Create Your Own Abstractions

1/6

There's an illusion in the software world that abstraction makes things easier, faster. Sometimes. But it also obfuscates the factors that lead to problems, making debugging often more challening than writing your own work from the ground up. 2/6

Working with a raw language is computationally universal, so there isn't anything you can't create (within the usual limits of computation).

"But writing it from scratch will take longer."

Not necessarily.

The Redundancy Cascade

1/11

To be effective at anything you need to find the overlaps between your many interests.

This ensures you are working on what you’re meant to do, and that you can scale your efforts, with higher ROI.

Let me explain. 2/11

There are many things you want to accomplish. Maybe start a business, build a product, teach a course, etc.

Finite time and resources seem to demand you focus on very few interests. But limiting yourself to few interests is unnatural and unsustainable.

@engexplain @nntaleb 1/10
Science is assumed to be “evidence-based” but that term alone doesn’t mean much. What constitutes good evidence? How is evidence being used? Is it supporting or refuting a hypothesis? Was the hypothesis and experimental design predetermined or found ex post facto? @engexplain @nntaleb 2/10
The reality is you can find “evidence” for almost any narrative. Limit the sample size, cherry-pick studies, etc. Systematic reviews, meta analyses, and randomized controlled trials are all susceptible to selective interpretation/narrative fallacy.

@paulportesi 1/n

It's been behind the scenes. The Canadian AI company BlueDot is who first alerted the world to coronavirus after the first case was detected in China.

This program predicts infectious diseases and locates and tracks their spread.

bit.ly/2UvLLWv @paulportesi 2/n

It combines with domain knowledge from epidemiologists, analyzes over 100,000 reports daily (multiple languages), then sends out regular alerts to health care, government, business, and public health clients. Highlights outbreaks discovered by AI and their risk.

Get Started Building Software (in 1 twitter thread)

I believe everyone should build. Building is the only real validation of our ideas. There is no better way to learn.

Today, that usually means software. I think of software as today's woodworking. Today's metalsmithing. Anyone can build a basic application, with all the major pieces needed to seed more elaborate projects and learning down the road.

Software is about expression. If you want to express your ideas with more than just form, you need to add function.

Let's get started.

1/n

VCs only want to invest in startups that can be bought by incumbents, otherwise they don’t foresee ROI.

So if you are dealing with a VC-backed startup you can be fairly certain they are doing nothing innovative, for reasons depicted in my original tweet.

https://twitter.com/kmlyounes/status/1255204450471419907

2/n

Attempts to break this model usually focus around bootstrapping, enabling companies to grow organically, from the bottom-up; albeit much more slowly than their VC-back counterparts.

1/12

Great talk by @stephen_wolfram introducing his Physics Project (wolframphysics.org)

Important work here; goes well-beyond physics. I hope upcoming generations of scientists pay close attention.

Some of my quick thoughts: 2/12

What is the qualifier for unification under this framework? How will QM and GR demonstrably work together enough to say they are unified"?

@spartanowner1 @paulportesi 1/8

If you over-manage your distractions you’ll miss out on the surprisal needed to fuel creative efforts. But, if everything is unstructured you’ll fail to maintain momentum on your most important tasks. @spartanowner1 @paulportesi 2/8

Nature exhibits pareto-esque distributions for a reason. It needs to tap into variation (surprisal) to make serendipitous discoveries, but those discoveries must benefit some structured goal.

@spartanowner1 1/12

First, we have to be clear. Mastering a craft and doing what's needed to get a job are 2 very different things. @spartanowner1 2/12

Companies, particularly large ones, hire "cogs." You fit into an existing recipe that has made the company money for years. Cog work is highly-focused labor that allows companies to scale by division of work.

On Challenging Ideas

1/5

Many are unwilling to challenge established ideas for fear they lack the knowledge necessary to advance opposition.

But truth accommodates the dissident far more than those hoping to maintain the commonly accepted. 2/5

2 important points to understand:

1) The power of refutation lies in epistemic asymmetry; it requires far less information to refute a theory than to support it.

2) All attempts to assess a model’s validity must remain agnostic to the tools used to build and promote it.

There Are No Recipes

You cannot engineer your career. You cannot craft your success. Your accomplishments will not come by following a plan, mimicking your hero’s habits, or staying up-to-date on the latest trends. Your luminaries are statistical aberrations, with stories no more interesting or unique than those who have failed. You are reading but one of countless plausible narratives in an ocean of silent evidence.

@iwangulenko @birdxi1988 @nntaleb I'll give you some background first, then bring in entropy. Note I am not speaking for Taleb here. @iwangulenko @birdxi1988 @nntaleb Kelly betting is the application of information theory to investing. This conceptual leap makes sense since information theory tells us how to quantify information to make decisions under uncertainty.

@MoneyEvil @nntaleb @yhazony @clairlemon Reality is nonlinear, involving higher dimensions than anything simplistic statistical techniques can model correctly. If you want to model reality you need to use high-dim approaches that can adequately approximate complexity. @MoneyEvil @nntaleb @yhazony @clairlemon Forcing a statistically convenient model onto something overtly complex is not science. This is caused by "physics-envy" where those studying complex phenomena want to anchor their narrative on simplistic models that are easy to understand (and influence policy with).