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"Machine learning is to big data as human learning is to life experience," says Christopher Nguyen, the co-founder and CEO of big data intelligence company Adatao. Sure, but then, what IS big data? (especially as it's become a buzzword that captures so many things)... 

On this episode of the a16z Podcast, Nguyen puts on his former computer science professor hat to describe 'big data' in relation to 'machine learning' -- as well as what comes next with 'deep learning'. Finally, the former Google exec shares how Hadoop and Spark evolved from the efforts of companies dealing with massive amounts of real-time information; what we need to make machine learning a property of every application (why would we even want to?); and how we can make all this intelligence accessible to everyone.

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The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
 
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: Making Sense of Big Data, Machine Learning, and Deep Learning

Think of our inaugural episode of The AI Podcast as a guide for the perplexed.  Host Michael Copeland speaks with NVIDIA's Will Ramey about the history behind today's AI boom and the key concepts you need to know to get your head around a technology that's reshaping the world.

Ep. 1: Deep Learning 101 - Will Ramey, NVIDIA Senior Manager for GPU Computing

Bryan Catanzaro, vice president for applied deep learning research at NVIDIA, talks about how we know an AI technology is working, the potential for AI-powered speech, and where we’ll see the next deep learning breakthroughs.

Ep. 2: Where Deep Learning Goes Next - Bryan Catanzaro, NVIDIA Applied Deep Learning Research

The culture of open source has changed across generations, from previous ones that had to fight for the brave new way -- to the current "GitHub generation" that not only accepts open source, but expects it as the default. Which makes sense given that open source powers so much of the software world today... and by the way, that's not just tech companies but hospitals and banks; it touches everyone.

Open source culture has also moved away from cults of personality and top-down models to drive the vision for open source projects, to decentralized individual contributor identities and more micro-sized projects within projects. So what does that mean for the governance of open source, whether it's by institution or foundation, or a "healthy" or "popular" project? Should we invert, always invert to make sure open source code "lands" and is committed by default -- as opposed to going through a cabal of gatekeepers first?

This episode of the a16z Podcast -- featuring Nadia Eghbal (who formerly researched the sustainability of open source projects for Ford Foundation, and is now in community programs at GitHub) and Mikeal Rogers (community manager and more at Node.js Foundation) in conversation with Sonal Chokshi -- covers all this and more. Is open source simply too loaded a term? Is there no sense of ownership? How best to manage a project or resolve conflicts? After all, at the end of the day, it's about people, not just code...

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The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation.
 
This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments and certain publicly traded cryptocurrencies/ digital assets for which the issuer has not provided permission for a16z to disclose publicly) is available at https://a16z.com/investments/.

Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: The Changing Culture of Open Source

There's feedback and there's guidance; there's praise and there's criticism. All of it is important to do better work, but to develop a better and more productive workplace and relationships -- especially given how much time we spend at work! -- the way we give and receive feedback really matters. "One of the great things about having a great boss," observes Kim Scott, "is that a great boss will help you grow as a person. And for a lot of people, a big part of what gives work meaning is personal growth." That's another reason why feedback matters.

But doesn't so much feedback take too much time when you're busy building things, especially in fast-growing startups where you're also focused on survival first? Or what if you're not so into the touchy-feely aspects of soliciting feedback? In fact, what is the best way to give feedback, so that you're not being obnoxiously aggressive or even worse, "ruinously empathetic"?

You actually don't have to choose between those two things, argues Scott, because the answer lies somewhere in between, with "radical candor". Finally, how does this fit with other management wisdom around how much to develop someone -- or when to just "call it" and fire them? How does this affect women and under-represented minorities in the workplace? Or how about creatives, millennials, and remote workers? In this episode of the a16z Podcast, Sonal Chokshi explores these questions with Scott, who came out of Google, Apple University, and her own startups... and literally wrote the book on Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity.

a16z Podcast: Giving and Getting Feedback -- for Bosses and Employees

with Frank Chen, Steven Sinofsky, and Sonal Chokshi

There are many reasons why we’re in an “A.I. spring” after multiple “A.I. winters” — but how then do we tease apart what’s real vs. what’s hype when it comes to the (legitimate!) excitement about artificial intelligence and machine learning? Especially when it comes to the latest results of computers beating games, which not only captures our imaginations but has always played a critical role in advancing machine intelligence (whether it’s AI winning Texas Hold’em poker or beating the world human champ in the ancient Chinese game of Go).

But on learning that Google DeepMind’s AlphaGo can master the game of Go without human knowledge — or more precisely: “based solely on reinforcement learning, without human data, guidance, or domain knowledge beyond game rules” — some people leap too far towards claims of artificial generalized intelligence. So where can we then generalize the findings of such work — unsupervised learning, self-play, etc. — to other specific domains? What does it mean for entrepreneurs building companies (and what investors look for)? And what does it mean for how we, as humans, learn… or rather, how computers can also learn from how we learn?

Deal and research operating team head Frank Chen and a16z board partner Steven Sinofsky ponder all this and more, in conversation with Sonal Chokshi, in this episode of the a16z Podcast. We ended last time with the triumph of data over algorithms and begin this time with the triumph of algorithms over data … is this the end of big data?

a16z Podcast: Revenge of the Algorithms (Over Data)... Go! No?

Author and professor at George Mason University, Peter Leeson describes himself as not just an economist but as a "collector of curiosa." In his latest book, WTF?! An Economic Tour of the Weird, Leeson looks at just that -- the strangest beliefs, superstitions and rituals humankind has engaged in -- and using economics, uncovers the incentives and rational behavior that makes them, well, make a whole lot more sense.

In this Halloween Special, Leeson and a16z's Hanne Tidnam dive into the weirdest historical mysteries -- everything from ecclesiastic courts that put rats and rodents on trial, to judicial ordeals that determined guilt or innocence by boiling the accused's hands in cauldrons of hot water, to the economics and laws that governed pirate ships. All these practices, Leeson argues, use superstitions and beliefs like tools: a kind of technology -- in the broadest possible terms.

a16z Podcast: The Why Behind the Weird

with  Brandon Ballinger (@bballinger), Mintu Turakhia (@leftbundle), Vijay Pande (@vijaypande), and Hanne Tidnam (@omnivorousread)

There’s been a lot of talk about technology -- and AI, deep learning, and machine learning specifically -- finally reaching the healthcare sector. But AI in medicine isn’t actually new; it’s actually been there since the 1960s. And yet we didn’t see it effect a true change, or even become a real part our doctor’s offices -- let alone routine healthcare services. So: what's different now? And what does AI in medicine look like, practically speaking, whether it's ensuring the best data, versioning software for healthcare, or other aspects? 

In this episode of the a16z Podcast, Brandon Ballinger, CEO of Cardiogram; Mintu Turakhia, cardiologist at Stanford and Director of the Center for Digital Health; and general partner and head of a16z bio fund Vijay Pande in conversation with Hanne Tidnam discuss where will we start to see AI in healthcare first -- diagnosis, treatment, or system management -- to what it will take for it to succeed. Will we perhaps see a "levels" of AI framework for doctors as we have for autonomous cars?

a16z Podcast: Putting AI in Medicine, in Practice

Hiring a VP of Product -- especially as the founder of the company -- can almost feel like handing over your baby to someone else to hold, observes a16z executive talent team partner Caroline Horn, who hosted an event on this topic earlier this year (which this podcast is based on). Featuring Vijay Balasubramaniyan, founder/CEO of Pindrop; Shishir Mehrotra, founder and CEO of Coda; Gokul Rajaram, Production Engineering Lead at Square; and Alan Schaaf, founder/CEO of Imgur -- and moderated by general partner Martin Casado -- the discussion covers everything from what the VP of Product role really is to how to hire and integrate it into your company.

Because if you're going to be handing your "baby" over... how can you avoid common pitfalls? And know that you pick the right person for the job?

a16z Podcast: How Founders Hire a VP of Product

There's a new C-level role in town: the CCO, or Chief Customer Officer. This episode (based on a previous event) is all about the rise of this new role, why it's so important -- and what the actual scope and function of the role should be.

a16z's Matt Levy, partner on the exec talent team, discusses with (CCOs all) Allison Pickens of Gainsight; Krista Anderson-Copperman from Okta; and Hatima Shafique from Databricks why it is that the Chief Customer Officer is becoming more prevalent across a number of different kinds of companies; what the strategic value of a CCO is (and how it's actually very different from a VP of Customer Success!); and finally, the career pathing of the Chief Customer Officer.

a16z Podcast: The Rise of the CCO

When you have “a really hot, frothy space” like AI, even the most basic questions — like what is it good for, how do you make sure your data is in shape, and so on — aren’t answered. This is just as true for the companies eager to adopt the technology and get into the space, as it is for those building companies around that space, observes Joe Spisak, Head of Partnerships at Amazon Web Services. “People treat it like magic,” adds a16z general partner Martin Casado.

This magical realism is especially true of AI, because by definition — i.e., machines learning — there is a bit of a “black box” between what you put in and what you get out of it. Which may be fine… Except when you have to completely change the data being fed into that black box, or you’re shooting for a completely different target to come out of it. That’s why, observes Scott Clark, CEO and co-founder of SigOpt, “an untuned, sophisticated system will underperform a tuned simple system” almost every time.

So what does this mean for organizations going from so-called “toy” problems in R&D to real business results tied to KPIs and ROI? In this episode of the a16z Podcast, Casado, Clark, and Spisak (in conversation with Sonal Chokshi) share their thoughts on what’s happening and what’s needed for AI in practice, given their vantage points working with both large companies and AI startups. What does it mean for data scientists and domain experts? For differentiation and advantage? Because even though we finally have widely available building blocks for AI, we need the scaffolding too… and only then can we build something powerful on top of it.

a16z Podcast: AI, from 'Toy' Problems to Practical Application

The a16z Podcast discusses tech and culture trends, news, and the future -- especially as ‘software eats the world’. It features industry experts, business leaders, and other interesting thinkers and voices from around the world. This podcast is produced by Andreessen Horowitz (aka “a16z”), a Silicon Valley-based venture capital firm. Multiple episodes are released every week; visit a16z.com for more details and to sign up for our newsletters and other content as well!