u/vanisle_kahuna

Lately I've been deep in a personal project by writing chapter summaries of Richard McElreath’s Statistical Rethinking textbook and applying them to wildfire models, and somehow found a way to elegantly (in my opinion) combine the two through storytelling. The tl;dr: I built a whole narrative around a wildfire forensic investigator named Prof. Markov, rolling an eight-sided die to decide which direction to search a burnt forest grid, to explain how the Metropolis-Hastings algorithm (the earliest variant of Markov Chain Monte Carlo (MCMC)) actually works.

MCMC sits at the foundation of modern Bayesian computation and probabilistic programming frameworks like PyMC and Stan so it could be genuinely useful to anyone looking to level up in these topics. Roast me, tell me what you liked and didn’t like. Regardless, it was a fun little mini-project!

https://pub.towardsai.net/explaining-markov-chain-monte-carlo-using-wildfire-forensics-a334fecaefb3

Open-weight models like Kimi K3 and GLM 5.2 are within a few points of frontier closed models on most benchmarks. Removing a model's safety fine-tuning after download is cheap and well-documented.

Once that's done, there's no vendor, no API, no classifier watching the queries... Right? If so, then I think the only safeguard left is whatever the lab scrubbed from the training data on building bioweapons before release (if the Chinese labs even did this at all) which nobody outside the lab can verify and doesn't reliably stop someone from reconstructing the missing pieces by asking around the edges.

I know there are mechanisms such as synthesis screening, controlled reagent tracking, or DNA synthesis order screening that monitor wet lab supply chains but are they really robust enough to guard against a plotting open-source, open-weight AI who's had it's guardrails removed? For example, how much does a frontier-model help someone route around synthesis screening?

To be clear, I'm very pro open source but this threat vector now worries me so I'm curious about what wet lab experts have to say since this is far outside my expertise.

American frontier labs steal IP from every published author on the Internet. Chinese labs distill American frontier labs. Shit if everyone's stealing from each other, why doesn't every other nation just join the party and distill from Chinese labs since it's open source anyways? 😅

Is it that talent intensive or capital intensive to accomplish this? I get that hosting and running inference will be another story and that the competition will be over chips and energy but at least the model layer will be solved.

Anyone wanna trade me a Chikorita?

Preferably with a water stone attached? I can offer a Cyndaquil or Bulbasaur if you're looking for other starters.

My in-game name is Friede and my friend code is:

0564 3757 9126

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90s to about 2005: The Dead Ball Era. Characterized by slow half court offenses that gravitated towards 1v1 post play. Defenses mostly sagged as there wasn't much 3 pt shooting during this period. Games sometimes feel like a wrestling match as players would physically expand themselves jostling for position in a shrunken floor.

2005 to 2013: Pace and Space Era. Kicked off and largely influenced by the success of the 7 seconds or less Phoenix Suns. Rather than two post players, teams would deploy only one centre at max in an effort to space the floors. It started to become more common for highly skilled guards/wings like Tony Parker, Nash, or Wade to be the primary initiator for offenses. Spread pick and roll become the dominant offense for most championship teams like the 2008-2010 Lakers who utilized a hybrid Triangle-PnR offense along and the prime example Heatles. Spacing bigs were at a premium if they could pop out but 5 out wasn't common here yet.

2014 to 2023: Beautiful Game Analytics Era. Kicked off by the 2014 Spurs and mastered by the Curry Warriors from 2015 onwards. Ball movement was a premium. 5 out spacing was the ideal. By this point, being able to shoot 3s above league average and competently defend are almost minimum skills by this point in order to stay on the floor. Coaches are also expected to come up with complex tactical adjustments and adapt on the fly with the boom in analytics and camera tracking technology.

2024 to present: Perhaps the Parity Era? The story hasn't been completely written for this era but the trend seems to be peak physical fitness, strength, conditioning, and skillset is the requirement for all players in order to play meaningfully from pg to centre. Teams now regularly pick each other up and full court press so that all 94 feet is utilized, just like in Futbol. Teams shoot almost as much 3s now as layups. Nearly every wing has the capability to shoot difficult 3 point shots whereas this ability used to belong to just Curry and a few other players. Even bigs like Victor and Jokic are capable of difficult shot making and playmaking beyond the 3 point line. Successful team building also relies more in internal development in order for the the salaries to remain within the cap structure whereas in the previous era, championship teams were largely built through free agency. And again to emphasize, Multi dimensional (almost guard like) skillsets are the minimum now from your centre to point guard.

A few weeks ago I posted about building a causal DAG for BC wildfire growth and got some great discussion going about why causal reasoning doesn't get nearly enough airtime in ML. So I went and tested the DAG with regression, utilizing both the Bayesian and Frequentist flavours where appropriate rather than sticking with one approach dogmatically. Here were some of my key findings:

It turns out that atmospheric predictors alone were weak drivers in accounting for fire size and that I underestimated the complexity that influences how big or small they can get! A Frequentist Regression R² score of 0.067 on the full dataset is, by most ML benchmarks, a model you'd throw out 💩

But if I hadn’t approached this project through a causal lens, throwing it out would have meant missing the most interesting insights! What I found interesting was that when you stratified the same model into “zones” by fire centre, the performance nearly doubled without adding a single new predictor. The global model wasn't just underperforming, it was averaging over structurally different regional realities and hiding it entirely. Essentially the main insight here is that there’s a really good chance that future projects will have better success by fitting hierarchical models that account for the geographic differences since there’s so much inter-provincial diversity if you consider the infrastructural differences, climate, geography, topography, institutions, etc.

That's not a predictive insight, that's a causal one. And it only became visible because the DAG gave me a reason to look for it.

Other key things the data pushed back on:

• One predictor dominated across every region… but not for the reason I originally assumed.
• Two predictors I hypothesized as meaningful mediators turned out to be redundant based on multiple lines of evidence from the regression models. 
• Dropping them from the predictive model moved the R² by 0.004 which prompted me to update my hypothesized causal DAG based on the evidence, which is similar in principle to how Bayesian updating works 🙂

For those who appreciated that Part 1 used real wildfire data instead of toy examples, Part 2 goes even deeper into the same dataset with all the code included.

The article is written for people who are earlier in their data science, machine learning, or stats journey but curious about causal inference. If that's you, hopefully you find it accessible! And if you're more advanced, I'd genuinely appreciate the feedback. I hope that projects like these get more people in the data community excited and thinking about ways to apply their skills towards meaningful problems like disaster response, wildlife conservation, or renewable energy 🐺

Thank you all for your support!

https://pub.towardsai.net/putting-dags-to-the-test-what-regression-reveals-about-wildfire-drivers-part-2-c03d4f8a9b13

How to fix tanking and meaningless games

Tanking has become embarrassing. Nine or ten teams are openly punting this season and the league's own fixes haven't worked. Here's a fun ground-up solution I'm proposing that could potentially solve it.

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THE REGULAR SEASON — The League Cup

• League expands to 32 teams

• Double round robin format — every team plays every other team twice (62 games)

• Best overall record at the end wins the League Cup

• Every game matters because every game affects standings. No more dead rubber months.

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MIDSEASON TOURNAMENT - The Dominion Shield Tournament

• Season pauses at the halfway point after ever team has played each other once

• All 32 teams enter a single-elimination, best-of-3 tournament called the Dominion Shield

• Seeded by current standings at the halfway mark

• Shield results do NOT affect regular season standings

• Winner earns a direct path to the NBA Supercup Championship

The Shield is essentially March Madness dropped into the middle of the NBA season. Every team is alive. Upsets happen. It's must-watch television.

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END OF SEASON — The Lottery Tournament

• Bottom 16 teams by record enter a single-elimination, best-of-3 tournament

• Winner gets the #1 draft pick

• Teams that lose keep their original seeding-based draft position — no punishment for losing

• Remaining picks 2–16 ordered by regular season record

This is the key tanking fix. Bad teams are now competing to win for the top pick instead of competing to lose. The worst games of the year become meaningful overnight.

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THE POSTSEASON SUPERCUP — Three Round Championship

After the Lottery Tournament concludes, giving all teams time to rest:

Round 1 — Wildcard Series (7 games)

Shield runner-up vs League Cup runner-up

If the same team finished 2nd in both, the spot goes to 3rd place in the League Cup, then 3rd in the Shield, cascading down.

Round 2 — Semifinal (7 games)

Wildcard winner vs Shield winner

Round 3 — NBA Championship (7 games)

Semifinal winner vs League Cup winner

• League Cup winner gets a full bye — they only play ONE series to win the championship. That's the reward for sustained excellence over 62 games.

• Shield winner plays two series. They have to prove the midseason run wasn't a fluke.

• Wildcard winner has to win three straight series. If they pull it off, it's the greatest underdog run in league history.

• If one team wins both the League Cup AND the Shield, they are automatically NBA Champions. No Supercup needed.

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WHY THIS WORKS

✓ Tanking becomes irrational — bad teams fight to win their way to the #1 pick

✓ The regular season is watchable from game 1 to game 62

✓ The Shield creates a brand new tentpole event mid-calendar

✓ The postseason has three escalating 7-game series with distinct storylines

✓ The reward structure is fair — the hardest competition (League Cup) earns the easiest postseason path

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What do you think? Does this fix the league's tanking, revenue, and overall anticompetitiveness problem?

The past few months, I've been teaching myself Bayesian stats from the Statistical Rethinking textbook (highly recommend btw) and I went down a rabbit hole on causal reasoning which I found really compelling! It's a completely different framework from the "maximize predictive accuracy, throw everything in" approach I learned in bootcamps and instead called for thinking deliberately about the causal mechanisms generating your data.

Anyways, I thought it might be useful to write up an article summarizing some key ideas of causal reasoning like DAGs, mediators, and confounders for those that haven’t come across it yet. I also made a case for why adding more predictors may actually make your models worse if you don’t think carefully about the relationships your predictors have with one another. And to make these concepts more practical, I applied them towards a wildfire dataset to form a hypothesis on the data generating process behind total hectares burnt in a wildfire.

This is Part 1 (theory + DAG construction) of a two-part series. Part 2 will test the causal model with regression.

If you find this stuff interesting, useful, or even just inaccurate, I’d love to hear your feedback! Has anyone else gone down the causal inference rabbit hole? It feels like a whole different lens on ML that doesn't get talked about much but definitely needs more attention.

https://medium.com/towards-artificial-intelligence/rethinking-predictors-why-causal-reasoning-matters-in-data-science-part-1-f1d4c1e08068

https://preview.redd.it/n7isqm44v00h1.png?width=2779&format=png&auto=webp&s=fb4def19be69150c19bff3805d80243540eb6f2c