trentmkelly/LessWrong-43k huggingface dataset:
2021 Darwin Game - Benthic
by lsusr — 2021-10-06T04:39:05.037Z

Welcome to the depths of the sea, far beyond the reach of sunlight. Here ye shall feast upon the dead.

Name
Carrion
Leaves
Grass
Seeds
Detritus
Coconuts
Algae
Lichen

Benthic
10
0
0
0
1000
0
0
0

72 species were submitted to the Benthic.

Generations 0-25

We start with a population crash, as usual. After that, the populations almost look like they stabilize. Less than half of our species have gone extinct.

Goes Extinct in Generation
Species

6
Salpophore

6
Tasty

7
Marine Snow Worm

7
benthic bottom feeder

8
Scavenger

9
Isopoid

9
Detritus Eater Eater

10
Scavenger Shrimp

10
Rubble on Double

10
Sea Zombie

11
Fangliphore

11
Grumpy Jellyfish

11
Doomed

11
Happy Hunter

11
A new hope

12
Krill1

12
Sp0r3

12
Cnidophore

12
Cephalophore

12
Not Picky

13
Detritus Eater Eater Eater

14
Sea Snail

14
Hermit Crab

14
Space Horse

14
BeauOmni3

14
aBa-CG751

15
Flounder

16
Microbet

16
Slowmo

17
aBa-D273

18
Hund

18
Glyptoderm

18
Squid Kid

19
Anglerfish

19
Toxic Squid

20
Torpedo Jelly

23
Willi

24
cg-riverfish

Here are our populations at generation 25.

Population
Species

170
Barnacle

158
Deepwater Devourer

124
The Infinite Depths

114
FOERDI 1,6,4,3,D

98
Flutz

88
DetriusSpeedTank

66
Spanish Beard

55
BeauOmni1

50
Silli

46
FOERDI 1,0,10,1,D

45
aBa-D262

43
Tilli

40
FOERDI 0,0,0,10,D

39
FOERDI 1,3,7,3,D

35
Pilli

34
Blood Shark

33
FOERDI 1,10,0,1,D

32
Xilli

30
Chilli

29
omastar

27
Elephantfish

27
Abyssal eel

24
Billi

19
Kraken2

17
Space Fiddler

15
Giant Squid

10
The Tideless Depths

5
FOERDI 1,9,1,1,D

5
Soonbegon

2
gonnabiteit

2
Deap Sea Tortoise

2
Flesh-Eating Clam1

1
Killi

1
Tachyphore

As you might guess, the Barnicle is an invincible Detritus eater and the Deepwater Devourer is a carnivore. However, the Deepwater Devourer is not an apex predator. It has only 3 Attack and 6 speed. (It also has Antivenom and the ability to consume Detritus.)

Generations 25-100

Barnicles dominate.

Goes Extinct in Generation
Species

6
Salpophore

6
Tasty

7
Marine Snow Worm

7
benthic bottom feeder

8
Scavenger

9
Isopoid

9
Detritus Eater Eater

10
Scavenger Shrimp

10
Rubble on Double

10
Sea Zombie

11
Fangliphore

11
Grumpy Jellyfish

11
Doomed

11
Happy Hunter

11
A new hope

12
Krill1

12
Sp0r3

12
Cnidophore

12
Ceph

...
2021 Darwin Game - Ocean
by lsusr — 2021-10-06T01:59:19.067Z

The Ocean has lots of algae, a little carrion and a little detritus. It was the most popular biome, with 131 native species.

Name
Carrion
Leaves
Grass
Seeds
Detritus
Coconuts
Algae
Lichen

Ocean
10
0
0
0
10
0
10,000
0

To thrive in this game as a forager you must survive the initial predators. One way to do this is to breed faster than they can eat you. Another way is to use armor or another defense.

Random guy in NY took a third route. He created the First Round Predator Distractor, a cheap species with almost zero nutrition and exactly zero chance of survival that kept the predators in check for a single round.

Goes Extinct in Generation
Species

1
First Round Predator Distractor

Now that Random guy in NY's silliness is out of the way let's get on to the real show.

Generations 1-50

At first the defenseless foragers thrive. Increasingly large carnivores's populations grow and crash.

Name
Venom + Antivenom?
Attack
Armor
Speed
Forager?
Creator

Phytoplankton
Neither
0
0
0
Algae
aphyer

Algae Tribble
Neither
0
0
0
Algae
simon

CookieMaximizer
Neither
1
0
1

Saran

gkdemaree-9-eater-of-drifting-forager
Neither
1
0
1

Grant Demaree

Pike
Neither
2
0
3

Yair

Herring
Neither
2
0
2

Measure

Bhuphin
Neither
0
2
0
Algae
phenx

Snail-Eating Snail
Antivenom only
5
0
2
Carrion;Seeds;Detritus
Taleuntum

Jilli
Neither
3
0
1
Algae
Milli

Right whale
Neither
5
0
5

EricF

gkdemaree-10-small-shark
Neither
6
0
6

Grant Demaree

gkdemaree-1-fast-shark
Antivenom only
10
0
10

Grant Demaree

gkdemaree-6-large-omnivore-fish
Antivenom only
7
0
7
Algae
Grant Demaree

Goes Extinct in Generation
Species

7
Cheerless Tidehunters

12
Krabs

12
Qoxeadian

13
Bloade

13
Squidheads

14
Sleeping Sealions

16
Sanguine Scavenger

17
Terror Whale

17
Sea Snake

17
Whale

17
Algae Sprinter

17
FOERDI 1,0,10,0,CDA

18
Blue whale

18
Chybcik

18
op511

18
Crab

19
Kraken1

19
The Aparatus

19
ocean vulture

20
magikarp

20
sea tortoise

20
1-4-1 carrion

21
osc511

22
Manta Ray

22
Grerft

24
Plankton2

24
Poisonous Sponge

24
cg-fish

25
Speedy Algivore

26
cg-whale

28
RiverShark

28
Yellow Krill

28
0-0-0 algae-seed

29
Armored Algivore

30
Salt Water Crocodiles

30
speedFish

30
FOERDI 1,1,0,1,A

31
ploplo

31
Shellder

32
gkdemaree-9-eater-of-drifting-forager

33
CookiePaladin

35
CookieMaximizer

36
Veno

...
Appropriately Gray Products
by Adam Zerner — 2021-10-06T00:56:41.586Z

Epistemic status:

I've been in the startup world for a while, have read almost all of Paul Graham's essays, have read The Lean Startup cover-to-cover, etc. However, it's possible that I am misunderstanding/misrepresenting the idea of an MVP here. If so, I apologize and would like to be corrected.

As for my opinions, I feel solid about them. Not too, too confident though. To justify that amount of confidence, I would want to have had stress tested my ideas by having other people critique them. That one of the goals I have in writing this, actually.

This is a "starting the conversation" type of post. Not an "I've spent a ton of time researching, discussing with others, and iterating, and now I am presenting to you a finished product" type of post.

Here is how I understand the idea of a minimum viable product (MVP).

Starting out, you have some hypothesis that you want to test. Eg. that people will want to buy pet food online. How would you go about testing this hypothesis?

You could go to town and spend 18 months building out a beautiful website that has pixel perfect designs with an accompanying iOS and Android app, but that is a little bit excessive. To test your hypothesis, doing all of that isn't really necessary.

On the other hand, you could just throw together a Google Doc with products, pictures and prices and tell people to email you if they want to place an order. But I'd argue that this wouldn't be enough. You wouldn't get a good enough test of your hypothesis. People who otherwise would be willing to buy pet food online might not place orders because a Google Doc like this feels sketchy. In other words, the risk of a false negative is too high.

With that context, let me explain what an MVP says. An MVP says to keep "moving to the right" until you have a hypothesis test that is "viable", and then once you hit that point, stop. You don't want to be to the left of the line because that would mean you can't trust the results of your experiment. But you also don't want to be to the right of the line because that would mean acting on assumptions that haven't been validated.

Shades of gray

Something has always rubbed me the wrong way about this. I don't like the idea that there is this line where you declare that something is viable. "To the left of this line things aren't viable, to the right of this line things are viable." You don't hear it said this expl...
An Investigation into the Passage of Time
by hivemindsingularity — 2021-10-06T00:31:04.062Z

Time past and time future
Allow but a little consciousness.
To be conscious is not to be in time
But only in time can the moment in the rose-garden,
The moment in the arbour where the rain beat,
The moment in the draughty church at smokefall
Be remembered; involved with past and future.
Only through time time is conquered.

- Burnt Norton, Four Quartets, T.S. Eliot, published in 1909–1935

Abstract:

There are predominantly two major views on the ontology of time, as phrased by McTaggart (1908) as ‘A-series’ of time and ‘B-series’ of time, although I prefer the more direct and precise way of identifying the two theories—the dynamic theory and the static theory. The general camp of dynamic theory includes Growing-Block Theory (GBT), Moving-Spotlight Theory(MST), and presentism. What is common among those views is the fundamental belief that time passes and that time has a dynamic nature. On the other hand, static theorists, who mostly endorse the Block-Universe Theory (BUT), deny the existence of any apparent dynamic quality of change nor any special nature of the present. Hence, the passage of time is thought to be illusionary to their beliefs.

It is essential to divide the question of the passage of time into the question of subjective passage and objective passage. The following two questions arise when we attempt to distinguish between subjective passage, as experienced phenomenologically, and objective passage, which concerns the fundamental ontology of time itself:

The phenomenological question: Why does time seem to us as if it passes?

The ontological question: Is the nature of time dynamic?

Full essay available

here
2021 Darwin Game - Desert
by lsusr — 2021-10-06T00:24:57.230Z

Our Desert is an inhospitable environment. Carrion, while nutritious, is hard to digest and there's not a lot of it. Species must be adapted to the heat too.

Carrion
Leaves
Grass
Seeds
Detritus
Coconuts
Algae
Lichen

100
0
1
1
1
0
0
0

The most dangerous part of the desert is the predators. Two people submitted sandworms.

Name
Venom
Weapons
Antivenom
Armor
Speed
Creator

Desert Carnivore

10
✓
0
10
Antihaas

dp511
✓
10
✓
0
10
horu

Sand Drake

6

0
10
Yull-Rete

Sandworm

10

7
8
Vanessa

Shai-Hulud[1]

10
✓
0
7
simon

Only 36 organisms were submitted to the Desert. 23 of them could digest Carrion. Carrion is very nutritious so all 23 were viable foragers.

Generations 1 to 10

A few apex predators did well.

Goes Extinct in Generation
Species

6
Lesser Desert Rat

7
Cordova

8
Hyena

8
Desert-Ants

8
Desert-Foxes

8
Desert-Lizard

9
Yonge_Hot

10
Josep Raich

Generations 11 to 50

The apex predators did so well they wiped out all the foragers without maxed out speed or defense. Having extinguished the easy prey, the apex predators proceeded to starve.

Goes Extinct in Generation
Species

11
Desert-Coyotes

11
Cockatrice

11
cg-wildcat

12
Venomous Snark

12
Desert-Snakes

14
Bitey-the-scary

14
Smok

16
Titus

19
Desertio

20
Qanon

23
Wurm

26
Sandworm

33
Shai-Hulud

35
lDha-C293

38
Sand Drake

38
slow vulture

41
Desert Carnivore

45
Chocolattos

45
Dongles

46
dp511

Generations 51+

Name
Venom
Weapons
Antivenom
Armor
Speed
Eats Carrion?
Other
Creator

Desert Viper
✓
0
✓
0
10
✓

aphyer

dsc511

0
✓
10
0
✓
Eats Leaves, Grass, Seeds, Detritus, Coconuts, Algae, Lichen
horu

Booyahs

2

0
10
✓

CK

Rock Beetle

0
✓
10
0
✓

Taleuntum

Desert Tortoise1

0
✓
10
0
✓

Multicore

Desert Tortoise2

0
✓
10
0
✓

Yull-Rete

Armadillo v2

0
✓
10
0
✓

Henny

Sol Invictus

0
✓
10
0
✓

simon

Most of these speccies are identical. They're carrion foragers with maxed-out defense (including antivenom) and nothing else. Everyone who isn't a maximally-efficient invincible forager dies by generation 61.

Goes Extinct in Generation
Species

55
dsc511

60
Booyahs

61
Desert Viper

It's a random walk among equals from here.

Winners

Species
Player
Social Media

Armadillo v2
Henny
None

Desert Tortoise1
Multicore
None

Desert Tortoise2
Yull-Rete
None

Rock Beetle
Taleuntum
None

Sol Invictus

...
How can one identify the absolute safest car in practice rather than theory?
by rockwell — 2021-10-05T21:19:19.765Z

I've been considering getting a new, safer car and I am wondering how to best utilize the data available. Organizations like the IIHS and NHTSA publish information on crash test performance and safety features, and have lists of cars which should theoretically be the safest. There's also data on driver death rates, fatal accidents, and crashes by make and model.

Assuming the data is accurate, what would be a good way of approaching it to find the safest car in practice? My thinking is that the lists of safest cars should be a fairly reliable way to quickly find top contenders, and then the ones that are the safest should have the lowest driver deaths per miles driven (available in the IIHS Status Reports).

An obvious problem with this is that there's no way to factor in the driver's capability, road conditions, and other important external factors. There's also no data on new models (as there hasn't been enough time to collect it), so I would be estimating the rates of those based on the previous years' models. Another issue is that many have zero deaths recorded, which makes for lower resolution comparisons.

What else should I consider, and what other important factors am I not thinking of if my end goal is finding cars that are really a cut above the rest in terms of safety?
How should an interviewer evaluate management skills in a candidate?
by Maxwell Peterson — 2021-10-05T18:25:51.746Z

My small Data Science team is hiring a new manager. I have some skill at evaluating the technical abilities of a candidate, but very little in evaluating their management abilities.

In Data Science technical interviews I don’t think one needs a picture of what domain a person works in or what the size of their team was etc. to evaluate their technical and mathematical abilities and their intelligence. Is the same true of a manager? Is so, what are the underlying dimensions of management ability that I should be trying to uncover and evaluate?
D&D.Sci 4th Edition: League of Defenders of the Storm Evaluation & Ruleset
by aphyer — 2021-10-05T17:30:50.049Z

This is a follow-up to last week's D&D.Sci scenario: if you intend to play that, and haven't done so yet, you should do so now before spoiling yourself.

RULESET

Code is available here for those who are interested.

CHARACTER STATS

A character has two stats: an Element and a Power Level.

18 of the 19 characters are power levels 1-6 of the elements Fire, Water and Earth:

Power Level	Fire	Water	Earth
1	Volcano Villain	Arch-Alligator	Landslide Lord
2	Oil Ooze	Captain Canoe	Earth Elemental
3	Fire Fox	Maelstrom Mage	Dire Druid
4	Inferno Imp	Siren Sorceress	Quartz Questant
5	Phoenix Paladin	Warrior of Winter	Rock-n-roll Ranger
6	Blaze Boy	Tidehollow Tyrant	Greenery Giant

The remaining character, the Nullifying Nightmare, has a Power Level of 5 with the unique element of Void.

The NPC team consists of Fire 5, Water 6, Earth 3, Earth 4, Earth 6.

Congratulations here to abstractapplic, who was the first to figure out the elements.

HOW CHARACTERS FIGHT

A fight between two teams is composed of fights between the individual characters. When two characters fight one another, it works as follows:

Some elements counter others:

Fire is countered by Water
Water is countered by Earth
Earth is countered by Fire

If one character's element is countered by the other, that character loses the fight (regardless of power level). For example, if Oil Ooze (Fire 2) fights Greenery Giant (Earth 6), Oil Ooze will win.
If the characters are the same element, the higher-power one will win. For example, if Phoenix Paladin (Fire 5) fights Fire Fox (Fire 3), Phoenix Paladin will win.
If the characters are the same element and the same power, each has a 50% chance to win.
There are two special cases:

The 1 of each element counters the 6 of the same element, and beats it rather than losing to it. So if Volcano Villain (Fire 1) fights Blaze Boy (Fire 6), Volcano Villain will win. (Congratulations to Yonge, who I think is the first person to have explicitly noticed one of these counters).
The Nullifying Nightmare has Power Level 5, and the unique element of Void. Void does not counter any elements, and is not countered by any elements - the Nightmare just fights with Power Level directly, as if it were the same element as its opponent. So it will lose a fight to any Power 6, have a 50% chance against any Power 5, and beat any Power 1-4.

HOW TEAMS FIGHT

To find the outcome of a game between two t...
Covid Home Test Experiment
by SebastianG  — 2021-10-05T15:24:08.441Z

I was exposed to COVID 4 days ago. I received the Pfizer vaccine second dose 4 months ago. I have done 2 rapid tests today. One came back positive, one negative. I would like to do 18 more and report back my data. However, I cannot afford that many home tests.

Would anyone like to sponsor this home science project?
Modelling and Understanding SGD
by J Bostock — 2021-10-05T13:41:22.562Z

I began this as a way to get a better understanding of the feeling of SGD in generalized models. This doesn't go into detail as to what a loss function actually is, and doesn't even mention neural networks. The loss functions are likely to be totally unrealistic, and these methods may be well out-of-date. Nonetheless I thought this was interesting and worth sharing.

Imagine we have a one-parameter model, fitting to one datapoint. The parameter starts at W=0 and the loss is L=−exp(−(W−2)2). The gradient will then be dLdW=2(W−2)exp(−(W−2)2).

An imaginary continuous gradient descent will smoothly move to the bottom of the well and end up with W=2.

A stepwise gradient descent needs a hyperparameter T telling it how much to move the parameters each step. Let's start with this at 1.

This gives us a zig-zag motion. The system is overshooting. Let's pick a smaller T value.

Hmm. Now our model converges quite slowly. What about 0.5?

Much better. Seems that there's an optimal value of T, which we will later see depends on the spikiness of the loss function.

T is not dimensionless and has the dimension of W2/L, as ΔW=−TdLdW. Certain function-minimising methods like Newton's method use (for example) ΔW=−kdLdW/d2LdW2 where k is dimensionless. This is why different loss functions require different T values.

SGD and Local Minima

What if we have two datapoints? Now L=(l0+l1)/2. Let l0=−exp(−(W−2)2) as above but l1=−exp(−(W−2)2)−exp(−100(W−1)2).

Now our loss function L has a new local minimum I think of this as "the pit". Where we end up depends on where we start. If we start at W=4 then we'll clearly end up at W=2 but if we start at W=0, then:

Huh, this isn't what I expected at all! The gradient must have been too high around the local minimum. Let's try with T = 0.05, which will be slower but ought to work better.

This is more like what I expected. Now our system is stuck in the local minimum.

But most modern gradient descent algorithms use stochastic gradient descent. We can model this here by randomly picking one of the two datapoints (with associated loss function) to descend by each step.

What happens now? Well now we have a chance to escape the pit. Let's say we're at the minimum of the pit. How many consecutive descent steps performed on l0&nb...
Force neural nets to use models, then detect these
by Stuart_Armstrong — 2021-10-05T11:31:08.130Z

Research projects

I'm planning to start two research projects on model splintering/reward generalisation and learning the preferences of irrational agents.

Within those projects, I'm aiming to work on subprojects that are:

Posed in terms that are familiar to conventional ML;

interesting to solve from the conventional ML perspective;

and whose solutions can be extended to the big issues in AI safety.

The point is not just to solve the sub-problems, but to solve them in ways that generalise or point to a general solution.

The aim is to iterate and improve fast on these ideas before implementing them. Because of that, these posts should be considered dynamic and prone to be re-edited, potentially often. Suggestions and modifications of the design are valuable and may get included in the top post.

Force model use and then detect it

Parent project: this is a subproject of the value learning project.

Background

I've seen human values residing, at least in part, in our mental models. We have a mental model of what might happen in the world, and we grade these outcomes as good or bad. In order to learn what humans value, the AI needs to be able to access the mental models underlying our thought processes.

Before starting on humans, with our messy brains, it might be better to start on artificial agents, especially neural-net based ones that superficially resemble ourselves.

The problem is that deep learning RL agents are generally model-free. Or, when they are model-based, they are generally constructed with a model explicitly, so that identifying their model is as simple as saying "the model is in this sub-module, the one labelled 'model'."

Setup

The idea here is to force a neural net to construct a model within itself - a model that we can somewhat understand.

I can think of several ways of doing that. We could get a traditional deep learning agent that performs on a game. But we might also force it to answer questions about various aspects of the game, identifying the values of certain features we have specified in advance ("how many spaceships are there on the screen currently?"). We can then use multi-objective optimisation with a strong simplicity prior/regulariser. This may force the agent to use the categories it has constructed to answer the questions, in order to play the game.

Or we could be more direct. We could, for instance, have the neural net pass on ins...
2021 Darwin Game - Tundra
by lsusr — 2021-10-05T05:03:04.813Z

Our Tundra is an inhospitable[1] environment. The only significant food available to herbivores is Lichen, which has a tiny nutritional value of 1. The Tundra is cold too. Staying warm requires the cold tolerance adaptation, which costs +2 size.

Carrion
Leaves
Grass
Seeds
Detritus
Coconuts
Algae
Lichen

1
1
1
1
1
0
0
300

An organism must expend 20% of its energy just to survive. A herbivore foraging for lichen cannot have a size greater than 5 or else it will expend more energy in metabolism than it is possible to acquire from eating Lichen.

All organisms have base size 0.1. The cold adaptation (+2) plus the Lichen digestive tract (+1) costs a total of +3 size. A Tundra herbivore has a minimum size of 3.1. A herbivore with size 5.1 is untenable since it expends more energy (1.02) than is possible to obtain from Lichen (1.00).

Players submitted 39 species native to the Tundra. Only 4 of them were viable herbivores: Micropas, Arctic Slug, Northern Nibbler and "lichen" (not to be confused with the foragable "Lichen"). (Multicore's Arctic Fox was a carnivore.)

These species could support little in the way of weapons, armor and speed. They were defenseless. In the first 8 turns, all four of our viable foragers are eaten to extinction.

Goes Extinct in Generation
Species

5
Pristol

7
Micropas

7
Arctic Slug

8
Northern Nibbler

8
lichen

After the viable herbivores were eliminated, total ecological collapse was inevitable.

Goes Extinct in Generation
Species

9
Yonge_Cold

9
Boreakeet

9
Beck’s Penguin

10
SmolFire

10
Arctic Ambusher

10
Zlorg

10
Arctic Fox

10
Orange-Krill

10
abominable_snowman

12
Antasvara

12
Unfortunately Large Cockroach

12
cg-mouse

13
Porostozer Malutki

13
1994 Mazda RX7

14
Raburetta

14
Pittsburgh-Penguins

15
Louse-lion

15
Wolverine

16
Jtp

16
Wolves

17
Seals

19
Direwolf

24
Tsc

27
Tundrus Rex

29
Frankenstein

32
Broken Fetters

34
Alaskans

37
Dragon

37
Porostozer Mamuci

39
Rocks

41
Duckofants

43
White-Whales

50
tp511

52
Frostwing Snipper

The Frostwing Snipper

An honorable mention goes to Nem's Frostwing Snipper, a Speed 10 species that could digest both Lichen and Seeds. The maximum speed made the Frostwing Snipper immune to predation which let it survive the initial carnage. The ability to digest seeds meant that the Frostwing Snipper did consume enough energ...