Tag: Artificial Intelligence

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Artificial Intelligence Education Innovation Reading

Latest Read: How Smart Machines Think

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.

How Smart Machines Think by Sean Gerrish

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.

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Artificial Intelligence Education Network Reading

Latest Read: Grokking AI Algorithms

Grokking Artificial Intelligence Algorithms: Understand and apply the core algorithms of deep learning and artificial intelligence by Rishal Hurbans. Rishal is a technologist, founder, and international speaker.

Grokking Artificial Intelligence Algorithms

He previously launched Viszen, a SaaS platform in his native South Africa. Today Rishal is a Business Solutions Manager at Entelect. In addition, Rishal founded Artificial Intelligence South Africa (AISA).

The Grokking series from Manning is certainly a wonderfully illustrated series of books helping users of all ages embrace algorithms.

At the same time I would recommend the Grokking series to long time computer users. The reason for their success is indeed a combination of illustrations with code examples. This is providing readers with multiple views of the numerous algorithms that provide the base structure of algorithms.

In addition, the series is a very plain-language approach. This makes learning a certainly challenging topic perfect for readers of any age, providing easy to learn concepts and terms. Rishal has even all the python code from this book online.

The code provided allows easy experimentation. In addition, Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts. Okay, it may be fair to say you will be recalling high school algebra.

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Artificial Intelligence Education Google Network Reading

Latest Read: Grokking Algorithms

Grokking Algorithms: An Illustrated Guide For Programmers and Other Curious People by Aditya Y. Bhargava. Aditya is an Engineering Manager at Etsy. He blogs at adit.io.

Grokking Algorithms

This is not a book for seasoned programmers. However, this is certainly a wonderful book to learn the basics of algorithms.

Grokking Algorithms provides an illustrated overview of algorithms which certainly provides a foundation to common algorithms used everyday by programmers.

For anyone interested in learning the basic structures of algorithms, Aditya also provides code samples in Python. In addition, the programming community has shared java versions of the book on Github.

On the surface, everyone learning about various algorithm options will not secure a programming job in their short term future.

However, I feel it is very fair to say many readers interested in learning about algorithms will not be overwhelmed.

If you are interested in learning to code, then this book is required reading. There are 400 illustrations within Aditya’s 256 page book. easy-peasy. The 256 page count is not lost on me.

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Artificial Intelligence Education Google Network Reading Technology

Latest Read: Algorithms of Oppression

Algorithms of Oppression: How Search Engines Reinforce Racism
by Safiya Umoja Noble. Safiya is an associate professor at UCLA and is the Co-Founder and Co-Director of the UCLA Center for Critical Internet Inquiry. Safiya’s research as a result, considers how bias has been embedded into search engines.

Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble

Clearly, search engine algorithms are not neutral by any means. This was indeed proving to be a very disturbing issue at the time of publication in 2018.

So, how did this happen in the first place? It is rather shocking to understand that a seemingly simple search term “black girls” results in such disgusting results.

Safiya certainly reveals this unforgiving gap and Google has made efforts to fix their errors. The result of her work has brought about the term algorithmic oppression.

Safiya explores how racism, especially anti-blackness, is generated and maintained across the internet, yet is focused squarely on Google.

In addition, Safiya reveals the impact of AdWords, Google’s advertising tool. I found it interesting that since search results are altered by paid advertising, Google is more of an advertising company than a search engine company.

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Artificial Intelligence Education Innovation Reading

Latest Read: The Master Algorithm

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.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

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.

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