Nikolaj Kuntner Nikolaj-K

"web-app, dapps, token smart contracts, ERC20, NEP5" : NEO 8

https://youtu.be/cnK1E3yZGx0

"Hypertext Transfer Protocol (subsidiary clip)" : NEO 7

https://youtu.be/aBz_IjJng3Q

Videos

What's a hash and what's the point?

https://youtu.be/wZiZjXOpaEs

Presented in Vienna with Chris, Dean & Erik

(that was 2 weeks ago - I forgot to make a gist - but this week we have video proof of the event!)

Tech talks and Q&A at the Vienna meetup - part 1 and 2

Video 1:

https://youtu.be/PMvx-H2qgNQ

(Overview at 3:56, talk starts at 11:04)

Generic viva questions

This file contains generic questions about your work, thesis, project, etc. and they are presented in this video:

https://youtu.be/gqTihL-7FbA

To compile it, I split some sensible thesis questions into reasonable categories and then added good stuff from the top ten or so internet search results for "viva questions" on university and news articles. Many questions are very similar, but each with a particular spin to them.

Original list on the MIRI website:

https://intelligence.org/all-publications/

E Hubinger, C van Merwijk, V Mikulik, J Skalse, and S Garrabrant. 2019. “Risks from Learned Optimization in Advanced Machine Learning Systems.” arXiv:1906.01820 [cs.AI].

39 pages ++

V Kosoy. 2019. “Delegative Reinforcement Learning: Learning to Avoid Traps with a Little Help.” Forthcoming at the Safe Machine Learning workshop at ICLR.

	'''
	git clone
	https://github.com/jhogan4288/coinmarketcap-history

	python2.7 coinmarketcap_usd_history.py neo 2016-09-09 2017-12-12 > neo.csv
	'''

	import csv
	import matplotlib.pyplot as plt

	import matplotlib.pyplot as plt


	def bonusNormalized(levelHalf, level):
	return 1 - levelHalf / float(levelHalf + level)

	def levelFactor(levelHalf, level, bonusMax):
	return 1 + bonusMax * bonusNormalized(levelHalf, level)

	def reward(levelHalf, bonusMax, marketFactor, levelOld, levelDelta):

	import math

	ICO_SUPPLY = 10**9
	DECIMALS = 8
	FLOAT_FACTOR = 10**DECIMALS

	IMUSIFY_OPERATION_STACK = (3-1) * ICO_SUPPLY * FLOAT_FACTOR
	SOME_USER_ADDRESS = 'ASsudg...poor soul'
	IMUSIFY_MAIN_IMU_ADDRESS = "AXjsdb...much IMU here"
	IMUSIFY_OPERATION_IMU_ADDRESS = "AKjsdb...less IMU here"

	import matplotlib.pyplot as plt

	PRICE_TODAY = dict(dag=[0.003599], ftm=[0.021852])

	def parse(name):
	filename = "{}.csv".format(name)
	prices = [float(line[13:21]) for line in open(filename)][::-1] # line[13:21] cuts out the right column values
	return [y / prices[0] for y in prices + PRICE_TODAY[name]]

	dag = parse("dag")

	"""
	A compact feedforward AI. Script explained here:

	https://youtu.be/z2aq21lMw40

	In this video we're implementing a feed forward neutral network that is able to discern
	handwritten digits in vanilla Python.

	References:
	http://static.latexstudio.net/article/2018/0912/neuralnetworksanddeeplearning.pdf # deep learning book