danlaudk / graph_gist_researchquestions.adoc

Last active April 3, 2016 16:53 — forked from cheerfulstoic/graph_gist_template.adoc

CHANGEME: GraphGist Template. Fork to make your own, view source to see instruction comments

Uses of Research questions related to High School education

danlaudk / README.md

Created December 11, 2016 21:15 — forked from metmajer/README.md

Zoomable Sunburst with Labels

danlaudk / dfsRecursive.scala

Last active January 22, 2017 17:11

rc dfs

	import scala.annotation.tailrec

	trait Tree[+T] {val value: T}
	case class Node[+T](value: T, children:List[Tree[T]]) extends Tree[T]
	case class Leaf[+T](value: T) extends Tree[T]

	// returns Some(Node) or Some(Leaf) if targetValue is found, else None
	// usage: use 'map' or 'foreach' to get the node from the result eg.
	// getNodeInTree('g',tree).foreach{ node => println(s"Found a node: $node") }
	def getNodeWhereTarget[T](target: T, tree:Tree[T]):Option[Tree[T]] = {

danlaudk / dfsHandlingCycles.scala

Created January 22, 2017 17:13

dfs recursive able to handle cycles

	import scala.annotation.tailrec

	trait Tree[+T] {val value: T}
	case class Node[+T](value: T, children:List[Tree[T]]) extends Tree[T]
	case class Leaf[+T](value: T) extends Tree[T]

	// returns Some(Node) or Some(Leaf) if targetValue is found, else None
	// usage: use 'map' or 'foreach' to get the node from the result eg.
	// getNodeInTree('g',tree).foreach{ node => println(s"Found a node: $node") }
	def getNodeWhereTarget[T](target: T, tree:Tree[T]):Option[Tree[T]] = {

danlaudk / swapforth.hs

Created May 27, 2017 20:23

forth at rc wk1

	-- Control.Monad.State.Strict

	import Control.Applicative (liftA3)
	import Control.Monad (replicateM)
	import Control.Monad.Trans.State

	type Stack = List[Int]
	pop :: State Stack Int
	pop = state $ \x ->
	case x of

danlaudk / dining.sc

Created November 1, 2018 02:29

dining philosophers

	import com.twitter.concurrent.Offer
	import com.twitter.util.{Await, Future}
	import scala.concurrent.duration._
	import scala.util.Random
	import scala.collection.mutable.Seq

	val deadline = 3 seconds fromNow
	val r = new Random()
	val numPhilosophers = 3

danlaudk / window.R

Created January 5, 2019 10:04

library(purrr)

danlaudk / gist:b8f8bea01608ee7172effa7a388fd343

Created August 25, 2023 13:56

	techniques which increase statistical power by leveraging formal models, are particularly relevant when human interaction (or limited datapoints from scaled compute) will be more relevant to run these experiments ("field experiments"). This is because such interaction or resource is limited and statistical power can be amplified by pairing statistical techniques with formal models derived from AI internals or from models of incentivized behavior (of the human and of the AI system). Within statistics, this long been the purvey of econometrics

	One organization that already does research in this space is Apollo research
	https://www.lesswrong.com/posts/MrdFL38Zi3DwTDkKS/apollo-research-is-hiring-evals-and-interpretability

	Other orgs also indicate that experiments and causal models play a productive role ( DARPA XAI https://arxiv.org/abs/2106.05506
	, and in deception too https://arxiv.org/pdf/2307.10569.pdf

	Transparency methods https://newsletter.mlsafety.org/p/ml-safety-newsletter-6 like analysing circuits

Daniel Lau danlaudk