Category: Artificial Intelligence

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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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Artificial Intelligence Education Innovation Reading

Latest Read: AI and Machine Learning for Coders

AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence by Laurence Moroney.

AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence by Laurence Moroney

Laurence holds a BSc in Physics and Computer Science from Cardiff University, Postgraduate Diploma in Microelectronics Systems Design from Birmingham City University and a Graduate Certificate in Artificial Intelligence from Stanford University.

He was Global AI Advocacy Lead at Google for 10 years and Senior Developer Evangelist at Microsoft for 7 years. In fact, today he is Senior Fellow at The AI Fund, and Artificial Intelligence Advisor and Fellow at RealAvatar. Laurence has also taught millions of students through online platforms such as Coursera, Udacity, and edX.

This book is certainly an amazing guide designed for programmers looking to transition into AI and machine learning with a focus on computer vision, natural language processing (NLP), and sequence modeling with code samples and projects.

To no surprise Laurence is driving this on TensorFlow, Google’s tool for for building models on multiple platforms including Raspberry Pi for deployments on web, mobile (Android and iOS), cloud, and embedded systems.

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Artificial Intelligence Education Innovation Reading

Latest Read: Evolutionary Deep Learning

Evolutionary Deep Learning: Genetic algorithms and neural networks by Micheal Lanham.

Evolutionary Deep Learning: Genetic algorithms and neural networks by Micheal Lanham

Today Michael is a Lead AI Developer at Brilliant Harvest Inc. and also a Technical author at Manning Publications. Previously he worked at Symend as a Principle AI Engineer and Manager of ML Engineering.

This books expands upon his knowledge in deep learning, evolutionary computation, and genetic algorithms. This was a very enlightening read and will be very interesting for those interested in a comprehensive understanding of deep learning today.

The book is certainly written for data scientists using Python. This offers a very deep perspective on the combination of evolutionary principles with deep learning.

This results in enhanced model performance with the ability to solve complex problems with machine learning.

Michael is able to address multiple themes related to the link with computation and deep learning techniques. The book is structured in three main parts:

  1. Getting Started:
    Introduces evolutionary deep learning, evolutionary computation, and genetic algorithms using DEAP (Distributed Evolutionary Algorithms in Python).
  2. Optimizing Deep Learning:
    Covers automated hyperparameter optimization, neuroevolution optimization, and evolutionary convolutional neural networks.
  3. Advanced Applications:
    Explores evolving autoencoders, generative deep learning with evolution, NeuroEvolution of Augmenting Topologies (NEAT), and future directions in evolutionary machine learning..
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Artificial Intelligence Education Innovation Reading

Latest Read: Teaching with AI

Teaching with AI: A Practical Guide to a New Era of Human Learning by Jose Antonio Bowen and C. Edward Watson.

Teaching with AI: A Practical Guide to a New Era of Human Learning by Jose Antonio Bowen and C. Edward Watson

Bowen holds a PhD from Stanford in musicology and humanities. Watson holds a PhD in Curriculum and Instruction from Virginia Tech.

The general aim of the book is to address AI in education. Regrettably the book takes very broad brush stokes on a fast moving technology. As a result, they have missed an opportunity. It appears to many that a rush of books addressing AI in education has been underway since the introduction of ChatGPT.

Unfortunately the book misses key, critical requirements for AI integration into workflows, business thinking, and how practical strategies for faculty to leverage AI within the classroom.

The book is divided into three sections:
First, Thinking with AI is very much a basic AI 101 course with broad overviews to how work, literacy and creativity will be shaped by AI. Second, Teaching with AI is focusing on how AI will assist faculty but is not able to address how facutly at a private four year college would be for factuly teaching at a public two year college. Again, lots of broad brush strokes. Finally, Learning with AI is exploring the feedback and how AI can design assignments for students, how AI will change student writing and also assessments.

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Artificial Intelligence Education Innovation Reading

Latest Read: Fusion Strategy

Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future by Vijay Govindarajan and Venkat Venkatraman.

Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future by Vijay Govindarajan

Vijay holds an MBA from Harvard Business School and is Professor of International Business at Dartmouth College. Venkat holds an MBA from the Indian Institute of Management and a PhD from the University of Pittsburgh and is Professor of Information Systems at Boston College.

Mix AI innovation and digital strategy and you can certainly understand how real-time data can transform companies and their products. Furthermore, this book is addressing industrial and manufacturing firms., data-rich information was limited to a select few like Boeing.

So parsing terabytes of interconnected datasets, industries can drive new value by creating strategic connections not possible even 10 years ago. However Facebook, Amazon, and Google proved repeatedly that the collection of real-time data can drive innovation in a fast changing world. Especially for design and manufacturing firms, the deployment of inexpensive sensors, enhanced wireless technologies and real cheap computing wrapped around artificial intelligence will make industries shift overnight.

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