🚀 Blueprints for Retail’s Autonomous Future Foundations of Agentic AI for Retail (Full-Color Edition) Are you ready to revolutionize your retail business? This book is the definitive, end-to-end playbook showing you how to design, code, and deploy autonomous agents that think, learn, and act in real time—transforming every aspect of your retail operations.Dive into the future where […]
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In our previous AI series, we delved into the realm of adversarial machine learning and introduced a system for classifying AI attacks. In this fourth installment of our AI Series, our focus will shift towards methods to mitigate these attacks. Let’s follow the same structure.. Previously, we categorized AI-related attacks into two main categories: Generative
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In this part of the AI series, we will cover the topic of adversarial machine learning and attempt to create a taxonomy of AI attacks. We will also discuss mitigation strategies in Part #4. Q1. What is Adversarial Machine Learning? Adversarial machine learning is a technique employed by threat actors to manipulate machine learning models.
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OpenAI introduced a “Preparedness Framework” just before the 2024 holiday season. This framework grants the company’s board of directors the authority to revise decisions made by CEO Sam Altman and the leadership team. It is currently a work in progress, labeled as “beta”, and was developed in response to the events of November 2023 when
The UK AI Safety Summit, along with G7 and US actions, signalled progress in AI last week. Over 100 attendees from various sectors met at Bletchley Park for the world’s first international AI Safety Summit, exploring the future of AI. Several big tech companies and nations signed a ‘landmark’ voluntary agreement to allow governments, including
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Are you ready for the next revolution in retail? Think of an AI that doesn’t just automate tasks but actually acts with intention, much like a well-trained store manager who’s been in the trenches for decades—observing, planning, deciding, and learning on the fly. That’s the vision behind my upcoming book, Foundations of Agentic AI for
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Introduction Imagine you have a new type of user visiting your website or app – not a human customer, but an AI-powered agent. This agent can read, converse, and even take actions on behalf of humans. It’s as if millions of super-smart interns are out there, ready to interact with digital services. Welcome to the
Executive Summary Retailers face complex decisions in assortment planning – determining the optimal mix of products to stock across stores and channels. Traditional methods struggle to account for changing customer preferences, demand uncertainty, and the myriad factors influencing product performance. Reinforcement Learning (RL) offers a data-driven, dynamic approach to optimize assortments in real time. By
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Executive Summary Introduction Business leaders are witnessing a rapid evolution in artificial intelligence (AI) capabilities. What began with simple predictive analytics has advanced through prescriptive and generative AI, and is now entering the era of agentic AI. Unlike earlier forms of AI that mainly analyze data or generate content, agentic AI systems can autonomously make
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At AWS re:Invent 2024, Amazon Web Services (AWS) unveiled a comprehensive suite of advancements across artificial intelligence (AI), cloud computing, data management, and analytics, reinforcing its commitment to innovation and customer-centric solutions. 1. Introduction of ‘Nova’ AI Models AWS expanded its generative AI capabilities by launching the “Nova” series of foundation models: 2. Development of
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