Tag: Artificial Intelligence

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Artificial Intelligence Education Innovation Reading

Latest Read: HBR Guide to AI Basics for Managers

HBR Guide to AI Basics for Managers by Harvard Business Review. The ‘HBR Guide’ series offers articles addressed in multiple sections. This is not a single author’s interpretation.

HBR Guide to AI Basics for Managers

Published in January 2023 this Guide is critical for managers regardless of organization, vertical market, or seniority within executive teams. In fact, prior to the pandemic AI had already shifted the fundamentals of business and society. Many to this day never saw it coming and this Guide is mandatory.

While business as usual is often overstated, our post pandemic world shifted so rapidly and radically, that organizations will simply fail if they do not adopt. In more and more business cases, the adopt or die mindset will continue to become painfully evident as consolidation, mergers, acquisitions and divestitures (MAD) only accelerate via AI solutions.

For managers the Introduction is aiming squarely at your future: How AI Will Redefine Management is not to be taken lightly. However in the presented articles the adoption of which cannot be overstated, miss the fundamentals of AI changing your organization, and you might as well begin refreshing your resume. For the CEO or President, this will empower you to drive change long desired but slowed by organization’s noise. And you will have to address your supporters, detractors and fence sitters to get the AI ball rolling.

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Artificial Intelligence Education Innovation Reading

Latest Read: Succeeding with AI

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.

Succeeding with AI: How to make AI work for your business by Veljko Krunic

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.

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Artificial Intelligence Education Innovation Network Reading

Latest Read: GANs in Action

GANs in Action: Deep learning with Generative Adversarial Networks by Jakub Langr and Vladimir Bok. Vladimir is a Data Product Manager at Intent. In addition, I really welcome his statement: Why I Donate All of My Book’s Proceeds to Girls Who Code. Jakub is Co-Found of Hypermile, a UK startup deploying AI across transportation solutions.

GANs in Action by Jakub Langr and Vladimir Bok

Jakub and Vladimir have certainly written a wonderful book on machine learning algorithms that generate realistic imaging. However, this book is really intended for readers who already have some experience with machine learning and neural networks.

Whereas many consumers view new imaging services as a kind of magic, super computing power delivered by AI. There are indeed large machine learning datasets in play that even make this imaging possible.

GANs in Action is a very worthy followup to John Kelleher’s Machine Learning, Melanie Mitchell’s excellent book Artificial Intelligence: A Guide for Thinking Humans, and Sean Gerrish’s How Smart Machines Think. Each author is in fact, addressing in various scales, the introduction to Neural Networks and GANs. Thankfully, Jakub and Vladimir have taken the necessary next step in delivering a wonderful introduction and coding deep dive to GANs.

In fact, for many consumers the Grokking series of books are a must read. Grokking Algorithms: An Illustrated Guide For Programmers and Other Curious People by Aditya Bhargava and Grokking Artificial Intelligence Algorithms: Understand and apply the core algorithms of deep learning and artificial intelligence by Rishal Hurbans. Thus, both are wonderfully illustrated books to begin anyone’s journey into understanding Artificial Intelligence, Machine Learning, and Deep Learning.

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Artificial Intelligence Education Innovation Reading

Latest Read: Deep Learning

Deep Learning by John D. Kelleher. John is the Academic Leader of the Information, Communication and Entertainment research institute at the Technological University Dublin. He has previously taught at Dublin City University, Media Lab Europe, and DFKI (the German Centre for Artificial Intelligence Research).

Deep Learning by John D. Kelleher

This is a very good introduction to specific subsets of artificial intelligence that are indeed powering imaging, speech recognition, machine translation, and autonomous cars today.

Consumers may forget as they are engaging various technologies, their interactions are via Deep Learning systems. This includes interactions with Siri on iPhones, and Alexa on all things from Amazon. To a lesser extent is Cortana from Microsoft. Actually, John provides a wonderful glossary. This serves the reader well in helping to further develop their understanding of Deep Learning systems.

Likewise, his introduction illustrates how Deep learning delivers data-driven decisions from very large datasets. The key is Deep Learning deliver immediate ‘learning’ as the large datasets grow.

In addition, his insights on autoencoders, recurrent neural networks, and Generative Adversarial Networks (GAN) are very stimulating. At the same time, addressing gradient descent and especially backpropagation is amazing in of itself.

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Artificial Intelligence Education Reading

Latest Read: The Myth of Artificial Intelligence

The Myth of Artificial Intelligence Why Computers Can’t Think the Way We Do by Erik Larson. Erik is an entrepreneur and former research scientist at the University of Texas at Austin where he focused in machine learning and natural language processing.

The Myth of Artificial Intelligence by Erik Larson

In addition, Erik founded two DARPA-funded AI startups and works on core issues in natural language processing and machine learning. Erik has written for The Atlantic.

Artificial Intelligence seems to be the buzzword of the last twenty years, for better or for worse. For some it is the savior of humanity. For others, the spawn of the devil.

So, does AI actually deliver on superior knowledge systems surpassing human capabilities? Actually, there are valid points by Erik to reveal quite the opposite.

The real challenge proposed by Erik is that so many noted authors on AI, and all their books promising AI’s coming revolution have really all missed their target dates. All of those noted experts made bold predictions to delivery dates of systems that surpass all human knowledge and the downstream effect AI will play upon both markets and society. So why in 2022 have they all missed the mark?

The book is broken into three parts: The Simplified World, the Problems of Inference, and the Future of the Myth. These opening chapters should be considered mandatory reading for every middle school student as Part 1 is certainly well researched. At less than seventy pages, Erik provides a grounded explanation to the early foundation that brought AI forward. From Chapter Two, Turing at Bletchley, to Chapter Five, Natural Language Understanding.

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