Category Archives: Learning

Theory

I received some advice from a friend who worked as a software engineer and consultant. He recommended focusing on theory since technologies are constantly changing. Try to get a general understanding of why things are working.

Python Programming

I finished the front end section of the bento.io curriculum. It covered topics on HTML, CSS, and Javascript. I worked on a few projects in order to apply some of the knowledge (tictactoe and timer).

I started on the back end section with Python. I decided to take a break and read Python Programming. I’d learned some Python with the automatetheboringstuff class, but I wanted to get a better grasp of the fundamentals. I heard that this book also does a good job of covering basic computer science topics and software design principles.

 

 

 

Embracing the Struggle

I’m currently working through the second front end section of the Bento.io curriculum. I worked through the Codeacademy portion and created a project for myself so I could apply what I’ve learned. I do want to eventually try to redo the project, but I decided to read through JavaScript: The Good Parts first.

My first attempt at reading it was really frustrating. About 30 pages in, I realized, I wasn’t absorbing the information. I took a break and decided that I was going to force myself to understand this. I slowed down and looked up all the concepts that I didn’t understand.

Some people might consider this too expensive in terms of time, but I think it’s more wasteful to read a book and not understand it or retain the information.

 

 

 

Factory X – Future of Work

I attended an event yesterday in San Francisco. Factory X is an organization that studies the nature of companies. In the status quo, companies solely focus on amassing resources often resulting in negative externalities for individuals and society. Factory X wants to rethink the concept of companies as wealth amassing institutions to simply a way of organizing work. Factory X did this by launching a company every 10 weeks. In the process, they experimented with various structures and ideas.

Rapid prototyping was the focus of the hand-ons section of the event. We were to build, test, and iterate an idea all in 30 minutes. I’ll try to roughly describe the process.

  • 2 min – We were placed into random groups of three. We were to discuss a problem that we wanted to tackle.
  • 1 min – We decided which problem we wanted to tackle.
  • 2 min – We talked about the problem in detail, but were not allowed to discuss solutions.
  • 2 min – This was a quiet brainstorming session. Everyone writes down their proposed solutions. When brainstorming is a discussion, only one person is coming up with ideas and the others are listening. This limits idea generation and biases other people’s solutions and ideas.
  • 2 min – Everyone goes over their most practical solution and impractical solution.
  • 1 min – Team decides which solution to use
  • 11 min – Create a very rudimentary prototype. The focus was on how the product would work and what it would roughly look like. We made mockups with sticky notes.
  • 10 min – Get a new member from another team who is role playing your customer. You can only tell them 1) who they are, 2) where they are, and 3) what they want to do. There were a few rules for the role playing customer: 1) you are reasonable 2) you are not unreasonably interested in tinkering with everything, and 3) think aloud. We quickly  found that our customer was confused or didn’t understand the product. We would then retinker with it and try again.

I found the workshop and the discussion afterwards incredibly insightful. Many times people are focused on the product, but lose sight of what the market or user wants. People become invested in what they are building and get defensive when customers don’t understand their product. The ultimate deciding factor though is how the customer interacts with the product. If the customer doesn’t know how or why to use the product then the failure is on the creator.

Another important point was learning to avoid generalities. When people were going over what they learned, Tom challenged some of the attendees to go deeper in their conclusions. Generalities can be meaningless, abstract, and hard to prove. You can only know what you observed so be specific about exactly what you observed.

  • We learned we could solve this by gamification BECAME
  • We could get users to act if we included a leader board BECAME
  • We could get users to want to participate if we included a leader board that had their friends

When I first arrived at the event, I thought I might have made a mistake. I generally don’t like events where I’m walking around talking to random people, but I decided to make the best of it. Learning to connect with people is something I’m working on improving. I kept a couple rules in my head: 1) Learn about the persons motivations and background, 2) stay away from boring topics, 3) Shift to another topic if the person doesn’t seem animated, 4) if the person becomes animated about something expand and explore this topic. I had some conversations that I really enjoyed and the networking session ended before I knew it.

I really enjoyed the talk. I often think about how I can pursue meaning in conjunction with my work. I don’t have an answer right now. My main focus right now has been on acquiring skills that I can eventually use to effect change.

The hands on session was my favorite. I’ve been a hermit the past few months and I forgot how fun it can be trying to tackle a problem with people.

My biggest takeaway from the event was to not forget about the end user. I’ve been becoming more interested in technology and computer programming recently. I want to bring some of my ideas to life, but if I ever want this to be more than a hobby, I can’t forget that the ultimate deciding factor will be at the point of interaction between the product and the end user.

 

 

Action and Knowledge

Action + Knowledge > Action > Knowledge

Learning without execution is useless. That being said, I think it’s important to learn enough to know how to act. After I quit my job, I read for about two and a half months. I absolutely loved it. I didn’t finish my book list and likely never will because it keeps growing, but I reached a critical level where I felt compelled to act on my knowledge.

I started applying systems thinking to my everyday life. I focus on base subset of skills and deliberate practice in learning. I try to connect with people by attempting to develop genuine interest and goodwill.

I believe meaningfulness is about having a unique impact that benefits other people. This is a very fuzzy long-term goal so my main priority currently is developing my skills and abilities. As I do this, I hope to get more clarity on what kind of impact I can and want to have.

Write Everyday

Scott Adams says people are moist robots. We are random inputs of the environment. It’s a fun metaphor, but maybe he’s not that far off.

In Thinking, Fast, and Slow, Daniel Kahneman showed that people are susceptible to priming and anchors. They also tend to greatly underestimate the impact of such influence.

For some reason, I’ve been compelled to write something everyday. The suggestion was from James Altucher’s podcast with Seth Godin. I was listening to it in the background while I was driving somewhere. I don’t even remember the details of the conversation.

Now here I am trying to write everyday, even if it’s something small. I don’t mind though because I think it’s a good thing to take time to really think about things.

Anyway, I think it just reinforces how important it is to control your inputs and make sure you are programming your mind the way you want it to be programmed.

How I Got Started In Coding

I was interested in learning to code, but I didn’t get started on it earlier, because I didn’t know what I wanted to do with it. I hear the best way to learn is by having some project to work on. I tried learning for the sake of learning a few years ago and that didn’t work out too well.

I was browsing through free courses one day and I came across Automatetheboringstuff. It teaches Python for beginners and non-engineers.  Learning to automate certain things seemed like it would be valuable regardless of what I decided to get into.

Also, one of the things I liked about my previous job was working with data (Visualization, analysis). It seemed like a lot of people in that field used Python so I thought this would be a good way to get some exposure to it.

I’m finishing up Chapter 11, I’m learning a lot and having a lot of fun.

 

 

 

Experts

In Scott Adam’s book, How to Fail at Almost Everything and Still Win Big, he gives the below observation about Experts. Just to be clear, Scott Adam says you shouldn’t mindlessly take advice from a cartoonist. Vet everything yourself.

My observation and  best guess is that experts are right about 98 percent of the time on the easy stuff but only right 50 percent of the time on anything that is unusually complicated, mysterious, or even new.

I was reminded of the above point as I was reading through my next book, Think Like a Freak. These are some excerpts on studies of experts. The short summary is that experts are not good at making predictions, but tend to be massively overconfident.

One of the most impressive studies was conducted by Philip Tetlock, a psychology professor at the University of Pennsylvania. His focus is politics. Tetlock enlisted nearly 300 experts– government officials, political-science scholars, national security experts, and economists– to make thousands of predictions that he charted over the course of twenty years

The results of Tetlock’s study were sobering. These most expert of experts–96% of them had postgraduate training– “thought they knew more than they knew,” he says. How accurate were their predictions? They weren’t much better than “dart-throwing chimps,” as Tetlock often joked.

Tetlock and other scholars who have tracked prominent pundits find that they tend to be “massively overconfident,” in Tetlock’s words, even when their predictions prove stone-cold wrong.

A similar study by CXO Advisory Group covered more than 6,000 predictions by stock-market experts over several years. It found an overall accuracy rate of 47.4%. Again, the dart-throwing chimp likely would have done just as well–and, when you consider investment fees, at a fraction of the cost.

Thinking, Fast, and Slow attributes this overconfidence to the illusion of understanding and the illusion of validity. The illusion of understanding occurs because people assign a larger role to talent and intentions than to luck. The illusion of validity occurs because confidence arises from explanations that are coherent and follow a clear narrative, even if they may not be true. So people find a coherent story and assume they understand why events occurred and discount the impact of luck. The ultimate test however is whether you can make accurate predictions, but clearly this does not occur because there is too much randomness.

I recently heard a statement about how research is often wrong so took a look and here are some of the articles that came up.

How science goes wrong- The Economist

A rule of thumb among biotechnology venture-capitalists is that half of published research cannot be replicated. Even that may be optimistic. Last year researchers at one biotech firm, Amgen, found they could reproduce just six of 53 “landmark” studies in cancer research. Earlier, a group at Bayer, a drug company, managed to repeat just a quarter of 67 similarly important papers. A leading computer scientist frets that three-quarters of papers in his subfield are bunk. In 2000-10 roughly 80,000 patients took part in clinical trials based on research that was later retracted because of mistakes or improprieties

Why Most Published Research Findings Are False – NCBI

Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias

Why Scientific Studies Are So Often Wrong: The Streetlight Effect

In 1992 a now-classic study by researchers at Harvard and the National Bureau of Economic Research examined papers from a range of economics journals and determined that approximately none of them had conclusively proved anything one way or the other

In several confidential surveys spanning different fields, anywhere from 10 to 50 percent of scientists have confessed to perpetrating or being aware of some sort of research misbehavior. And numerous studies have highlighted remarkably lax supervision of research assistants and technicians. A bigger obstacle to reliable research, though, is that scientists often simply cannot get at the things they need to measure.

In many cases it is painfully obvious that scientists are stuck with surrogate measures in place of what they really want to quantify.

Maybe Scott Adams wasn’t so far off.

So where does that leave us? I don’t think it means to ignore “science”, but I think it means we have to learn to be even more vigilant and exercise critical thinking. I think a lot of business books cite scientific studies, but it’s up to you to actually take a look at those studies and see if they would support the claims that the authors are making while keeping in mind the studies may be wrong.

 

Learning to Draw in 20 Hours

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I was inspired by Josh Kaufman’s book, The First 20 Hours, to learn a new skill. According to the book, if you follow the right approach, you can become decently good at any skill with 20 hours of deliberate practice.

Steps for Rapid Skill Acquisition
  1. Deconstructing – the smallest possible sub-skill
  2. Learning – Practice intelligently and able to self-correct
  3. Removing – Any physical, mental, emotional barriers
  4. Practicing – At least 20 hours of dedicated practice

I picked drawing because I wanted to do something creative and outside of my normal range of activities.

I searched around and decided to follow Monika Zagrobelna’s free online guide. This tutorial really stood out because it deconstructed the skills to a super basic level. The exercises were easy to follow, though hard to do really well.

I was also influenced by Josh Waitzkin’s book, The Art of Learning. He recommends focusing on depth over breadth and internalizing the most fundamental units of skill. The tutorial is broken into three sections: manual skills, precision, and visual database. I spend about 10 hours on part one, and five hours on each of the other sections. I didn’t make it very far on the third section though.

I felt pretty good about the first section, but really inadequate on the other two. If I wanted to really get serious, I could easily see myself spending much more time on the last two sections. For this particular project, I just wanted to see how far I could get with 20 hours.

I wanted to see my progress so on the first day, I drew a mini football helmet and redrew it as I hit 19 hours and 30 minutes. In the beginning, I felt really depressed. It seemed like it was shaping out to really suck. It was discouraging, but I decided I came this far so I might as well finish this last part.

As I started working, I started noticing little things. I could tell when a line didn’t quite match with what I was looking at. As I drew, I knew how to draw lightly before committing with a darker line. I started looking at everything as small shapes and focusing on where things were in relation to other things.

The end result was very emotionally rewarding. I’m not an artistic person and seeing the improvement was very gratifying. If I had to do it again, I’d allocate some time every 7-10 days to just drawing so I can see incremental improvements. It’s not particularly rewarding only seeing your lines and shapes get better.

I’ve uploaded all my practice in the album above. 

Link to Book Notes:

The First 20 Hours

The Art of Learning

Finding Purpose and Getting Good

In Man’s Search for Meaning, Viktor Frankl says everyone has purpose in life. It tends to be specific and changes from day to day. A supermeaning also exists, but it’s possible you may not understand it until the very end. Viktor Frankl’s idea is uplifting, but a little lacking in terms of direction.

I finished rereading Cal Newport’s book, So Good They Can’t Ignore You, and his advice on finding purpose provides some clarity, at least for my circumstances. Good missions are unique and tend to happen at the cutting edge of a field. He talks about how major scientific breakthroughs occur in tandem because at the time only a certain amount of knowledge is understood. Discoveries are more likely to happen in the adjacent possible.

He brings up examples of people who quit their jobs to pursue a lifestyle company about “a life well lived.” They ultimately end up failing, because they don’t have the requisite career capital to succeed. They don’t have any interesting insights or skills. The only thing they have is enthusiasm which is a commodity.

Looking back, one of the reasons I left my jobs was because I felt that I wasn’t learning and growing as fast anymore. It wasn’t the only reason. I also wanted to take some time to get my head straight. Think about what I want in life, focus on my systems, habits, psychology. Also, I wanted to learn some new skills and do some traveling.

If someone else can do what you’re doing, it seems less worthwhile. We have a desire to be unique. Maybe it is biological. Perhaps groups of people who were more likely to do unique things were more likely to make discoveries or have ideas that increased their survival rate. There likely wouldn’t have been much progress back then if everyone did the same thing.

I buy what Cal says. If you want to have some unique impact in this world, you will need some unique skills to make it happen.

Links to Book Notes:

So Good They Can’t Ignore You

Man’s Search for Meaning