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Peter Wang Profile picture
@pwang
May 2, 2022 • 10 tweets • 3 min read • Read on X
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Given the interest in PyScript and some of the common questions that are coming up, I figured I'd share some of my slides which may answer some questions.

Also I highly recommend checking out our engineering blog post on it: engineering.anaconda.com
For context: each square is 1 million people. The gray squares represent the population of earth.
For those who don't want to do the math, it's about 0.3%.
(The people who can be said to "know" DS/ML/AI is much, much smaller - maybe a few million?)

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More from @pwang

Peter Wang Profile picture
Dec 5, 2022
We are at this stage of "AI".

Just keep that in mind as you board pressurized Mach 0.9 passengers jets in a few weeks to see your family for the holidays. Image
42 years after the Wright Flyer, the Bell X-1 broke the sound barrier. Image
19 years after that, the SR-71. Mach 3.2+ (faster than a bullet), service ceiling 85,000 ft. Image
Read 4 tweets
Peter Wang Profile picture
Dec 4, 2022
An alternative way to consider what’s happening with LLMs is that they are transforming the *serendipity industry*.
Investors and pundits have been talking about “network effects” forever, but seldom use the word “serendipity”, instead favoring “scale” and “long tail”…
1/N
When a platform creates markets that can serve “the long tail” of customer needs, the result can feel serendipitous.

LLMs have mined the *entire digitized corpus* of historical human thought and creativity, and put a compressed version of that on the Supply side of the market.
What kinds of serendipitous connections can emerge between each of us and *the entirety of human history*? This is a question which very few have ever contemplated, and certainly we have no answers for it.
Read 5 tweets
Peter Wang Profile picture
Jun 1, 2021
The last twenty years of software development have gotten away with not giving a flip about security, really.
Recalling some of the horror stories I've read on HN, reddit, and other forums, it's clear that the entire industry shamelessly plows ahead despite horrible tech debt.
This level of blithe carelessness should give all AI & ML practitioners pause.
The lesson of the past is this: Business decision makers will gleefully roll forward with whatever garbage you threw at a wall that happens to stick just long enough for bonuses to be paid out.
Today's tech architectures are more and more being built by teams that are amalgams of multiple specialists: data scientist, data engineer, software dev, infra ops, PMs, ML researchers,... Lots of Franken-elves piecing together a beast that no single party is responsible for.
Read 8 tweets
Peter Wang Profile picture
Dec 5, 2020
@widenka @RealSexyCyborg The long explanation is that Westerners are raised in a cultural tradition built on Enlightenment-era concepts of individualism and liberty as the paramount concerns. This has become even more pronounced in the post-war consumer society of last 50 years.
@widenka @RealSexyCyborg Eastern cultures only started interacting with this philosophical mode of individualism about 100 years ago, with the advent of industrialization and labor economics.

So in China, India, and other heavily-socialized countries, ppl can’t just “do what they want”.
@widenka @RealSexyCyborg Americans are generally blind to the fact that our model of liberty and individual egocentrism is a privilege they earned through centuries of oppression, and having limitless resources and the good luck to be bordered by oceans and nice Canadians & Mexicans.
Read 7 tweets
Peter Wang Profile picture
Jun 30, 2020
The day is here—we’re excited to share findings on the 2020 State of Data Science!

Every year, we check in with the data science community with a survey to see what’s on their minds re: responsibilities, challenges, & processes.

The TL;DR from this year is… (1/5)
Hype is cooling down, but there’s still work to be done to help data science achieve business maturity.

Here are my top 3 takeaways:

1️. Turning data into value is difficult. Data scientists report that almost half of their days are spent on data loading/cleansing. (2/5)
2. Data bias and privacy are top of mind. Yet, only 15% of surveyed universities include courses in ethics and 15% of data science teams are actively addressing issues of bias. (3/5)
Read 5 tweets
Peter Wang Profile picture
Feb 17, 2020
My thesis is that since the value prop of ML/AI efforts are complex-valued function ƒ(Software component, Data component), the value of ML/AI companies are extremely non-linear with respect to either partial derivative ∂/∂Software or ∂/∂Data
The disruptive potential of an ML/AI startup depends a LOT on whether the hurdles to predictive success within a problem domain are primarily technological or organizational. Conway's Law applies to data systems even more so to software; and bad data arch can doom DS/ML/AI.
So a successful ML/AI startup requires lightning to strike TWICE. Virtually every ML/AI startup in B2B will suffer the organizational/GTM challenges of B2B software, IN ADDITION to needing to earn a bunch of early-stage revenue that looks an awful lot like services ("bad") ARR.
Read 5 tweets

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