With the increasing prominence of artificial intelligence and machine learning (AI/ML), numerous state governments are creating taskforces on AI to help determine their state’s AI strategy.

For the United States Navy, data has become a cornerstone of its operations, guiding the course of missions, and ensuring the safety of personnel. Every facet of maritime endeavors, from intelligence gathering, to navigation and logistics, relies on a steady stream of accurate and timely data. Harnessing and making the best use of that data, though, comes with its own challenges. These can include limited bandwidth, stringent security protocols, and the sheer volume of information generated across a host of platforms.

What is the difference between AI and ML?
Artificial Intelligence and Machine Learning are two of the most transformative technologies in the computing space that are changing the world in which we live. However, there is often confusion in in what constitutes Artificial Intelligence, and what constitutes Machine Learning.

President Joe Biden signed the National Defense Authorization Act (NDAA) for fiscal year 2022 (FY22) into law on December 27, 2021. It authorizes $770 billion in defense spending which is a 5% increase over last year. This marks 61 consecutive years that a bill received bipartisan support from congress (a display of agreement that has become increasingly rare for DC politics).

On January 1, 2021, the House and Senate overrode President Trump’s veto of H.R.6395, the National Defense Authorization Act (NDAA) for Fiscal Year 2021, rendering the bill law. The FY2021 NDAA includes the National Artificial Intelligence Initiative Act (NAIIA), one of the most ambitious attempts by Congress to date, seeking to establish the United States leadership in artificial intelligence (AI).

Since the release of the final Federal Data Strategy Action Plan (henceforth FDSAP) the end of last year, much has been written about the Federal Data Strategy. However, little has been written to connect the important role it can play in enabling the adoption of AI within the federal public sector.

Machine learning (ML) and artificial intelligence (AI) could very well be the next major technological advancements that change the way federal IT pros work. These technologies can provide substantial benefits to any IT shop, particularly when it comes to security, network, and application performance.

The state of our nation’s infrastructure is grim. The most recent report card from the American Society of Civil Engineers (ASCE) gave U.S. infrastructure an overall score of “D” for “poor” or “at risk”.

Artificial intelligence (AI) is coming. Contrary to the stuff of science fiction, however, AI has the potential to have a positive impact within the federal IT community. The adoption of AI will likely be the result of the adoption of hybrid and cloud IT computing.

The history of hacking shows that bad actors use good technologies for bad purposes.  Machine learning is no different: it has never been easier for white hats and black hats to obtain and learn the tools of the machine learning trade. Software is readily available at little or no cost, and machine learning tutorials are just as easy to obtain.