Tag: machine learning

Latest Read: Thinking With Machines

Post author By Don Kasprzak
Post date February 28, 2026

Thinking With Machines: The Brave New World of AI by Vasant Dhar.

Vasant holds a BA Chemical Engineering from the Indian Institute of Technology and an MS and PhD in Artificial Intelligence from the University of Pittsburgh. He previously worked at Morgan Stanley, where he created the Data Mining Group focused on predicting financial markets and customer behavior.

He founded SCT Capital Management, one of the first machine?learning?based, automated hedge funds bringing predictive modeling and AI directly into real?world trading. Today Vasant is a professor at New York University’s Stern School of Business and also a professor at NYU’s Center for Data Science.

Thinking With Machines is one of the useful books on AI that I have read recently. Vasant is sharing with the reader not just what AI is, but how we can make decisions in today’s world that is now shaped by intelligent machines.

He succeeds delivering a practical guide for anyone trying to figure out when to trust AI—and when not to and insist on human judgment.

Tags Artificial Intelligence, Education, Innovation, machine learning, Thinking With Machines, Vasant Dhar

Latest Read: Generative AI in Action

Post author By Don Kasprzak
Post date October 22, 2025

Generative AI in Action by Amit Bahree.

Amit holds an MSc in Software Engineering from the University of Oxford and a BSc in Computer Science from the University of Pune. He previously served as Chief Technology Innovation Officer at Avanade. He is also a co-author of Pro WCF: Practical Microsoft SOA Implementation. Today he serves as a Principal Group Product Manager for the Azure AI engineering team at Microsoft.

Generative AI in Action certainly provides a high-level roadmap for integrating Large Language Models into organizational enterprise environments. He is linking the “magic” of AI’s hype cycle with the demands of production architectures across diverse organizations.

This is not a “how to use ChatGPT” guide for non-technical users. This is however a good book for experienced software developers, architects, and data scientists who are new to AI. It explains the core concepts from scratch and provides a learning path sans PhD in mathematics. So, generating quick prompts from poetry to dinner recipes, the demands of building enterprise, scalable AI services is rather challenging.

Tags Amit Bahree, Artificial Intelligence, Generative AI in Action, machine learning

Artificial Intelligence Education Reading

Latest Read: The Alignment Problem

Post author By Don Kasprzak
Post date October 2, 2024

The Alignment Problem: Machine Learning and Human Values by Brian Christian.

Brian holds a BA in Computer Science and Philosophy from Brown University. And holds an MFA in Poetry from the University of Washington. He is a visiting scholar at the University of California, Berkeley. His focus is cognitive science and human-compatible AI.

He received the Eric and Wendy Schmidt Award for Excellence in Science Communication for his work on The Alignment Problem. Yes, I fully agree this is a wonderful book to read.

His previous books include The Most Human Human and Algorithms to Live By. Both address AI technology and the human experience. When AI services (ChatGPT, CoPilot, Dalle 3, etc.) attempt to execute tasks that result in errors, ethical and potential risks emerge. So, researchers call this the alignment problem.

At the book’s core, Brian is examining ethical and safety issues as machine learning technologies are both advancing into society more rapidly than projected. Perhaps the key element is the ability for machine learning services to execute tasks on behalf of humans across almost every aspect of our lives. This also markets from education, retail, supply chain, and energy to name just a few.

Tags algorithms, Artificial Intelligence, Bias, hallucinations, machine learning

Artificial Intelligence Education Reading

Latest Read: Managing Machine Learning Projects

Post author By Don Kasprzak
Post date September 14, 2024

Managing Machine Learning Projects: From design to deployment by Simon Thompson.

Simon holds a Phd in Philosophy from the University of Oxford UK. His is the former Director of AI research at BT Labs. Today he serves as the Head of Data Science at GFT Technologies.

Simon is revealing that managing ML projects to production can seem like navigating uncharted waters.

By revealing the challenges of accounting for large data resources to tracking multiple models, machine learning has very different requirements when compared to building traditional software applications. Simon does acknowledge this challenge and asks readers to execute this on a more even scale.

Managing Machine Learning Projects is an end-to-end guide for delivering ML applications on time and under budget. Simon is revealing the tools, approaches, and processes designed to handle the challenges of machine learning project management. To help readers Simon is deploying a full case study.

Tags Artificial Intelligence, machine learning, project management

Artificial Intelligence Education Reading

Latest Read: Privacy-Preserving Machine Learning

Post author By Don Kasprzak
Post date July 17, 2024

Privacy-Preserving Machine Learning by J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera.

Morris holds a BSEE from Tatung University, Taiwan and MS and PhD in computer engineering from North Carolina State University. He teaches at the University of South Florida. Di holds PhD in Computer Engineering from Iowa State University and the University of South Florida. He is a Security / Privacy Engineer at Snap Inc. Dumindu holds a MSc in Enterprise Application Development from Sheffield Hallam University and PhD in Electrical Engineering and Philosophy from University of South Florida. Today he is Assistant Professor of Data Science at Embry-Riddle Aeronautical University.

This was a book that places into perspective the need for ensuring privacy in our fast paced AI marketplace. The authors express the need not only to understand privacy within Machine Learning systems, but understanding methodologies to preserve user’s private data while maintaining performance on LLMs.

They address how personal data well embedded across various sectors increases the risks of data breaches. Just realize how your smartphone is tracked by marketing companies. In fact, they review the Facebook-Cambridge Analytica scandal and call for robust privacy measures in data-driven applications.

Tags Artificial Intelligence, machine learning, privacy