How The US Government Is Using AI To Help Procure Trillions Of Dollars Of Products And Services ...

How The US Government Is Using AI To Help Procure Trillions Of Dollars Of Products And Services …

The United States government is one of the largest buyers in the world, if not the largest, spending over $4.1 Trillion annually overall with hundreds of billions spent on technology. As part of all this, the General Services Administration (GSA), a key agency in the US federal government is responsible for managing many of the operations of the federal system including many aspects of procurement.

Increasingly the GSA is leveraging AI and machine learning to help optimize, manage, and advance procurement functions. AI and ML are providing key ability to optimize procurement processes, provide visibility into key metrics, and generate insights and forecasts to procurement trends. In this article, Keith Nakasone, Deputy Assistant Commissioner, Acquisition, Office IT Category at the GSA shares insights on how AI is impacting federal government procurement as a follow-up to a recent podcast interview on this topic.

Why is it important for the Federal Government to adopt AI, especially for procurement?

Keith Nakasone: Artificial intelligence is showing very promising potential. As is often the case, the private sector paves the way on new and emerging technologies. There’s a real opportunity to apply what we’re seeing there to the federal government. We’re particularly interested in exploring technologies that reduce the burden of low-level tasks.

How are various government groups working with each other to advance the state of AI adoption in the government?

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Keith Nakasone: In terms of how the federal government is working with each other, GSA’s Technology Transformation Services established an AI Community of Practice. This community aims to unite federal employees who are active in, or interested in AI policy, technology, and programs to accelerate the adoption of AI across the federal government. This is a platform where we host talks, panels, and workshops that highlight work within and beyond government, e.g sharing use cases, lessons learned, and best practices.

We are also forming working groups to focus on various issues related to the implementation of AI. A part of this is in gathering various resources in these areas. This is in the early stages of development, but we want to come together with a greater focus on specific issues and aggregate tools, perspective, and other resources that can help federal agencies address critical issues, whether privacy, AI documentation and the responsible use of AI. We’re also looking for opportunities to engage externally and bring in external perspectives that can help government think through these issues.

How is the GSA incorporating artificial intelligence to help with government procurement?

Keith Nakasone: GSA is exploring the use of AI to inform our way forward on reducing the time to review end-user license agreements (EULA). In the near term, we will be looking at opportunities within the acquisition process to handle low-level tasks related to legacy acquisition policies, or updating procedures related to new/updated regulations/statutes, etc.

How do you see process automation and more intelligent forms of automation helping with procurement?

Keith Nakasone: By leveraging emerging technologies, procurement professionals will be able to spend more time working on complex acquisitions, innovation, and acquisition reform rather than spending time doing repetitive tasks — we hope to leverage AI, where possible, to automate these functions.

Where do you see federal agencies today in their AI adoption?

Keith Nakasone: Agencies are continuing to explore the opportunities that are available through commercial off-the-shelf tools for AI. They’re learning the best practices they need to adopt in order to be successful with leveraging emerging technologies, for example: data governance, business process re-engineering, etc. Agencies are meeting regularly with GSA’s AI Community of Practice, which is led by Steve Babitch, Head of Artificial Intelligence Portfolio in GSA’s Technology Transformation Services.

What are some of the most interesting use cases you’ve seen so far with the government’s adoption of AI?

Keith Nakasone: The agencies are in the early stages of adopting AI. However, there is more information as it relates to the different sectors (Healthcare, Financial, Agriculture, and more) available on the White House AI for American Innovation website.

How can the Federal government learn from industry adoption and use of AI?

Keith Nakasone: There are a number of ways that the government can learn from industry in adopting AI. In addition to the important work of the AI COP, the government is interested in having collaborative sessions on what they have experienced, learning from lessons learned from early adopters, research into emerging best practice, and understanding the sustainability of using emerging technologies.

What are some challenges the Federal government has in regards to data privacy and data usage?

Keith Nakasone: In regards to data privacy, the government must adhere to statutory and regulatory requirements, policies and procedures in regards to data privacy. In addition, the government must ensure that we do not release any data that is considered contractor proprietary information. In regards to data usage, consumption may be duplicative when information is stored and shared across organizations. The data governance and framework within each agency will be key as we implement artificial intelligence.

What do you see as critical needs for workforce development around AI?

Keithe Nakasone: We see three critical needs:

Training courses that are adapted to the government’s framework and use cases in adopting emerging technologies.
Understanding the constraints within each of the respective agencies.
Understanding the statutes and regulations that may be challenged and/or in conflict with the AI solutions.

What AI technologies are you most looking forward to in the coming years?

Keith Nakasone: AI combined with other emerging technologies will further the efficiencies and effectiveness of agency missions as well as the customer experience. Applying the right information technology tools based on functional user requirements can eliminate the low-level tasks and leverage the workforce with more complex work to include other agency priorities.

Published at Sun, 16 Aug 2020 18:00:00 +0000

Indigenous AUM Photonic System for Real-Time Remote Air Quality Monitoring

Article By : PIB Delhi

GVP-SIRC researchers has developed an indigenous photonic system for real-time remote monitoring of air quality parameters…

World Health Organisation (WHO)’s reports show that the worsening state of poor air quality is responsible for more than 7.5 million fatalities worldwide annually. This highlights the necessity for accurate, yet cost-effective monitoring of air quality parameters as monitoring is critical to solution. The current systems and technologies used for air quality monitoring are prohibitively expensive for wider deployment. This underlines the need for development of systems for real-time remote monitoring of relevant air quality parameters.

With the support from Department of Science and Technology’s Clean Air Research Initiative, Prof. Rao Tatavarti, Director of Gayatri Vidya Parishad-Scientific and Industrial Research Centre (GVP-SIRC) & GVP College of Engineering, Visakhapatnam, has developed an indigenous photonic system for real-time remote monitoring of air quality parameters. Prof Tatavarti was supported by Prof. P. Arulmozhivarman from the School of Electrical Engineering, VIT University, Vellore, and other team members. The system titled AUM (Air Unique-quality Monitoring) had CATS Eco-Systems, Nashik as the technology transfer partner for commercialization.

The AUM system (patent pending) is an innovative application of the principles of laser backscattering, statistical mechanics, optoelectronics, artificial intelligence, machine/deep learning, and Internet of Things. It can identify, classify, and quantify various pollutants simultaneously (of orders of less than one part per billion) and meteorological parameters, with very high precision, sensitivity and accuracy.

AUM was successfully evaluated during laboratory trials with gold standards (in collaboration with EffecTech, UK), and also compared in the field with imported systems from France, and Australia and operated by Karnataka State Pollution Control Board under the aegis of the Central Pollution Control Board of India.

It has been found to be highly sensitive and accurate and capable of simultaneous detection and quantification of all air quality parameters and offers a number of merits over any of the currently available conventional systems. It is portable, compact, low powered and economical, works on plug and play system, requires no setting uptime, and no additional civil infrastructure for housing. It provides information on all gases and meteorological parameters simultaneously. It is a non-intrusive remote, real-time monitoring system with very high sensitivities and accuracies and is capable of monitoring in both spatial and temporal domains, with very high sampling frequencies. Also, the data from spatially separated sensors can be seamlessly streamed to a cloud server, from where digestible real-time encrypted information on dashboard is made available to user at any part of the world.

This system can boost the nation’s efforts towards self-reliance in high-end technologies and can additionally be instrumental in supporting the endeavours in improving the nation’s health and economy.