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@danlaudk
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
danlaudk / README.md
Created December 11, 2016 21:15 — forked from metmajer/README.md
Zoomable Sunburst with Labels

Zoomable Sunburst with Labels

@danlaudk
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
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
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
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
library(purrr)
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