| AI/ML Model Forensics |
A&M AI & Analytics |
In today’s data-driven world, the adoption of artificial intelligence (AI) and machine learning (ML) models by companies has become increasingly prevalent. These models are leveraged to make critical business decisions, optimize processes, enhance customer experiences and predict future trends. |
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| AI Ethics: Navigating Pressures for Responsible AI |
A&M AI & Analytics |
Artificial intelligence (AI) ethics is becoming increasingly critical as AI deeply integrates into business and society. The need for comprehensive ethical frameworks has never been more urgent. With AI systems becoming more powerful and widespread, the risks of ethical oversights—such as bias, discrimination, privacy violations, and accountability issues—are growing significantly. |
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| AI Ethics: AI Framework Best Practices |
A&M AI & Analytics |
Ethical artificial intelligence frameworks are still emerging across both public and private sectors, making the task of building a responsible AI program particularly challenging. Organizations often struggle to define the right requirements and implement effective measures. So, where do you start if you want to integrate AI ethics into your operations? |
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| AI Ethics: Steps to Build an AI Ethics Framework |
A&M AI & Analytics |
In this article, you will learn the steps to set up an AI ethics framework, introducing A&MPLIFY’s framework model, governance practices and implementation guidelines. |
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| What are AI Agents? |
A&M AI & Analytics |
Agentic workflows represent a paradigm shift from traditional automated processes, enabling a new level of autonomy and intelligence in business operations across all operational verticals. These workflows can be created to be self-sufficient, able to learn from interactions and make decisions without constant human oversight. |
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| AI Agents: Separating Hype from Reality and Navigating Market Outlook |
A&M AI & Analytics |
Artificial Intelligence (AI) agents have emerged as a transformative force in enterprise technology, promising enhanced efficiency and automation. However, the rapid proliferation of AI agents has led to a mix of auspicious developments and inflated claims. Tech headlines are declaring 2025 the “year of the AI agent,” but it’s important for IT and business leaders alike to cut through the hype and understand what is, and what is not, realistically achievable with AI agents today. |
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| Leveraging OCR and AI for Modern Defense and Government Applications |
A&M AI & Analytics |
Defense and government sectors face an unprecedented influx of unstructured data from diverse sources. From battlefield intelligence to administrative records, quickly managing and analyzing information from documents is critical to mission success and national security. Yet, traditional approaches often fall short due to the scale, complexity and need for real-time insights. This is where the use of artificial intelligence (AI) models, such as optical character recognition (OCR) can accurately automate data handling and shorten the time to insights. |
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| A Framework for Auditing Data Center Energy Usage and Mitigating Environmental Footprint |
Purdue University |
As the Data Science field continues to mature, and we collect more data, the demand to store and analyze them will continue to increase. This increase in data availability and demand for analytics will put a strain on data centers and compute clusters-with implications for both energy costs and emissions. As the world battles a climate crisis, it is prudent for organizations with data centers to have a framework for combating increasing energy costs and emissions to meet demand for analytics work. In this paper, I present a generalized framework for organizations to audit data centers energy efficiency to understand the resources required to operate a given data center and effective steps organizations can take to improve data center efficiency and lower the environmental impact. |
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