When I ask about the weather, my smartphone chirps back an answer: “It doesn’t look so nice tomorrow.”

That’s doubly unsurprising. One, it’s February in Maine during a historic stretch of snowy weather. Two, a phone is supposed to answer questions.

But here’s a surprise. We tend to think that new ideas are the fuel for today’s technology. Yet voice recognition is one of the “many things that people have tried before that now work,” says Susan Dumais ’75, a Distinguished Scientist with Microsoft Research in Redmond, Wash.

A member of the American Academy of Arts and Sciences and the National Academy of Engineering, Dumais visited campus in February as the College Key’s Distinguished Alumna in Residence, meeting with students and giving a talk on Feb. 1.

College Key Distinguished Alumna in Residence Susan Dumais ’75 gives a talk on Feb. 1 in the Olin Arts Center Concert Hall in front of an image showing a bygone way to retrieve information: the card catalog. (Phyllis Graber Jensen/�ֲ���ý College)

Back in the 1980s, when Dumais was at Bell Labs, one ongoing project was to get a computer to recognize correctly whether “yes” or “no” was answered to the question, “Will you accept this collect call?”

“Years and years and years of work went into that,” Dumais says. The project did succeed in the 1990s (though collect calls are going the way of the typewriter).

So, what makes today’s “new” technology possible? She points to better computational models, more computational horsepower, and access to much richer data.

In the case of voice recognition, it means access to vast amounts of spoken language with corresponding transcriptions, which makes it “easier to map various acoustic features to the resulting words,” she says.

“So when you search for ‘Who is the best teacher?’ the results include pages that use the word ‘professor’ even if they don’t use the word ‘teacher.'”

Another old idea whose time has come belongs to Dumais herself. In 1990 — way before Google or Bing — she and fellow researchers developed an information retrieval process known as “latent semantic indexing.” In recent years, LSI-related ideas have “been a big splash,” she says.

That’s because LSI enables search engines to retrieve the information that we’re actually looking for. “So when you search for ‘Who is the best teacher?’ the results include pages that use the word ‘professor’ even if they don’t use the word ‘teacher,’ ” Dumais explains.

Susan Dumais ’75 laughs with Professor Emeritus of Psychology John Kelsey before her talk on Feb. 1. Dumais first met Kelsey in the 1970s when she was a doctoral student at the University of Indiana, where he was starting his faculty career. (Phyllis Graber Jensen/�ֲ���ý College)

Simply put, LSI helps a search engine respond to queries by “discovering words that are semantically the same even though they aren’t literally the same,” she says.

The idea existed 25 years ago but “the time wasn’t right,” she says. Today, computers can swiftly learn these more nuanced word relationships thanks to the availability of much larger collections of text and more powerful computational resources.

Though advances in computing power are turning yesterday’s ideas into today’s tools, the demands of today’s tools are also keeping those ideas viable.

“How we use this technology — such as everyone carrying a smartphone — makes these ideas even more necessary now,” Dumais says.

Students do pre-computer information retrieval, circa 1974, using the library’s card catalog. (Muskie Archives and Special Collections Library)

The next time you get a good answer from a web search, you can thank Susan Dumais ’75.

Beginning with landmark research in the 1980s, Dumais has made fundamental contributions to how we find, use and make sense of information on the Internet and in our computers.

For her achievements, Dumais was recently named the 2014-15 Athena Lecturer by the Association for Computing Machinery’s Council on Women in Computing.

Susan Dumais ’75 of Microsoft Research is the 2014-15 Athena Lecturer.

“Her sustained contributions have shaped the thinking and direction of human-computer interaction and information retrieval,” said Mary Jane Irwin, who heads the Athena Lecture awards committee.

The Athena award celebrates female researchers who have made fundamental contributions to computer science.

The author of more than 200 articles on information science, human-computer interaction and cognitive science, Dumais is a and deputy managing director of the Microsoft Research Lab in Redmond, Wash.

A native of Lewiston who grew up on Nichols Street near campus, Dumais was a magna cum laude mathematics and psychology major at �ֲ���ý.

As a student, she did research with Drake Bradley, the Charles A. Dana Professor Emeritus of Psychology, co-authoring a research article on that appeared in the journal Nature.

Being published as a student? “Pretty awesome,” she recalls. The pair also explored ways to use computer software to improve the teaching of statistics.

“She was one of the best students I ever had,” says Bradley, a comment echoed by Robert Moyer, professor emeritus of psychology, who says that Dumais “was one of the very best researchers I ever worked with — student or otherwise.”

Dumais headed to Indiana University for doctoral work, but didn’t leave �ֲ���ý behind.

In 1979, she teamed with Moyer and a University of Michigan researcher to publish the article “Semantic association effects in a mental comparison task,” in the journal Memory & Cognition.

The researchers measured how long it took subjects to say which item in a pair was larger, such as flea or dog, needle or boat, penguin or iceberg.

Subjects were able to judge the size of semantically related terms (such as dog and flea) more quickly than unrelated terms (needle and boat).

To be sure, the researchers’ conclusions went deeper than that, but the thrust of Dumais’ work — modeling how human memory works — was a leaping-off point for a career exploring how humans can better communicate with their machines.

(Fun �ֲ���ý connection: At Indiana, Dumais met a “fantastic teacher doing research in biopsychology” who joined the �ֲ���ý faculty the next year. That professor was John Kelsey, who retired in 2012 after a great career at �ֲ���ý.)

Ph.D. in hand from Indiana, Dumais was on her way. After looking at how humans retrieve information from our own memories, she began looking at how we retrieve information from external sources, such as computers.

In the 1980s, Dumais was researcher at Bell Labs when she exploring “the vocabulary problem,” the fact that computer users always had to key in the exact, correct word to get what they wanted from their machine.

Reading like a manifesto for the information age, the paper explained the problem:

Many functions of most large [computer] systems depend on users typing in the right words. New or intermittent users often use the wrong words and fail to get the actions or information they want. This is the vocabulary problem. It is a troublesome impediment….In what follows we report evidence on the extent of the vocabulary problem and propose both a diagnosis and a cure. The fundamental observation is that people use a surprisingly great variety of words to refer to the same thing.

From that observation came a solution, , published three years later in the journal of the with Dumais as a co-author.

Today, in terms of how we find stuff on the Internet, whether cute kittens or Red Sox highlights, LSI acts as an indexing and retrieval method. It helps our search experience by deciding if words that share similar contexts also share a similar meaning, and thus should be elevated as a search result.

“For example,” Dumais says, “LSI would find that the words ‘physician’ and ‘doctor’ are similar not because they co-occur together but because they occur with many of the same words, such as ‘nurse,’ ‘patient,’ or ‘hospital.'”

Essentially, she adds, “LSI induces the similarity among words by looking at the company they keep.”

Dumais’ most recent research at Microsoft analyzes how web content changes over time, how people revisit web pages, and how searching can be improved by

By context, she means “who is asking, what task they are trying to accomplish, and what documents are of interest.”

That means you get your own search results. “So if I, Susan Dumais, search for SIGIR, I probably mean the ‘Special Interest Group on Information Retrieval’ and not the ‘Special Inspector General for Iraq Reconstruction.'”

In a good sense, Dumais sees no end to the work ahead.

The ability to search for information, she says, will support an “ever-increasing range of information, services and communications.”