue queries with respect to known key players – persons, places, and other things – in the data. The system can filter the data based on real-world things and the analyst can refine the results.

In addition, a system centered on key players instead of keywords can help users track “ghosts” – entities appearing in raw data but that are otherwise unknown. To illustrate, let’s imagine a hypothetical terrorist incident. A few hours before, the computer sees social media messages on networks of interests. By focusing on key players not keywords it realizes that, although the level of “chatter” is normal, a single person is being frequently mentioned in these messages by a variety of names. Although this person is unknown to the system, it’s able to connect all the names together as referring to him and to flag him as receiving an unusually high amount of attention. An analyst is alerted to this, reviews the messages which the computer has grouped, and takes action.

It’s an exciting time to be applying human-computer collaboration to the challenges before us. Systems like Watson may need us as much as we need them.

David Murgatroyd is vice president of engineering at Basis Technology. He has been building human language technology systems since 1998. Twitter: @dmurga

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