RA_Fisher

As AGI economizes general human intelligence, people will scramble to acquire relatively specialized intelligence since it is now much more scarce relative to general intelligence (and both are highly demanded).

I don’t think the same is as true of LLMs unless they’re put into a system (that can acquire knowledge and skills).

Things like AutoGPT and BabyAGI seem intelligent to me.

Intelligence is defined as the ability to acquire and apply knowledge and skills.

How could it be denied that things like AutoGPT can acquire knowledge and apply skills?

That wishful thinking stood out to me, too. “High-quality Soviet …”

They ostensibly noticed the bank was insolvent and shared that important information with their startups. The bank should never have been anywhere near insolvent.

It requires exceptional executives and an exceptional executive data scientist today, no doubt. Though, based on my experience it is possible given mutual due respect. Executives are domain experts, and I’m a business economist-statistician / econometrician. We work together to pool our understanding given our vantage points and differing abilities, skills and knowledge. By working together, and applying the right skills to important situations, it’s possible to avoid a lot of conformation bias it would be difficult to avoid otherwise (and the result is growth and smooth operation). ie- through analysis, it’s possible gain alignment & prevent all kinds of erroneous assumptions (resulting in increased growth).

Both are unfortunately very deadly and there’s a lot of societal value in preventing both (without inducing more costly trade offs). Covid appears to be killing at a rate of 138,000 per year in just the United States.

I’ve been performing it informally for many years and more formally for the last year and a half as Zapier’s Principal Data Scientist. I really enjoy combining my advanced technology, business, economic and statistical skills with large scale business opportunities to produce innovation for people and earnings for the company.

My goal is ensuring executives are making decisions using the best information possible. It’s highly collaborative to maximize the amount of pooled information (because executives are deep experts, too). Based on how it’s gone so far, I think it’s innovative and has a strong beneficial affect on the quality of decisions (and output as you’d expect). ie- applying more technology & skills improves output volume and quality.

I think it’d be wise for more companies to employ an executive data scientist.

And the stock seems down 7% vs. the Nasdaq.

I get to apply my causal inference and measurement skills to a lot of compelling situations in collaboration with some really intelligent and skilled people. I truly feel like I get to perform technically excellent work, and I feel very privileged in that way.

My day to day is mostly economic / econometric analysis, conceptualization and formulation of technology, and writing, but also occasional meetings to gather information, feedback, answer questions, teach and collaborate with others.

I try to effectively “sample” the org by interacting with people up and down the org, so I can assist Execs in incorporating directors, managers and ICs knowledge / pool good information / systematize planning, etc.

I think the recent industry layoffs reflect that in part.

Though I think there’s a better way— that is executive data science, what I do at Zapier. The key is that I’ve built up a huge amount of econometric and economic / business skills that I apply to affect good change in collaboration with company leaders. It allows me to work with Execs using sensible analysis. I improve growth and output by helping us catch errors of assumption before they go into production and cost growth / bad surprises. I also help the executives gain alignment around good information. This multiplies their departments’ output by allowing them to work better together, more in concert. That helps avoid issues with data being bent to decisions.

They typically carry a lot of hard-won valuable domain knowledge (that I combine with my economic-statistic knowledge and skills for rigor).

It’s my job to ensure Execs start with good sensible information regarding objectives. They usually ask fantastic questions and share a lot of great analysis of their own.

There are times I learn about what I might call controversial implications. This is typical of innovation using technology. It’s in these moments that I feel I create the most value by highlighting the trade offs I believe we face / potential regret.

Both suffered ‘08 and the pandemic and have responsible central banks.

It doesn’t sound to me like they’ve put in the work to earn the ownership share. I think it’s unfair to yourself and remaining team to keep them around.

We mostly type these days and block writing’s good enough, so it’s a waste of time for the masses in my view.

It’s nice, but it’s hard to beat the clarity and expressiveness of dplyr and purrr.

That’s awesome customer service! Thanks for sharing.

So true, causal inference is much more valuable than prediction which is a relatively cheap commodity.

The person advertising has all the data they need to determine performance, just add statistics know-how.

Yes! I love Apple Arcade for this reason, too.

Exactly, RCTs take the mystery out. Nice work!

The groups aren’t segmented by freeway vs. non-freeway use, but it’s an important omitted lurking variable. After conditioning on the variable, the takeaway changes.

Yep. I wonder how realistic a 51% option would be?

True. One would want to continually decorrelate services or model the dependencies. Redundancy will help even with some dependency, but you raise an important point.

Bingo.

True with two independent servers at 90% each, that’s 0.1^2 = 1% chance both fail— so redundancy can add a lot of reliability.

The competition benefits consumers. Shopping is much more convenient than before Walmart and Amazon and they help keep prices low— that’s why I wouldn’t be a fan of the government interfering with the two companies.

I agree, it shows the company cares about its employees.

Finally multi-level/hierarchical modeling is starting to permeate industry thanks to Stan and company.

I use hierarchical modeling regularly to help build Zapier. So do other companies like Generable: https://www.generable.com/

I suspect hierarchical models will become the next “new” hot data structure in software engineering due to their ability to compact logic. https://twitter.com/statwonk/status/1363104221747421184?s=21

I agree, the degree of expressiveness that dplyr achieves is impressive. The library has had a great impact on my work. The syntax is easy to remember so you don't have to constantly look up minor variations in usage. Also, the pipelining aspect makes it dead-simple to debug. It's especially powerful when combined with the purrr library: https://purrr.tidyverse.org/