Technology

The full version of "We All Become" from the Transistor Original Soundtrack. Get it here: http://bit.ly/TransistorOST

Transistor - We All Become

This is a series about what happens when someone who knows nothing about business starts one. It's called StartUp.  In episode one, Alex Blumberg botches his pitch to Silicon Valley billionaire Chris Sacca.

#1 How Not to Pitch a Billionaire

Dreaming big is harder than you'd think. 

#2 Is Podcasting the Future or the Past?

By John G. Cramer, University of Washington: https://faculty.washington.edu/jcramer/BBSound.html

Related story: http://www.theatlantic.com/science/archive/1857/11/a-brief-history-of-noise/422481/

Frequency Spectrum From the Big Bang (WMAP Satellite Data)

Moore's Law -- putting more and more transistors on a chip -- accelerated the computing industry by so many orders of magnitude, it has (and continues to) achieve seemingly impossible feats. However, we're now resorting to brute-force hacks to keep pushing it beyond its limits and are getting closer to the point of diminishing returns (especially given costly manufacturing infrastructure). Yet this very dynamic is leading to "a Cambrian explosion" in computing capabilities… just look at what's happening today with GPUs, FPGAs, and neuromorphic chips. Through such continuing performance improvements and parallelization, classic computing continues to reshape the modern world.

But we're so focused on making our computers do more that we're not talking enough about what classic computers can't do -- and that's to compute things the way nature does, which operates in quantum mechanics. So our smart machines are really quite dumb, argues Rigetti Computing founder and CEO Chad Rigetti; they're limited to human-made binary code vs. the natural reality of continuous variables. This in turn limits our ability to work on problems that classic computers can't solve, such as key applications in computational chemistry or large-scale optimization for machine learning and artificial intelligence. Which is where quantum computing comes in.

But what is quantum computing, really -- beyond the history and the hype? And where are we in reaching the promise of practical quantum computers? (Hint: it will take a hybrid approach to get there.) Who are the players -- companies, countries, types of people/skills -- working on it, and how can a startup compete in this space? Finally, what will it take to get "the flywheel" of application development and discovery going? Part of the answer comes full circle to the same economic engine that drove previous computing advances, argues Chris Dixon; Moore's Law, after all, is more of an economic principle that combined the forces of capitalism, a critical mass of ideas, and people moving things forward by sheer will. Quantum computing is finally getting pulled into the same economic forces as well.

a16z Podcast: Quantum Computing, Now and Next

Three themes from Detroit: Become Human, each focusing on one of the title's main characters, and each scored by a different composer.

Detroit: Become Human launches May 25, 2018 on PS4.

Detroit: Become Human — Kara Main Theme By Philip Sheppard

Detroit: Become Human — Connor Main Theme By Nima Fakhrara

Mission Impossible-Fallout

A little ditty from the guts of our iPhone app

Cha-Ching!

In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains. And as data science and data engineering teams continue to expand, tools need to enable and facilitate collaboration.

As someone who specializes in helping teams turn machine learning prototypes into production-ready services, I wanted to hear what Doddi has learned while working with organizations that aspire to “become machine learning companies.”

Simplifying Machine Learning Lifecycle Management

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Dispute resolution is the process by which contracting parties settle disagreements. Whether in the form of litigation, arbitration, or other means of mediation, every contract defines a dispute resolution mechanism and jurisdiction. It is the metaphorical Lady Justice, measuring the strength of each party's arguments, and reaching a decision based on evidence.

Smart contracts are unique in this sense. Unlike traditional contracts, they are rigid and deterministic. Written in computer code, nuances in human language and vagueness of terms do not exist in this realm. There are no judges, no jury, just calculated execution. 

The DOA hack and other similar events have prompted observers of the space to express the need for smart contract dispute resolution. Some have suggested "exit switches" (https://epicenter.tv/episode/142/) that would allow for human intervention when edge cases appear. But could the arbitration process be integrated into the smart contract and on the blockchain?

We're joined by, Federico Ast and Clement Lessage, respectively CEO and CTO of Kleros. This dispute resolution layer provides contracting parties with a fast and secure process for arbitration. The system is broken up into courts and sub-courts, each specializing in specific matters like e-commerce, insurance, and transport. In the event of a dispute, parties submit their case to Kleros, where a crowd of expert jurors analyses the evidence. When all votes are cast, the decision is enforced by the smart contract, which may unlock funds, or provide parties with additional time to fulfill the terms of the agreement. Clever incentive mechanisms reward jurors who vote with the crowd, making Kleros resistant to bribe attacks and collusion between jurors.

Topics discussed in this episode:
- Federico and Clement's respective backgrounds, including a crowd arbitration project called Jury.
- The vision behind Kleros and the problem it addresses
- The case for crowd-sourced jurors as a means to find the best judgment
- The game theory and incentive mechanisms embedded in Kleros
- Kleros' hierarchical system of courts and sub-courts
- How jury selection works and who administers courts
- The system's built-in governance mechanism and its purpose
- The Kleros token, Pinkaion coin, and it's utility in the system
- “Doge on Trial,” a clever experiment to find authentic doges
- The current status of the project and roadmap

Links mentioned in this episode: 
- Kleros Website: https://kleros.io
- Kleros White Papper: https://kleros.io/assets/whitepaper.pdf
- Doges on Trial: https://dogesontrial.dog
- Why Decentralization Matters by Chris Dixon: https://medium.com/@cdixon/why-decentralization-matters-5e3f79f7638e

Sponsors: 


This episode is also available on :
- Epicenter.tv: https://epicenter.tv/250
- YouTube: http://youtu.be/efzSmpac2pw
- Souncloud: http://soundcloud.com/epicenterbitcoin/eb-250

Watch or listen, Epicenter is available wherever you get your podcasts.

Epicenter is hosted by Brian Fabian Crain, Sébastien Couture & Meher Roy.

#250 Clement Lessage & Federico Ast: Kleros – Crowdsourced Arbitration for Blockchain Applications

In this episode of the Data Show, I spoke with Alan Nichol, co-founder and CTO of Rasa, a startup that builds open source tools to help developers and product teams build conversational applications. About 18 months ago, there was tremendous excitement and hype surrounding chatbots, and while things have quieted lately, companies and developers continue to refine and define tools for building conversational applications. We spoke about the current state of chatbots, specifically about the types of applications developers are building today and how he sees conversational applications evolving in the near future.

Using Machine Learning to Improve Dialog Flow in Conversational Applications

with Benedict Evans (@BenedictEvans) and Steven Sinofksy (@StevenSi)

In this hallway-style conversation episode of the a16z Podcast, Benedict Evans and a16z board partner Steven Sinofsky discuss Apple’s September 2018 keynote event and share their thoughts on the new innovations -- and lessons -- that really matter.

With something that’s gone from toy to phone to fashion item -- and just pivoted to a health monitor that can literally save lives -- where are we now? How closely aligned is health to the overall value proposition, and what are some of the characteristics of how Apple innovates as a company as a whole... from components and building blocks to how it all comes together?

image: Integrated Change/ Flickr(CC 2.0)

a16z Podcast: What's at the Core of the Latest Apple Announcement?

with Fred Wilson (@fredwilson) and Chris Dixon (@cdixon)

There's all sorts of interesting tech trends happening right now, including AI, VR/AR, self-driving cars and drones (as well as interesting stuff happening in verticals like healthcare and finance) -- and there's a lot also happening in seemingly more "mature" tech revolutions, such as mobile and cloud. But where are we now, really, with these shifts... and how does that inform how we think about the next couple decades?

And does a framework like Carlota Perez's -- as outlined in Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages and summarized by venture capitalist and longtime internet investor Fred Wilson (of Union Square Ventures) -- fully apply when it comes to software? Because, argues Chris Dixon (general partner on a16z crypto), software "has so much more plasticity, ability to adapt, ability to evolve" that unlike hardware, "the core itself will also dramatically change... not just the apps around it". The total economic value that will be unlocked with the software revolution, observes Wilson, should be orders of magnitude bigger than what we saw with manufacturing for sure.

But just how much internet innovation is actually powering true disruption (i.e., is more than just a sustaining innovation, to use Clayton Christensen's terminology)? How do new business models change everything? Dixon and Wilson consider all this and more in this hallway-style episode of the a16z Podcast, where we recorded the two having a think-aloud conversation about everything from the history of the internet and startups, the evolution of capital and infrastructure, to the advent of crypto. How do they they both define "decentralized", what do they think of dApps, and where do NFTs and "crypto goods" come in?? One thing's for sure: It's the most interesting time they've both ever seen in over 30 years of internet work, life, and play.

Please note that the a16z crypto fund is a separate legal entity managed by CNK Capital Management, L.L.C. (“CNK”), a registered investor advisor with the Securities and Exchange Commission. a16z crypto is legally independent and operationally separate from the Andreessen Horowitz family of fund and AH Capital Management, L.L.C. (“AHCM”). 

In any case, the content provided here is for informational purposes only, and does NOT constitute an offer or solicitation to purchase any investment solution or a recommendation to buy or sell a security; nor it is to be taken as legal, business, investment, or tax advice. In fact, none of the information in this or other content on a16zcrypto.com should be relied on in any manner as advice. You should consult your own advisers as to legal, business, tax and other related matters concerning any investment.

a16z Podcast: Technological Trends, Capital, and Internet 'Disruption'

In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. It turns out that each of the standard approaches (anti-classification, classification parity, and calibration) has limitations, and their paper is a must-read tour through recent research in designing fair algorithms. We talked about their key findings, and, most importantly, I pressed them to list a few best practices that analysts and industrial data scientists might want to consider.

Technology

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