Unlike other military technologies driven by national security needs and developed with federal funding, AI is predominantly funded and advanced by commercial industry for civilian applications. However, there is a lack of understanding of the reasons commercial AI firms decide to work with the DoD or choose to abstain from the defence market. This thesis argues that the contract law and procurement framework are among the most significant obstacles. This research indicates that the commercial AI industry actually views the DoD as an attractive customer. However, this attraction is despite the obstacles presented by traditional contract law and procurement practices used to solicit and award contracts. Drawing on social exchange theory, this thesis introduces a theoretical framework, optimal buyer theory, to understand the factors that influence a commercial decision to engage with the DoD. Interviews from a sample of the participants explain why the AI industry holds such perceptions, opinions, and preferences about contracts generally and the DoD, specifically, in its role as a customer. This thesis concludes that commercial AI firms are attracted to contracts that are consistent with their business and technology considerations. Additionally, it develops best practices for leveraging existing contract law, primarily other transaction authority, to align contracting practices with commercial preferences and the machine learning development and deployment lifecycle.

We propose a transformer-based rhythm quantization model that incorporates beat and downbeat information to quantize MIDI performances into metrically-aligned, human-readable scores. We propose a beat-based preprocessing method that transfers score and performance data into a unified token representation. We optimize our model architecture and data representation and train on piano and guitar performances. Our model exceeds state-of-the-art performance based on the MUSTER metric.

The crucial field of Optical Chemical Structure Recognition (OCSR) aims to transform chemical structure photographs into machine-readable formats so that chemical databases may be efficiently stored and queried. Although a number of OCSR technologies have been created, little is known about how well they work in different picture deterioration scenarios. In this work, a new dataset of chemically structured images that have been systematically harmed graphically by compression, noise, distortion, and black overlays is presented. On these subsets, publicly accessible OCSR tools were thoroughly tested to determine how resilient they were to unfavorable circumstances. The outcomes show notable performance variation, underscoring each tool's advantages and disadvantages. Interestingly, MolScribe performed best under heavy compression (55.8% at 99%) and had the highest identification rate on undamaged photos (94.6%). MolVec performed exceptionally well against noise and black overlay (86.8% at 40%), although it declined under extreme distortion (<70%). With recognition rates below 30%, Decimer demonstrated strong sensitivity to noise and black overlay, but Imago had the lowest baseline accuracy (73.6%). The creative assessment of this study offers important new information about how well the OCSR tool performs when images deteriorate, as well as useful standards for tool development in the future.

In today's rapidly evolving military landscape, advancing artificial intelligence (AI) in support of wargaming becomes essential. Despite reinforcement learning (RL) showing promise for developing intelligent agents, conventional RL faces limitations in handling the complexity inherent in combat simulations. This dissertation proposes a comprehensive approach, including targeted observation abstractions, multi-model integration, a hybrid AI framework, and an overarching hierarchical reinforcement learning (HRL) framework. Our localized observation abstraction using piecewise linear spatial decay simplifies the RL problem, enhancing computational efficiency and demonstrating superior efficacy over traditional global observation methods. Our multi-model framework combines various AI methodologies, optimizing performance while still enabling the use of diverse, specialized individual behavior models. Our hybrid AI framework synergizes RL with scripted agents, leveraging RL for high-level decisions and scripted agents for lower-level tasks, enhancing adaptability, reliability, and performance. Our HRL architecture and training framework decomposes complex problems into manageable subproblems, aligning with military decision-making structures. Although initial tests did not show improved performance, insights were gained to improve future iterations. This study underscores AI's potential to revolutionize wargaming, emphasizing the need for continued research in this domain.

Should artificial intelligence or machine learning (AI/ML) be allowed to alter FDA approved software in medical devices? If so, where should the guardrails be set? The discussions and debates surrounding AI/ML are heated; some believe the technology may destroy humanity, while others look forward to the speed of advancement it will allow. The FDA is getting out ahead on this debate. This week the agency drafted a list of “guiding principles” intended to begin developing best practices for machine learning within medical devices. A new framework envisioned by the FDA includes a “predetermined change control plan” in premarket submissions. This plan would include the types of anticipated modifications, referred to as “Software as a Medical Device Pre-Specifications”. The associated methodology used to implement those changes in a measured and controlled approach that manages risk the FDA calls the “Algorithm Change Protocol.”

Both generate a huge amount of data, information, and records. This humongous amount, along with complicated calculations, complex decision making, and timebound result expectations, increases the difficulty in dealing with different processes in finance. Though computerization and digitalization have taken this responsibility and become a fundamental support base for the modern finance market, the real transformation is now being brought by Artificial Intelligence (AI) and Machine Learning (ML). This is the era of rapid technological progress, with new disruptive breakthroughs coming in daily. Every time companies cannot quickly absorb the sudden changes brought in by such technologies.

This is part 1 of a multi part series covering Realm's development journey and some of the complex decisions we have made in order to leverage the power of Artificial Intelligence and Machine Learning. Quickly create engaging virtual experiences (realms) with no code. We knew by simplifying the creation process, we could quickly grow the number of realms, kickstart network effects and enable everyone to find experiences they enjoyed. We made 2 core product design decisions that were against the grain compared to other Web3 metaverses. The decision around creating a mobile first product was a no brainer, 61% of the entire gaming revenues in 2022 came from mobile devices.

A digestible introduction to how quantum computers work and why they are essential in evolving AI and ML systems. Quantum computing is a rapidly accelerating field with the power to revolutionize artificial intelligence (AI) and machine learning (ML). As the demand for bigger, better, and more accurate AI and ML accelerates, standard computers will be pushed to the limits of their capabilities. Rooted in parallelization and able to manage far more complex algorithms, quantum computers will be the key to unlocking the next generation of AI and ML models. This article aims to demystify how quantum computers work by breaking down some of the key principles that enable quantum computing.

We are seeking an RPA developer responsible for building Bots and Automation using industry standard automation tools like UiPath, automation anywhere and others. You will be responsible for End-to-End process of implementing solutions, right from Requirement gathering to Production deployment. You ll also act as a lead and will take the necessary initiatives related to designing and managing workflow automation projects, testing, and fixing bugs. Design, Develop, Test Automation Workflows,and Provide Guidance for the Process Design. Create Process and End-User Documentation.

Artificial intelligence may have begun as mere fiction in books like "Frankenstein" and "Robots," but it is now very real. As technology advances, we see more and more uses for AI.