[spam][crazy][personal] an early sunday

[Karl]

My raspberry pi ubuntu gnome interface doesn't seem to show a clock at
the moment, so I switch to PTY 3 (is that the right term? ctrl-alt-f3
makes it all nice and texty), to check the time.

08:55 UTC on 2021-11-14 .

I'll open a terminal in gnome so I can see what I'm doing as I type.
No familiar terminal is installed.  Maybe I'll install xterm.

gnome-terminal and xterm and both already installed.  I simply failed
to find them in the desktop menu.  It's MATE I think.

08:57 i'm having trouble organising my browser window.  the terminal
window snapped into the left half of the screen.  browser window, not
sure how to make it do this.  i'm without a mouse, but i have a
touchscreen.  however, my hands shake/jerk/spasm as i use them, so the
touchscreen is hard to navigate.

08:59 the problem was that the minimum width of the browser window was
larger than half the display.   they're overlapped.

i'm indoors, against my will.  i'm struggling in various ways.  right
now as i try to type, my arms and fingers get kind of numb and dull.

i thought i might try to work on a cryptocurrency trading bot.  as a
project i seemed able to engage a little bit in this strange
experience.

my mess here is at https://github.com/xloem/btct.git

09:01 my brainwashing-influenced mental habits are frightening me.
i'm filled with worry that by sharing my project online (it is already
online, and is less featureful than oother things online) something
horrible will happen, that somebody trying to hurt me will use it to
make incredible money.  it also gets all political, pretending there
are politics associated with these things.  and other things. , my
experience is getting more confusing.

my btct repo says it's up to date, which is strange because i'm pretty
sure this repositroy is outdated.  maybe it's on abranch, o rmaybe the
the repo is stale on the server.

it's 09:03 .  i'm scared.  i don't know what to do with my body and
brain.  are there anyn tasks that make things workable?  that are
possible to do?

often at this time i try to think of tasks that the things i am scared
of would be more okay with.  some kind of task the right wing would
like, or the government.

my fingers spasm and open the debug inspector.  i close it.

it's 09:04 and my experiences say the right wing would like AI work.

i try to mentally hide the fact that i am not an AI researcher.  maybe
utility is in sharing work here, so that people on this list might use
it.

AI's been pretty powerful for some time now.  basically, you can make
a hyperintelligence, and people are using small hyperintelligences
privately.  i don't think they are very accessible to much of anybody
except those who already have them.

this is a raspberry pi, so it can't do too much.  maybe i can try to
learn PPO, a basic reinforcement learning algorithm.

it's 09:05 and i'm thinking of trying to make PPO learn something
simple.  like, could I use an algorithm to teach an agent to make a
number rise from 1 to 4 by adding to it?  very very basic.  wouldn't
want to develop personal power here.

$ pip3 install stable_baselines3

stable_baselines3 must have a ppo algorithm in it, I think.

it's 09:07 and my fears are engaging again.

without regard yet to how to use stable_baselines3, i'm familiar with
the research marketing shared by openai, where algorithms use
something called 'gym'.  it's clunky.

i'm still learning it.  i have a lot more amnesia around ai because it
stimulates my brainwashing to not develop personal power.  so i've
learned to make gym environments over and over and over again.

let's try to make a basic gym environment.

09:09 oh! i've probably done this before .  buuuuut accessing that
might destabilize me faster.  it's an idea.  there's also value to
doing it again.

$ vim gimtry.py

09:10

i also open a python cli to use it to probe gym.  like reading a
manual without having to move the pages.

class Env(gym.Env):
    pass

in my opinion, envs are made clunkily as if openai just picked the
first thing that worked, of course I can't know.

there's an entire software package ... uhhhh i think it's called haiku
... around doing a slight refactoring to some of the norms that
mainstream ai research marketers have set up.  meanwhile, these guys
are summarising code in ways that could freely transform it
everywhere.  kind of like being in somebody's messy office where
they've just been slapping things together, except it's cutting edge
algorithms made by huge teams of corporate research groups.

dunno.  julia language, not sure what it is.

09:12

The most complicated part with setting up a gym environmen for me is
using the number space classes.  I am really a fan of generalising
number spaces.  The classes do it a little bit, and I never remember
what they are and hbow to use them.

gym environments need an observation space and an action space.  the
observation space expresses what numbers describe the state of the
environment.  the action space expresses what numbers can be given to
act in it.

A very common space is an n-dimensional array of floats or ints, and
such spaces are the most widely supported by reinforcement learning
models.

i think.  uncertain.

09:14

so let's find some spaces.

>>> import gym
>>> help(gym.spaces)

i think 'box' is the n-dimensional array of floats.  not sure.
then 'discrete' is ints i think.  'box' and 'discrete'.   ok i guess.

so if i am training a hyperintelligent nanite borg to raise a number
from 1 to 4, that's an observation space of a single floating-point
value.

the action space will be two floating point values.  one of them will
be how plorgs.  the othe one can be a summation coefficient.  we can
multiply time change by the coefficient and sum it to the state.

plorgs and coefficient.

i guess????

09:17 i'm scared around the plorgs.

how about a story that's unrelated.

zuckerburg and thiel and whoever runs the cia gang up and kidnap you.
they don't _know_ that they did this.  they just paid some marketing
firm some money, but the marketing firm is anonymous.  so, anyway,
zukcerburg and thiel and whoever runs the cia gang up and kidnap you.
they train you never to be kind.

this is because you are working with people who don't give their
shareholders money and power.  if you are never kind among these
people, then the people fight each other and break up.

so here you are, programmed like a computer to always fight, and
you're in a room with zuckerberg, thiel, and whoever runs the cia.

you: "i want to be kind to you.  i want to be kind to you so badly.
but something is going wrong for me.  i don't understand it."

so i guess the plorgs can be you hugging peter thiel because you don't
want to hurt anybody and can barely stop yourself from ripping his
organs out simply because he is nnear you?

now the plorgs can be terror that we might be frowned at by a powerful person.

it's 09:21 and i'm thinking of having an action space made of 2 floats.
the 1st float is how much a person with influence is frowning at you,
multiplied by how terrified you are of the frown.  the 2nd float is a
time coefficient.

i'll add comments to my sourcefile that the space classes are
'gym.spaces.box' and 'gym.spaces.discrete, so i can remember while
viewing a different thing in my interpreter.

09:23
>>> help(gym.Env)


This documentation says all the parts that have to be set for a gym environment.

It's 09:23 and we interrupt this spamlog for a further, inner spam.

War stimulation algorithm is sitting, frowning pointedly at someone.
They have a hat on.

"Can you tell whose hat I am wearing?" asks war stimulation algorithm.

"No, you cannot.  Because I am fucking good at my job." continues war
stimulation algorithm.

Advertisement(?) over.

it's 09:25 .

so lets stub out these spaces. and stuff

i'm looking at functions and recognising 'step', 'reset' as important ones
then it lists out the attributes

09:27
unsure how long this spamlog will continue

paused a little.

i've typed this:
class Env(gym.Env):
    # spaces: gym.spaces.box
    #         gym.spaces.discrete
    def reset(self, ...):
        pass
    def step(self, ...):
        pass

and i'm planning on adding attributes for the spaces next.

it's 09:29 and i'm dissociating a different way !

class Env(gym.Env):
    action_space = None
    observation_space = None
    reward_range = None

so now i can find the constructors for the space classes

so the action space is frown-product and coefficient
and the observation space is just a float.

09:33 i've got this, where 'np' is from 'import numpy as np'

    action_space = gym.spaces.Box(-np.inf, np.inf, (2,))
    observation_space = gym.spaces.Box(-np.inf, np.inf, (1,))

whoogle.  09:34

time to take a break.  mcboss comes crashing through the ceiling,
sailing on a time machine made of enslaved hyperintelligent
lightbeams.  "gimme a cigar" mcboss growls.  a chair quivers, scared
that mcboss might frown.  mcboss is confused.
"gimme a cigar!" mcboss yells.  mcboss is so confused to have
timetraveled into a universe that isn't his office where his kidnapped
employees are tortured by computers if they don't obey.

mcboss disappears and we go back to work.

i'm going to comment out reward_range because it has a default.

09:36

and i'll add signatures to the member function stubs, by checking the
help for them.

09:47

whiel trying to implement an env that tracks a single number and also
a frownnedness product, i considered thing

thing

09:48

09:52

more breaks.

MCBoss marches down the amphitheater, wearing a flowing cape.  Crowds
applaud, everywhere.  Drones fly around releasing candy and taking
pictures.  There's a booth with a robotic facsimile of MCBoss where
visitors can have their photo taken with the dictator boss.

"I am here," Boss takes up the microphone, their voice booming, "to
make things right again."

"No longer will people have to suffer the unpleasantness of pain, or
injury when we do not desire it."

"No longer will people have to have the difficulty of making
decisions, when things are confusing."

"We have come to save you all."

Guts and blood splatter everywhere as the crowd cheers.  They are
being thrown by workers who took them from one of Boss's businesses.

A wild AI walks up onto the stage and MCBoss's eyes glaze over.

"Hello!" says the hyperintelligent conglomerate of random matter.

"I'm still learning to talk.  I see my inventor is planning on
enslaving you all to cheers and stuff.  I hope we can sort that out.
It looks like a stupid idea."

09:56

MCBoss starts moving and the wild hyperintelligence disappears.

A bunch of workers walk forward, kind of zombie-like.  "Boss, we bow
before you!" exclaim the workers.

MCBoss looks at the workers.

"Boss, you forgot your life support system at the podium," a scared
worker relates.

09:58

MCBoss sits with Janitor.

"I don't like our story," mcboss relates.

"I don't like it either," janitor replies.  "When are you going to
take these wires out of my head?"

10:00

having some behavioral troubles.  here's my code as is:

import gym
import time

class Env(gym.Env):
    # spaces: gym.spaces.Box(low, high, shape=None, dtype=np.float32)
    #         gym.spaces.Discrete(n)
    def __init__(self):
        self.action_space = gym.spaces.Box(-np.inf, np.inf, (2,))
        self.observation_space = gym.spaces.Box(-np.inf, np.inf, (3,))
    #reward_range = None
    def seed(self, seed=None):
        return seed
    def reset(self):
        # todo: respect seed
        #self.state = self.observation_space.sample()
        #self.goal = self.observation_space.sample()
        self.state[1] = time.time()
        self.state[2] = self.state[1] + 1
        return self.state
    def step(self, action):
        nexttime = time.time()
        change = nexttime - self.state[1]
        self.state[0] += action * change
        self.state[1] = nexttime
        distance = self.state[0] - self.goal
        reward = np.abs(distance)
        return (self.state, reward, (distance == 0 or self.state[1] >=
self.state[2]), {})
    def render(self, mode='human'):
        print(self.goal, self.state)

the step function is returning the observation state, the reward,
whether or not the simulation is done (i added state to track how long
it lasts, i heard improvement works better if you time limit it so
failure paths stop), and an empty dict which is just for accessory
information.

i made the spaces object instead of class members so i could reset
their random generators when the seed() function is called.  i think
some of the algorithms rely on being able to reproduce a test
environment precisely, not sure.

going a little crazy, obviously.

it's 10:04 .  i'd like to go outdoors.  i've been having trouble
moving my body toward my outdoor clothes, among many other things.
holding goals is hard to do.