Tag: Artificial Intelligence

Latest Read: AI at the Edge

Post author By Don Kasprzak
Post date June 30, 2024

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.

Tags Artificial Intelligence, Innovation, Internet of Things, machine learning

Artificial Intelligence Education Innovation Reading

Latest Read: AI and Machine Learning for Coders

Post author By Don Kasprzak
Post date June 26, 2024

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.

Tags Artificial Intelligence, Innovation, machine learning

Artificial Intelligence Education Innovation Reading

Latest Read: Evolutionary Deep Learning

Post author By Don Kasprzak
Post date June 15, 2024

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:

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

Tags Artificial Intelligence, deep learning, Innovation

Artificial Intelligence Education Reading

Latest Read: Generative AI in Practice

Post author By Don Kasprzak
Post date May 3, 2024

Generative AI in Practice: 100+ Amazing Ways Generative Artificial Intelligence is Changing Business and Society by Bernard Marr.

He holds degrees in business, engineering and information technology from the University of Cambridge and Cranfield School of Management. He has written several books and two that I have read include his 2015 release Big Data Using smart big data and his recent book Artificial Intelligence in Practice. That said, this book falls short. I feel this was a rushed effort to get into the Generative AI hype cycle.

So here, Bernard is focusing on Generative AI as the biggest advancement in technology in the history of the world and how ChatGPT is driving this new somewhat magic service. Actually the metrics seem to confirm: 10 million users within 30 days of launch and then a stunning 100 million within the next 60 days. Simply put, the fastest adoption of technology in the history of computing. But don’t forget the cost Bernard.

In the rush for all things Generative AI, this new subset of Machine Learning is driving the AI hype cycle even higher than many would conclude possible. Generative AI can of course create visual graphics, computer code, and music. Seems to be the ‘generative’ in Generative AI.

Tags AI hype cycle, Artificial Intelligence, bernard marr, Generative AI, Generative AI in Practice

Artificial Intelligence Education Reading

Latest Read: AI and Machine Learning for On-Device Development

Post author By Don Kasprzak
Post date April 24, 2024

AI and Machine Learning for On-Device Development: A Programmer’s Guide by Laurence Moroney.

Laurence holds a Bachelor of Science in Physics and Computer Science from Cardiff University, PgD, Microelectronics Systems Design from Birmingham City University and Graduate Certificate in Artificial Intelligence from Stanford University.

Today he is Chief AI Scientist at VisionWorks Studios. He previously was an AI Advocate at Google for 10 years and served as a Senior Developer Evangelist at Microsoft. He wrote this book in 2021 and previously published AI and Machine Learning for Coders in 2020.

While OpenAI’s ChatGPT kick started the LLM surge, AI will simply be a component installed upon our mobile devices. The OpenAI/Microsoft partnership is certainly enterprise focused, Google Gemini and Apple will drive their Android and iOS devices to simply adopt AI as part of their mobile ecosystem. Phones will simply remain the go to device.

So, there was a lot of buzz regarding new AI devices including Humane’s AI Pin or the Rabbit R1. Their rush to market to capitalize on the AI hype cycle leads to critical mistakes.

Tags Artificial Intelligence, Laurence Moroney, machine learning, On-Device Development, TensorFlow