Tag: machine learning

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Artificial Intelligence Education Reading

Latest Read: The Alignment Problem

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

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.

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Artificial Intelligence Education Reading

Latest Read: Managing Machine Learning Projects

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

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.

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Artificial Intelligence Education Reading

Latest Read: Privacy-Preserving Machine Learning

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

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.

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Artificial Intelligence Education Innovation Reading

Latest Read: The DevSecOps Playbook

The DevSecOps Playbook: Deliver Continuous Security at Speed by Sean D. Mack.

The DevSecOps Playbook: Deliver Continuous Security at Speed by Sean D. Mack

Sean holds a BS in Computer and Information Sciences from UC Santa Cruz and MBA from Seattle University. He is CIO and CISO at Wiley, VP of Operations and Applications at Pearson, Director of Global Product Development and Delivery at Experian, and Senior Director of Technical Operations at RealNetworks.

In fact, the term Development, Security, and Operations (DevSecOps) stands for a framework that integrates security into all phases of the software development lifecycle. Today more than ever before DevSecOps must deliver continuous security at the speed of business. DevSecOps can only succeed when the organization supports the triad of people, process, and tech to delver strong cybersecurity infrastructure and practices.

To simplify, DevSecOps emphasizes incorporating security measures from the beginning of the development process, rather than treating them as an afterthought or post deployment requirement. This approach identifies and mitigates potential security risks early on.

Sean outlines why it’s critical to shift security considerations to the front-end of the development cycle, how to do this, and how the evolution of a standard security model since the pandemic has impacted modern cybersecurity.

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Artificial Intelligence Education Innovation Reading

Latest Read: AI at the Edge

AI at the Edge: Solving Real-World Problems with Embedded Machine Learning by Daniel Situnayake and Jenny Plunkett.

AI at the Edge: Solving Real-World Problems with Embedded Machine Learning by Daniel Situnayake and Jenny Plunkett

Jenny holds BS in Electrical Engineering in from the University of Texas at Austin. She is a senior developer relations engineer at Edge Impulse. Daniel is Director of ML at Edge Impulse and holds a BSc Computer Networks and Security from Birmingham City University. He is the former Developer Advocate at Google for TensorFlow Lite.

This book is a guide to exploring how machine learning is being implemented on both edge devices and embedded systems. A bit of caution however, both authors are at Edge Impluse and their company product is referenced.

While not surprising, it can be viewed as a marketing product rather than addressing market leading solutions. The target audience is engineering professionals, including product managers and technology leaders.

We continue to deploy Internet of Things (IoT) devices. Many readers will think thermostats like Google Nest and X. However this focus is on industrial, automation, healthcare, agriculture, and autonomous vehicle devices which brings a lot of real-time data and machine learning driven decision-making at the remote device location. It is a very fast paced environment.

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