A brain is a complex organ of human body. It took a lot of time to study Brain and its functionalities. But as soon as we understood Brain the next thing we focused on is to create one. This process involves imparting intelligence to the Machines, a more popular name for this is Machine Learning. Machine LearningÂ gives â€œcomputers the ability to learn without being explicitly programmed.â€ It explores the study and construction ofÂ algorithmsÂ that can learn from and make predictions onÂ the data. Nowadays, Machine Learning has become a hot topic because of its widespread applications.
Machine Learning has made significant contributions to various organizations. It can reduceÂ the human-factors burden for the government across national securityÂ and public safety, policy making, financial services, entitlements andÂ benefits or infrastructure by:
- Automating detection, tipping and cueing ofÂ patterns and anomalies and determining whether they are threats or opportunities.
- Classifying, labeling and/or tagging entitiesÂ and events as well as discovering the non-obvious relationships betweenÂ them.
- Aiding in decision-making and operations.
Also, MachineÂ Learning and AI represent incredible opportunities toÂ enrich the operational data on whichÂ government runs. This transformation signals a number of implications for government:
- They will be at the center ofÂ initiatives to modernize agency systems and business processes, driving theÂ need to break down data silos.
- The human-computer interaction will drive enhanced feedback, more-granular data access controlsÂ and greater security, creating a closed loop forÂ continuous improvement of algorithmsÂ and techniques.
- Design, development, testing and application of algorithms must be done inÂ a â€˜fail fastâ€™ and iterative experimentalÂ environment while not triggering complete â€˜ripÂ and replaceâ€™ of legacy systems.
- Agencies will need a databaseÂ management system, such as an enterprise data hub,Â that can take advantage of new technologies. Systems should reduce data wrangling time and complexity so the insights derivedÂ from AI and machine learning can be easily operationalized.
Other Applications of Machine Learning in Government Agencies:
Cybersecurity: Identify abnormal activity, correlated nefarious patterns across multiple data types and inputs, and prescribe actions based on all the factors. Examples include insider threat, network design/operations, threat detection/alerting and software defined networking.
Situational Profiling:Â Based on the information at hand, identify what is important and where to look. Customized view of complex data. Examples include risk heat map, identity intelligence, activity based intelligence (ABI)/object based production (OBP) and event/activity prediction.
The pattern of Life:Â Identify trends and correlations among different groups to detect various subtle and complex patterns. Gain a deep customization of response, based on a thorough understanding of the players involved.
Machine Learning is mature enough to start accurately predicting medical eventsâ€”such as whether patients will be hospitalized, how long they will stay, and whether their health is deteriorating despite treatment for conditions such as urinary tract infections, pneumonia, or heart failure. Advanced machine learning can discover patterns in de-identified medical records (that is, stripped of any personally identifiable information) to predict what is likely to happen next, and thus, anticipate the needs of the patients before they arise.
Editor’s note:Â Original Sources
Billy Sokol. (Jul 11, 2017). How AI and machine learning can drive government efficiency. Retrieved from https://gcn.com/articles/2017/07/11/ai-machine-learning.aspx
Katherine Chou. (MAY 17, 2017). Partnering on machine learning in healthcare. Retrieved from https://www.blog.google/topics/machine-learning/partnering-machine-learning-healthcare/