Good Ol' Fashion Book Reports

Sept 21st, 2019 2:42 PM

Good Ol' Fashion Book Reports
Share this

This post is to collect summaries and key takeaways from the books I read. All summaries range from mostly to totally positive. This is because I only summarized books I’ve read in their entirety. If I didn’t like a book I would have stopped reading it. I’ve kept track of every book since 2019, and often read a couple books at a time.

The Culture Code - Daniel Coyle

Why do some groups come together and add up to be greater than the sum of its parts?

If I were to ask you who could do linear algebra better: a group of kinder-gardeners or a group of MBA students, you would likely pick the latter. How about something simpler: build a tower with nothing but spaghetti, tape, string, and a marshmallow. In this regard, the kinder-gardeners triumph. Why? This is the question that “The Culture Code” exists to answer. Culture, no matter the type or scale, is essential to every choice, action, and thought one has. This is because none is made without culture in mind, yet all of them ultimately influence culture itself.

Key Takeaways

  • The psychological effects of just a handfull of words and the way those words are delivered is overwhelmingly powerful. To ask a stranger at the bus station on a rainy day “Can I borrow your phone?” versus “I’m so sorry about the rain, can I borrow your phone” is the difference between a 422% increase in response rate. 6 words = 4x as likely to get what you want. Unreal.
  • No one knows his name, but Jeff Dean made Google the giant that it is today. He is the reason why when you search Kawasaki H1B motorcycle on Google AdWords, you get the motorcycle and not a H-1B foreign visa application. Jeff did this, not because it was his job to, but because he was so in tune with what Google stood for, he took it upon himself to work on a problem that wasn’t even his department. When confronted about his solution, he said “It didn’t feel special or different. It was normal. That kind of thing happened all the time.”
  • In the event of a catastrophic failure (the official term the National Transporation Safety Board used for United Airlines Flight 232) the words you use are a matter of life or death. Pilots use what is known as notifications; maximize information, vulnerability, and openness with as little words as possible.
  • Tony Hsieh, the eccentric founder of LinkExchange (sold to Microsoft) and Zappos (sold to Amazon), has a unique take of building culture and making new friends; he is notorious for mediating warm introductions between new people he meets and people he already knows. Basically leveraging arguably the most valuable resource; human resources.
  • Pixar saved Disney from making any more animated movie atrocities. They did this with more than just creativity. Pixar’s “Inside Out”, “Nemo”, and “Monster’s Inc,”, literally taught Disney how to make “Frozen”, “Big Hero 6”, and “Zootopia”. When Disney acquired Pixar, they acquired more than just creativity; they acquired Pixar’s community and culture of communication, bar-setting, and vulnerability.

  • The coaches of top basketball teams, high ranking officers of the Navy SEALs, and owners of some of the best restaurants in the world (11 Madison Park and Shake Shack) all have something in common. The pre and post. Each highly successful group has a different name for them, but simply put, what happens before and after a big game, a dangerous operation, or a busy Friday dinner service, is key to community growth.

The Talent Code - Daniel Coyle

Key Takeaways

Zero to One - Peter Thiel

How do you build an impactful, sustainable, profitable, and successful startup; how do you go from 0 to 1?

Peter Thiel is the “veteran of a hundred battles” of the startup world. Best known for PayPall, Thiel started more than just a company; his story influenced the very culture, methods, and ideas that are central to startups. Just look at the “PayPall Mafia”, a group of individuals who, after leaving PayPall, went on to start more highly impactful companies, analogous to the way the splitting of an atom causes a domino effect.

Peter Thiel takes common misconceptions about startups and shreds them apart, whilst revealing the truth behind the secrets of any successful startup. Drawing from both his own experiences and observations, he details everything from the economics to the operations, from the market to the team, and covers all corners. It’s like a cheatsheet for founders, entrepreneurs, and techies.

Key Takeaways

  • For good companies are great - when done correctly. In the first decade of the 2000s, there was a boom in green-tech/ clean-tech startups. Almost all of them, save for Tesla and a handful of others, survived.
  • PayPal and (an online banking company built by Elon Musk) put aside their competition to survive the dot-com crash; a move that saved them both in a crash that took down hundreds of companies.
  • There is a reason why most successful co-founders are shut-ins. The population can be represented by a normal distribution (black line) Founders Curve

this normal distribution is universal. What makes founders, entrepreneurs, and tech nerds so different? It turns out, they are exactly the opposite (orange line). They are polarizing to the point of irony. Name any notable founder and they belong in one of the extremes.

Brief Andwers to the Big Questions - Stephen Hawking

Is there a God? How did it all begin? Is there extraterrestrial intelligent life? Can we predict the future? What is inside a black hole? Is time travel possible? Will we survive on Earth? Should we colonize space? Will A.I outsmart us? How do we shape the future?

In honor of the late and forever great Stephen Hawking, BABQ was a book I had to read. Hawking’s final work focuses on the final 10 questions he was striving to answer. I found myself gazing up at the sky more often this month than even before in my life, even as a child. It fascinating to read about the history of the universe in a style reminiscent of “Sapiens”, only this time, from the perspective of a world renowned physicist. This book made me realize how unfortunate it is that most people hear the voices of less intelligent people more often than the smartest people. It made me think and realize that the smart people tend to be careful when making statements; they are experts at discerning fact from fiction or hypothesis. One can then infer that we should be paying attention to any statements smart people say. Today, this fact cannot be understated.

This book, regardless of your background, beliefs, or knowledge, is a must read. You will finish it feeling enlightened, inspired, and best of all, optimistic. May he rest in piece, next to the ashes of Sir Isaac Newton and Charles Darwin. He will be immortalized by his contributions and remembered as one among the pantheon of the smartest people in history, and, to match his scale of vision, the smartest creatures in the universe.

Key Takeaways

  • Hawking subscribes to the Weak Anthropic Principle, that is, he takes the values of the physical constants as given. A principle that states that only in a universe capable of eventually supporting life will there be living beings capable of observing and reflecting on the matter.
  • The universe has 3 ingredients: matter, energy, and space. Einstein discovered (with E = mc²) that mass and energy are basically the same thing, hence there are only two ingredients: energy and space.
  • “Do we need a God to set it up so that the Big Band could bang?… It is possible that nothing caused the Big Bang… Time didn’t exist before the Big Bang so there is no time for God to make the universe in”
  • Black holes exist because of a concept best described as analogous to escape velocity. That is to say that, like how a rocket must reach above 11 km/s to escape an object with earth-like mass, an object must reach more than 300k km/s (light speed) to escape a black hole.
  • “If you know how something works, you can control it” - Hawking, echoes the now famous “What I cannot create, I do not understand” - Fyenman. It seems as is if all the great scientists followed the same train of thought.
  • Building upon the takeaways of Yuval Noah Harari’s “Sapiens” trilogy, the future of humanity relies on a handful of technologies and industries: Artificial intelligence, Biotechnology, Brain computer interfaces, Fusion energy (Hawking’s response to “What would you like to see implemented), and Space exploration.
  • To end, Hawking leaves us with this: “We stand at a threshold of important discoveries in all areas of science… We will find out what happened at the Big Band. We will come to understand how life began on Earth… We will continue to explore our cosmic habitat… We must look outwards to the wider universe, while also striving to fix the problems on Earth… And one final point - we never really know where the next great scientific discovery will come from, nor who will make it.

Architects of Intelligence - Martin Ford

I was window shopping in a book store one day when the title caught my eye. After only having read the premise of the book, I followed my instinct and bought it right then and there. “Architects of Intelligence” is the aggregation of various interviews conducted with the greatest minds in the field of Artificial Intelligence. Martin Ford previously published “Rise of the Robots”, an in depth look at the direction of the industry and how it will impact society business, and government. The key takeaways here outnumber that of any other book I read this year. I have no doubt this is partly due to my extensive research in the field, but also because Martin Ford does a tremendous job aggregating, filtering, and condensing all the juiciest tidbits of knowledge from some of the smartest people in the world. The book almost radiates with value.

Key Takeaways

  • Not any of the big names in A.I are vehemently against “A.I taking over our jobs”. More specifically, the common agreement seems to be that technology as a whole will always continue to make life easier for humankind (when used correctly), and that the effect technology will inevitably have over jobs is a government and regulation issue, and the technology should not be “blamed”
  • China is, according to mainstream media and a significant portion of Americans, the monster under the bed come to life. When asked if China is a threat, especially with their breakthroughs and contributions in A.I, the interviewees all seemed to have a perspective from a technological and progressional point of view; progress in A.I is always good, what is bad is when A.I is used for things like automated weaponry or surveillance.
  • This one got me particularly hyped: we should be very suspicious of Back-propagation. According to Hinton himself, BP is considerably more efficient and effective than any preceding method, BUT research has shown that BP is not how humans “learn”. Neural Networks, to be sure, are indeed the fundamental bricks of our brains, but Hinton and many others think that we should be on the look out for the next big step after BP. Here’s Hinton himself doing a light criticism on the very algorithm he popularized:
  • This one will be obvious to the learned data scientist and machine learner: supervised learning is being used to overhype A.I as a field, and unfortunately it’s not sustainable. Yann LeCun:

    “If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning (RL).” - Yann LeCun

  • Interesting point about the history of Deep Learning; there was a time when the majority of researchers in A.I were bashing Deep Learning, thinking that the so called “symbolic A.I” methods of the time would prevail. LeCun, Hinton, and Bengio, and a handful of other leaders and followers persevered through the shit-storm and survived the A.I winter with groundbreaking results/victories/discoveries at around the 2010s
  • A common trend in the discussions about society’s fascination with A.I points out that we really shouldn’t be listening to people like Elon Musk and Stephen Hawking (both of whom share many opinions about the risks of A.I). While they are both well meaning and undoubtedly true masters in their craft, neither of their opinions should be valued as high as they are, with respect to the opinions of people who actually do this research. As quoted by Jeffery Dean, head of A.I and director of the Google Brain team:

    “I want regulation to be informed by people with expertise in the field” - Jeffery Dean

  • Another common trend in the discussions about A.I x Society is the opinions of what needs to be done in terms of policy, regulation, government, etc. The unanimous response is some variation of “this is beyond my expertise”, but many were receptive, borderline supporting of UBI. Andrew Ng is actually an advocate of a variation of the original idea, dubbed “conditional basic income”.
  • Common trend #3 about A.I and society is centered around education and further research. Educating people of all backgrounds is why Fei Fei Li started AI4ALL, and why Andrew Ng and Daphne Koller built Coursera. Further research into different architectures that can be made with neural networks is a highlight for Bengio. Further research into policy, with respect to employment, economics, and education is emphasized by LeCun. An algorithm that trumps backpropagation is the interest of particular interest to Hinton.
  • I want to end this book’s highlights with what might be my favorite quote of the year, one that might be more relevant today then ever before. It was quoted with regards to the concern of the Terminator A.I scenario, but I have no doubt it was also to throw shade at certain political persons:

    “The desire to take of the world is not correlated wth intelligence it’s correlated with testosterone” - Yann LeCun

How to Create a Mind - Ray Kurtzweil

I serendipitously came across this book while reading “Architects of Intelligence”. Among Martin Ford’s list of all-star A.I researchers, was Ray Kurtzweil. I could spend the entirety of a post talking about him alone, but it’s best if you give his name a Google (where he has spend much of his time working at). His scale and magnitude of contributions are on par with that of Thomas Edison. “How to Create a Mind” is a look at one of the greatest mysteries known to mankind; the brain.

A little about Ray Kurzweil; this is the man who brought back connectionism A.I from the dead (the precursor to Deep Learning), has received honors from academia, government, and industry, and is what many people think of when they think of the smartest person in the world, whether they know it or not. The chapter of AoI on the interview with Ray was what got me very interested in him and his work, and that’s how I found “How to Create a Mind”.

Warning: This book was easily the most challenging read this year. It’s not something you could read on a subway home with music playing. It’s best appreciated when given full attention (which is something that is discussed thoroughly in the book). Kurzweil manages to break down some super complex topics into simple concepts that left me feeling like this:

Self Chess

Key Takeaways

  • When I was a kid, remember realizing that the world is made out of patterns. Technically, nothing is random. If you “zoom out” and increase your scope enough - to infinity - a pattern must emerge. It turns out, our brains take advantage of this fundamental fact. That is to say that the brain is just a biological apparatus (that evolved, grew, and changed over time) to be super good at recognizing patterns.
  • The brain is made of neurons yes, but not just randomly. These neurons are organized into “modules” each with a subset of neurons. These modules form a hierarchy, with low level modules responsible for prediction the activations of low level abstractions like shade, color, shape, etc. (in the case of visual perception), and high level modules accepting concepts like who’s face, what object, or what structure something is.
  • This one really had me baffled. Language is largely debated in the neuroscience, cognitive science, and machine learning communities as either being very correlated/inclined with intelligence, or as a simple and naturally occurring by-product of intelligence.
  • In any case, it turns out that there are some truths to the notion that language concepts like sarcasm and metaphor is a strong sign of intelligence.
  • Geniuses like Einstein, Darwin, and others show strong evidence that a major contribution to their ideas have been their ability to use metaphors to theorize and eventually explain their discoveries.
  • Metaphor is a language concept that most commonly occurs when the subject has a strong knowledge base of multiple areas (so that we can compare and find similarities to make metaphors), and when the subject has a high degree of freedom-of-thought (so that they have the desire and audacity to make connections between areas)

The Craft of Research - Wayne C. Booth and Co.

Side note; this was the first time I ever directly highlighted in the physical copy of a book. I found it very useful for future reference. There were so many good one-liners in the book, and it would often make self-referential statements (I suppose they had to do a lot of research about the very act of doing research, so it makes sense). I found that many of the key takeaways are concepts you could acertain by simply reading a lot of papers, but much of their advice provides a methodological approach. Finally, I think this book helped me read as well as write better academic content.

Key Takeaways

  • “When you write for others you demand more of yourself than when you write for yourself alone” summarizes why I blog.
  • Applied Research : Pure Research :: Industry Research : Academic Research
  • “A question raises a problem when if not answering it keeps us from knowing something greater than its answer” is the key to asking good questions.
  • I notice the emphasis of focusing on the PROBLEM first and not the solution is a common point from the startup world (echoes Peter Thiel’s words in 0-1) Research Cycle
  • Take advantage of both forwards and backwards citation. When optimizing for recency, the strategy is especially useful.
  • “When you acknowledge the views of others, you show that you not only know those views, but you have carefully considered and can now confidently respond to them” is an excellent argument strategy
    • Reminds me of advice from Shaan; To acknowledge your own shortcomings and insecurities and use them as a tool or a weapon
  • When to quote, paraphrase, or summarize:
    • If you want/need to fairly challenge a view, respect the authority of the quoter, or frame an argument with a compelling statement
    • Paraphrase when specific words are less important than it’s meaning
    • Summarize when useful for context but not directly relevant
  • “In a research argument you make a claim, back it with reasons supported bu evidence, acknowledge and respond to other views, and sometimes explain your principles of reasoning” is actually how we commonly communicate. Research Warrants
  • A warrant is a principle that connects a reason to a claim (instead of validity, the relevance might be challenged)
  • Your ethos is the character you project in your arguments; I supposed even this blog has an ethos of sorts.
  • Assume the opposite; a strategy that is useful for evaluation as it is for exploration. To test the fallibility of your claim, consider the opposite. If the opposite is obvious or trivial, the claim is not worth an argument.
  • To “hedge one’s bets” applies to the realm of research as well. Writing an assertion like “we state the” vs a hedged request “we wish to propose” makes all the difference. (But don’t sound like a wuss)
  • Remember the predictable disagreements:
    • There are causes in addition to the one you claim (No cause has a single effect and no effect has a single cause)
    • Qeueu the counterexamples (Be wary when you make claims that have a high degree of variation or opinion)
    • I don’t define x the same way you do (If you argument relies on the definition of a term or concept, define it, perhaps with a subordinate argument as support)
  • It’s better for the reading to say “I don’t agree” than for them to say “I don’t care”
  • Remember active vs passive voice, simple subject, whole subject, verb, noun, clause

The Richest Man in Babylon - George S. Clason

As a gift for my birthday, my good friend Agosh Saini bought me a book I would have quickly judged by its cover had I seen it on a shelf in some bookstore. Written decades ago by George S. Clason, this book reiterates the lessons that resonated through time since Babylon was the greatest civilization in history, or since. Lessons that would have otherwise remained buried in the desserts of the middle east are now excavated and translated for modern eyes. These lessons are still relevant to this day. This books is easily the most quotable thing I’ve read all year.

Key Takeaways

  • Good luck waits to come to the man who accepts opportunity - to attract good luck, one must take advantage of opportunities
  • Procrastination is the enemy of opportunity
  • Our acts can be no wiser than our thoughts
  • Better a little caution than a great regret
  • The hungrier one becomes, the clearer one’s mind works. Also the more sensitive one becomes to the odour of food
    • The “odour” is therefore analogous to opportunity, and “food” is analogous to money, at least in the context of this book.
    • The point is, that your ability to both survive and thrive depend on your desire to do both, which leads us to the next lesson:

Angry Lion

  • Where the determination is, the way can be found - Where there is a will, there is a way

The Book of Why - Judea Pearl (IN PROGRESS)

A really dense book. I’m struggling to follow along at times and find myself rereading very often. This being my first introduction to A.I and congnitive sciences beyond Deep Learning, I find it’s ideas very compelling.

Key Takeaways

A Programmer’s Introduction to Mathematics - Jeremy Kun (IN PROGRESS)

Loving this book, it’s perfect for people who’s programming is stronger than their math, allowing you to maximize on those transferable skills. I read the corresponding chapters interleaved between episodes of 3Blue1Brown videos for that extra visual reinforcement. Notes and practice questions provided by UofT and Waterloo were also very helpful.

Key Takeaways

  • There are many direct analogs between programming and mathematics
    • Set builder notation is literally just a list comprehension
    • Proof by induction is just a recursive algorithm
    • \(\mapsto\) is the mathematical analog of anonymous functions

Gödel, Escher, Bach - Douglas R. Hofstadter (in progress)

the preface was long and a bit intimidating, but it aptly set up the rest of the book.

Key takeaways

Cracking the Coding Interview (6th Edition) - Gayle L. McDowell (in progress)

So far I’d describe the book as curt and to the point. Some ideas are obvious, but they’re a good reminder. There are some nuggets of interesting and very compelling ideas scattered in each section.

Key takeaways

  • When selecting candidates, false negatives are acceptable (rejecting people who are actually very good), but false positives (accepting people that are actually very bad) are not.
  • Whiteboards let you focus on what matters. Like isolating the specific part they want to assess (your ability to think, analyze, and communicate)
  • Nugget first; give a one-line summary of your story before speaking about the story
  • Arrogance is mitigated with specificity
  • Situation. Action. Result.
  • Mind blowing: sometimes internet speeds are so slow, it might actually be faster to drive/fly across the country or world to deliver your data (linear time)
  • When you see a problem where the number of elements in the problem space gets halved each time, that will likely be a 0( log N) runtime.

Platform Revolution - Geoffrey G Parker, Marshall Van Alstyne, and Sangeet Paul Choudary (Audiobook in progress)

Recommended by Ben Blaizik. I’m loving the ample example-set provided with every argument presented. The authors don’t rely so much on large tech giants and often use niche but relevant real world examples to prove their points.

Key takeaways