Can Artificial Intelligence Outsmart COVID-19?
In 2013, the City of Detroit made history when it filed municipal bankruptcy. Seven years later, COVID-19 related budget shortfalls loom large and cities nationwide face fiscal crises that could eclipse those seen in Detroit.
Tax revenue in cities across the country has been in free-fall, courtesy of COVID-19, while expenditures continue to rise. As a result, many economists fear that the coronavirus pandemic is on pace to hit municipal finances even harder than the Great Recession.
Recent economic indicators, however, paint a more optimistic picture; CNBC reported that unemployment dropped well below expectations with governments doing the most hiring. As the pendulum swings out of the red – albeit gradually – municipalities must stay the course with sustainable smart city projects for the long-term public good.
We sat down with Sharon Daniels, CEO of Arria NLG, to discuss how artificial intelligence (AI), machine learning, Internet of Things (IoT) and other technologies foster Smart City adoption, as well as help municipalities navigate their way through the pandemic. She is also a member of the Forbes Technology Council Member and one of the leading entrepreneurs in the artificial intelligence
TSCJ.- How is legacy technology hindering municipal leaders’ ability to take quick, decisive actions for the betterment of their cities?
SD.- Access to real-time, actionable intelligence is crucial for government leaders who must reallocate their increasingly limited resources to maintain citizens’ safety. Yet, despite ubiquitous advancements in the private sector, most government employees are deprived of innovative technologies like AI and machine learning.
Municipal workers are hamstrung by obsolete systems that mandate tedious manual data entry and put up the proverbial blinders of disparate technology environments that limit or block visibility into critical data. Instead of tapping into up-to-date performance analytics with a single click, data silos create bottlenecks as each of the aforementioned disparate systems are queried.
Whereas contemporary data management, analytics and reporting tools leverage automation and AI, outdated systems make accessing historical financials or performance metrics an arduous, inefficient grind. The time it takes to manually compile and prepare reports denigrates the value and utility of findings.
Static data gets stale quickly, making it difficult to draw accurate conclusions from reports built on month-old statistics. This means that civic leaders are not only making decisions based on potentially outdated data, but also burdened with costly maintenance and management that waste time, money and man hours - as well as stifle progress.
TSCJ.- Artificial intelligence is an umbrella term for a number of different technologies. Please explain how natural language generation technology and machine learning help governments adapt without sacrificing productivity?
SD.- Everything starts with data.
Natural Language GenerationNatural Language Generation (NLG) is an advanced form of AI that automatically transforms structured data in contextual, dynamic narratives. Producing actionable intelligence on-demand enables users to monitor real-time business performance against the metrics they set, drill down into details, identify trends, and make decisions quickly.
Actionable intelligence contained within your data is instantly discovered, analyzed and communicated in natural language (in real time)—resulting in improved decision-making. NLG lets you understand all underlying data – not just the visuals – with the click of a button, generating instant insights that may otherwise have been missed or misunderstood.
In advocating for universal modernization across its technology ecosystem, the U.S. government is emphasizing the need to improve how it leverages data. For data to have value, governments must be able to extract meaningful insights in real-time that lead to decisive actions.
For government analysts and their superiors, automating reports with NLG recovers countless hours wasted on manual data compilation while improving the accuracy and utility of analytics.
Intelligent chatbots that combine conversational AI with NLG offer citizens a convenient channel for engaging governments without having to wait on hold or sending an email to a nameless email address. Tools like Arria Connect, an SDK accelerator, connect NLG to any API-based platform, such as business intelligence (BI) dashboards, allowing users to have legitimate, multi-turn conversations with their data.
For the municipal analyst presenting financials to elected officials who may lack backgrounds in finance, NLG summaries provide supporting analyses in everyday language. Most important to note is that responses are dynamic, allowing for follow-up questions in context without having to repeat trigger words.
AI and machine learning enable better decision-making and transparency, mitigating risk of bias and ensuring public trust with communications backed by data. The multitude of applications for these technologies help municipalities minimize waste while improving operational efficiency and quality of life for citizens.
TSCJ. - A lot of what we read and hear about NLG and augmented analytics relates to financial reporting. How could the application of NLG help municipalities protect funds allocated to smart city programs?
S.D. - NLG platforms like Arria have the ability to automate, the process of going from data analysis to data insights to data reporting. This allows for speed to decision making and a broader adoption of data understanding supporting the intelligence city financial officers need for both long-term strategic planning and day-to-day operational efficiencies.
Also, NLG-authored analyses are almost indistinguishable from those prepared by human experts with one notable exception: NLG reports are compiled in minutes, not weeks.
Why does this matter?
Municipal Bonds are common investment vehicles used to repair—or, in some cases, rebuild—outdated infrastructure, with sustainable smart city developments among the funded beneficiaries.
Unfortunately, with COVID-19 having stalled commercial development projects across the country, the many mutual funds that bought municipal bonds backing new construction have since taken hundreds of millions of dollars in paper losses. Dennis Pidherny, a municipal-bond analyst at Fitch Ratings, explained to the Wall Street Journal that, “If a project falls behind, the question is whether the revenues can be recaptured in the future or they have been lost forever.”
In municipal bond markets, antiquated auditing and missed reporting deadlines can disintegrate investor confidence. The most recent findings from Merritt Research’s annual study on municipal bonds’ audit performance shows that slower completed audits were generally found among governments and organizations that were lower rated and had inferior credit metrics.
Report automation reduces the time it takes to identify and communicate key insights for speed to decision-making. (Arria NLG recently introduced a Microsoft Excel Add-In that brings natural Language summaries and report automation to spreadsheets, turning Excel data into contextual narratives.)
If municipalities and bond managers can accelerate audits and transform reporting processes using natural language generation, they will improve credit ratings and investor confidence – mitigating risks to smart city funding.
TSCJ.- For many smart city projects, IoT sensors are embedded in devices to collect and communicate data. What role does data analytics play in utilizing this information?
SD.- Data collection is an integral part of the way the IoT works. Departments of public works, for example, collect data from waste management facilities and other municipal services to analyze hauler efficiency, street cleanliness and dispatch scheduling.
Embedded with predictive analytics and machine learning, IoT devices provide cities with access to data intelligence and real-time insights on resource utilization. Once the data is cleaned and structured, NLG then automatically transforms this data into explanatory, insightful summaries.
Speed is the key, as the value of intelligence is contingent upon its timely delivery. NLG automates report-writing, saving countless manhours spent on manual data entry and validating data integrity by eliminating human error. Best of all, employees are free to focus on more important activities.
Smart city programs combine IoT with AI, data visualizations platforms, network infrastructure, and sensors to generate dynamic, actionable insights in real-time for data-driven decision-making.
Sticking with the waste management theme - sanitation practices become eco-friendlier by optimizing collection practices, reducing the number of unnecessary trips for haulers, and by extension, their carbon footprint, fuel consumption and time spent blocking traffic.
TSCJ.- How can NLG Enhance Public Awareness and Transparency with Augmented Analytics?
SD.- 20-year-old systems hinder a government’s ability to communicate and inform constituents with trusted, transparent, unbiased information.
For York City, PA, a lack of staff with backgrounds in epidemiology and data analysis has left the York City Health Bureau struggling to study, interpret and release to the public a pile of data on the coronavirus it has collected. The inability to access real-time insights – or get answers from public health officials as to the spread of COVID-19 in their communities can erode constituents’ trust in their elected officials.
Working with leading business intelligence (BI) platforms like TIBCO Spotfire and Microsoft Power BI, Arria has developed purpose-built dashboards that leverage NLG to eliminate ambiguity about the spread of COVID-19. With NLG, COVID-19 dashboards communicate data’s full story, augmenting visualizations with dynamic, actionable intelligence.
The combination of data visualizations and written narratives presents comprehensive insights in easily understood formats; effective data storytelling allows consumption by a broad audience.
Artificial Intelligence and machine learning help extend the reach and value of analytics through data storytelling.
In fact, according to Gartner, by 2025, data stories will be the most widespread way of consuming analytics, and 75 percent of stories will be automatically generated using augmented analytics techniques.
TSCJ.- How can NLG and automation protect critical infrastructure and industrial control systems?
SD.- Over the past few years, glaring vulnerabilities regarding cyber-physical security for public works, power plants and other major infrastructure have come to light.
Industrial Control Systems (ICS) are a critical part of infrastructure that interact with power plants, oil rigs and refineries, public works, as well as IoT devices. Compared to traditional IT, which is more focused on internal enterprise risks, ICS security vulnerabilities are less monitored and reported.
ICS security managers need efficient information gathering mechanisms to avoid potential catastrophes.
NLG reporting is dynamic and can highlight abnormalities. By presenting only the most valuable information, such as unexpected spikes in water pressure or power grids, users can immediately address critical issues.
Real-time visibility into insights help ensure an ICS is operating safely, and if it isn’t, provide engineers with the data to justify shutting things down, preventing destruction of equipment, potentially life-threatening accidents, or an environmental disaster.
Any final thoughts?
A recent Deloitte report eloquently surmised that, “Smart cities are paramount for the future of our country: not only is the safety and security of our citizens and businesses at risk as infrastructure assets age and fall into disrepair, but so too is the broader economic well-being and global competitiveness of our cities and our country.”
Artificial intelligence is affecting real change in the impact of environmental sustainability in both public and private sectors. In agriculture, AI is revolutionizing production through enhanced access to insights for monitoring and managing climate conditions and crop yields – while reducing water consumption. By more accurately predicting weather patterns, AI can optimize operations to reduce carbon footprints.
With natural language generation, machine learning and automation, smart city and modernization programs are empowered to municipal budget expenditures, improve workforce efficiency, maximize revenue potential, and improve quality of life for constituents.