Interesting Books In 2020

There have been a lot of things we haven’t been able to do during the last nine months. But it’s been a good time for reading ebooks and listening to audiobooks. So my on-again-off-again tradition of highlighting interesting books that I have read in the year is on again.

These books have not all been published during the last year, but are ones I’ve read this past year and thought worth mentioning to other folks who read this blog.  You’ll note that this is an eclectic combination of books on technology, government, the economy and other non-fiction – but that’s the range of topics that my blog is about.

Anyway, here’s my list for 2020 and a blurb as to why each book is on the list.  I have obviously eliminated from the list the many other books that I’ve read, which I would not recommend you spend your time on. ?

Technology, AI/Machine Learning and Science

  1. David Carmona – The AI Organization: Learn from Real Companies and Microsoft’s Journey How to Redefine Your Organization with AI (2019). Perhaps too many examples from Microsoft, but it is a really good book from A to Z on artificial intelligence.
  2. Cliff Kuang and Robert Fabricant – User Friendly: How the Hidden Rules of Design Are Changing the Way We Live, Work, and Play (2019). Very interesting review of the leading good (and sometimes bad) user interfaces.
  3. Matthew O. Jackson – The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors (2019). Good, understandable explanations of network measures and phenomena in various domains.
  4. Damon Centola – How Behavior Spreads: The Science of Complex Contagions (2018). Provides a nuanced view of the best time to use weak or strong ties, especially in leading changes in an organization or community.
  5. Eric Topol – Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again (2019). Although it is mostly about the ways that artificial intelligence can re-humanize the patient-doctor relationship, it even has a pretty good, understandable review of general artificial intelligence and machine learning concepts.
  6. Lisa Feldman Barrett – How Emotions Are Made: The Secret Life of the Brain (2017). The title highlights emotions, but this book is not just about emotions. It instead offers a paradigm shift about how the brain works.
  7. Jodie Archer and Matthew L. Jockers – The Bestseller Code: Anatomy of a Blockbuster Novel (2016). interesting book, better and more nuanced than the usual summaries about machine learning models to predict the success of books.
  8. Leonard Mlodinow – The Drunkard’s Walk: How Randomness Rules Our Lives (2009). Interesting explanations of the implications of probability theory and how most people get probability wrong.
  9. Scott Rigby and Richard M Ryan – Glued to games: how video games draw us in and hold us spellbound (2011). Good review of computer-based games, especially the psychological aspects.

Leadership And Business

  1. Jim McKelvey – The innovation stack: building an unbeatable business one crazy idea at a time (2020). Good, insightful and sometimes funny book by one of the co-founders of Square, with the proposition that success is the result of a chain (better word than stack) of innovations rather than just one big one.
  2. Scott Kupor – Secrets of Sand Hill Road: Venture Capital and How to Get It (2019). If you want to know how venture capitalists look at startups, this tells you how.
  3. Geoffrey G. Parker, Marshall W. Van Alstyne, Sangeet Paul Choudary – Platform Revolution: How Networked Markets Are Transforming the Economy – and How to Make Them Work for You (2017). While other books on the subject go more deeply into the broader policy implications of platforms, if you want to start a platform business, this is your best, almost required, user manual.
  4. Daniel Coyle – The Culture Code: The Secrets of Highly Successful Groups (2018). Culture is a frequently used word to explain the forces that drive behavior in organizations, but too often the concept is fuzzy. This book is one of the clearest and best on the subject.
  5. Dan Heath – Upstream: The Quest to Solve Problems Before They Happen (2020). Good, as usual for the Heath brothers, well written down to earth, but important concepts underneath and guidance at looking at the more fundamental part of problems that you are trying to solve.
  6. Matt Ridley – How Innovation Works: And Why It Flourishes in Freedom (2020). Includes many short histories of key innovations, not just invention, with an emphasis on the iterative and collaborative nature of the innovation process. Ridley advocates curtailing IP protections, thus providing more tolerance of risky experiments/innovations.
  7. Rita McGrath – Seeing Around Corners: How To Spot Inflection Points In Business Before They Happen (2019). Columbia Professor McGrath has made clear that no strategy is sustainable for a long time and in this book, she helps you figure out when you are at good or bad inflection points.

The Economy And Government

  1. Robert H. Frank – Under the Influence: Putting Peer Pressure to Work (2020). Frank is one of the most creative economists around and in this review of behavioral economics, he highlights how people pursue relative positions of wealth, rather than merely being rational maximizers of wealth.  He also offers a good discussion of public policies to pursue, that are based on this understanding of economic behavior.
  2. Stephanie Kelton – The Deficit Myth: Modern Monetary Theory and the Birth of the People’s Economy (2020). Well written, clear exposition of modern monetary theory and the positive and negative consequences of having completely fiat money (no gold standard or fixed currency exchanges). Professor Kelton is an increasingly influential economist and her ideas – whether or not she is given credit – have enabled the US Government to spend more with less angst than used to be the case.
  3. Abhijit V. Banerjee and Esther Duflo – Good Economics for Hard Times: Better Answers to Our Biggest Problems (2019). A review of economics research – and, more important, its limits – in addressing major socio-economic problems.
  4. Matthew Yglesias – One Billion Americans: The Case for Thinking Bigger (2020). Although no one (including me) will agree with everything he proposes, this is an interesting book with some original forward thinking – something we need more of as we face a very changed future.
  5. Michael Hallsworth and Elspeth Kirkman – Behavioral Insights (2020). This is a good overview of the application of behavior research to mostly public policy, especially about the UK.
  6. Paul Begala – You’re fired: the perfect guide to beating Donald Trump (2020). Smart and realistic proposals for the campaign to oppose Trump with many very funny lines.
  7. Jane Kleeb – Harvest the Vote: How Democrats Can Win Again in Rural America (2020). Along with Begala, explains her own success in rural America and more generally what needs to be done by Democrats to regain their old reputation as the party of the majority of people.
  8. Mark Lilla – The Once and Future Liberal: After Identity Politics (2017). Short review of how the Democratic party became dominated by identity politics and, for that reason, provides a bit of background for the previous too books.

Have a happy holiday season and a great, much better, year in 2021!

© 2020 Norman Jacknis, All Rights Reserved

Are Computers Learning On Their Own?

To many people, the current applications of artificial intelligence, like your car being able to detect where a lane ends, seem magical. Although most of the significant advances in AI have been in supervised learning, it is the idea that the computer is making sense of the world on its own — unsupervised — which intrigues people more.

If you’ve read some about artificial intelligence, you may often see a distinction between supervised and unsupervised learning by machine. (There are other categories too, but these are the big two.)

In supervised learning, the machine is taught by humans what is right or wrong — for example, who did or did not default on a loan — and it eventually figures out what characteristics would best predict a default.

Another example is asking the computer to identify whether a picture shows a dog or a cat. In supervised learning, a person identifies each picture and then the computer figures out the best way to distinguish between each — perhaps whether the animal has floppy ears 😉

Even though the machine gets quite good at correctly doing this, the underlying model of the things that predict these results is also often opaque. Indeed, one of the hot issues in analytics and machine learning these days is how humans can uncover and almost “reverse engineer” the model the machine is using.

In unsupervised learning, the computer has to figure out for itself how to divide a group of pictures or events or whatever into various categories. Then the next step is for the human to figure out what those categories mean. Since it is subject to interpretation, there is no truly accurate and useful way to describe the categories, although people try. That’s how we get psychographic categories in marketing or equivalent labels, like “soccer moms”.

Sometimes the results are easy for humans to figure out, but not exactly earth shattering, like in this cartoon.

https://twitter.com/athena_schools/status/1063013435779223553

In the case of the computer that is given a set of pictures of cats and dogs to determine what might be the distinguishing characteristics, we (people) would hope that computer would figure out that there are dogs and cats. But it might instead classify them based on size — small animals and big animals — or based on the colors of the animals.

This is all sounds like it is unsupervised. Anything useful that the computer determines is thus part of the magic.

How Unsupervised Is Unsupervised Machine Learning?

Except, in some of the techniques of unsupervised learning, especially in cluster analysis, a person is asked to determine how many clusters or groups there might be. This too limits and supervises the learning by the machine. (Think about how much easier it is to be right in Who Wants To Be A Millionaire if the contestant can narrow down the choices to two.)

Even more important, the computer can only learn from the data that it is given. It would have problems if pictures of a bunch of elephants or firetrucks were later thrown into the mix. Thus, the human being is at least partially supervising the learning and certainly limiting it.  The machine’s model is subject to the limitations and biases of the data that it learned on.

Truly unsupervised learning would occur the way that it does for children. They are let out to observe the world and learn patterns, often without any direct assistance from anyone else. Even with over-scheduling by helicopter parents, children can often freely roam the earth and discover new data and experiences.

Similarly, to have true unsupervised learning of machines, they would have to be able to travel and process the data they see.

At the beginning of his book Life 3.0, Max Tegmark weaves a sci fi tale about a team that built an AI called Prometheus. While it wasn’t directly focused on unsupervised classification, Prometheus was unsupervised and learned on its own. It eventually learned enough to dominate all mankind. But even in this fantasy world, its unsupervised escape only enabled the AI machine to roam the internet, which is not quite the same thing as real life after all.

It is likely, for a while longer, that a significant portion of human behavior will occur outside of the internet 🙂

(And, as we saw with Microsoft’s chatbot Tay, an AI can also learn some unfortunate and incorrect things on the open internet.)

While not quite letting robots roam free in the real world, researchers at Stanford University’s Vision and Learning Lab “have developed iGibson, a realistic, large-scale, and interactive virtual environment within which a robot model can explore, navigate, and perform tasks.” (More about this at A Simulated Playground for Robots)

https://time.com/3983475/hitchbot-assault-video-footage/

There was HitchBOT a few years ago which traveled around the US, although I don’t think that it added to its knowledge along the way, and it eventually met up with some nasty humans. (For more see here and here.)

 

Perhaps self-driving cars or walking robots will eventually be able to see the world freely as we do. Ford Motor Company’s proposed delivery robot roams around, but it is not really equipped for learning. The traveling, learning machine will likely require a lot more computing power and time than we currently use in machine learning.

Of course, there is also work on the computing part of problem, as this July 21st headline shows, “Machines Can Learn Unsupervised ‘At Speed Of Light’ After AI Breakthrough, Scientists Say.” But that’s only the computing part of the problem and not the roaming around the world part.

These more recent projects are evidence that the AI researchers realize their models are not being built in a truly unsupervised way. Despite the hoped-for progress of these projects, for now, that is why data scientists need to be careful how they train and supervise a machine even in unsupervised learning mode.

© 2020 Norman Jacknis, All Rights Reserved