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Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical concerns and explores the latest methodologies, tools, and real-world examples relevant to today’s dynamic ISE landscape. Readers will gain a practical toolkit for effective integration and utilization of AI in system design and operation. The book also presents the current state of AI across big data analytics, machine learning, artificial intelligence tools, cloud-based AI applications, neural-based technologies, modeling and simulation in the metaverse, intelligent systems engineering, and more, and discusses future trends. Written by renowned international contributors for an international audience, Advances in Artificial Intelligence Applications in Industrial and Systems Engineering includes information on: Reinforcement learning, computer vision and perception, and safety considerations for autonomous systems (AS) (NLP) topics including language understanding and generation, sentiment analysis and text classification, and machine translation AI in healthcare, covering medical imaging and diagnostics, drug discovery and personalized medicine, and patient monitoring and predictive analysis Cybersecurity, covering threat detection and intrusion prevention, fraud detection and risk management, and network security Social good applications including poverty alleviation and education, environmental sustainability, and disaster response and humanitarian aid. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is a timely, essential reference for engineering, computer science, and business professionals worldwide.

Financial institutions are racing to harness the power of AI, but the path to implementation is filled with challenges. From feature engineering to model deployment, the technical complexities of AI adoption in finance require careful navigation of both technological and regulatory landscapes. How do you build AI systems that satisfy strict compliance requirements while still delivering business value? What skills should teams prioritize as AI tools become more accessible through natural language interfaces? With the pressure to reduce model development time from months to days, how can organizations maintain proper governance while still moving at the speed modern business demands? Vijay is a seasoned analytics, product, and technology executive. As EVP of Global Solutions & Analytics at Experian, he runs the department that creates Experian's Ascend financial AI platform. Promoted multiple times in eight years, Vijay now leads a team of more than 70 at Experian. He is one of the youngest execs at Experian, believing strongly in understanding and accepting risk. He has built and run data, engineering, and IT teams, and created market-leading products. In the episode, Richie and Vijay explore the impact of generative AI on the finance industry, the development of Experian's Ascend platform, the challenges of fraud prevention, education and compliance in AI deployment, and much more. Links Mentioned in the Show: ExperianExperian AscendConnect with VijayCourse: Implementing AI Solutions in BusinessRelated Episode: How Generative AI is Transforming Finance with Andrew Reiskind, CDO at MastercardRewatch RADAR AI 

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Welcome to our June 2024 Data & AI Meetup, We have two great sessions with Pizza and Networking between them.

Krishna Yogi Bio: I'm Data Consultant with over 15 years of experience in Data engineering and data science. I've worked with various companies like Microsoft, Deutsche Bank in the past, I am currently heading the data division at Scrumconnect a public sector-focused consultancy. I founded 2 start-ups in the past Maptags (geotagging to a simple word), Sashwat (certificate fraud prevention using blockchain) and I am the Author of the short book 'Why Bitcoin'. Outside work I'm a Star Wars geek, board game nerd, and Bitcoiner.

Session Abstract: This session explores the limitations of traditional data warehouses and the rise of data lakes for flexible data storage. We'll discuss how data lakehouses merge the structured schema of warehouses with the scalability of lakes with a focus on Delta Lake, an open-source layer from Databricks.

------ Break for Networking and Pizza ------

Marc Dunbar Bio: Marc has been working in the IT industry for over 15 years, specialising in Infrastructure, Networking and Security as well as Microsoft Cloud Technolgies. He has spent most of that time working with clients to navigate the ever changing world of IT and has worked for some of the industries best know consultancies like Microsoft and Coeo. Session Abstract: The Digital Transformation is coming to an end with Quantum Computing around the corner and AI is the next technological revolution. What if we combine these Technolgies? In this session we'll explore where we are now, and where we are going with Quantum and AI. You might be surprised how far we have come already. Bring an inquisitive mind as we consider ways to prepare ourselves for the future.

We talked about:

Angela's background Angela's role at Sam's Club The usefulness of knowing ML as a data engineer Angela's career path Transitioning from data analyst to data engineer/system designer Best practices for system design and data engineering Working with document databases Working with network-based databases Detecting fraud with a network-based database Selecting the database type to work with Neo4j vs Postgres The importance of having software engineering knowledge in data engineering Data quality check tooling The greatest challenges in data engineering Debugging and finding the root cause of a failed job What kinds of tools Angela uses on a daily basis Working with external data sources Angela's resource recommendations

Links:

LinkedIn: https://www.linkedin.com/in/aramirez1305/ Twitter: https://twitter.com/angelamaria__r Github: https://github.com/aramir62 Previous podcast talk: https://twitter.com/i/spaces/1OwGWwZAZDnGQ?s=20

Free ML Engineering course: http://mlzoomcamp.com

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Fighting fraud with engineering and machine learning - Angela Ramirez

Outline:

• Data engineering
• ML for Fraud protection

About the speaker: Angela is a data engineer working at Sam’s Club with their fraud team. She has 4 years of experience as a data engineer. Currently specialising in machine learning for fraud prevention, Angela works on designing and maintaining the data for a machine learning system identifying fraudulent transactions.

In addition, Angela has a masters in Natural Language Processing where she focused on coreference resolution and dialogue systems. Her most recent research includes generating quality synthetic data from LLMs for dialogue systems.

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