Tag: Artificial Intelligence

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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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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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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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Latest Read: Hello World

Hello World: Being Human in the Age of Algorithms by Hannah Fry, Today Hannah is a senior lecturer at University College London’s Centre for Advanced Spatial Analysis.

Hello World Being Human in the Age of Algorithms by Hannah Fry

Generally speaking, Hannah has written a wonderful book addressing algorithms and artificial intelligence. Society has certainly fallen behind the moral implications of algorithms and Hannah speaks truth to power.

Above all, do not let the idea of learning about algorithms, artificial intelligence, or machine learning intimate you. Hannah explains all of these terms with easy to understand examples. This is why her book is popular and well regarded.

I really appreciate how Hannah is addressing algorithm technology across the following chapters: Power, Data, Justice, Medicine, Cars, and Crime. However, I will save her best lesson for last.

Machines that see

So, Hannah reveals artificial intelligence allows a computer to identify dogs. Once a computer has identify over one million dog photos, artificial intelligence can identify dogs like an expert.

Yet, when applying this to breast cancer diagnosis the magic of machine learning can truly shine. Feed a computer millions images of breast cancer tissue images and a local doctor at a small community hospital in remote Iowa can tap into machine learning to help diagnose with a better degree of accuracy once only for a doctor with 20 years of breast cancer diagnosis at Memorial Sloan Kettering Cancer Center in New York City.

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Latest Read: A Brief History of Artificial Intelligence

A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going by Michael Wooldridge. Michael is Head of Department of Computer Science and Professor of Computer Science at the University of Oxford.

A Brief History of Artificial Intelligence Michal Wooldridge

Is artificial intelligence intimidating to you? Above all this is a very easy, enjoyable book. So, Michael states in his introduction “I’m writing a popular science introduction to artificial intelligence.”

Accordingly, Michael has researched artificial intelligence for over 30 years. He is focusing on multi-agent systems drawing upon ideas from game theory, logic, computational complexity, and agent-based modeling.

A short history begins with Alan Turing’s work in 1935 at Cambridge during World War II. This is beyond America’s cultural understanding of Turing’s life from the 2014 movie The Imitation Game. Alan Turing actually defined artificial intelligence.

Machine Learning

Chapter 5: Deep Breakthroughs, addresses why Google acquired DeepMind Technologies, a British-based research laboratory in 2014. Founded in September 2010, DeepMind was introducing a term bounced around a lot: Machine Learning.

There is certainly a great misunderstanding regarding machine learning and deep learning. Additionally, Micheal’s efforts are to be complimented in making this topic understandable.

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