Government leans into machine learning
It was just two years ago that artificial intelligence seemed to burst onto the government agenda.
In August 2016 then-President Barack Obama was the guest editor for an issue of Wired magazine and spoke with head 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, A broad overview of the emerging technology, the report dedicated just a few pages to how the government could benefit from AI. A second report followed just 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,” it concluded.
Since then, the Trump administration has provided additional guidance to agencies outlining machine learning and AI as research priorities. It has also set up 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 May 2018 White House report. The work of the committee would include encouraging “agency AI-related programs and initiatives,” the report reads.
Government interest in — and initiatives using — AI and machine learning beganlongbefore 2016, of course. But over the past two years, agencies at every level — local, state and federal — have increasingly looked to machine learning in particular to better understand data and make back-office tasks more efficient.
Machine learning techniques developed by researchers at Oak Ridge National Laboratory have been used by the Federal Emergency Management Agency to find man-made structures that have been eaten up by lava flow. Kansas City, Mo., has developed a machine learning algorithm to help predict when potholes will form on city streets. And the military has begun using AI to predict component failure on tanks.
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 modelings.”
These systems are given input – whether that is satellite photos, 311 calls or sensor readings from vehicles — and are asked to predict an output — an airfield, a pothole or a part going bad on a tank. Machine learning models are trained on historical data to recognize patterns. The inputs and outputs, however, have to be clearly defined if machine learning is to be useful, Lipton said.
Machine learning can be considered the logical next step in analytics, Accenture CTO Dominic Delmolino told GCN.
“It’s interesting, there are growth areas where government agencies who 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,'” Delmolino said.
Machine learning can be a great tool for finding non-linear relationships. Linear relationships, like the cost of a house related to its size (as one goes up, so does the other), are better explained by classic regression techniques. But sometimes relationships aren’t linear, he added.
The relationship among words in a sentence is not linear, nor is the relationship between pixels in a photo. These relationships are complicated, but machine learning has proved to be a way to find them and others.
Getting the data house in order
At the end of each year, state agencies often issue an annual report capturing what they see as their successes from the previous year and their goals going forward. In 2017, the Illinois Department of Innovation and Technology focused on accelerating the use of AI, chatbots and advanced data analytics tools to “advance Illinois’ overall effort for improving citizen services in a more efficient manner through innovation,” its report noted.
DoIT Chief Data Scientist Krishna Iyer said the state issued a request for information last year to get a better understanding of the machine learning and artificial intelligence landscape. From those conversations with vendors, it became clear the state wasn’t taking advantage of the technologies’ potential.
“There is a huge gap between what it can do and what it’s being used for today,” Iyer said.