Category: Artificial Intelligence

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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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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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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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