Tag: machine learning

Latest Read: Artificial Intelligence in Practice

Post author By Don Kasprzak
Post date September 24, 2023

Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems by Bernard Marr. He holds degrees in business, engineering and information technology from the University of Cambridge and Cranfield School of Management.

Today, Bernard also enjoys teaching for Oxford University, Warwick Business School, the Irish Management Institute, and the and Institute of Chartered Accountants in England and Wales. He is also a contributor to Forbes. Bernard is focusing on strategy, business performance, digital transformations, and AI.

He has certainly advised many of the world’s most recognized brands including: Amazon, Google, Microsoft, Astra Zeneca, The Bank of England, BP, NVIDIA, Cisco, DHL, IBM, HPE, Ericsson, Jaguar Land Rover, Mars, The Ministry of Defense, NATO, The Home Office, NHS, Oracle, T-Mobile, Toyota, The Royal Air Force, Shell, The United Nations, Walgreens, and Walmart.

Bernard has written 20 books having been recognized with the 2022 Business Book of the Year award, the CMI Management Book of the Year award, the Axiom book award and the WHSmith best business book award. I enjoyed reading one of his previous books, Big Data: Using smart big data analytics and metrics to make better decisions and improve performance.

Artificial intelligence and machine learning are continuing to be cited as the most important trend in society today. However, rather than list where AI is deployed, it would be more important to understand how AI was deployed. Bernard is providing comprehensive overviews, including technical details as key learning summaries for each case study.

Tags Artificial Intelligence, Artificial Intelligence in Practice, bernard marr, business planning, machine learning

Artificial Intelligence Education Innovation Reading

Latest Read: Machine Learning: The New AI

Post author By Don Kasprzak
Post date September 5, 2023

Machine Learning: The New AI by Ethem Alpaydin. A Fulbright scholar, Ethem holds a Ph.D. in Computer Engineering from Ecole Polytechnique Fédérale de Lausanne. He has held visiting research positions at University of California, Berkeley, and MIT.

Ethem is delivering an exceptional overview of machine learning. If you want to understand the foundations of machine learning without any programming details, this is the perfect book. The math and statistics are delivered at a conceptual level. Anyone can follow along. He provides a solid foundation addressing algorithms, artificial intelligence, and neural networks. Again for anyone interested, this book is not technical. You will not be overwhelmed, but rather inspired to learn.

Today, Machine Learning (ML) certainly is the most popular subset of artificial intelligence. With ML certainly now a core AI service, we can more easily understand the growing range of ML apps we use everyday.

This includes product recommendations to voice recognition. Spread across just seven chapters, readers will come to understand ML, Statistics and Data Analytics. However chapter four: Neural Networks and Deep Learning is a strong delivery of ML’s core services. This is perhaps the most important chapter for readers new to ML. Ethem provides the much needed context that the foundations were first tested in 1946. This helps set a level playing field in following onto neural networks and the core of deep learning.

Tags algorithms, Artificial Intelligence, Ethem Alpaydin, machine learning, neural networks

3D Printing Artificial Intelligence Education Reading Technology

Latest Read: HBR’s 10 Must Reads on AI, Analytics, and the New Machine Age

Post author By Don Kasprzak
Post date September 2, 2023

HBR’s 10 Must Reads on AI, Analytics, and the New Machine Age by Harvard Business Review. Harvard Business Review has selected 10 articles focusing on intelligence machines and why your strategy cannot ignore their impact.

Published in January 2019 these article certainly provide insights to the new machine age. Each article is presented with three ideas: Problem, Causes, and Solutions.

This very new class of intelligent machines are indeed revolutionizing business through data. Sensors now embedded can gather and transmit data. So, data analytics and machine learning are uniting to provide organizations powerful, data driven insights not possible even ten years ago. The cornerstone of these technologies is miniaturization and supply chain manufacturing costs.

When Organizations and smart machines are correctly brought together, teams will being hiring, not firing. The data outcomes will release new levels of productivity in a much shorter timespan. organizational Leaders will see the big idea new machines provide. As a result, leadership must rethink strategy, managing people, and introducing new services.
The downstream impact: Drone deployments will need retooling of current services into the cloud via mobile apps. Just wait until drones mature and move into commercial spaces. This is a far cry from perceived military only use for drones.

Tags 3D printing, analytics, Artificial Intelligence, Drones, Innovation, machine learning

Artificial Intelligence Education Innovation Reading

Latest Read: Succeeding with AI

Post author By Don Kasprzak
Post date February 21, 2022

Succeeding with AI: How to make AI work for your business
by Veljko Krunic. He holds a PhD in Computer Science from University of Colorado Boulder. Today he is CEO of Health Saver AI and holds two AI patients.

Based upon the AI books I have been reading, none certainly addresses the business side of AI. Veljko is writing this book for business leaders. At first glance one may believe technology project managers would be a target audience. This is perfect for business leaders and analysts with no AI programming.

For any business seeking to define investments for data driven decision making, this is a worth title to read. The approach is completely a business approach to AI projects, how they are different and when to fail quickly early in a project.

In addition, Veljko’s underlying message for business teams is the profit is the resulting data outcomes, as it seems almost every business seeking to gain an upper hand have already kickstarted small AI projects. Veljko certainly helps leaders understand the foundation requirements (developers, and data scientists) required to succeed.

The hard requirements for AI if overlooked, will not be enough to prove a business case and result in wasted investments. This is along the lines of The Myth of Artificial Intelligence Why Computers Can’t Think the Way We Do by Erik Larson.

Tags Artificial Intelligence, Business AI projects, machine learning, Succeeding with AI, Veljko Krunic

Artificial Intelligence Education Innovation Reading

Latest Read: How Smart Machines Think

Post author By Don Kasprzak
Post date October 23, 2021

How Smart Machines Think by Sean Gerrish. Sean is a Senior Engineering Manager at Google leading machine learning and data science teams. He holds a PhD in machine learning from Princeton.

This book is providing readers with a wonderful overview to advances in artificial intelligence, and specifically how machine leaning is now the most popular subset of AI.

How Smart Machines Think is addressing three key areas that reveal the leaps in advancements of machine learning development: The DAPRA Grand Challenge, the Netflix recommendation engine, and Neural Networks.

Each section is well written, providing above all, deep insights tied to objectives driving new business models.

While Sean is certainly providing a solid grounding in algorithms and their methodologies, I was certainly surprised at the depth of autonomous vehicles, recommendation engines, and game-playing. The larger lessons from his book include amazing progress in neural networks.

Machine Learning for autonomous vehicles

Clearly Sean understands the full picture of how this emerging technology began. The 2004 initial contest found team only to achieve a small distance, perhaps less than twenty five percent of the course before their AI systems failed.

Tags Artificial Intelligence, Innovation, machine learning, Sean Gerrish, Smart Machines