Zavain Dar

Critical Thinker, VC

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Math Puzzle

Context

Jake and I found ourselves watching New Girl and solving math puzzles last Friday. The. Good. Life.

Problem:

Screen Shot 2015-05-03 at 8.16.30 PM.png

Solution

Alice and Bob each pick the index that has the first 1 in their respective arrays.

Intuition

For each permutation length, Alice and Bob nail one case where they necessarily win.

Example

Bob’s array: 001011…

Alice’s array: 011100…

Bob’s guess: 3

Alice’s guess: 2

Note: In this case they do not win, as the second number in Bob’s array (0) is not equal to the third number in Alice’s array (1).

Python script simulation

import random

#this creates the randomized bit arrays
def makeBitString(bitlist, length):
    for i in range(0, length):
        bitlist.append(random.randint(0, 1))

#this is the guessing algorithm. The guess output is the index of your first 1
def guess(bitlist):
    index = 0
    while (bitlist[index]!=1):
        index = index+1

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Announcing our Investment in Clarifai

Today is about [Clarifai](clarifai.com), it’s about an ambitious entrepreneur in Matt Zeiler, it’s about a true 0 to 1 step function breakthrough in technological ability, it’s about the reawakening of the New York deep tech scene, and as a least important bit, it’s about Lux’s investment alongside USV in Clarifai’s Series A.

Before going further, it makes sense to explain: How did we get here?

At Lux we’ve been investing within the intersection of deep tech and contrarian for over 10 years. The thesis and conviction to be differentiated and shoot for true outsized venture returns has led to investments in nanomaterials, electric grids, drones, digital pathology, and even satellites. Along the way we’ve questioned what it means to be VCs and rolled up our sleeves to play active hands in directly founding companies in Nuclear Waste, semiconductors, and even biotech.

However, time

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20 Bullets on Artificial Intelligence

On Artificial Intelligence:

  1. The prior two decades’ work on digitization of big data sets, infrastructure to manage big data sets, and paradigms to compute over big data sets is the primary causal driver explaining this era’s emphasis first on ‘Data Science’ then on ‘AI’.
  2. Once we have digitized data and made data programmatically available, the obvious next step is to lever it for future automation and prediction making. As our predictive capabilities give the optics of greater ‘intelligence’ we evolve our vocabulary from ‘Data Science’ to ‘Artificial Intelligence’. In reality there is no strong distinction between the two, rather only the perception of novelty and difficulty. Novelty and difficulty are normative to time; today’s ‘Artificial Intelligence’ is tomorrow’s ‘Data Science’ p1aa.jpg
  3. AI that learns from data is called Machine Learning. Traditionally ML has taken raw data

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Alan Turing, Theory, and the Diminishing Hegemony of the Status Quo

(This piece initially appeared in TechCrunch, here)

Like many in Silicon Valley, I recently saw Morten Tyldum’s “The Imitation Game”. Admittedly, I have a soft spot for underdog academic narratives and actually teared up. However, I couldn’t shake the feeling the film pigeonholed the breadth and depth of Turing’s work to early cryptography and its mechanized instantiation in WWII.

Cryptography aside, Turing’s work, theory, and models still underline undergraduate curriculums in Computer Science, Mathematics, and Philosophy. His models for computation form the basis for how mathematicians and computer scientists structure both what is solvable and the efficiency with which we can algorithmically solve answerable questions. His Church-Turing Thesis coupled with Godel’s Incompleteness Theorems still has philosophers debating the existence of universal constraints around human knowledge

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The League: Implications & Responsibilities as Technologists

Silicon Valley -

Here are a few things I believe:

  1. Capitalism is a technology, not an ideology
  2. Technology can be used both to disrupt systems and oppression, and conversely, to strengthen existing stratifications

Here are a few things about the Bay Area, where you live:

  1. The Bay Area has the fastest growing gap between the rich and the poor in the entire US. From 2007-2012 the richest 5% saw their incomes increase $28000, while the bottom 20% saw theirs drop $4000
  2. If San Francisco were a country, its GINI coefficient (a measure of income distribution), would rank as 14th worst in the World
  3. Minorities in SF are decreasing both in aggregate and percentage

I recently saw press of an app, The League. As an app that trumpets ‘weeding out’ the ‘less-than-desirable applicants’ I can’t help but wonder what you’re actually getting at with homepage qualifiers including ‘classy’

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Reflections on current movements in the Bitcoin Ecosystem

An earlier version of this was published on VentureBeat.

A few interesting bits of cryptocurrency news were announced in the recent past. First, a team and project near and dear to me, Blockstream, publicly launched with a white paper for the first time rigorously documenting sidechains. Second, Oliver Bussmann, CIO of UBS, publicly stated “blockchain technology will not only change the way we do payments but it will change the whole trading and settlement topic.” Finally, a group I advise, Counterparty was one of many Bitcoin based teams receiving inquiry letters from the SEC pertaining to unregistered securities.

[Update (11/3/2014): Early reports of SEC letters to Counterparty were incorrect. Despite speculation, Counterparty — to its knowledge — has yet to be contacted by the SEC.]

I first met Blockstream cofounders Austin Hill and Adam Back in early 2014 when teaching a

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Joining Lux

At a recent lunch with two long time friends and mentors I found myself talking through what I thought the best VCs exhibited and how they approached their jobs. The conversation focused on founders vs investors, key differences between the two, and attached characteristics the top few from each group exhibited.

A point of contention came when discussing great founders as having a ‘knack for the dramatic’ vs exceptional investors as thriving on backend and operational initiative. While certainly not trivializing this (I’m happy to point to a handful of previous investments I’ve championed arguing the founder, above all, would dramatically run through brick walls on their way to success), I’ve found the strongest founders and investors alike as coupling strong technical and leadership qualities alongside unmatched applied-philosopher-like questioning, modeling, and proxying instincts.

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Railroads, Cars, and the Price of Bitcoin

I recently had a fun email exchange with a good friend regarding the recent drop in price of Bitcoin. The friend sent this New York Times’ piece by Sydney Ember. I’ve pasted my response below:

Agreed, I suspect there’s not much rationale behind using Bitcoin as a currency unless you’re in a very unstable economy where even with the recent downswing, Bitcoin has shown to be a safer store of value than the local currency.

That said, the article is missing the point and focusing its attention orthogonally to the core innovation within Bitcoin: the ledger. A decent analogue is a railroad track or network with an initial and faulty or problematic car. The article focuses on the faults with the initial car, while discounting the potential in creating entirely new cars and leveraging the core innovation: the track and transportation ability.

Example: Counterparty, an open source

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Decentralized Cloud Societies

I brain dumped on twitter a few nights ago on my view of a developing abstraction for decentralized ledgers.

I’ve since had some interesting conversations diving into another part of the ‘stack’ here, that I didn’t properly reference above. That is dynamic rules at the node communication layer. As my friend Clayton put it:

been thinking about crypto currencies with economic policy built in. bitcoin has anti-inflation built in. what other economic policies could you build in? what if you start putting lots of intelligence at the nodes and when transactions are verified the nodes intelligently manipulate the transactions by small amounts in a pro-social way? can we implement dynamic taxes and subsidies?

Worth rereading Balaji’s wired article on organizing communities/nations on the cloud, keeping in mind Clayton’s above lens on decentralized datasets and network protocols automating

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The real promise of big data: It’s changing the whole way humans will solve problems

Note: A version of this piece was published on VentureBeat

Current “big data” and “API-ification” trends can trace their roots to a definition Kant first coined in the 18th century. In his Critique of Pure Reason, Kant drew a dichotomy between analytic and synthetic truths.

An analytic truth was one that could be derived from a logical argument, given an underlying model or axiomatization of the objects the statement referred to. Given the rules of arithmetic we can say “2+2=4” without putting two of something next to two of something else and counting a total of four.

A synthetic truth, on the other hand, was a statement whose correctness could not be determined without access to empirical evidence or external data. Without empirical data, I can’t reason that adding five inbound links to my webpage will increase the number of unique visitors by 32%.

In this vein, the rise of big

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