GovWebworks AI Lab

August AI Newsletter

Our AI Lab's roundup of the latest articles and news relating to artificial intelligence and online services in the public sector
GWW Staff

GWW Opinion

GWW Staff

August 17, 2021

Welcome to the August AI Newsletter from the GovWebworks AI Lab. The following articles touch on important topics in the industry, including:

  1. AI Day
  2. Predicting the future
  3. Non manual data labeling

We hope you find this information as interesting as we do. If so, feel free to share with your colleagues and encourage them to sign up for the AI Newsletter and join the conversation! Or email us at ai@govwebworks.com to talk about using AI to optimize your organization’s digital goals.


#1: Have an “AI Day”

Key takeaway: Having an AI day can help your organization identify opportunities for leveraging AI.

Reviewed by Adam Kempler

Tesla recently announced an “AI Day” where key stakeholders will discuss their current and future use of AI. While their goal is centered around attracting talent and likely will have a focus on autonomous vehicles, I think the concept of having an “AI Day” is a great idea for government organizations looking to leverage Artificial Intelligence as part of their digital transformation strategy. An AI Day can provide a structured opportunity for stakeholders, management, and the organization’s vendors to discuss ways AI is currently being leveraged as well as plans and opportunities to capitalize on AI in the near and long term.

These discussions can review the various technological categories of AI and their applications including RPA (robotic process automation), NLP (natural language processing), conversational assistants and chatbots, machine vision, predictive systems and other areas of machine learning.

Investors Watch Tesla (TSLA) AI Day for Next Growth Milestone


#2: Predicting the future

Key takeaway: “Forecasting as-a-Service” makes it easier for government agencies to make predictions based on their existing data.

Reviewed by Adam Kempler

Tools such as Amazon Forecast provide managed services that use machine learning to make “forecasts” about future events and outcomes by consuming and learning from data that it is supplied. Government agencies could leverage these types of tools for a wide variety of capabilities such as forecasting expenditures and costs, animal population growth, economic events, vehicle traffic, demand for various services, and contact/support center usage. These types of forecasts (predictions) can help guide decision making processes, resource allocations, and budget considerations. While not government specific, this recent post on predicting future sales details many of the aspects of utilizing a tool such as Amazon Forecast.

Accurately predicting future sales at Clearly using Amazon Forecast  


#3: Data Labeling no longer has to be a manual process

Key takeaway: A new breed of tools and services is helping to reduce the effort and increase the quality of data labeling efforts.

Reviewed by Adam Kempler

One barrier to many AI projects can be the time, resource, and cost intensive process of labeling data for generating Machine Learning models. Examples include labeling text data for content and document categorization and labeling images for object recognition.

While there are many existing models that can be leveraged, often there is the need to customize the model to recognize domain specific entities. For example, if you wanted a machine vision application to recognize breeds of dogs, you can leverage one of many existing models that has already labeled tens of thousands of images of dog breeds. However if want to have an application that recognizes cracks and potholes in roadways, or you want a natural language processing tool to recognize specific clinical and psychological categories from user submitted text, you may need to custom label example data.

This process can be extremely time consuming and resource intensive, however a fairly new breed of tools and services offered by companies such as Snorkel AI, Amazon, Scale AI, Appen, Labelbox, and Cloudfactory can augment the manual process by automating or semi-automating data labeling. Additionally these tools and services can help identify model inefficiencies, errors and facilitate improvements to the models.

Data labeling platform Snorkel AI nabs $85M 


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