Tag: machine learning

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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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Latest Read: The AI Advantage

The AI Advantage: How to Put the Artificial Intelligence Revolution to Work by Tom Davenport. Tom has written several well respected books. Reading Competing on Analytics in 2008, provides me stellar view of business metrics. Keeping Up with the Quants and Big Data at Work both reveal deep insights every organization must absorb to understand predictive analytics and big data. Surprisingly, the AI Advantage falls flat by comparison.

the ai advantage

The book’s pitch is well researched, yet there is a surprising lack of unique cases compared to his three books above. Likewise, the opening chapter “Artificial Intelligence Comes of Age—Slowly” provides a general overview to IBM’s Watson. Small hits, and yet a larger unfocused ability of Watson to move the needle on cancer research.

The promise of AI’s subset, machine learning (ML) is very over-promoted across today’s IT sales marketplace. The opening chapter reveals some deep AI shortcomings that should not be ignored.

Meanwhile, examples throughout the book refer to the Robotic Process Automation (RPA), a somewhat flavor of machine learning. Likewise, this extends into Chapter 2: AI in the Enterprise, the impact of AI for knowledge workers. This type of technology advance is impacting a cognitive advantage in healthcare.

Surprisingly, The AI Advantage released in 2018. But the role of AI driven Deep Fakes is missing. Launched in 2014, Generative Adversarial Network (GAN) technology may indeed be the most contested application of ML.

16 Million views of a Deep Fake application on YouTube

While amusing for movies and celebrities, certainly there are significant impacts upon society regarding government, corporations, and foreign relations. Shockingly the manipulation of voice has already resulted in financial transfers to criminal organizations.

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