Given recent breakthroughs in Generative Artificial Intelligence (GAI) and Large Language Models (LLMs), more and more professional services are being augmented through Artificial Intelligence (AI), which once seemed impossible to automate. This paper presents a modular model for leveraging GAI in developing strategic plans for large scale government organizations and evaluates leading machine learning techniques in their application towards one of the identified modules. Specifically, the performance of BERTopic and Non-negative Matrix Factorization (NMF) are evaluated in their ability to use topic modeling to generate themes representative of Vision Elements within a strategic plan. To accomplish this, BERTopic and NMF models are trained using a large volume of reports from the Government Accountability Office (GAO). The generated topics from each model are then scored for similarity against the Vision Elements of a published strategic plan and the results are compared. Our results show that these techniques are capable of generating themes similar to 100% of the elements being evaluated against. Further, we conclude that BERTopic performs best in this application with more than half of its correlated topics achieving a "medium" or "strong" correlation. A capability of GAI-enabled strategic plan development impacts a multi-billion dollar industry and assists the federal government in overcoming regulatory requirements which are crucial to the public good. Further work will focus on the operationalization of the concept proven in this study as well as viability of the remaining modules in the proposed model for GAI-generated strategic plans.

The 2024 National Defense Authorization Act (NDAA) includes a heavy focus on the continued development of artificial intelligence for the military, an investment that could be pivotal in the continued competition with China. "I think there are two key areas of the NDAA that point to a great strategic direction for the United States. The first is recognizing the threat China poses in both the physical and the cognitive domain of conflict, in the cold war we are currently in with them and the hot war they may consider in the future. AI, and protecting AI is a crucial part of that strategic calculus," Christopher Alexander, the chief analytics officer of Pioneer Development Group, told Fox News Digital. Alexander's comments come after President Biden signed the Pentagon spending bill into law last month, the yearly "must pass" legislation that lays out U.S. military spending.

Formulating a strategic plan aligned with a company's business scope allows the company to explore data-driven ways of business improvement and risk mitigation quantitively while utilizing collected data to perform statistical applications. The company's business leadership generally organizes joint meetings with internal or external data analysis teams to design a plan for executing business-related statistical analysis. Such projects demonstrate that the company should invest in what areas and adjust the budget for business verticals with low revenue. Furthermore, statistical applications can determine the logic of how to improve staff performance in the workplace. LendingClub, as a peer-to-peer lending company, offers loans and investment products in different sectors, including personal and business loans, automobile loans, and health-related financing loans. LendingClub's business model comprises three primary players: borrowers, investors, and portfolios for issued loans. LendingClub is about expanding the statistical analytics that consists of infrastructure and software algorithm applications to develop two meaningful solutions ultimately: a) estimating durations in which clients will pay off loans; and b) 30-minute loan approval decision-making. To implement these two capabilities, the company has collected data on loans that were granted or rejected over 12 years, including 145 attributes and more than 2 million observations, where 32 features have no missing values across the dataset.

The White House has made responsible AI development a focus of this administration in recent months, releasing a Blueprint AI Bill of Rights, developing a risk management framework, committing $140 million to found seven new National Academies dedicated to AI research and weighing in on how private enterprises are leveraging the technology. On Tuesday, the executive branch announced its next steps towards that goal including releasing an update to the National AI R&D Strategic Plan for the first time since 2019 as well as issuing a request for public input on critical AI issues. The Department of Education also dropped its hotly-anticipated report on the effects and risks of AI for students. The OSTP's National AI R&D Strategic Plan, which guides the federal government's investments in AI research, hadn't been updated since the Trump Administration (when he gutted the OSTP staffing levels). The plan seeks to promote responsible innovation in the field that serves the public good without infringing on the public's rights, safety and democratic values, having done so until this point through eight core strategies.

Leon Gordon is a leader in data analytics, a current Microsoft MVP based in the U.K. and a partner at Pomerol Partners. The digital revolution has set off near-panic among executives as they seek to find ways to use data and analytics to enhance their decision-making ability. The end result of data transformation is the creation of a digital core, a set of capabilities that are used to power the entire organization. Companies seeking to transform themselves with AI must establish a strategic plan for using analytics and insights derived from data. Organizational structure is important when considering an AI strategy because it can help ensure data are properly transferred and secure as they move through their lifecycle.

After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally starting to deliver real-life benefits to early-adopting companies. Retailers on the digital frontier rely on AI-powered robots to run their warehouses--and even to automatically order stock when inventory runs low. Utilities use AI to forecast electricity demand. A confluence of developments is driving this new wave of AI development. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI--data.

Artificial Intelligence (AI) is becoming more prevalent in all of our lives. The so-called "intelligence" is the result of powerful computers sorting through mountains of data to find patterns, using algorithms designed and optimized by computer scientists. Like all technology, AI is far from perfect. As we have started using AI for consequential decisions, we have realized that while AI can improve decision making, it too often compounds historical patterns of bias and deepens existing inequality. AI's reliance on biased data or design processes has led to systems that produce discriminatory, or otherwise harmful, outcomes.

This story is limited to Techwire Insider members. This story is limited to Techwire Insider members. Login below to read this story or learn about membership. An independent oversight agency charged with probing and making recommendations on government operations and policy has issued several recommendations on artificial intelligence. In a recent "Lessons from Research" post on "How California Can Better Harness the Power of Artificial Intelligence," the Little Hoover Commission (LHC) offers a bit of a follow-up to its 2018 report, Artificial Intelligence: A Roadmap for California.

Oracle, Google, Microsoft, and Amazon are archenemies in the competitive cloud computing market. But in late 2018, top executives from the four companies, including future Amazon CEO Andy Jassy, teamed up on an unpaid side gig: advising the president and US Congress on how artificial intelligence can bolster national security. The executives were named to the National Security Commission on AI, created by Congress. Its chair was Eric Schmidt, previously CEO of Google, who later said it would help the US "harness this transformative technology to benefit both our economic and national security interests." Schmidt, Jassy, and the other commission members from Big Tech also had an economic interest in the topic.

Executives worldwide placed artificial intelligence as a top strategic priority for 2021, yet plans have slowed or been curtailed. Juniper Networks recently released the report, "AI is set to accelerate...is your organization ready?" which addresses this very curious dilemma: Developers, organizations (95%) and consumers know the benefits, welcome and are excited about the potential. But how can companies accelerate their adoption? Today, AI's slow rollout includes the automation of daily tasks, such as chatbots for customer service, bank reconciliations and smart workflows for IT trouble ticket management. The aforementioned 95% of organizations believe their companies would benefit from embedding AI into daily operations, products and services. Curiously though, only 6% of C-level leaders reported adoption of AI-powered solutions across their organizations today.