Page 35 - GCN, August/September 2018

It was only two years ago that artificial intelligence seemed to burst onto the government scene.
In August 2016, then-President Barack Obama was the guest editor for an issue of Wired magazine and spoke with the director of the MIT Media Lab, Joi Ito, about AI and its implications.
“Early in a technology, a thousand flowers should bloom,” Obama said. “And the government should add a relatively light touch, investing heavily in research and making sure there’s a conversation between basic research and applied research.”
Two months later, the Obama administration released a report on AI. That broad overview of the emerging technology dedicated just a few pages to how the government could benefit from it. A second report followed weeks before Obama left office and focused primarily on the potential economic impacts.
“AI raises many new policy questions, which should be continued topics for discussion and consideration by future administrations, Congress, the private sector and the public,” the report states.
Since then, the Trump administration has provided additional guidance to agencies that highlights machine learning and AI as research priorities. The White House also created the Select Committee on Artificial Intelligence “to improve the coordination of federal efforts related to AI and ensure continued U.S. leadership in AI,” according to a report released in May. The committee’s work will include encouraging “agency AI- related programs and initiatives.”
Government interest in — and initiatives using — AI and machine learning began long before 2016,
of course. But in the past two years, agencies at every level have increasingly looked to machine learning in particular to help them better understand data and make back-office tasks more efficient.
A common theme of predictions
Machine learning techniques developed by researchers at Oak Ridge National Laboratory have been used by the Federal Emergency Management Agency to identify man-made structures buried by lava flow in Hawaii. Officials in Kansas City, Mo., have developed a machine learning algorithm to predict when potholes will form on city streets. And the military has begun using AI to anticipate when tank components might fail.
If there is a common theme, it is one of predictions. In machine learning, “prediction” means “you can infer something unknown given something known,” said Zachary Chase Lipton, an assistant professor at Carnegie Mellon University’s Tepper School of Business. “It turns out that a huge number of tasks can be expressed with predictive modeling.”
Those systems are given input — such as satellite photos, data from 311 calls or sensor readings from vehicles — and are asked to predict an output, such as the location of an airfield, a pothole forming or a part going bad on a tank. Machine learning models are trained to recognize patterns by ingesting historical data, but the input and output must be clearly defined for the technology to be useful, Lipton said.
Dominic Delmolino, CTO at Accenture Federal Services, told GCN
that machine learning is the logical next step in data analytics. “There are growth areas where government agencies that have been doing a lot of what we would call advanced analytics are starting to say, ‘OK, can we start to incorporate AI and machine learning now as the next step in analyzing our data for mission decision-making or mission value?’” he said.
Machine learning can be a valuable tool for uncovering non-linear relationships. Linear relationships — such as the cost of a house relative to its size (as one goes up, so does the other) — are better explained by classic regression techniques. But sometimes relationships are not linear.
The relationship among words in a sentence is not linear, nor is the relationship between pixels in a photo. Those relationships are complicated, but machine learning has proven to be a way to find them and others.
Getting the data
house in order
At the end of each year, many state agencies issue annual reports detailing what they see as their successes in the previous year and their goals for the coming year or years. In its 2017 report, the Illinois Department of Innovation and Technology said it intended to focus on accelerating the use of AI, chatbots and advanced data analytics to “advance Illinois’ overall effort for improving citizen services in a more efficient manner through innovation.”
Krishna Iyer, the department’s chief data scientist, said the state issued a request for information last
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