Category: Innovation

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Latest Read: How Innovation Works

How Innovation Works: And Why It Flourishes in Freedom by Matt Ridley. Matt is a British journalist best known for his writings on science, the environment, and economics in The Times. He holds a seat in the British House of Lords.

How Innovation Works: And Why It Flourishes in Freedom by Matt Ridley

Matt is certainly writing against government regulation and big business across many established and emerging fields: energy, transportation, food, and computing. Regulation and big businesses are hampering innovation due to oversight. He also addresses early innovations including farming and taming animals including dogs.

Every chapter is certainly intriguing. Vast amounts of stories and historical facts drove each innovation. Matt obviously makes it certainly clear that innovation is within democratic countries where freedom allows for ideas to flourish, leading to inventions.

Chapter 2, focusing upon Public Health innovation certainly reminds the reader how vaccines developed to curb the loss of lives across many continents in our global history. A very refreshing chapter for our COVID era. On the other hand, this would be publishing in May 2020, at the beginning of our pandemic.

The first airplane

Chapter 3, Transport has a particularly great recollection of The Wright Brothers innovation. Unquestionably, this storyline is parallel to Simon Sinek’s Start With Why, and reveals more details to the road both brothers took in finding success at Kitty Hawk.

Many readers will be captivated by the research Matt delivers in round after round of amazing stories of innovation not by the inventor, but rather by those who saw a vision of how inventions lay the foundation of innovations.

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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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Education Globalization Google Innovation Network Reading Technology

Latest Read: Algorithms To Live By

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian and Tom Griffiths. Brian is the author of The Most Human Human, a Wall Street Journal bestseller, New York Times editors’ choice, and New Yorker favorite book of the year. Tom is a professor of psychology and cognitive science at Princeton University. In addition, he directs the Institute of Cognitive and Brain Sciences.

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths

At first glance the idea of brining algorithms into our daily lives seems a bit too much, even for a budding computer nerd. At the same time, Brian and Tom prove that most of us are already doing this daily.

I recall spending many hours programming SQL while living in Chicago and realizing how much more efficient my grocery shopping would be if I actually transformed my shopping list into a SQL table:

SELECT * FROM FoodGroup
ORDER BY GroceryStoreIsle;

So I can certainly agree. Yet this idea still may seem daunting. If you begin thinking about repeating tasks you perform, even laundry should certainly make you believe there is a better way.

Algorithms will certainly make this possible. Therefore, you may be spending too much time repeating tasks. This is where the book reveals how you can become efficient, by sharing the history and development of many common algorithms. You will certainly discover a few frameworks.

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Artificial Intelligence Education Innovation Network Reading Technology

Latest Read: What Algorithms Want

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.

What Algorithms Want: Imagination in the Age of Computing by Ed Finn

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.

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Artificial Intelligence Education Innovation Network Reading

Latest Read: The AI Delusion

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.

The AI Delusion by Gary Smith

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.

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