- http://www.jinni.com/ (went defunct, relaunched) [S]
- https://movielens.org/ (U of MN created as recsys experiment) [S]
- http://foundd.com/ [S]
- http://www.metacritic.com/ – haven’t tried
[S] denotes storage of seen
2 really has the most information and coverage (amazing database) but I don’t find the recsys very valuable. It also allows you to download your data.
I’ve rated ~1000 on FOUNDD (sadly I’ve watched 1,000) and it does recommend some good stuff (which I have yet to see). Unfortunately, the coverage isn’t great, it’s hard to see what you’ve rated, etc.
Jinni is the hipster, mainstream solution. It feels like shopping netflix. This happens to mean it offers the most useful way to discover and browse. Recommendations are so, so. Hard to discover NEW media.
Be Humble, Honest, Scholastic
Or documenting and rectifying my ignorance, as best I can:
(introduced by Knuth, Art of Computer Programming v.2, p.4)
What is the electric field?
Because it was mentioned by Richard Feynman (https://www.youtube.com/watch?v=FjHJ7FmV0M4) as he described how light exists and propogates (asserting that they are not flowing vectors
Fool me once
Hopefully I save a fellow pythonista a few minutes of debugging –
Problem: SQLAlchemy is responding with UnicodeDecodeError, “UnicodeEncodeError: ‘ascii’ codec can’t encode character u’\u????’ in position ?: ordinal not in range(128)” for Postgres
- SQLAlchemy: Are you using importing sqlalchemy.String or Unicode?
- SQLAlchemy: Is you client / connect string set to expect utf-8?
- Postgres: What type of encoding are your tables using?
Solution(s): a) from sqlachemy import Unicode (instead of String) b) update your sqlalchemy.create_engine initializer to include a client_encoding=’utf8’ kwarg (or add ?client_encoding=utf-8 directly to your connection string) c) make sure your table has the correct encoding d) if it doesn’t, avoid future conflict: Make sure your postgresql template0/1 are using UTF-8 and not SQL_ASCII. To fix this, see: https://gist.github.com/ffmike/877447
linux kvm windows insufficient space
search: kvm windows out of hard drive space
- How to resize your kvm virtual disk | KVM - The Linux Kernel-Based …
Be Intospective, Objective, Quantified, Data Driven
I need to justify my decisions to myself: https://docs.google.com/spreadsheets/d/1k-zl-Ya4OAU7Lp8DsPGy7mSJVCeXSPqK673Wiq8ndt8/edit#gid=0
5 Year Goals
The optimal representations of paths through knowledge
(knowledge base) how do you represent all your existing knowledge / starting state, keep track of what you know
- expose your knowledge base / graph + state of knowledge via API
- obtain universal graph of knowledge gaps, inconsistencies
On Protocols and Sandboxes
What would your idea world look like if you could build your own sandbox(es); [a] subset(s) of our world restricted to/by concrete rules, limitations, and community members, formats?
Hint: you can.
- [programming|natural] languages
- [wikipedia|stackoverflow|irc|github|quora|fb|reddit|([0-9a-z]*)chans]-like communities
Personally, I enjoy brainstorming epistemology systems (not unlike wikipedia, freebase, and wolfram alpha) which aim to formalizes and relate all forms / instances of knowledge quasi bella fugientes metamathematics. If APIs for connecting such resources were standardized, first-order citizens to HTTP (read: adopted protocols, implemented by all services and/or browsers), the web would become a “unix shell” of glorious asynchronous piping and distributed analysis of information.
A powerful principle I often neglect in the design or consideration of such sandboxes is the importance of Protocol design (TCP/IP -> HTTP -> RSS), and how critical they can be (i.e. intimately tied to the lasting success) to the service (and other future services like it). w3c RDF and PROV are two protocols which (if adopted, mainstream) will make it much easier for me to create the types of sandboxes I want to see. A sentiment/mindset (protocol research) worth exploring to those interested in shaping their own little corner of this universe.
While I appreciate “the web” offers a creative outlet for people to say (and convince people of) pretty much whatever thing they want, even before they think it, I dream of a sandbox of knowledge which is axiomatically provable from ground up. Able to quantifiably show points of inconsistency or contention between ideas or disciplines, identify and possible resolve/merge duplicate knowledge. I basically mean Wikipedia (if the language was less “natural” (read human; english) and more meta-mathematical (some DSL; domain specific language)). Freebase is even closed but it fails (for me) to be a service whose data is integral to the problems I care about.
Which is selfish. But I want to see the a place where the world’s knowledge (with concrete rules for what constitutes knowledge and its types) is laid out in a universal graph I can zoom/travel through. I want to know what topics I know, and how well. Similarly, what other people know and how well. I want I want to know which resources or authorities know topics best, objectively (coverage, comprehension, based on demonstration or achievement). I want to test run assertions against the knowledge base and get a % confidence of how right it is, or learn what other knowledge would be invalidated if this knowledge were correct. I want to be able to participate in a global initiative, a global conversation and single thread of consciousness and understanding, to further knowledge. I cringe when I imagine what minuscule percent of the “knowledge” we, as people, proliferate, believing it would pass axiomatic validation / rigorously challenge. And how many policies / propaganda / arguments / technologies / corollaries are drawn from these errant foundations.
- Learn Haskell
- Learn Clojure
read sheet music for piano + guitar
- [ ] Learn conversational Chinese (Mandarin)
- [ ] Learn conversational Spanish
Create a usb device + write device driver/module (C + EE)
- [ ] Read K&R C Programming Language
- [ ] The Unix Programming Environment
- [ ] Become better at bash
- Linear Algebra
Learn python better
- [ ] Port web.py to python3
- [ ] Learn scipy + numpy
- [ ] Debugging
Statistics - Housing in SF
- [ ] Learn Statistics
Complete Andrew Ng’s Machine Learning Course w/ passing grade
- [ ] Machine Learning
Create a smart email client w/ summarization (Math)
- [ ] Create a summarizer in NLP (read some ntlk book)
- Writing summarizer using nltk
- Learning scipy & numpy
- Neural network in clojure
http://www.fatvat.co.uk/2009/06/back-propagation-algorithm-in-clojure.html http://gigasquidsoftware.com/blog/2013/12/02/neural-networks-in-clojure-with-core-dot-matrix/ https://github.com/technomancy/leiningen/blob/stable/doc/TUTORIAL.md http://clojure-doc.org/
- World modelling (stats + ml)
- Build personal movie recsys
- Mathematics: Its Content, Methods and Meaning
- Gödel, Escher, Bach
How does a self come from things which have no self?
Why Nudge?: The Politics of Libertarian Paternalism (The Storrs Lectures Series) http://www.amazon.com/Why-Nudge-Politics-Libertarian-Paternalism/dp/0300197861
Nick Owens (2014-08-29)
Unix System Programming
Simulacra and Simulation
Human Action: The Scholar’s Edition
- How in depth people think about economics
The Art of War
Artificial Intelligence, a Modern Approach
How to Win Friends & Influence People
22 Immutable Laws Of Marketing
- Very short; high return for time investment
- Great empirical case studies
Stephen Balaban (2014-08-29)
The C Programming Language, The Unix Programming Environment (TuP)
The Art of Computer Programming TAoCP, SICP
The Introduction to Function Programming through Lambda Calculus
OnLisp > Land of Lisp
Dummit and Foote Abstract Algebra
Meditations, Marcus Aerelius
- One person’s perspective on how to live a good life
- How you spend your time, determining your purpose
1st paper on Denoising Autoencrypters Benjio
Drew Winget (2014-08-29)
- Represent patterns that are recapitulated in daily life, assumptions around which the world is organized
- These are good examples of works written by people who cared and were genuinely trying to figure things out.
- A lens into (trying to understand) your own opinions and thoughts
- Organize a field of questioning in terms of daily experience which is universal across time.
The Republic (Plato)
https://en.wikipedia.org/wiki/The_Republic_(Plato) Freedom, control, and purpose in society 1. 2. 3.
How to behave ethically and control/balance one’s passions in life?
What is knowledge?
- Attempts to separate the field of epistemology into recovering knowledge or creation
constructivism v. intuitionism 2. 3.
Montaigne’s Essays (Michel de Montaigne)
Drew’s 1st pick, read in french https://en.wikipedia.org/wiki/Essays_(Montaigne)
- Quotes nearly every book
- A lot of perspective, covers many topics
- On education
- Invented the term/genre essay to describe a piece of writing
essay – (exigere: to weigh, essayer: to try) ex·e·ge·sis – (critical explanation or interpretation of a text, especially of scripture)
Drew’s 2nd pick
- Biggest prerequisite mathematical book prior to 1900’s
- One of Brewster Khale’s favourite books,
1st book of apollonius’s conics
2. 3.Decartes Geometry Directly addresses apollonius
- discourse on method – how to think, do science, properly
- the geometry – solving open problems
- optics and meteorology
- Rudolf Carnap’s translation is the best
- read first two pages of each section
- the three laws of proper inference
Drew’s 4rd pick
- Joe Sachs translation – preserves etymology and context of words Aristotle invents
- Formalizing the way we experience time, motion, change, casuality, and chance. Named it phusei (natural, by nature)
- Attempts to give definitions for time, motion, causality, and chance.
(Optional intro to Kant)
Critique of Pure Reason (Immanuel Kant)
Drew’s 3rd pick repeats himself, seems unedited https://en.wikipedia.org/wiki/Critique_of_Pure_Reason How reason fits into preconceived notions, senses, etc., and as a result, what limits of reason and Summarising 2000 years of thinking about how one can know things. Create a vocabulary 1. 2. 3.
Decartes: Rules For The Direction Of The Mind
Great Books of the Western World
phenomenology of spirit
Georg Wilhelm Friedrich Hegel – quintissential german philosophy
Mcconnell And Brue Economics 17th Edition
The optimal representation of paths through knowledge
Basic building blocks for life Defines our reality
Book recommendation for Tesla Give Henry Ford
The Physical Properties of Radiation
- Green cloth textbook from 50s, monograph (supposedly at stanford)
live and grow rich
Be a Participant
GCC Great Conversation Congress
2014-04-25 Fri (Drew, Mek, Andrew)
Re: Structure of Scientific Revolutions & Against Method & Logic of Scientific Discovery
Defining a different level of granularity
Newness: Change in the size or location of the boundaries of reality. E.g. (paradigm shifts), the discovery of a piece of knowledge which alters the internal consistency, or the boundaries, of our observable world/discourse/reality (e.g. galileo). refuting integrity
Novelty (normal science): Change in the density/granularity (# inner circles, and inductively their sizes – therefore novelty requires newness?) or granularity (addition/removal of new consistent circles). The introduction of an association or classification which enhances our definition or understanding of an existing reality (e.g. filling in knowledge). E.g. A more effective model/approach, which further supports the internal consistency of our relativity / existing world
Any statement, by definition is a combination of newness and novelty, even if these values are zero or negative. This is because each universe of discourse is composed of subsets which are themselves isolatable universes of discourse. The definition of newness and novelty thus may have the potential to change depending on the context / scope / selection of the universe.
Parcellation: Or the re-partitioning/classification of a universe (from the context/pespective of this parcellated universe) is thus new, in that its size and location have changed, and novel, in that their densities/granularity have too changed.
a subset of a previously understood reality is the change of either size, location, … of a smaller universe of discourse.
Human perspective: People prefer novelty over newness because we want to be able to focus on problems without having the rug pulled from under our feet and having to restart (unless we can guarantee that we will be restarting something which allows us to reach greater heights in less time)
All knowledge can be represented as a presumably infinite graph (needs support/proof). Some of this graph is “known” (observable, and in some cases infinitely calculatively/verifiably so; the result dividing by 2), some of it is unknown (unobservable). An alternate presentation of Münchhausen’s trilemma yields:
circular argument - a branch of logic in the graph is a cycle. regressive argument - or rather recurrent argument (mathematical induction), a branch may be recursively expanded via a formula until either a base-case (ax axiomatic argument - a base-case reached via a recurrent argument which carries the accumulative uncertainty inherent of all its parent arguments
“entscheidungsproblem” -> “Gödel’s incompleteness theorems”
Epistemology - Definitions and Limitations of Knowledgeinventor Scottish philosopher James Frederick Ferrier (1808–1864) (Encyclopaedia Britannica Online, 2007)etymology from Greek ἐπιστήμη, epistēmē, meaning “knowledge, understanding”, and λόγος, logos, meaning “study of”) Terms
The Unmoved Mover - That which moves others but is not itself moved
The “unmoved mover” moves other things, but is not itself moved by any prior action. In Book 12 (Greek “Λ”) of his Metaphysics, Aristotle describes the unmoved mover as being perfectly beautiful, indivisible, and contemplating only the perfect contemplation: itself. He equates this concept also with the Active Intellect
If we ask of any knowledge: “How do I know that it’s true?”, we may provide proof; yet that same question can be asked of the proof, and any subsequent proof.
The Münchhausen trilemma is that we have only three options when providing proof in this situation:
The circular argument, in which theory and proof support each other (i.e. we repeat ourselves at some point) The regressive argument, in which each proof requires a further proof, ad infinitum (i.e. we just keep giving proofs, presumably forever) The axiomatic argument, which rests on accepted precepts (i.e. we reach some bedrock assumption or certainty)
The following tropes for Greek skepticism are given by Sextus Empiricus, in his Outlines of Pyrrhonism. According to Sextus, they are attributed only “to the more recent skeptics” and it is by Diogenes Laertius that we attribute them to Agrippa. The tropes are:
Dissent – The uncertainty of the rules of common life, and of the opinions of philosophers. Progress ad infinitum – All proof requires some further proof, and so on to infinity. Relation – All things are changed as their relations become changed, or, as we look upon them from different points of view. Assumption – The truth asserted is merely a hypothesis. Circularity – The truth asserted involves a vicious circle (see regress argument, known in scholasticism as diallelus)
Mtg w/ Drew
All watched over by machines of love and grace - Richard Brautigan (poem; Machines of Love and Grace)
Seeking 1% Self-Improvements:
One should spend their first 20 years learning about their lifestyles (or perhaps a computer should do this and learn your objectives based on your actions).
- How long should I sleep?
- When do I work best?
- Where are my biggest distractions? Where am I genuinely wasting time?
- How do I procrastinate better; more productively or efficiently?
- Which relationship are helping me live most healthfully?
Curating and Maintaining Community
(the world needs to specify functions and definitions for this general concept)
- How much effort is needed by experts to maintain quality per user added to the network (this is likely not be linear) – This is different than per contributor added (noam)
Web of Trust
(the world needs to specify functions and definitions for this general concept) It’s an optimization problem between: who to let in your network and what type of content you want. Consideration: You don’t want to miss out on good content but you also want to right content.
Prerequisite: Requires people must first determine their goals.
Automating Knowledge Recommendations
A recommender system which recommends content based on the entirety of your knowledge: optimizes for greatest entropy / opportunity to learn / fill gaps and fulfillment. It should also be forward looking and consider future knowledge (planning). This requires some degree of infrastructure and informed taxonomy / ontological organization.
On Improving Notification Systems
Planning for an event is not enough, it needs priorty levels + feedback
Calendars + push notifications lack priority / frequency () There is an 83% chance you will miss this meeting Feedback loops
Idiot - Archive + Entity Resolve every idea
See whose interested in a problem / solution. See how closely ideas overlap in requirements or impact Compare effectiveness of solutions
- continuity of parks
Akhil Aryan: Maximizing Experience
A Response to Akhil Aryan on Facebook ‘My objective in life is to maximize the number of unique experiences that I can experience. I’d like to “actually know” what everything “actually” feels like.’ https://www.facebook.com/doublea9000/posts/10152379108912307
Nor did I get the impression your objective was to achieve understanding or mastery – these are separate objectives from acquiring diverse, meaningful experiences (albeit unavoidable, natural consequences thereof).
First, let’s re-align on the points we are discussing. Your original post mentions “maximize[ing] the number of unique experiences” you have, which is a quantitative statement. In your followup, you also mention consciousness which is a qualitative property. In the realm of experientiality, I refer to quality as perceivable utility (how good the experience is to you either objectively or subjectively), as well as your physical capacity to utilize it; awareness (consciousness, appreciation).
Thesis: On an ideological level, let me begin by saying I support your general thesis that encountering more experiences has the potential to be better and statistically more impactful than sitting in house all day. I think your’s is a great paradigm and rule of thumb by which to live. What I will proceed to propose is a framework for ensuring you achieve this (as close as possible to) optimally, based on your definitions of optimality.
Limitations: The reality is, even if you are out “collecting” experiences 24/7, there are physical bandwidth limitations and constraints to what you can experience. For example, it’s likely difficult to fully appreciate eating at a 5 star restaurant, while having sex, at the same time as winning a $50M lottery, while watching Schindler’s List, having diarrhea, getting punched in the face by Mike Tyson, and simultaneously having it revealed that your life is being broadcast live to the world, like the Truman Show – granted this is an experience everyone should try at least once, if the opportunity presents itself.
- Being informed will induce selection bias
- System inaccuracy
- Decision making, itself, is a valuable experience (reinforced learning)
I appreciate the sentiments you may feel, that seeing data and following a formula will distort / impair your decision-making ability and be counter-productive to achieving spontaneity. This is why I do not suggest you build a system to make decisions for you. Instead, you may consider exploring how a model or framework can help (a) advise you on making choices which optimize your objectives*, (b) evaluate the effectiveness / balance of your approach compared to your goals, expectations, and success criteria and (c) discover or identify unexplored opportunities / low hanging fruit.
A second objection may be, a mathematical model is only “simulating” your decision making process, and it would be unacceptable for it to, even once, diverge/disagree from your thought process. On a scientific note, I encourage you to consider: you, as a person, are using some formula/algorithm to make your decisions, just like a computer does. Spontaneous, irrational decisions are a very real (stochastic) and important part of this formula. The key differentiation (especially as someone with a CEO mentality) is, when you personally make decisions, you feel in charge of, fully aware/informed, and likely responsible (ownership) for your decision. This mentality has many merits: there is both pleasure/utility in the uncertainty and accountability of physically being responsible for making a decision. Additionally, the act of making a decision (just as is true in a computational model) re-enforces your brain’s internal decision accuracy (hopefully). On the other hand, restricting/limiting your decision capabilities to that of the human mind (rather than augmenting it with models which enable you to think on bigger scales) is a risk and an opportunity cost.
I think it is wise (and imperative) to be skeptical of blindly following a mathematical model. Being over-controlling/planning (falsely assuming we know the optimal selection/sampling strategy) for our experiences defeats (what I believe to be) your underlying impetus (pure spontaneity). There is (presumably great, untapped) opportunity and value in experiencing things which are illogical, unintuitive, or generally misunderstood. I think of indie music, abstract art, underground restaurants, new startups. Many great things, wine for example, have the potential to be great but take time to mature (if for no other reason than circumstantial; the stars aligning). And these most significant experiences may not be surfaced by some algorithm (due to signal to noise ratio or insufficient data/exposure to show up on radar). They require intuition. But the biggest mistake is that this expertise/intuition cannot be encoded within a model, and that there is not greater benefit within a hybrid, augmented human-computer model.
If you’ve gotten this far, there are significant reasons why augmenting your goal with a mathematical model is (not just a good idea but is) essential.
Necessity: If I am agreeing with your reservations and your thesis, why did I say, “you should consider researching different selection heuristics / strategies for statistical sampling”?
- Your mission statement directly references maximization, which is a mathematical optimization (in your case, a stochastic optimization).
- People are objectively inferior to computers at estimating and random number generation. That is to say, relying on yourself, you likely won’t come close to achieving the “spontaneity” you’re after.
- We as humans are physically incapable of reasoning about more data than we can possibly digest (let alone reduce the dimensionality of).
Humans are notoriously bad at estimation and generating randomness (you may be significantly better than the mass majority, but if you were to graph your attempts at true randomness against an algorithm, I think you would be discouraged). It’s possible you feel your natural heuristics are better / more favorable models than pure randomness. If this is the case, you should strive to encode them so you can programatically make decisions at a larger scale. Again, I am assuming your goal is to maximize your number of spontaneous experiences – you should mathematically know, by the way, what your definition of spontaneity is and what behaviors (maybe always saying yes) achieve the outcome.
Risks: What are the dangers of not having a framework?
- Local Optima / Unequal distribution
Especially if your goal is spontaneity (which will mean your experiences will be heavily weighted by the effects of spatial and temporal locality – things which happen near your location and …), it’s easy
Like any graph search, you can get caught in loops or find yourself making the same choices, unintentionally.
- Etc …
Here’s a lecture I gave at the University of Vermont on the PageRank algorithm which discusses the above risks, as well as several other graph search caveats and gotchas: http://www.cs.uvm.edu/~xwu/kdd/PageRank-09.pdf
How / Proposal: What specifically should you be systematizing?
The ultimate problem I, in your shoes, would try to address with a mathematical model is, the equivalent of “How do you take a random sampling of the ocean without knowing the ocean’s size?”. The goal being to leverage tools to augment your brain power and help you reason about problems which require analysis of more data than you can physically keep in your head.
I don’t recommend you develop a system for automatically deciding things for you, only a model to post-facto examine and review your experiences to see if they are actually as “spontaneous” as your success criteria and, if so, potentially discover what the consequences of this are (maybe all your experiences are in one geography, maybe there’s low hanging fruit to try a certain class of experience; a sport, a relationship for example). As is done within evolutionary computation (see: genetic algorithms), I am referring to testing (manually reviewing, even) how good your selection process is for achieving your desired optimization criteria.
TL;DR (if you only read one section) How does this actually manifest? What is the concrete manifestation of such a model? I would create a googledoc spreadsheet and every day (Benjamin Franklin style – see his autobiography) add an experience entry (or two or three). At the end of each week, check a few key metrics which are important to you concerning spontaneity – perhaps location, people, domain/activity, emotional response elicited. Try to determine which weeks you had the most profound experiences and what the significant factors or circumstances were.
Challenge: (tongue and cheek) Just keep in mind, I am specifically referring to having a framework which allows you to gauge your effectiveness at accomplishing your own goal of constant experiential immersion, and maximizing the quality and quantity of your experiences. You can take this as a challenge (i.e. I can do better without a model), but the only way for you to win the challenge and construct an academically compelling proof would be to develop a mathematical framework to express your results – which is exactly what I am recommending to begin with. You could also do this anecdotally via approval of peers, a subsidiary metric like “success”, “happiness”, etc, but it is challenging to eliminate selection bias (one of the risks such a system aims to address).
Closing remarks: None of us truly survive our experiences (perhaps via books and in the near future cryogenic freezing). The best we can do is efficiently and effectively use out time here. In terms of choosing a heuristic, I think a good success metric is to see (as you put it best) if you feel you’re perpetually outside of your comfort zone.
re: (a) you probably wouldn’t / shouldn’t want to employ this strategy. It’s likely to instill a bias which is indeed counter-productive to your spontaneity. A parallel is the feel of solving a math homework problem and having an answer in the back of the book for comparison – such a safety net can be disadvantageous when you’re faced with a problem having no such solution. Similarly, the same strategy could help you determine which elements of an experience made you feel a certain way, but you expressed concern for knowing / “understanding” why an experience was good. This is something I would want to know but I understand the complications and why you might not.
 https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect Research conducted by Burson et al. (2006) set out to test one of the core hypotheses put forth by Kruger and Muller in their paper “Unskilled, unaware, or both? The better-than-average heuristic and statistical regression predict errors in estimates of own performance,” “that people at all performance levels are equally poor at estimating their relative performance.” In order to test this hypothesis, the authors investigate three different studies, which all manipulated the “perceived difficulty of the tasks and hence participants’ beliefs about their relative standing.” The authors found that when researchers presented subjects with moderately difficult tasks, the best and the worst performers actually varied little in their ability to accurately predict their performance. Additionally, they found that with more difficult tasks, the best performers are less accurate in predicting their performance than the worst performers. The authors conclude that these findings suggest that “judges at all skill levels are subject to similar degrees of error.”
Paul Graham’s On Munger
I was talking to a vp of estates at chase about paul graham’s “worldly wisdom” essay and I thought someone else may benefit from my response.
the original essay is here: http://ycombinator.com/munger.html
“As per pg’s worldly wisdom, the most insightful take home for me is considering what one gains or (more interestingly to me) loses while scaling. In our case, with Hackerlist, we have tremendous pressure to scale up our platform and network but a large part of our value proposition is maintaining an unmatched, high quality talent pool of software engineers. This, psychologically, is very important for our clients. We’re also trying to be cautious of how we scale our team (i.e.our strategy).
I am inspired by Git’s (http://git-scm.com/) distributed approach towards source code management and think many companies should aim to scale in a similar way. This may be interpreted as any of several advantages one gains in such a distributed system; removing a central bottleneck or point of failure, increased autonomicity, smaller working teams and less communication overhead. Our product reflects our belief that the future of employment is predominantly distributed and augmented by technology and tools. I believe in many teachings of Brooks’ Mythical Man Month, that continuously adding people to a project (scaling a team) results in diminishing and, at some point, negative returns. That said, I also believe there are tools and strategies which can be applied to change what this graph looks like; to mitigate when and at what rate this problem occurs and in many cases eliminate it as a practical concern (it’s like the difference between choosing a linear versus quadratic algorithm). As an anecdote, massive projects, like the Linux Kernel, continue to progress relatively quickly, despite their complicated code base and reasonably large contributor base, due to effective use of the right technologies and strict adherence of strategies + standards for guiding development (granted the project’s complexity may be a self-selector for reducing bike-shedding and allowing natural parallelization of work, but this still needs to be coupled with effective and modular code structure + a clear development strategy)
I typically find the real bottleneck in projects with large teams is intimately tied to communication, manual review, or ineffective management or development strategy. This can often be resolved by deconstructing and analyzing problematic workflow, identifying areas where expensive blocking occurs (i.e. periods where most people are blocked / waiting) and then (1) automating process – i.e. replacing human/manually operated components with faster alternatives, (2) introducing rules or practices to avoid or minimize these blockers (which perhaps we can call management approach), or by (3) physically altering the workflow (operations; e.g. substituting more efficient steps for less efficient steps or trading off; sacrificing/dropping inefficient steps in exchange for time savings).
I think the right management strategies and the right workflow are critical, but my bet is technology is currently the lowest hanging fruit and the largest, most rapidly evolving, opportunity for gains in productivity.”
Sean: A Uniftying Theory for Humor
My friend Sean forwarded me a very interesting read today about classifying humor. It look a while to get into, but after getting a page or two deep into the article, I read a passage which smacked me in the face like a wet noodle. I found this summary to be refreshing, elegant, and a great candidate for Occam’s Razor:
McGraw and Caleb Warren, a doctoral student, presented their elegantly simple formulation in the August 2010 issue of the journal Psychological Science. Their paper, “Benign Violations: Making Immoral Behavior Funny,” cited scores of philosophers, psychologists, and neuroscientists (as well as Mel Brooks and Carol Burnett). The theory they lay out: “Laughter and amusement result from violations that are simultaneously seen as benign.” That is, they perceive a violation—”of personal dignity (e.g., slapstick, physical deformities), linguistic norms (e.g., unusual accents, malapropisms), social norms (e.g., eating from a sterile bedpan, strange behaviors), and even moral norms (e.g., bestiality, disrespectful behaviors)”—while simultaneously recognizing that the violation doesn’t pose a threat to them or their worldview. The theory is ludicrously, vaporously simple. But extensive field tests revealed nuances, variables that determined exactly how funny a joke was perceived to be.
Enjoy the full article @ http://www.wired.com/magazine/2011/04/ff_humorcode
Jessy & Drew’s Parser Project
For anyone who hasn’t taken a Programming Languages or Compiler course (or who hasn’t written a parser), it’s worth knowing what parsers are why what Jessy is doing is cool.
Why should you care about parsers? They are the backbone and pre-requisites to nearly every important computer system we use (compilers, search engines, bioinformatics & gene sequencing, map systems, spell-checkers, translators).
Parsers, in one sentence, are what takes the text we type or enter, and, using rules it defines, turns it into pieces of data the computer understands. Compilers are similar (as they are built using parsers) in that they determine/decide what happens with this data, once the computer understands it.
Parsers, by themselves, are useful in any case where one wants to invent their own pattern, or way of representing information. Computer scientists call these specific patterns and their syntax Domain Specific Languages (DSLs). The concept is a lot more intuitive and accessible than you might think:
Acronyms, for example,are a type of DSL. It involves taking a long, complex, or difficult to say word and using its letters (or similar sounding letters) to shorten it. Each academic domain uses their own system of abbreviations to simplify complicated terms – like deoxyribonucleic acid becoming DNA. Or for hipsters, LOL become “laughing out loud”. In fact… DSL is an acronym, and may at first seem overwhelming because we as humans have to parse the acronym into other terms we understand! Enough about DSLs, back to parsers:
Parsers are special types of computer programs (software) which are responsible for: a) defining grammatical rules (like those used in natural language) b) lexing: reading in strings of raw text  c) tokenizing: identifying terms or clumps of characters in the text which are meaningful (such as a word or part of speech in English) d) parsing: applying its rules from (a) to each word/token to derive meaning between their relationship or semantics of each word (like a group of words/tokens being a correct/valid English sentence).
You can probably imagine a programmer using a parser to create a limited version of the English language which reads in English sentences (maybe it reads your email) and determines/tags the part-of-speech for each word. This isn’t too different from how spell-checkers and equivalent tools work.
If you’ve made it this far, you may want to jump deeper and learn how parsers work and how they are used. Parsers have strategies and implementations. A strategy describes how parsing is actually performed. Think of it like strategies for reading – some cultures read left-right, like English. Some right-to-left, like Hebrew, some top to bottom, like traditional Chinese.
An implementation is how a parser’s capabilities are exposed to a programmer (usually as a library or API for a specific programming language. For those interested, in Jessy’s case, ANTLR is the implementation and LL(*) is the parsing strategy.
LL(*) describes a strategy for how the computer program (parser) reads and analyzes each symbol. Suffice to say, there are many different strategies… LALR, LL, LR, SLR,
We use parsing every day. Imagine receiving an email with the text (computer scientists call this a “string” of text, because each character is strung together, almost like one of those candy necklaces)
At a high level, the difference between LL parsing and LR parsing is that LL parsers begin at the start symbol and try to apply productions to arrive at the target string, whereas LR parsers begin at the target string and try to arrive back at the start symbol.
 For CS majors (I am using parsers to mean both parser and lexer, given in many implementations, parsers also implement lexical analysis).
I have a double trouble, tricky, dirty two hair braided master puppet With six strings and rusted knife, and god knows where this dummy’s stuck it Fuck it, chucky snuck in filled his bucket full of blood, attack, throat hacked, mouth packed, stacked with layers of mud, smiling back through his two buck teeth and a laugh like Elmer, Fudd. Doc! This ain’t no funny dummy, I don’t see no bugs bunny,
Better check my flow, cause you know my rhymes are runny–er Than your nose, which blows and exposes you and shows beneath these tattered clothes frozen in that aw-kward pose—over exposed – by my polyphonic prose… which left you less composed than an un–imposing-rosey– may Queen-figurine transposed against an over-caff-i-nated wolverine
My sylla-bles are original, unpredicable, inexplicably … metaphysical, visual, miscible, yet so subtley sophistical and subliminal it’ undeniably undismissible capital hill might as well make it criminally unremissible.
And it’s not too supercritical to say if they were transmittable –
your pitiful weak spittle might too be passably admissible.
Can’t stop this rigor
Back with the most algebraically abstract rap it be making your thoughts endomorphically contract
Fractal with clever multi-sylabic alliteration Tactilely experiencing this poetic dissertation
Publish another paper they call it a field shaper watch them cite it and ignite it like vapor-Ware, hype, cycle back, pop the stack, to the matter and hand, getting people excited is all part of the plan The other part of the plan is for you to understand the discovery of Zahlrings makes David Hilbert the man
Fraenkel’s mandatory multiplicative IDENTITY-of-1 makes sense to me, But Noether rejects any NEED for such an entity:
So don’t stop your rigor, You might learn yourself
don’t stop this rigor, You might learn yourself
We’re getting smart with this niche We’re crafting art, getting rich
If you’re lost, I’ll drop some knowledge, and I’ll give it to you gratis. This mathematical rap requires high-order functioning status.
It’s as simple as boolean algebra with Shannon’s switching apparatuses, You can follow along if you get Heyting’s semilattices.
Just don’t make me repeat myself, I haven’t patience for antics so let’s get right down to the denotational semantics.
Your brain is trapt like a fixed point combinator. You’re stuck at the bottom with the most common denominators.
[Insert inspiration section on learning math]
Dominating finite fields harder than the Patriots wannabes. 4 rings? Big D, I have as many rings named after me.
It isn’t easy joining our Abelian group. First you gotta prove to Galois you have the roots,
Math is just the start, things about to get meta, we’re gonna launch these lucid lyrics right outa beta.
Pickled, curried, salted, cookie clearing my cache, inspector detects localstore still referencing the hash
Regret is this rotten slab of wood, before my hands how tall you stood. How delightful, your cedar sawdust smell, even as your timber fell. Your roots, we leave behind, I do not think you’ll mind.
I must deny your final boon, to live by helping others bloom, No sooner killed than be exhumed, to compliment a well lit room. Your imperfections, they must be treated soon.
The pure feel of your finely sanded grains, your identity my fingers deliquesce in moments. How many rings have your years painstakingly crafted? They will be painted soon.
Your rhytidome, unsightly, stripped; a fresh coat of colored poison drips. Should your new skin crack or chip, I’ll restore you to your former glory. How lovely you’ll look soon after, freshly manicured, brushed and stroked with tender care, with love. Love for paint, for that is which we display.
Do not fret, my cedar tree. This same paint too covers me. But lest we be truly seen, as we were meant to be?
The icy breeze speaks in whispers, with such crisp and chill, even the hair on my arms are raised in attention. And the trees with their few remaining leaves make no effort to include me in their dance and song. Alone, my reality I embrace, air’s frozen kiss upon my face, a reminder I live in borrowed time and space. But do I wish I instead were warm, kept safe from fret and prey and storm, to live each day since I were born ‘neath shrouds of safety never torn? No, die I might ‘neath ‘morrows sky, I would have naught to sigh, for while a troubled life I lead, I led that life and not a lie.
Of the nights’ cold and the wind’s bitter kiss. Below these heavens, I am but for the gentle landing of the sky’s refuse. Obliged, I wear its dark frost as my evening attire. It weighs upon me, more heavily my heart than my shoulders, but that I have accepted it, I cannot alone be the one to remove it. And so it became that it is not for the chill that I am cold.
- Reid Hoffman LinkedIn Series B Pitch Deck
- Upscale Notes
- Upscale Pitch Deck
Current Tech Challenges
Speaking With Customers
Speaking With Hackers
Speaking With Investors/Advisors
https://archive.org/details/Think_and_Grow_Rich - Think and Grow Rich https://archive.org/details/autobiographybe00frangoog - Biography of Benjamin Franklin https://archive.org/details/firstsixbooksofe00eucl - Euclid’s Elements
http://www.cs.virginia.edu/~robins/YouAndYourResearch.html – recommended by Mischief
Putting Time In Perspective http://waitbutwhy.com/2013/08/putting-time-in-perspective.html
Give it five minutes https://signalvnoise.com/posts/3124-give-it-five-minutesEpistemology Is Justified True Belief Knowledge? - http://www.ditext.com/gettier/gettier.html [U]
http://intuitionanalytics.com/other/knowledgeDatabase/ http://www.google.com/insidesearch/features/search/knowledge.html https://en.wikipedia.org/wiki/Resource_Description_Framework http://www.crystalinks.com/euclid.htmlKnowledge Bases http://dbpedia.org/AboutProvenanceFact verification https://github.com/neyer/dewDrop
Remote Teams Effective Management
Neuroscience Scienceshttps://en.wikipedia.org/wiki/List_of_important_publications_in_mathematicsProbabilistic Models https://class.coursera.org/pgm-003Linear Algebra https://class.coursera.org/matrix-001Topology Henri Poincaré, “Analysis Situs” 1895 [U] http://www.maths.ed.ac.uk/~aar/papers/poincare2009.pdf – translation (english) http://gallica.bnf.fr/ark:/12148/bpt6k4337198/f7.image – original (french)Statistics & Probability
Statistics Done Wrong http://www.statisticsdonewrong.com/
Probabilistic Programming & Bayesian Methods for Hackers http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/Physics The Feynman Lectures on Physics http://www.feynmanlectures.caltech.edu/Quantum Mechanics http://arstechnica.com/science/2012/05/disentangling-the-wave-particle-duality-in-the-double-slit-experiment/Artificial IntelligenceExpert SystemsRecommender Systems https://class.coursera.org/recsys-001/auth/auth_redirector?type=login&subtype=normalMachine Learning Caltech, Learning from Data – https://www.youtube.com/watch?v=mbyG85GZ0PI&hd=1 Stanford, Machine Learning - https://www.coursera.org/course/ml http://datasciencelab.wordpress.com/2014/01/10/machine-learning-classics-the-perceptron/ http://www.quora.com/What-are-some-good-books-on-machine-learning
16 Free eBooks On Machine Learning! http://efytimes.com/e1/fullnews.asp?edid=121516 http://natureofcode.com/book/chapter-10-neural-networks/Deep LearningNeural Networks & Deep Learning https://class.coursera.org/neuralnets-2012-001In
Deep Learning; An MIT Press book in preparation http://www.iro.umontreal.ca/~bengioy/dlbook/
Stanford UFLDL http://ufldl.stanford.edu/wiki/index.php/UFLDL_TutorialComputer Vision https://class.coursera.org/digital-001 https://class.coursera.org/images-002InCaptchas https://www.mashape.com/daizisheng/bypass-captcha http://www.boyter.org/decoding-captchas/Anthropometry http://www.openlab.psu.edu/tools/calculators/AnsurDimensionSelect.php
Performance of a 2D image-based anthropometric measurement and clothing sizing system PIERRE MEUNIER† and SHI YIN‡ http://www.vis.ca/paper/ICESS.html
Development of Digital Anthropometric Circumferential Measurement System Based on Two Dimensional Images http://apiems.net/archive/apiems2010/pdf/HF/572.pdf
Precise Evaluation of Anthropometric 2D Software Processing of Hand in Comparison with Direct Method Ehsanollah Habibi, Shiva Soury, and Akbar Hasan Zadeh http://pubmedcentralcanada.ca/pmcc/articles/PMC3967428/
Study of 2D Automatic Anthropometric and Pattern Generation System http://www.aicit.org/IJACT/ppl/IJACT2502PPL.pdf
Automatic Human Body Parts Detection in a 2D Anthropometric System http://link.springer.com/chapter/10.1007%2F978-3-642-33191-6_53
Anthropometry, Apparel Sizing and Design edited by Deepti Gupta, Norsaadah Zakaria http://books.google.com/books?id=swCjAgAAQBAJ&pg=PA65&lpg=PA65&dq=2D+image+anthropometric+sizing+system&source=bl&ots=r7tMTRkW7j&sig=V9tkblnKMebaaEowxUja5N54ieM&hl=en&sa=X&ei=oNu7U-acHY-BogSzroDgCw&ved=0CF4Q6AEwCw#v=onepage&q=2D%20image%20anthropometric%20sizing%20system&f=false
Statistical Estimation of Human Anthropometry from a Single Uncalibrated Image http://www.umiacs.umd.edu/~yaser/BookChapter.pdf
Performance of a 2D image-based anthropometric measurement and clothing sizing system. http://www.ncbi.nlm.nih.gov/pubmed/11059458
A practical synopsis of methods of measurement in physical anthropology. Montagu, M. F. Ashley http://psycnet.apa.org/books/12018/
Measurement Techniques in Anthropometry http://books.google.com/books?hl=en&lr=&id=Bkk8FuB0P4IC&oi=fnd&pg=PA25&dq=anthropometry&ots=u5VtM4Omn-&sig=jvw93c_hXtEKfbiUrwvFcAGC-aY#v=onepage&q=anthropometry&f=false
International Journal on Advances in Intelligent Systems, vol 6 no 3 & 4, year 2013, http://www.iariajournals.org/intelligent_systems/ A Human Surface Prediction Model Based on Linear Anthropometry file:///home/mek/Downloads/intsys_v6_n34_2013_5.pdfComputer Science / MetamathematicsConcepts Big O Notation https://www.interviewcake.com/big-o-notation-time-and-space-complexityToolsK-Maps K-Maps to simplify boolean algebra expressions https://en.wikipedia.org/wiki/Karnaugh_map#Don.27t_caresRegex http://nbviewer.ipython.org/url/norvig.com/ipython/xkcd1313.ipynbTopics Understanding Partial Application and Currying http://abdulapopoola.com/2014/08/18/understanding-partial-application-and-currying/Programming LanguagesGo http://learnxinyminutes.com/docs/go/Shell http://www.shelldorado.com/links/Python How fast can we make interpreted Python? http://arxiv.org/abs/1306.6047
Python performance http://www.huyng.com/posts/python-performance-analysis/
Debugging Python https://zapier.com/engineering/debugging-python-boss/
Python Decorators List https://wiki.python.org/moin/PythonDecoratorLibrary
http://akaptur.github.io/blog/2013/11/15/introduction-to-the-python-interpreter-2/ http://lucumr.pocoo.org/2014/8/24/revenge-of-the-types/ http://jessenoller.com/good-to-great-python-reads/ https://packaging.python.org/en/latest/ – packaging http://guillaume.segu.in/blog/code/487/optimizing-memory-usage-in-python-a-case-study/HaskellOCaml http://queue.acm.org/detail.cfm?id=2038036 http://www.fatvat.co.uk/2009/08/some-haskell-data-structures.htmlScaling Infrastructure http://www.kegel.com/c10k.htmlPhilologyLinguistics / NLP https://class.coursera.org/nlp https://class.coursera.org/nlangp-001
Natural Language Processing http://www.nltk.org/book/Philology / LanguagesChineseDictionary http://www.wordreference.com/zhen/Literature http://ctext.org/art-of-war/laying-plans?en=off
Library Genesis http://gen.lib.rus.ec/
Hardware-Software Interfaces Hardwarehttps://class.coursera.org/hwswinterface-002Electrical Engineering https://class.coursera.org/eefun-001/auth/auth_redirector?type=login&subtype=normal
SSH Kung Fu http://blog.tjll.net/ssh-kung-fu/http://editorconfig.org/Vi / Vim http://learnvimscriptthehardway.stevelosh.com/preface.html http://yannesposito.com/Scratch/en/blog/Learn-Vim-Progressively/ http://vimcasts.org/Emacs http://batsov.com/articles/2012/02/19/package-management-in-emacs-the-good-the-bad-and-the-ugly/Agile Workflows http://thehypertextual.com/2013/04/23/social-neuroscience-scarf-model-and-change-management/Improve Programming http://codingbat.com/ http://exercism.io/ http://bob.ippoli.to/archives/2013/09/18/exercism/ http://cm.bell-labs.com/cm/cs/tpop/Software Quality Assurance & ToolingGit http://marklodato.github.io/visual-git-guide/index-en.html https://github.com/cjbarber/ToolsOfTheTrade https://github.com/nvie/gitflow http://www.nolte-schamm.za.net/2013/05/git-rebase-fun/Dependency Management
Entity Resolution Information Retrieval / Search
https://www.academia.edu/4404136/Entity_Matching_in_Online_Social_Networks Entity Matching in Online Social Networks
- The best single feature is “Jaro Winkler Name Similarity” with AUC of 0.9504 (based on the “only x” procedure).
- The top five features are all name based features.
https://www.cs.umd.edu/~mbilgic/pdfs/gd05.pdf D-Dupe: An Interactive Tool for Entity Resolution in Social Networks (University of Maryland) http://vldb.org/pvldb/vol5/p2018_lisegetoor_vldb2012.pdf Entity Resolution: Theory, Practice & Open Challenges http://arxiv.org/abs/1404.3017 A Link-based Approach to Entity Resolution in Social Networks
Incorporation Business & Entrepreneurship
- Incorporating, Incorporation Questionaires
- RSPAs, Stock Certificates, 83(b)
http://andrewchen.co/2014/07/08/theres-only-a-few-ways-to-scale-user-growth-and-heres-the-list/ http://u-psa.de/cheat-sheets/seo-onpage-cheat-sheet.pdf http://gibbon.co/hatimotarid/master-inbound-marketing/the-persona-driven-keyword-research-process http://www.evanmiller.org/how-not-to-run-an-ab-test.html http://www.qubitproducts.com/sites/default/files/pdf/most_winning_ab_test_results_are_illusory.pdf
Userful UI Componentshttp://bost.ocks.org/mike/join/ http://bost.ocks.org/mike/circles/ http://bl.ocks.org/mbostock/3808218 http://bl.ocks.org/mbostock/3808221
The Presentation on how d3.js binds data under the hood.
To get deeper, read: http://bost.ocks.org/mike/selection/
Software to try Hackerlisthttp://careers.stackoverflow.com/jobs/tag/face-recognition https://www.employiq.com/jobs/all/c-programming
Learning, in general
Learning involves two stages. Learning how to learn, and then actually learning. That is to say, a substantial portion of the learning process for a new domain involves determining which practices, resources, and methods are most effective. Building an effective learning model or framework requires one first has choosen criteria for measuring or evaluating progress, which is typically satisfied by a set of goals.
My goals is to be able to have:
- text conversations
- translate and read a chinese classic piece of literature
- participate in basic conversation
- read signs in san francisco
- travel to china without dying, or worse, offending anyone
In some cases where we have prior experience, we can apply methodologies we’ve identified as successful to similar domains. Many techniques for learning French can be obtained, for instance, from having first learned another romance language. For a topic which is significantly foreign to us, we must rely on others’ knowledge (e.g. take a course, read a book, hire a tutor) or evolve our own strategies and frameworks to structure our learning.
If this is an exercise which you find challenging, consider reading a book like Polya’s, “How to Solve It”  or “How to Learn Anything Quickly” . It may seem silly, but learning strategies for approaching learning (while quite meta in principle) is much the same as “teaching a [wo]man to fish”.
Words of warning
My personal experience is, great resources can come from anywhere, but they are often incomplete (in terms of gaps in content or unsatisfactory features) and need to be supplimented with other sources. An example is relying on one website for learning stroke order and another for pronounciation. There is a lot of risk in relying on too many fragmented sources as they will likely have disagreements about what content should be introduced and when. Additionally, context switching (changing from concentrating on one source to another) is distracting – it takes time to adjust to different teaching styles.
Building your framework or strategies on top a central foundation, like a lesson book or a native speaking tutor is likely the most effective (not necessarily the cheapest) way to bootstrap learning.
My Learning Philosophy
My personal preference is, I prefer not spending money on resources. I’d much rather repay the community with learnings of my own. That said, I appreciate some resources require significant investment and care in order to exist and if I have to pay for anything, I’d rather it be knowledge. Wikipedia and the Internet Archive, for instance, have necessary server and electricity costs in order to be able to provide their services to the public – I take pride in supporting these costly initiatives which aim to make knowledge more open and accessible.
That said, as I believe knowledge should be accessible to everyone, I am going to try to highlight resources which can be legally obtained, free of charge.
Largest population. More than 5 times more people speak Mandarin Chinese than speak English. 
Applicability / Competitive Advantage. There is a large spanish speaking community in california. Spanish is a more widely spoken language than English, world wide. I took spanish for five years. Other than ordering food in Spanish restaurants, reading the occasional spanish news paper, and travelling to Mexico and Costa Rica, I haven’t had many practical opportunities for applying the language. Perhaps this is due to cultural differences or a side effect of not having many spanish friends I can speak with on a regular basis. My personal experience is, I found a lot more Chinese speaking people in my computer science classes than Spanish speaking and honestly, being able to communicate with academics and researchers who share my interests is very important to me. Finally, a great deal of the world’s manufactoring occurs in China. If you can’t ask questions as a native, it’s easy to be taken advantage of.
Friends. The top reason why I am learning chinese is, I have a lot of smart friends who I respect who decided to learn Chinese. I don’t want to be left out of conversations.
It’s different. German, french, spanish, and itallian are all relatively similar. They use roman characters, have comparable grammars, and even cognates. If you know a few words in a sentence, there’s a chance you can guess the other words. Moreover, you can at least sound them out and read these languages, even if you have no idea what you’re reading. Chinese, on the other hand, is like learning two languages: the written language and the spoken language. I am unaware of any way (patterns, strategies) of infering written characters from spoken Chinese or vice versa. By the way, did you know vice versa means “a change”, “an alternate order” (vice) “turn about” (versa) – thus the “order turned about”? I find etymology, as in this example, to be a great way to remember words, discover similar roots, and achieve greater understanding of a language.
Cultural Insight, Literature. The language has unique idioms and insights. There The Four Books & Five Classics , The Art of War
 http://www.infoplease.com/ipa/A0775272.html  https://en.wikipedia.org/wiki/Four_Books  http://books.google.com/books/about/How_to_Solve_It.html?id=z_hsbu9kyQQC  http://www.amazon.com/Learn-Anything-Quickly-Ricki-Linksman/dp/0760728364
If not Chinese
I contemplated learning japanese, but there just aren’t enough speakers outside of japan. I occassionally read manga or watch anime so I thought these activities could be a good way to keep myself engaged and accountable to practicing a language. I also have contemplated learning hebrew, croation, or russian, because I have family in these places and the languages offer similar challenges.
I can sound out basic german, french, and spanish phonetically. Given chinese characters (hanzi) I am complete illiterate.
- I am going to pick one piece of classic Chinese literature and start learning / copying its characters and translating it.
Resources (in order of discovery)
Learning Chinese so far has been a frustrating process, mostly because learning a language, itself, is an art. What is the best way to practice? What are the best resources? What are common mistakes?
I decided it could be helpful for me to document my progress.
Choose a Goals
My goals is to be able to have
- text conversation
- read ____
- speak basic conversation
- read signs in san francisco
- travel to china and not die
I keep an electronic document with words as
- Lined paper
I use paper flash cards as one of the most important elements to memorizing chinese characters (or hanzi) is drawing them using the correct stroke orders.
- Literal meaning
Don’t settle for colloquial translations, try to understand literal meaning. For instance, it is often taught that 你好 (Ni3 Hao3) means hello. It really means “you” (ni3) “good” (hao3). Once you commit to breaking phrases down to, and understanding, the language’s simplest components, you begin to notice trends which make future learning much faster.
What to read
The Browser Graph
Objective: Browser extension, passively record all links visited (and their order / path of access). Provide keyboard shortcuts to “favorite”, “ignore”, etc. Label + tag resources as collections.
Meta Provenance Example (Browser Graph)
The broken browser history - February 25, 2007 Pathway - Sean Voisen, “Pathway is the first step I’ve seen towards browser history done right. A Mac application designed for navigating Wikipedia, Pathway records your browsing history as a traversable spring graph, making it easy to see and immediately remember how you moved from one article in Wikipedia to another. Nodes are added to the graph as pages are loaded, with lines between nodes representing the path followed from one page to the next. Relationships are made obvious.”
http://ausweb.scu.edu.au/aw99/papers/eklund2/paper.html http://www.cs.umd.edu/hcil/pad++/papers/hypertext-98-padprints https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CCkQFjAA&url=http%3A%2F%2Fwww.academia.edu%2F2035515%2FNAVIR_Navigation_and_Visualization_of_Information_Retrieved&ei=Q1b4UoHmM8HooATh24LYCw&usg=AFQjCNEEMtqaGjw7F5_1UqSNQPVMZeVfNA&bvm=bv.60983673,d.cGU
http://joel.dumoulin.home.hefr.ch/Projects/NAVIR/index.html http://joel.dumoulin.ch/projects/ – found email: email@example.com http://joel.dumoulin.home.hefr.ch/Projects/NAVIR/Prototype/NAVIR.html http://www.ra.ethz.ch/cdstore/www6/Posters/710/hg.html
Santiago Ortiz (the master)
Guess - Graph Exploration System
Google Hangouts + OAuth
- Google+ Server Side Oauth Flow
- For python3.4 pip install git+https://github.com/enorvelle/GoogleApiPython3x.git
- Get/Set Oauth Credentials
- Hangout App settings
- Revoking OAuth Access (for testing)
- Hangout Buttons
- Your or may need to enable OnAir for HoA
- Youtube live coding session on Hangouts
- Step by step tutorial on hangouts (1 of 2)
- Step by step tutorial on hangouts (2 of 2)
- Youtube video on: Including Hangout in your Website
Steps for starting a startup
Presence tracking + notification systems
- [UNREAD] http://harvardmagazine.com/2006/03/the-marketplace-of-perce.html
- [IMPORTANT FIGURE] http://en.wikipedia.org/wiki/Dan_Ariely
- Machine Learning
Be Accountable, Fair & Just
Every time I read a link (or at least for some class / category of resource) I should need to write a sentence as to why I am visiting the link, what it accomplishes. This friction is necessary. ^ PROVE IT. (document or it didn’t happen)
Keeping time in perspective. Women didn’t have the right to vote in the US until August 18, 1920 (that’s 95 years ago / ~1 generation ago). There are still women alive today (~2M US, probabilistically half of which are women) who in their lifetime were denied their right to vote at citizens! There’s no stronger and truer indicator of a systemic problem than one’s opinion legally suppressed and invalidated.
For this reason, I am not surprised by Lea’s (and similar) accounts (at Stanford, and otherwise). People are still under the generational affect of their elders, many who grew up valuing women differently.
No, I am not surprised by this account. I am, however, disappointed, even ashamed. Not in the capacity of a guy, who should feel bad or personally accountable (although who isn’t personally accountable at some level; if not directly, than for lack of empathy/sensitivity, tact, or for not doing too little to stop such discrimination), but rather as an academic witnessing the efforts of hard working, intelligent people undermined by those who would deter progress. For this reason, it is especially disappointing to see at an acclaimed academic facility.
A challenging thing about being human is, (multinomial; binary or similarly discrete-classed) logistic classification is too often employed where a continuous classifier would better serve us. There are times when logistic regression is the right tool – when there is an absolute answer (e.g. is this shape a square or a triangle). However, in many real-world scenarios, we compress the dimentionality of spectrums (autism, skin color, gender, personality-types) into generalizations whose limits serve to address our political goals (a generalization we make at sometimes great cost).
In some cases, it’s a consequence of real world limitations. In fact, this is a great example of where algorithm complexity analysis (rigorously quantifying the properties of algorithms) can give us a foundation and better understanding of real-world optimization. In practice, should a student fall just short of an “autistic” classification, they may be entirely ineligible (e.g. IEP) to receive or benefit from special instruction. That is to say, the model is limited in that not everyone benefits to a degree proportionate to their needs. An analogous continuous, linear approach, would be a custom curriculum for every student which addresses their every individuality. As you can imagine, while this has tremendous benefits (and we are + will see technology increasingly achieve this) it is challenging to standardize and costly to implement. It’s like making dinner for a family. The optimal case is to make everyone what they want, but preparing several different dishes with different ingredients takes a lot of time, money, and skill. As a result, an attempt is made to serve the majority by choosing a single menu (outcome). Restaurants are a model which serves to capitalize this inefficiency.
Corollary to modelling something continuous via generalized logits, there will (by definition, otherwise it wouldn’t be continuous) exists inefficiencies (mis-labellings) and gaps wherein thrive the Hacker. You see it with tax brackets; someone creatively structuring their finances to pay less tax. Or in gaming a job interview; preparing for the expected/common questions. Even the way some people dress or speak are attempts at triggering or activating some sigmoid (logistic) function to appear / be perceived as associated with a certain class or set of values.
Society tends to classify Hackers, posers (I’ll call them social engineers), and the likes, as evil, as they (presumably) opportunistically exploit our societal expectations/contracts/promises (mutual agreements/understandings) for personal gain.
Some people just like to better understand our contracts, and what these inefficiencies are. Some people do indeed want to discover ways to draw value. One could argue, this inquisitive hacker spirit (irrespective of the outcome) is both the biggest thread and the most essential imperative to our advancement. As inefficiencies are discovered, we as people (more broadly, ‘the government”) devise new legislature, social contracts, and or popularize trends (read: movements) to either minimize or ubiquitize this exploitation for optimal benefit of society as a whole (e.g. drug enforcement, police).
Of course, Hacker itself is a binary logistic, perhaps we can do a little better and make it ternary (admittedly still not perfect). The difference between white hat, grey hat, and black hat hackers, in my mind, is that the white hats discover inefficiencies between classes or how contracts can physically be subverted or ignored, but don’t themselves act towards exploiting them. The grey hats live in the ambiguity or inefficiencies of social contracts (blurring the lines) and attempt to legally derive benefit. The black hats ignore social contracts altogether in pursuit of gains.
While hackers are indispensable for discovering and solving inefficiencies, capitalism is one of the driving factors for sustaining such solutions at scale. It’s no mystery why many hackers are entrepreneurs, and vis versa. The general public (99%) may feel discouraged by capitalism because in “ameliorating” (reallocating) the system as a whole, their disproportionate gains may be compromised/diminished. It’s unfortunate that re-drawing the lines can results in negative side effects, there are likely interesting strategies to address this (e.g. exchanges + recompense, like eminent domain), and this should be addressed in essays by people much more qualified than I. On the other hand, slavery was one of those lines. Women voting was one of those lines. And some lines need to be redrawn and or removed altogether. Another case against capitalism is, many ventures are a net negative on the world, or help only people with money. Economics is a very difficult field and I prefer addressing problems I can rationalize against specific algorithms, so again I’ll leave this legitimate concern to someone more qualified.
Why are we talking about classifiers, hackers, and capitalism, and what does this have to do with sexism you ask? Well, sexism is a result of logistic classification. Society buckets individuals into specific gender categories which serve social or political purposes. Being identified as a boy or a girl has (and it shouldn’t, unless health related, have) significance to policies like marriage, taxation, and (absurdly) voting. Additionally, the classification is a principal component of sexual interest. And while we hate to admit it, there are both hackers who exploit their classifications and people who suffer from the classifications existing.
Continuing this essay is left as an exercise to the reader.
Be a Curate
The Laws of Science
The Branches of Mathematics
Topology -> group theory
Towards a navigatable dependency graph for all topics of mathematics and everything.
Prototype: The Compendium Mathematica
I’ve created a prototype of what I hope represents an important next step in connecting the disparate subfields of Mathematics and making them more accessible, for the benefit of math enthusiasts. http://michaelkarpeles.com/math.html
Credits: I have tremendous amount of gratitude for Michael Bostock (who deserves all credit for the interface; zoomable treemaps, d3), the American Mathematical Society’s Mathematics Subject Classification taxonomy (AMS’s MSC2010), and most importantly Dave Rusin who curated the most impressive mathematical atlas I’ve ever seen, way back in 2000
- Improve the taxonomy by having formula-level granularity
- Treemap search box to zoom to a specific topic’s frame
- Add a list of vetted navigable resources for each topic
- Allow crowd sources-wikipedia style taxonomy / resource editing
- Settings files which allows users to keep track of what math they know and what resources they prefer (to visually achieve a holistic understanding of their knowledge gaps)
- my json taxonomy of the MSC2010 taxonomy is available here: http://michaelkarpeles.com/static/js/math.json
- Run it, revise it yourself: https://github.com/mekarpeles/mathd
The ultimate goal is to be able to programatically/dynamically generate an exhaustive curricula (concrete DAG of resources / shortest path through knowledge) for learning a specific mathematical principle / formula.
http://mathworld.wolfram.com/letters/ http://www.math.niu.edu/~rusin/known-math/index/ https://www.khanacademy.org/exercisedashboard http://jeffbaumes.github.io/standards/# https://dl.dropboxusercontent.com/u/11459286/ccssmgraph.pdf http://meta.math.stackexchange.com/questions/6479/a-graph-map-of-math-se http://forums.xkcd.com/download/file.php?id=29015&sid=8ae1604e5e548bed844ae0f1275bad43&mode=view http://www.aaamath.com/grade6.htm#topic1 - elementary https://github.com/stared/tag-graph-map-of-stackexchange http://www-old.newton.ac.uk/preprints/NI12010.pdf
Epistemology: Types of Questions
What formal research (mathematics) is being done towards enumerating all types of questions into axiomatic classes? Who researches epistemology whom I can speak with?
Who, what, when, where, why, and how are fine top-level axioms (emergent from language) but it’s unclear whether they are exhaustive. More importantly, by themselves / absent of context, they only communicate (or constitute) part of a question. It does make me curious, from a philology perspective, whether there are languages which present additional directives for phrasing questions.
I am more interested (than these directives) in a formal computer system being capable of composing (read: asking) any question a human can, and constructing questions we haven’t thought to ask yet. I don’t think an “exhaustive” framework is achievable, but I do think we can approach it within the context of different dimensions. That is to say, I assume as we create more advanced frameworks for asking questions, we’ll be able to ask higher dimensional questions (just like, in the context of math, a full understanding of a point in 1D allows us to speculate about lines in 2D and, inductively, that of planes in 3D, and so on).
I believe there is a limit (namely time; lifespan, human race, and rate of biological evolution) to what a person can think and conceptualize. Computers have the potential to help us frame and ask questions we can’t conceptualize or reason (there’s a limit to what we can experience, in terms of biological sensors, signal processing & computational power, and dimensionality reduction). I believe this is the only practical, sustainable solution to the future of research and towards our understanding of topics like quantum mechanics (which increase in complexity and often experiment cost and diminish in returns). Here’s an incredibly corny video which anecdotally demonstrates the limits of our understanding : https://www.youtube.com/watch…
- How do we classify x (classification is a top-level axiom, perhaps?)
– What are the properties of x – What is the functional relationship between x and y — How does x behave —- Why does x exhibit y —- What does x ___ like? (where ___ is a sense) — What depends on x / does x depend on (DAG) — What happens when one applies, maps, reduces y / x – What is the probability of x (given a … y, z) – What is measurably similar to x (and how/what do we measure)
Many of these questions have native encodings / abstractions within programming languages – compareTo, map/apply/reduce. Maybe I am applying too many preconceived CS biases.
- Leslie Wilson suggests the general categories:
- 6 Types of Socratic Questions:
These are more universal axis/dimensions of problem solving than “questions” – and maybe this is a more fruitful approach. I’ve taken some liberties in summarizing them.
- clarification, disambiguation, relevance
- test assumptions
- test evidence/reasoning
- viewpoints and perspectives (contexts)
- hypothesis / implications / applications & consequences
- meta-questions (semantics, relations) – seems similar to 1
- Bloom’s Taxonomy:
What is the best system for discovering/sharing curricula on specific topics, similar to (and maybe the best answer is) university curricula + recommended books/resources?
Quora, Reddit, Stackoverflow, and even Github “curation” repos have been pretty good sources for communities surfacing good resources/content, but I’ve yet to find a resource which proposes curricula in a a standard format.
I’d love to know where you folks start when you want to learn / enter a new field (inb4 “Wikipedia”). I think University web pages, the aforementioned communities, Wikipedia, youtube, Khan Academy + Coursera are great starts, but it would be amazing to select a “subject” (e.g. linear algebra), see a list of checkboxes (read: requirements) which you already know (like discriminants, transpositions, tensors, eigen vectors) and then either generate or search manually curated curricula. Even if someone (the proverbial “someone” which usually ends up being me talking to myself, motivating myself to act, or convincing myself to avoid distraction) were to just crawl + curate academic curricula for a single field (computer science) across schools, present the data in a standard format, and allow diffs / frequency analysis of recommended books or common lectures, this would be an amazing start and value add to academia. I’m sure professors would contribute content to such a system, in place of their 80’s style academic static html pages, assuming it saved them time and offers a better experience to them + their students.
Anyway, if there’s not already a great source, what would such a solution look look like – a structured DAG / directed acyclic dependency graph of resources?
I’m sure thought leaders, bloggers, etc, could earn significant affiliate sales by selling their curriculum (as a bundle, including the books / resources or additional content). I’m sure plenty of people would contribute their curricula, github + SO + gibbon.co style, as creative commons, etc.
At first glance, here are some resources available:
Implementation: I’d wish to see something more like… Wikipedia / wikibooks “create a collection” feature (except, perhaps as a chrome extension across the web).
- Michael Spivak’s Calculus
- [[Mathematics: Its Content, Methods and Meaning]]
Create the next Web Browser
Stub: Decision Trees and Flow Charts for Automating Business
A Framework for Life Goals
Noam Samuel, I think being more quantitative helps, i.e. being able to objectively measure your impact. If no such framework exists for doing this within a given problem space, then I believe this itself is the most noble goal one can have (and the lowest hanging fruit, I think, in all of society). Einstein, Franklin, Euclid, Newton, Sun Tzu, accomplished fantastic things, but above all they left us frameworks for constructing, framing, and evaluating problems. Sometimes the framework itself becomes an entire discipline/school of thought. The computer is the single greatest manifestation of such a framework (the underlying framework being math, computer science being its accessible implementation; metamathematics). It’s purity, in my mind, comes from its axiomatic construction (infinitely provable from the ground up, giving rise to exciting corollaries and possibilities, like Curry-Howard Isomorphism).
Assuming an appropriate framework exists, identifying motivation will help you choose the right axis on which to measure impact/success. Do you want to create ideas which will last for 3000 years? I do: this is a criterion/principle on which my life is guided.
A framework for accountability and self-evaluation (quantified self) is equally as important as the framework / life goal you aim to pioneer. Ben Franklin, in his autobiography (appropriately named the Autobiography of Ben Franklin) presents several strategies. It’s important that your motivation also be quantifiable and not “to help as many people as I can” or to “spread happiness”. At the same time, there is a science to setting goals (and sometimes the psychology of setting a goal can limit your success, or accelerate it).
If you’re motivated by a mindset or philosophy (e.g. people should care about knowledge), it’s important to be able to envision the world you want to exist (what such a society looks like) and leave a comprehensive path/breadcrumbs (framework) towards achieving/realizing as much of this world as possible. This impetus, the big picture / destination, is the source of your motivation.
With the right destination in mind, and the right motivation, the correct accountability framework for realizing your goals, and the right framework for documenting and evaluating your progress (e.g. literate programming, paper trail + notes, observations), you’ll have an informed foundation for answering the questions you ask:
“How do you know whether a focus you have chosen in your life enriches it? Whom do you trust to tell you?”
Or at very least, you’ll leave behind a language or framework (system and axioms) which empowers others to continue your work.
Towards More Efficient Political Voting
Call me “unpatriotic”, our current implementation of voting in the United States seems less effective than bikeshedding.
We, the moderately informed people, passionately cast our vote for a painter whom we think we can trust to execute the few bikeshed features we think we understand and endorse, based on anecdotes from media which shows us only certain angles of “some” bikeshed-in-progress. The painter then proceeds to do whatever she or he wants, without much liability, in accordance with rules and regulations from the painters union of which we, the average people, can’t reasonably be expected to understand.
Americans understand a lot of other things, like what we believe, what we value, and what we want to see. But manually reducing/converting these sparse, abstract, highly dimensional beliefs into a binary decision for a single person (of whom we have limited and biased information) seems like a recipe for a badly painted bikeshed. (Voting on propositions is a better idea on the basis it bypasses this detrimental “encoding” / translation process and provides a scenario wherein it is clearer the issue being voted.)
While Baidu has produced technology to predict the 2014 FIFA World Cup results with 100% accuracy , 284 US “experts” who make their living “commenting or offering advice on political and economic trends,” scored worse on 82,361 forecasts (presented with 3 possible futures in a statistical study) than had they assigned each option an equal probability . These results might suggest both that our current system isn’t well suited for the laymen or the expert and that there is a massive opportunity to augment the electoral process with technology.
In terms of electing qualified candidates, wouldn’t it make more sense to inform the public by building a matrix (user-item collaborative filtering system) of political candidates (on one axis) and their actual/recorded voting history on policies (on the other axis) and then have an online survey with concrete questions (whose weights / importance can be specified by the participant) in order to see, statistically, which candidate has performed in a manner which best aligns with your position? The media (or your private online dashboard) can then statistically show, over time, to what degree a politician is deviating / has changed course from their past voting history. I know there’s plenty of research on such political recommender systems and likely implementations which are being used in practice (which I’d love to hear about).
The TL;DR is, I genuinely believe voting for candidates (versus propositions) using our current electoral system (even if you identify as an informed citizen) has the potential to be more detrimental that abstaining your vote.
I’m not claiming that voting doesn’t matter – it does. I am claiming that for which we vote is not the same as what we get, and thus that an honest vote can empower injustice.
 http://trends.baidu.com/worldcup/events/knockout?locale=en  http://www.newyorker.com/magaz…/…/12/05/everybodys-an-expert Expert Political Judgment: How Good Is It? How Can We Know?  http://www.researchgate.net/…/235767787_Election_candidates…
Be Retrospective, Thoughtful, Open-Minded, Respectful
Out of necessity (for survival), the world will approach/approximate greater accuracy. One challenge will be changing accordingly, and forfeiting luxuries which are unfairly bestowed upon us.
Architecture and Modelling
I recently heard a Knuth talk (I can send you the question he was asked) where he addresses one of his biggest mistakes. He couldn’t think of any, other than to say he had made many But he brought up a case of LaTeX. He, “based Tex on binary arithmetic inside (based on a scaled point) but decimal arithmetic outside, for the user”
Where the internal representation of font ratios was different than the external rendering because other libraries required certain things And by trying to come up with an analogy, side effects occurred which now still reside in TeX The key point, whether it perfectly follows from the example or not, is, if there’s a way to model things as they are (like a fraction instead of a decimal), there’s probably an advantage Granted the real world is both not fully understood and has tons of stochastic components we can’t practically model well. But when we can, it’s something to consider. The wise words of Knuth.
Lillian Rosanoff Lieber (via Daniel Fleisch) Tensors are the Facts of the Universe
As the nature of tensors is such that they hold for all observers (each rank addresses all 3 directional basis vectors; i.e. all forces which can be applied on the R3 cartesian plane)
Don’t let individuals affect your life decision
Don’t let people influence how you think about yourself
Be well rounded
be more proactive
Listen to people who have knowledge
Follow the traits of successful people
Find environments (niche) where your traits will be recognized
If you need to be noticed, make people notice you
People are wrong more often than we think
Don’t let perception rule your actions
Criticize by Questioning, not Advising
The best way to criticism is not to criticise. Criticizing is a way to get straight to the point. It is done between friends. Before this trust exists, don’t assume you know what they know (until you would both be comfortable agreeing this mutual understanding of each others’ knowledge exists). Instead, ask questions which attempt to understand why others do things a certain way. Without asserting yourself, this will demonstrate what you know and you will benefit from hearing about the experiences driving their decisions.
Compare yourself to who you were a year ago, not to others.
Erik Parker: Don’t ask permission for everything
The people around you change you
It doesn’t matter who’s right, only that the right answer outs
Give it 5 minutes
Organize your life as early as possible
Things I organize
- My file system
- My daily, weekly, montly, and yearly goals
- My thoughts – via commonbooks
- Reading lists
The power of effort, up-front – Research people before meetings
Find out who they know (know who you want to be introduced to)
Marginal Gains and Compound Interest
- All things come to an end. Some people have trouble allowing themselves to enjoy things because they think about / can imagine them ending. Take anime for example. If you’re on episode 26 out of 30, it’s hard to enjoy it fully knowing it soon will end. It is a protective mechanism to prevent us from getting too hurt, becoming blinded, falling into a trap.
- We must face our fears
- We must get the important things out of the way so we don’t have to worry about them later, let them pile up, or more importantly so you don’t forget. Spacial and temporal locality.
The Re-Discovered Importance of Practicing Fundamentals
My brain’s hard-wired state of being has had me convinced me for a while that systems like Khan Academy are important and useful, but especially since Khan is geared toward more elementary learning, I had discounted it as a tool for myself. I decided to challenge this assumption and, in turn, was awakened to a truly important reminder (which should be common sense, but which I as a human so frequently brainwash myself to neglect):
Hubris. The degree to which hubris prevents me, and likely you, from staying up to date with important, no critical, fundamentals of our education. I’m talking about arithmetic, algebra, pre-calc/trig, probability, geometry, linear algebra … all the way to more challenging topics like differential, integral and multivariate calculus and diffeqs – axiomatic tools for measuring, understanding, and conceptualizing the world. In the past, labeling topics like arithmetic or algebra as easy has prevented me from sufficiently practicing them. But I was in middle school when I learned algebra. I don’t think I had the attention/discipline or the faculties to absorb the material as well as it deserved. I have had so much faith “I can learn it again quickly when I need it” that I have not invested the time to stay sharp: the quality of being so well versed in a subject you can instantly infer insights intuitively without having to divert or dilute your attention.
Learning isn’t binary. It’s a continuous process. I also discovered the degree to which I treat learning topics as binary (I either know them / have learned them before, or I do not know them). There’s also the tricky and very dangerous conflation of knowing something versus knowing “of” something (we as humans often blur the lines here and even fool ourselves into knowing things we don’t, simply because we can convince others we know them – a fundamental which perpetuates the general problem of which I speak). But I’ll save this for a different essay.
You don’t know what you don’t know and you don’t know what you forget. Not only is learning not binary, but it is not persistent (rather, it’s transient). I’ll let you explore the research on this point for yourself: http://www.spring.org.uk/…/how-quickly-we-forget-transience…
Staying relevant. I recently spoke to my sister Jen Shainess who is an elementary school educator in Cambridge, MA, and I was pretty surprised by some of the new / alternate strategies which are now being used to teach elementary mathematics. It reminds me of what I’ll call the Tenure Paradox (there’s probably a better name for it). The stigma questioning how a professor who has been in academia for 20 years is able to teach the latest / most relevant content to students. Chances are, their thesis entailed techniques which are now outdated. The only answer is to stay relevant, and that means constantly relearning your field – not just the delta of new topics introduced but from ground up, if need be, as new breakthroughs arise. In this respect, each new generation does have a significant advantage of skipping directly to the obtainment of these important conclusions without having to go through a re-integrated learning process. However, I claim these intermediary learnings are undervalued in our society and that these processes (failed or otherwise) by which those important conclusions were obtained are the most necessary prerequisite for being able to repeatedly obtain future conclusions.
Why it pays to re-visit and re-learn. Not every breakthrough is an addition to our existing knowledge base. Some axiomatically change the whole structure of fields, like the discovery of gravity, of integrals, of geometry, or more recently, of deep learning as an alternative away from features-by-hand. From an algorithmic perspective, not only can new features and axioms be introduced, but we discover more algorithmically efficient (in terms of big O complexity or implementation complexity) ways of accomplishing them.
Re-integration. This notion of continuously analyzing and evaluating your personal knowledge graph is called (re-)enforced or (re-)integrated learning. It is applied within scientific contexts, such as Six Sigma, as Continuous Improvement of Processes and in artificial intelligence / machine learning as reinforcement learning.
It’s a personal roadblock. I want to reach a state in my life where I can open up a wikipedia page and gain a good intuition about complex mathematical topics or research papers. Right now it takes me far too long.
For these reasons, I intend to continue using Khan Academy, and investing time every day into learning not only new topics (Artificial Intelligence, Chinese, Computer Science, Math), but also the fundamentals.
My personal definition of becoming a professional, to become a truly versatile expert, is to invest enough time and learn enough about one’s industry in order to truly know what’s possible to do with the language.
For music, this means learning how to read music, more than one instrument, vocals, lyrics, writing music, music theory.
Imagine the disadvantages faced by a poet who could not read or write, who could explain what they know only through a single language, a single interpretation. Perhaps their one interpretation is very deep and resonates with many people, but it also prevents, or rather limits, them from translating their ideas and feelings and making them universal, cross-cultural, universal, and timeless.
This doesn’t mean someone who only sings isn’t a good musician. It just means that the learnings and skills one can achieve through such cross-pollination can potentially allow or prepare them to achieve even greater heights, with greater speed.
How much faster the car was made after the assembly line. How much sweeter the sound whose fundamentals had been tuned and justified by music theory and intimate understanding of harmonic overtones.
Mastery, in my opinion, is being able to apply, transfer, and convert learnings from one discipline seamlessly to another. The ability to perpetually adapt and grow, of never being limited or restricted by a single medium.
This is why I have such a hard time admiring rap. Not because rappers can’t be talented or because rap doesn’t require talent – it does. Perhaps I simply fail to recognize and appreciate the poetry and entertainment qualities in rap and consider these its primary value propositions. Perhaps I unfairly classify rap as music and hold it to unfair standards.
Or perhaps it is a personal issue and bias, that I become discouraged when artists blend mediums (music and presentation), though this is exactly what now I recommend they do, because I, the uneducated interpreter, lose the ability to assess their value.
I have a very specific definition of music. It’s something I hear. When there’s a song I really love as a song, I do not like for it to be “diluted” with visuals or performance. I often find it exceedingly difficult for me to separate rap from the person who’s performing, and to enjoy it by itself.
My general mentality is, I can take an accomplished musician, like Paul Simon, and he is able to both write great lyrics and rap (http://youtu.be/kDxQr-s26t8?t=3m44s). However how many rappers can sing or compose music like Paul Simon? In fact, how many rappers even write their own samples? Some don’t even write their own lyrics.
Some rappers, like Ryan Leslie, can sing, write, and plays all the instruments in his tracks.
Coincidentally, he also got a degree from Harvard and, while there, did a cappella with Krokodiloes. I find it a lot easier to appreciate rap when I can sense the intention and work behind it; the phrasing and timing, the diction, the rhyming.
Progress for the sake of progress, is Progress
TODO: Add Euclid quote about fetching coin for student who needs earn from their efforts
TODO: Discuss relationship between politics v. progress (why fear shouldn’t stint progress)
I actively make an effort to avoid thinking systems are inherently evil, even if perhaps there does exist what we perceive to be strong empirical/anecdotal evidence in history for showing one system to be “worse” or more “exploitable” than another. Systems are just universes of discourse with parameters. They provide rules, generalizations, boundaries, and a means through which to communicate ideals. Depending on the way they are structured, they can introduce friction or deter/encourage specific behaviors, but at the end of the day, in my (skewed and likely flawed) opinion, people (actors) and conditions (environments) play a determining role in the outcome of such system.
People come from different walks of life, economically, culturally/socially, academically. We all face differing constraints, circumstances. Some of us have it “easier” than others. I am likely one of them. Someone can be born brilliant and with unparalleled aptitude and, due to circumstances of their birth (geography, economic endowment, an abusive family, oppression/war, etc) may by chance never have the opportunity to explore/use it. And this and other such cases are our losses as a people – something which I certainly don’t pay the attention it deserves.
However, life is stochastic and people opportunistic. Someone with a below average IQ is unlikely to abstain from having a child on their own accord, in consideration of raising the world’s average IQ. Nor will every millionaire donate 10k to stop world hunger. People have different, not uncommonly conflicting, motivations. And where one is insufficient for accomplishing one’s own goals, there becomes two, or two thousand. Or in the construct that is Facebook, 700+ million (given dupes + in-actives).
Structures, organizations, and societal constructs, like economies, governments, or corporations, are what help us as individuals overcome hedge these differences, generally to the collective benefit of … well, some subset of people. Sometimes this works to our advantage. We’ve build roads, interactive maps, space shuttles, gps, voice recognition – and there likely isn’t a individual (irrespective of intelligence) capable of fully recreating these systems in their lifetime.
There’s always going to be inequity in the world, if for no other reason than we as a people are statistically and probabilistically astronomically unlikely to converge on a mutually agreeable definition (or interpretation, for that matter) of what is equitable – to be honest, I’m sure there are people who won’t even agree with that statement (que sera, sera).
Instead of worrying about how to fix inequality, I like to explore ways of deprecating it into obsolescence. Software is an interesting (but not perfect) example. Benevolent dictatorships are not uncommon within a software project and are occasionally even more successful than their democratized rivals (GNU/Linux, for instance). As we continue to increase our connectedness, learnings, and accomplishments as a people, we will inevitably provide more avenues for people to achieve better qualities of life.
TL;DR – Crazy people will still be crazy. Murderers will still be murderers. But at least we’ll be complaining about the missing cheese from our cheeseburger rather than the missing roof over our heads.
TL;DR on finding friends: Friendships grow and they have to grow from/on/through something.
- Love work, then meet with people at work to learn from
- Find friends through successful/healthy procrastination (gym, reading group, game night, hikes, music, art)
- Through academic learning (perhaps through work, or arrange activities with people you discover wish to learn similar things)
- Through “dampers” (see below; people you already trust who expose you to new things/people)
I have a different challenge: finding people I enjoy spending time with. I psychologically have a hard time escaping a “work” mindset because I have compulsively crafted my life around learning and applying knowledge. I also have little patience for people who I feel lack ambition, don’t push me, and question why things are. Arguably, this is a character flaw of mine and something I should work on – at the same time I am not sure I want to compromise my ideals (unless a person shows me how wonderful their qualities are that I am forced to make an exception – these are often my favourite friends). Life is short and we become more like those we spend out time with, thus I treat the process seriously. Perhaps some may call this being too risk adverse. I actually think it’s the opposite: after all, it’s essentially a general comparison of a depth first (my strategy) versus breadth first search (having no filter), and my strategy is proven to be mathematically incomplete (although I may actually suggest my search is more of an A*).
One thing I have learned is, to be successful at hitting goals, you need to also be successful at effective (yes, there is such a thing) procrastination (i.e. unwinding). It’s in these types of healthy, unwinding activities I often find meaningful friendships.
Also, make sure you genuinely love what you do and ask people for help when you need it. It’s much easier finding friends when you love your work; i.e. work is not work but life itself. Most of the people I meet with for coffee during work hours are extremely inspiring as they are solving problems I so deeply care about (it’s and incredible vicarious feeling) – each one in some way teaches me something and I end up considering some degree of a mentor.
Here are the places / activities where I find myself meeting people:
- Noisebridge Hackerspace
- Rock Climbing @ Mission Cliffs Indoor Rock Climbing Gym
- Five Minutes of Shame (Hackerquarters bi-monthly event)
- Mentors @ coffee shops. (stanza, fourbarrel, sightglass, cafe centro)
- The MoMa (which is now defunct)
- Mellow Beats & Board Games
- Damping Factors (see below)
Damping factors. Some of the important people I associate with are like damping factors (see explanation as it pertains to PageRank algorithm) . Dampers are good for introducing stochasticism into your life (as if we really need more!). I choose these types of friends because I fundamentally respect them and their abilities and thus know I will enjoy their company even in different (read: strange) scenarios, even if I would not otherwise enjoy the type of event they encourage me to participate in. This is also a good way to explore edges along your friends graph and meet new people.
Be a Hacker
Monty Hall and St. Petersburg
A few moments ago, someone posted about the Monty Hall problem and Marilyn vos Savant.
The Monty Hall problem is challenging for people to grasp for reasons demonstrated by the St. Petersburg Paradox. The reason is, some people rely on their “gut” (their ability to interpret subtle nuances) more than they do mathematical logic, statistics, and probability. There is a difference between a decision made based on mathematical principle, and a decision which results from “economic” or utilitarian constraints. The difference is, the answer to the St. Petersburg problem depends on your finances and risk aversion, whereas the Monty Hall problem has a irrefutable, yet perhaps counter intuitive, best-case solution.
The St. Petersburg Paradox proposes a game we’ll call the “Consecutive Flipping Heads Game”. The objective of the “Consecutive Flipping Heads Game” is to flip a fair penny as many consecutive times as you can until it lands on tails (wherein the game ends and you get to collect whatever money is in the pot). Now, originally, the pot contains 1 penny (i.e. if your first coin flip lands tails, you still keep this 1 penny from the pot and the game is over. If you flip heads first, and then a tails second, the pot doubles from 1 to 2 cents, the game ends, and you collect the 2 cents).
The HEATED question is, what price would YOU pay to play this game? If the cost to play is 1 penny, everyone (except hipsters, maybe) would play because you are GUARANTEED to earn at very least your penny back. You will never lose money.
But would you pay $1 dollar? Would you pay… $10?
The “Paradox” in the St Petersburg Paradox is, mathematically, you should pay ANY amount of money to play this game, IRRESPECTIVE of the cost (so long as it doesn’t cost “infinity” dollars). $1,000 to play? DO IT. $1,000,000 to play? WHAT ARE YOU WAITING FOR.
Wait, this sounds crazy, why is math telling us to spend $1,000,000 (which I might not even have) on a game where I could flip a tails and then instantly be broken. Math doesn’t care about how much money you have, or your risk aversion. Math only cares about the expected average case of the game and how much money you could win in this average case. And for the “Consecutive Flipping Heads Game”, this Expected Value (average-case payout) of playing the game (let’s call the game X for short-hand) an infinite number of times is…. INFINITY $$ .
You can think of Expected Value like someone’s batting average in baseball. Throughout their career, baseball batters play the “Try To Hit The Baseball” game hundreds of times. The more they play, the closer they approach to a certain long-run average (their batting score), which changes slightly (becomes more accurate) with each additional at-bat.
For those who care to understand the math, the formula for computing the Expected Value (let’s call it E for short-hand) of playing the “Consecutive Heads Flipping Game” (let’s call it capital X for short-hand) is show below. Otherwise skip to “GET ON WITH IT”:
Expected Value of [Consecutive Heads Flipping Game]: E[X] = SUM(xi * pi) … which expands to: E[X] = (x1*p1) + (x2 * p2) + … all the way up to … + (xk * pk) Understanding the formula
What does x# mean or p# mean? Think of “x#” (e.g. x1 or x2, etc) in this problem as the $ you earn if you achieve the #th consecutive heads flip. “p#” is the probability of achieving that # of consecutive heads flip. In this case, the probability of getting 1 consecutive heads is 1/2 (this is the probability of a coin landing heads) and the payout is 2 cents (the 1 cent initial pot specified by the game, doubles from the heads) … the probability of 2 consecutive heads is p=1/4 (this comes from 1/2 for the first heads * 1/2 for the second heads) and the payout is x=2 cents. 3 consecutive heads is p=1/8, the payout is x=4 cents. You can see a pattern of the probability halving each time, and the payout doubling each time. If you’re wondering what the xk and pk means… k is just a placeholder which represents the final/last iteration of the game. Since the game could last for 1 turn or 10,000 flips (we don’t know!) we use the arbitrary symbol k to represent/denote the kth (final) flip of the sequence (whatever that number k may actually be).
So in our case of X being the “Consecutive Heads Flipping Game”, we have: E[X] = (1/2 chance * 2 cents) + (1/4 * 4 cents) + … and on and on If we add (sum) all of these up, we get: E[X] = 1 + 1 + 1 + …. E[X] = infinity!
GET ON WITH IT!!!! Ok, ok. If you work through the math, you’ll see why the Expected Value (the average expected returns, if you were to play the game over and over again) would be infinity – a great value proposition!
That doesn’t mean you will actually pay to play the game. In economics and utility theory, it can be demonstrated that money has a different value, relative to the amount of money you have. If you have $100 to your name, paying $50 to play this game may seem ludicrous and irresponsible. But if you have $100,000,000 dollars and the game costs $50, it may make a lot of sense for you (because of the Expected Value justifies and suggests, if you keep playing, you stand to earn a nearly infinite sum – of course not guaranteed, just supported/informed by the average case probability of the event).
This paradox is similar to the Monty Hall problem. The Monty Hall problem suggests you’re on a game show with 3 doors. Behind one of them is something you want. Behind the other 2 are things you don’t want (let’s say worthless coal). You are entitled to choose 1 of the 3 doors (thus with a 1/3 confidence). The game show host then reveals one of the 2 remaining un-selected doors which contains coal (one of the items you don’t value). The question is, if the game show host were to allow you to change doors to the other remaining door, would/should you?
Many argue it doesn’t matter because you are now faced with a new, independent problem which has 2 doors, each of which have a 50/50 probability of containing coal. However, the problem is, the probability distribution does NOT magically raise from 1/3 to 1/2 just because there are only 2 doors remaining. Here’s why:
Mathematically, there are 3 doors (buckets for your combinatorics folks) and 3 items which have 3! (3 factorial; i.e. 3 * 2 * 1) unique arrangements / ways of being ordered. And 3! (i.e. 3 * 2 * 1) = 6 unique orderings. That is to say, when the game show host sets up the doors, he must choose 1 of those 6 arrangements.
Door: 1, 2, 3 a) Coal1 Coal2 Car b) Coal2 Coal1 Car c) Coal1 Car Coal2 d) Coal2 Car Coal1 e) Car Coal1 Coal2 f) Car Coal2 Coal1 Mind you, these are not actually 6 “unique” permutations. You may notice, that a&b, c&d, and e&f are… well, when you get right down to it, a piece of coal is a piece of coal and the two coal doors are the same crappy prize, as far as we’re concerned. So really, out of these 6 orderings, only 3 of them are unique: a&c&e (we could have just as well said b&c&f or a&d&f, etc – you should ask if you don’t understand why this is. It’s important, just not the emphasis of this piece). Needless to say, the real 3 unique outcomes are: Door: 1, 2, 3 a) Coal Coal Car b) Coal Car Coal c) Car Coal Coal
The door you originally select – let’s call this door #1, you choose with a (by definition) 1/3 probability of being right. When the game show host reveals one of the doors (such as door #3) as containing coal, we never learn if our door is wrong, only about one of the doors which was wrong. After the revealing, since there are only two doors left (ours and the other), and we know our door has a 1/3 chance of winning, then we know the remaining 2/3 probability lies with the other unrevealed door. While it may appear to be a 50 / 50 choice, this ignores the initial probability distribution of the problem setup. Just because there are only 2 choices remaining, doesn’t mean it the problem is a 50 / 50 spit. Here’s how to explain it. Here is the #1 key learning to understanding the Monty Hall Problem:
- With our initial selection, we always choose the same door.
- On the other hand, the same door is NOT always eliminated! If we always choose door #1, then it can be either door #2 or door #3 which is eliminated, depending on which contains coal. There are 2 ways for the car to exist within door #2 or door #3 but only 1 way for the car to exist in door #1.
One could argue the host uses knowledge of the Monty Hall problem to purposely mislead contestants into choosing the wrong door (i.e. only offering the option to switch when their 1/3 probability original selection is correct). But assuming the placement of the valuable item is done perfectly randomly (fairly), I would switch doors. By switching doors to the final remaining door, you are basically making a new decision which includes both of the 2 doors: the probability of the new door, as well as the probability (the added information) which you get from the door which has been revealed (1/3). As a result, switching your decision to the other door means you have a 2/3 chance of being right. Given the opportunity, I’d switch. DISCLAIMER: I’m not perfect, corrections appreciated.  https://en.wikipedia.org/wiki/St._Petersburg_paradox  https://en.wikipedia.org/wiki/Expected_value  Monty Hall Program + Proof
Be Philosophical: Meta, Reflective, Curious, Inquisitive
Analogies of Life and Computer Science (and their inferences)
The Perception of Realities
Reality is the conjectured state of things as they actually exist, rather than as they may appear or might be imagined. 
Namely, there are two types of reality. I independently came up with the terms Social and Material reality to serve this distinction. Ironically, Franz Jakubowski  (http://en.wikipedia.org/wiki/Franz_Jakubowski), Schütz, et al  came up with nearly identical distinctions 100 before (ideal v. material reality and social reality). Given they spent a lot longer thinking about this problem than I have, I am going to concede to their terminology.
- Our universe has not been fully observed
- Our universe is not fully understood
- Within reality, there are systems which are (for now) of known computable probability distribution
 Compact Oxford English Dictionary of Current English, Oxford University Press, 2005  http://en.wikipedia.org/wiki/Franz_Jakubowski  http://en.wikipedia.org/wiki/Social_reality  http://en.wikipedia.org/wiki/Occam’s_razor  http://en.wikipedia.org/wiki/Solomonoff’s_theory_of_inductive_inference  https://en.wikipedia.org/wiki/M%C3%BCnchhausen_trilemma
Why is the World Broken?
Occam’s Razor (Ockham’s razor; William of Ockham ) – or more formally Solomonoff’s theory of inductive inference  – demonstrates among other things
there’s only one way to be right and a nearly infinite number of ways to be incorrect.
When we discover a new theory, we’re presuming every assumption and axiom it relies on is also sound. Münchhausen’s trilemma  presents an interesting perspective on the challenges of achieving such “truths”.
The world really is broken. But this is because we don’t really understand it. And if we think we do, Occam’s razor should likely convince us otherwise.
See the Blub Paradox (http://www.paulgraham.com/avg.html)
And will remain broken, by definition, so long as our observations and methods are insufficient to achieve deterministic understanding.
Given that the number of bits required
There is a difference between the existence of moral truth and ab
In the same way that statistics offer us
The Žižek trilemma
In order for “moral truth” to have practical meaning, holistic and perfect understanding of others.
- continuously updated, shared knowledge base
- equal ability to process and interpret said knowledge base
Occam’s razor would likeyl suggest the world is as broken as our understanding of it. Or rather than it is broken because our understanding of it.
That is to say, it is binary whether the universe has a purpose or not.
We live within a context we don’t fully understand and thus have no way to verify how our system does or is supposed to work. This is a problem because it exposes our inability to fully control the which are important to us.
It’s challenging to quantify “the” problem, suffice to say there is one. Our interpretations of this context is, perhaps, part of the problem.
Life is a constant struggle to persist knowledge. How many hard drives have failed, libraries have burned, years of progress have been reverted? That our efforts keep building up these corpuses
Ideal and Social Reality
As far as most of us are concerned, there’s one reality that matters: ours (i.e. individual cognitive reality ). We acknowledge others are no different than us, in that they similarly experience an individual reality. We thus accommodate each others’ realities to reinforce our own, enabling us to mitigate confrontation, refine our beliefs, and exploit the shared/mutual beliefs and accomplishments of others.
The resulting network of ideas and ideals, this aggregation of all societies individual cognitive realities, is sometimes referred to as an accumulative Social Reality . This definition of reality manifests as our shared perception. It is an understanding of the world shaped by social interactions, emotion, advertising, politics, among other things.
For some, social reality is the only context worth acknowledging and these individuals will fight to preserve their “individuality”, their “privacy”, and their “competitive advantages”. However, beyond the confines of our contrived, disparate social realities lies a bigger context. A universe of possibilities more meaningful than our shallow perceptions allow. It is the underlying system on which we base and derive all our truth, even those we contrive, and the framework which constrains or progress.
Reality is fundamentally broken and is heavily gamed to remain this way. The masses, afraid, unmotivated, or unequipt to push against the tide, strengthen its flow.
When I say broken, I mean that our expectations have been violated or compromised; that the internal consistency of our system has been challenged or threatened. I mean Broken in the same way the algebraic or calculus proof demonstrating 2 = 1.999… [http://en.wikipedia.org/wiki/0.999…].
This deinition or sense of reality is fundamentally broken both in that it is incomplete and that we all experience it differently. How can we possibly consider the realities of those we have never met? Are they not part of reality?
I imagine, for the many, the realities we choose to acknowledge are limited to the scope of our immediate surroundings and anything beyond this, we discount. E.g. global warming is a problem for a future generation. Some people never escape this short-sightedness; this inability to look outside their “little town”, towards a holistic understanding of some bigger picture.
This gap is filled by CNN, twitter, et al. It is wrought with error, misinterpretation, and selection bias, in (among other ways) the form of advertising.
Herein lies a major source behind our dogma. Each of us has an agenda and are thus cautious of which reality we embrace and which reality we allow others to see / choose to proliferate. This decision process is often done without respect of (and sometimes with direct disregard to) overwhelming evidence on the topic.
Some connive or deceive in order to protect their image; to avoid appearing abnormal or rather to preserve their golden image. Some do so to gain control of others, to appear attractive, or to ensure their fortunes. But in each case, the individual prioritizes their immediate well being over challenges which may impact the masses. These are the people who monopolize knowledge, who file patents, and close the source to their software and hardware.
It is the brave few who look past this social reality and challenge us. Those who pose the world is flat, that the earth is not the center of the universe. Those few who devote themselves to academia, who poke holes in our assumptions and theories and compel us to alter our perceptions of reality. Seekers of a truth deeper than social perception.
But society often rejects these people and their ideas, until their merits become so compelling that they can no longer be controlled with propoganda and ignored; Alan Turing, Nichola Tesla, Galileo, Pythagoras, and Marie Curie.
With science and mathematics, we often construct controlled systems to test hypothesis. Through these controlled systems, we are able to separate concerns, reduce complexity, and deconvolute and deconflate variables. The result is greater understanding of how variables act independently, as well as relate and impact each other (i.e. their functional dependencies).
But the world we live, what we call reality, is in much more complex than these artificially constructed, simplified sandboxes.
There’s the additional argument that these sandboxes are themselves run within our reality (and thus impacted by stochasticism we can’t fully understand and appreciate).
Social Reality v. Material Reality
|Social Reality||Material Reality|
But this isn’t why reality is broken. Reality is broken because of us; people who make it that way and want it that way.
Dogmatism: People are afraid to admit some problems haven’t been solved. They cling
We feel accountability because we are dogmatic. We feel obligated to preserve our beliefs and those who mutually embrace them.
Towards Universal Reality
Working towards achieving this aligned reality enables us to make more educated, respectful decisions.
The answer lies with technology and deeper communication – with the possibility of a distributed, shared reality everyone can understand and independently validate. The Internet holds great promise as a medium for connecting and communicating knowledge in a way which enables everyone to participate and collaborate. The HTTP RFC, Wikipedia, Wolfram Alpha, Google Maps (Scholar and Finance), The Internet Archive, Gitub, and Coursera are great early efforts towards democratizing access to high quality data.
That such great margins exist to objectively improve quality of life; to reduce poverty, increase accessibility to knowledge, and thoughtfully reconcile conflicting views.
That we might spend less time engrossed in trivia, acting selfishly, inhibited by defensive dogma, and misled by jealousy, and instead spend more time understanding which problems are worth solving and which people are worth assisting.
How do I think?
Should others think differently?
It’s wonderful there’s such diversity within peoples’ thoughts and life philosophies. Some living for today, for nobel causes in 3rd world countries, to get rich, to find love. It’s reassuring in the same way a breadth first search offers completeness, in the same way random restart and simulated annealing helps avoid becoming infintely trapt within local optimas. I would not stake humanities success on the correctness of my ideals. Perhaps I should think differently and I look forward to shaping my direction as new information becomes available and circumstances emerge.
As a single thread, even when weaved between others
That doesn’t mean it isn’t lonely, feeling like a single thread weaving an path through a tapestry whose composition we don’t fully understand and cannot fully observe. I want to understand this composition. I want others to want to understand this composition, why it is we wave, and what opportunities exist for us all to weave better. Irrespective of our different goals, mutual understanding is imperative to us making informed decisions. Knowledge should never be held hostage or become collateral damage, unless to protect knowledge as a whole (a most saddening exception, one of the most sad things there is).
Who’s to say one’s strategy for weaving is universally superior to another? I suppose one can painstakingly calculate the costs of having to repair said weaves, or the value that a “correct” weave actually offers, for and to whom. One may even speak of opportunity costs, of risks, and on the cost of not risking enough. One can even debate what value is, how it is measured, and for that matter, what a “correct” weave actually means.
And people try. Economists, statisticians, computer scientists, mathematicians, classical scientists, game theoreticians. But this struggle is constantly undermined by the pressures of being human. The pressure of conforming to society, achieving personal happiness, and marketing/promoting one’s ideas in order to inflate their importance and have them adopted to a degree one believes accurately reflects their merits.
I feel like there should be a better way; a better global effort towards an internally consistent and accessible world. This is the world I strive to see realized, though I know it is not to be achieved in my lifetime.
On choosing a philosophy
Choosing a life philosophy is challenging, especially without first setting a context. For many, I would guess their context is around 150 years; approaching the upper bound of one’s life and their usefullness as pertaining to their children and grand children.
The window I live in is three thousand years. Arbitrary perhaps. It is the duration of time the past feels relevant to my life today. I look at the past three thousand years and I am distraught, how much information has been lost? How many great minds have returned to the earth without their learnings to survive them? This summed lost impact is surely greater than my own, should it even survive.
And of the learnings of others which have survived, how have people’s lives improved because of them? And, I’d be remiss if I did not also consider, in which ways have they become worse? On this I am not prepared to answer.
Sacrifice; The consequences of my decision
Having a contextual window of 3 millennia has consequences. It means that my actions do not optimize for the those living around me. Their window only practically represents 1/20th of mine.
I necessarily have to invest in other people. My mission is not one that can be accomplished individually. I thus constantly feel I can only afford to keep company with those who will push me on my service towards these goals.
- I become anxious about not using my resources efficiently
- I have started to become increasingly upset by trivia and experience even greater disdain when I take pleasure in it.
- I have sold most of my possessions to eliminate distractions
- I try not to purchase expensive things unless there is a practice reason and the delta in price is justifiable
- I am willing to spend money on others
- I have automated much of my life (laundry, groceries, rent) so I can spend as much time as possible focusing on problems I feel are more important.
Fear of missing out
Our number of seconds in this world are perpetually diminishing. Every moment we deviate from our objective is a moment we lose forever. It’s painful knowing we need to rest and take breaks. At the same time, this reality comes with opportunities for optimization. Effective procrastination is thus a topic of personal interest.
It’s also upsetting knowing there are only a certain number of forks we can make and still be on track to hit our goals. Only so many things we can be mutually good at.
I often wonder if my limited travelings, cultural exposures, relationships, meditations, are actually inhibiting my success. It’s likely that learnings fundamental to my success lie in one of the experiences I have not yet had.
I am only human. I frequently fight the urge to change my path. Sometimes I wish to dissapear in a different country and lose contact with anyone important to me. Sometimes I consider prioritizing happiness.
Growth versus execution
Balancing growth and execution is one of my biggest presonal challenges. Recently I’ve felt pressure to prioritize execution over learning and I have great fear this strategy will work against me (in terms of the absence of compound interest of my knowledge) in the future.
A leap of faith
I believe that (1) striving to create a universal knowledge base, (2) working towards discovering and eliminating inconsistencies, and (3) making this knowledge base accessible, are the root of progress and advancement.
Is certain creativity dangerous?
The words “just” (etymology: via Old French from Latin justus, from jus ‘law, right.’), “merely” (etymology: from Latin merus, ‘of wine’, ‘unmixed’ – antonymous to Promiscus Promiscuus), and “literally” (etymology: littera, “letter”, litteralis “of a translation”) have been perpetuated into diluted, hyperbolic statements which show disregard for the rigor and specificity under which they were formed.
I’ll likely look back and find this aforementioned claim naive and ironic – I am creating a commentary on the dangers of creativity. I’ll document my reasoning for the sake of provenance.
Your digital self: secure, organized dropbox for specific verticals: storing health records, genetics. Setup notifications in case of an emergency (contact your doctor, email your contacts). An encrypted server which stores your health (or other) records in the event of an emergency.
Goal driven book selection bests book on topics, endorsed and verified by leading experts
- If you’re not an expert in X, how do you find the best book on X?
- There are too many books to read, you can’t read them all
- People want to know the fastest way to learn
- Book selection is not outcome driven; what are your goals?
Solutions / Competitors
Verticals (+ justification)
- only phds/experts can comment
- Technical books via irc
DomainsMonetization M1. MVP, Search/Discovery Engine
- Affiliate Links (amazon)
Monetization Marketplace on thebestbookon.com M2. Marketplace
How to learn x (sequence of books) The best sequence / syllabus on X M3. Syllabuses
- mashups of content
- sell sylabi?
- Promote Bloggers, split affiliate fee
- Login with facebook, send books to friends (learn who likes what)
- Every purchase can be sent to 2 friends for free
– every additional friend is 25% off, you earn 25%
provenance - This tool is a way to quickly, selectively preserve and make re-accessible the knowledge and search history of every individual, and to both share and receive highlights with others
a mobile application which allows you to manage various contexts (work, home, etc). Programs are installed within the container environment with a specific set of permissions (2-factor, etc). It’s like having a separate, secure, contained user/environment. Manages contexts apps run in, prevents man in the middle, etc.
Be Supportive, Philanthropic
I need to support the people who are meaningful to me. I need to support people who can create a better world.
Drew’s Paper on Zooming
Jessy’s JTag Controllers
Be Observant, Logical, Reasonable, Analytical, Analogical
How making life decisions can be informed Computer Science (algos)
A vertex is worth as much as three edges
Pay people for finding mistakes