Tag: machine learning

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

Artificial Intelligence Education Innovation Reading

Latest Read: The Master Algorithm

Post author By Don Kasprzak
Post date September 20, 2021

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos. Pedro is Professor Emeritus of Computer Science and Engineering at the University of Washington.

In 2018 he launched a machine learning research group at D.E. Shaw, but departed after one year. Shaw’s well known former employees include Jeff Bezos, Lawrence Summers and Cathy O’Neil, author of Weapons of Math Destruction.

The Master Algorithm is a wonderful inspection of Machine Learning technology. However, do not be intimidated, this book is not for computer science majors. Pedro is addressing Machine Learning for consumers. The benefit here is the concepts around Machine Learning for every day consumers.

Actually, the goal of the book is two fold: learn about the development of machine learning, and then inspire the reader to build a master algorithm of their own. Do not worry, there is no coding in this book.

So, the premise for Pedro is to strive to build a master algorithm, which in turn will generate all algorithms needed by human kind. While a bit ambitious, it does not feel an out of bounds question. As Pedro conveys, give it twenty years. This is powering Google, Amazon, Microsoft in the enterprise and Apple’s iPhones.

Machine Learning is surpassing the parent, Artificial Intelligence as the most popular deployment of advanced computing over the last twenty years. What is remarkable is that only within the last ten years the integration of advanced processors and cloud computing, with quantum elements are now making the long promise of Machine Learning possible.

Tags Artificial Intelligence, machine learning, Pedro Domingos, The Master Algorithm

Artificial Intelligence Education Innovation Network Reading Technology

Latest Read: What Algorithms Want

Post author By Don Kasprzak
Post date September 14, 2021

What Algorithms Want: Imagination in the Age of Computing by Ed Finn. Ed is an associate professor at Arizona State University’s School for the Future of Innovation in Society and the School of Arts, Media and Engineering. He also serves as the academic director of Future Tense, a partnership between ASU, New America, and Slate Magazine.

I really appreciate reading this book as a follow up to The AI Delusion. What Algorithms Want takes a liberal arts approach. This is very appealing and brings a valued perspective.

Ed is communicating that society innocently believes magical algorithms as a tool to a better life. For this purpose, Ed shares that Eric Schmidt indicated that people do not want Google to just provide search results. Rather, they “want Google to tell them what they should be doing next.” I find this difficult to believe.

However, Ed also is viewing this from a practical perspective. His view is that algorithms are not only for mathematical logic, but rather for philosophy, cybernetics, and creative thinking.

Accordingly, there is a gap that Ed identifies between theoretical ideas and pragmatic instructions. This is a view outside of traditional computer science books.

Clearly, most users are not aware of how Facebook’s timeline and Google search queries are executions that benefit their data collection and profits. Many would not even consider the impact of Facebook’s timeline as nothing more than the latest news from friends, when in reality it is far from that idea.

Machine Learning

What Algorithms Want takes a deeper dive on Google’s efforts to drive profits from the data mining services across every service they deploy. What is also emphasized is the automatic assumptions by society that Google has their own interests protected because of a flimsy “do no evil” pledge.

Tags Artificial Intelligence, deep learning, Ed Finn, machine learning, What Algorithms Want