Tag: neural networks

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

Artificial Intelligence Education Innovation Network Reading

Latest Read: GANs in Action

Post author By Don Kasprzak
Post date February 8, 2022

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.

Tags Artificial Intelligence, deep learning, GANs in Action, Generative Adversarial Networks, Jakub Langr, neural networks, Vladimir Bok

Artificial Intelligence Education Innovation Reading

Latest Read: Deep Learning

Post author By Don Kasprzak
Post date February 4, 2022

D eep 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).

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.

Tags Artificial Intelligence, backpropagation, deep learning, Generative Adversarial Networks, gradient descent, John D. Kelleher, neural networks

Artificial Intelligence Education Innovation Network Reading

Latest Read: The AI Delusion

Post author By Don Kasprzak
Post date September 11, 2021

The AI Delusion by Gary Smith. Gary holds a Ph.D. in Economics is a professor Economies at Pomona College. He was a 1967 Woodrow Wilson Fellow and a 1968 Yale University Fellow. He was awarded a Stanford Research Institute Grant in 1978 and a NSF grant for an economics computer lab beginning in 1995.

Gary certainly provides a solid narrative that artificial intelligence is not perfect. On the contrary, it is quite far from perfect. As a result, we should be aware of how much blind faith is given to so many artificial intelligence services. We do this at our own peril.

IBM’s Watson is an example. Gary explains why Watson, a question-answering computer system capable of answering questions posed in natural language is a bad match for healthcare but can be an absolutely wonderful solution in other markets.

The AI Delusion certainly also reveals how many times artificial intelligence systems have simply failed. These lead to important lessons. At the same, time Gary does acknowledge that today’s machine learning has solved problems thought impossible just twenty years ago.

For example, the Obama campaigns in 2008 and his 2012 re-election deployed data analytics that were critical in his win and re-election. Yet, the Hilary Clinton campaign followed data insights from a machine learning system named Ada. This big data system advised against campaigning in Michigan and other states. This so upset former President Bill Clinton that he attempted to persuade the campaign to change strategy, however he was overruled by Ada. A powerful example of big data going off the tracks.

Gary is certainly acknowledging that machines in the future will have the ability to think, however today many are mislead by deep neural networks. Many on the surface associate brain neurons to artificial intelligence’ neural networks. Neural networks do not mimic the brain. Neural networks are indeed powerful programs that execute complex mathematical programs. However, today’s neural networks do not understand words, or images.

Tags algorithms, Artificial Intelligence, Gary Smith, machine learning, neural networks, The AI Delusion