The Power of Open Source: In conversation with Mike Volpi, General Partner, Index Ventures

Our most recent VC guest at Data Driven NYC, Mike Volpi of Index, has had a pretty amazing last couple of years, with three of his venture investments going public:  Zuora, Sonos and Elastic. 

Before becoming a VC, Mike ran Cisco’s routing business where he managed a P&L in excess of $10 billion in revenues, and acquired over 70 companies (note: probably a pretty good way to make a lot of friends in Silicon Valley).

A partner at Index Ventures in San Francisco, Mike invests primarily in infrastructure, open-source and artificial intelligence companies, so he was a perfect guest to have at the event.  In particular, he invested in two prior presenting companies: Confluent and Cockroach Labs (in which FirstMark is also an investor). 

We had a really interesting conversation about open source, AI and venture capital.  Here’s the video below, and l have jotted down a few notes as well, below the fold. 

Notes from the chat:

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The Killer App for Machine Learning: In Conversation with Pedro Domingos, Head of Machine Learning, D.E. Shaw

Best-selling author, Professor of Computer Science at the University of Washington, recent recipient of the prestigious IJCAI John McCarthy Award for excellence in artificial intelligence research (among other awards) and Head of the Machine Learning Research group at D.E. Shaw:  Pedro Domingos has one of the most incredible resumes in the world of AI, and we were thrilled to host him for a fireside chat at our most recent Data Driven NYC. 

We covered a bunch of things, including why finance is a killer app for machine learning, his much-lauded book, ‘The Master Algorithm’ and what’s truly scary about AI (hint: not the Terminator).

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AI’s Trust Problem: In Conversation with Gary Marcus (Video + Book Notes)

Should we be worried about the prospect of AI superintelligence taking over the world?

“In the real world, current-day robots struggle to turn doorknobs, and Teslas driven in ‘Autopilot’ mode keep rear-ending parked emergency vehicles […].   It’s as if people in the fourteenth century were worrying about traffic accidents, where good hygiene might have been a whole lot more helpful”.

This is one of my favorite quotes from “Rebooting AI: Building Artificial Intelligence We Can Trust,” a new book by Gary Marcus – scientist, NYU professor, New York Times bestselling author, entrepreneur – and his co-author Ernest Davis, Professor of Computer Science at the Courant Institute, NYU.

Gary did us a big honor recently: he chose to speak at Data Driven NYC on the evening of the publication of the book.  He also signed a few copies. Our first book launch party!

Particularly if you’re trying to make sense of the still-ongoing hype around AI, including predictions of global gloom, Gary’s book is a fantastic read: a lucid, no-nonsense and occasionally provocative take on the current state of AI, that distills complex concepts into simple ideas, and includes plenty of interesting and often funny anecdotes.

The book builds on Gary’s earlier assessment of deep learning (see Deep Learning: A Critical Appraisal), and advocates for a hybrid approach to AI.

Below is the video of his talk at the event, plus a notes I derived from both the talk and the book.  I’ll keep those brief as the book is worth reading in its entirety.

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Decoding the Human Nervous System: In conversation with Thomas Reardon, CEO, CTRL-labs

In its largest acquisition since Oculus in 2014, Facebook just announced last night it acquired CTRL-labs, a 4 year old startup based in New York, for a reported $500M-$1B.

Coincidentally, CTRL-labs CEO, Thomas Reardon (who goes by Reardon) was our guest at Data Driven NYC just a couple of weeks ago. Reardon is a particularly compelling entrepreneur, and this was a fascinating fireside chat, where we dove into machine learning, neuroscience, VR and all sorts of cool topics.

CTRL-labs builds what it calls “neural interface technology”: algorithms that decode the activity of individual motor neurons and turns that into control over machines, thereby completely redefining the interaction between humans and machines. Because the technology captures your intentions without requiring any physical movement, you can do things that you could never do by moving, and you can start “imaging experiences where you would have 20 fingers… or 8 arms or legs”.

The video (below) is well worth a watch in its entirety, including the audience Q&A at the end, and I’ve jotted down a few notes as well, for a quick review.

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Part II: Major Trends in the 2019 Data & AI Landscape

Part I of the 2019 Data & AI Landscape covered issues around the societal impact of data and AI, and included the landscape chart itself. In this Part II, we’re going to dive into some of the main industry trends in data and AI. 

The data and AI ecosystem continues to be one of the most exciting areas of technology. Not only does it have its own explosive momentum, but it also powers and accelerates innovation in many other areas (consumer applications, gaming, transportation, etc).  As such, its overall impact is immense, and goes much beyond the technical discussions below.

Of course, no meaningful trend unfolds over the course of just one year, and many of the following has been years in the making. We’ll focus the discussion on trends that we have seen particularly accelerating in 2019, or gaining rapid prominence in industry conversations.

We will loosely follow the order of the landscape, from left to right: infrastructure, analytics and applications.

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A Turbulent Year: The 2019 Data & AI Landscape

It has been another intense year in the world of data, full of excitement but also complexity. 

As more of the world gets online, the “datafication” of everything continues to accelerate.  This mega-trend keeps gathering steam, powered by the intersection of separate advances in infrastructure, cloud computing, artificial intelligence, open source and the overall digitalization of our economies and lives. 

A few years ago, the discussion around “Big Data” was mostly a technical one, centered around the emergence of a new generation of tools to collect, process and analyze massive amounts of data. Many of those technologies are now well understood, and deployed at scale. In addition, over the last couple of years in particular, we’ve started adding layers of intelligence through data science, machine learning and AI into many applications, which are now increasingly running in production in all sorts of consumer and B2B products.  

As those technologies continue to both improve and spread beyond the initial group of early adopters (FAANG and startups) into the broader economy and world, the discussion is shifting from the purely technical into a necessary conversation around impact on our economies, societies and lives.

We’re just starting to truly get a sense of the nature of the disruption ahead. In a world where data-driven automation becomes the rule (automated products, automated cars, automated enterprises), what is the new nature of work? How do we handle the social impact? How do we think about privacy, security, freedom? 

Meanwhile, the underlying technologies continue to evolve at a rapid pace, with an ever vibrant ecosystem of startups, products and projects, heralding perhaps even more profound changes ahead. In that ecosystem, the year was characterized by the early innings of a long expected consolidation, and perhaps a passing of the guard from one era to another as early technologies are starting to give way to the next generation.

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Data, AI & Hedge Funds: In Conversation with Matt Ober, Chief Data Scientist at Third Point

 

The hedge fund world has been evolving dramatically over the last few years.

Just like in other industries, software, data and AI/ML have been playing an increasingly important, and disruptive, role.  Many hedge funds have been scrambling to embrace this evolution – not just to gain an edge, but also to avoid becoming extinct.

Certainly, quantitative hedge funds have been making heavy use of software and data for a while now.  The “quant” funds rely upon algorithmic or systematic strategies for their trades – meaning that they generally employ  automated trading rules rather than discretionary (human) ones, and they will trade tens or hundreds of assets simultaneously.

But another big part of the industry, the “fundamental” hedge funds, had been operating very differently.  Those funds will perform a bottoms up analysis on individual securities to  value them in the marketplace and assess whether they are  “undervalued” and “overvalued” assets.  They’ll often have a much more concentrated portfolio.

In part because the entire hedge fund industry has been performing generally poorly recently (years of performance trailing the stock market), there’s been mounting pressure on hedge funds to evolve rapidly, particularly fundamental ones.

A couple of years ago, Third Point made a big splash when they hired Matt Ober, who was 32 at the time, to become their Chief Data Scientist.  Dan Loeb, the billionaire founder of Third Point, was a prime example of a fund manager who had reached tremendous success through a fundamental approach.  His efforts to hire Matt away from his previous employer and make him Third Point’s head quant was widely viewed as a sign of the times. Continue reading “Data, AI & Hedge Funds: In Conversation with Matt Ober, Chief Data Scientist at Third Point”

Scaling AI Startups

Not so long ago, AI startups were the new shiny object that everyone was getting excited about.   It was a time of seemingly infinite promise: AI was going to not just redefine everything in business, but also offer entrepreneurs opportunities to build category-defining companies.

A few years (and billions of dollars of venture capital) later, AI startups have re-entered reality.  Time has come to make good on the original promise, and prove that AI-first startups can become formidable companies,  with long term differentiation and defensibility.

In other words, it is time to go from “starting” mode to “scaling” mode.

To be clear: I am as bullish on the AI space as ever.  I believe AI is a different and powerful enough technology that entire new industry leaders can be built by leveraging it, as long it is applied to the right business problems.

At the same time, I have learned plenty of lessons in the last three or four years by being on the board of AI startups, and talking to many AI entrepreneurs in the context of Data Driven NYC.   I’ll be sharing some notes here.

This post is a sequel to a presentation I made almost three years ago at the O’Reilly Artificial Intelligence conference, entitled “Building an AI Startup: Realities & Tactics“, which covered a lot of of core ideas about starting an AI company:  building a team, acquiring data, finding the right market positioning. A lot of those concepts still hold, and this post will focus more on specific lessons around scaling.

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AI & Blockchain: An Introduction

 

At the kind invitation of Rob May and the Botchain team, I had the opportunity recently to keynote Brains and Chains, an interesting conference in New York exploring  the intersection of artificial intelligence and blockchain.

This is both an exciting and challenging topic, and the goal of my talk was to provide a broad introduction to kick things off, and frame the discussion for the rest of the day: discuss why the topic matters in the first place, and highlight the work of some interesting companies in the space.

Below is the presentation, with some added commentary when relevant. Scroll to the very bottom for a SlideShare widget, if you’d like to flip through the slides.

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Great Power, Great Responsibility: The 2018 Big Data & AI Landscape

 

It’s been an exciting, but complex year in the data world.  

Just as last year, the data tech ecosystem has continued to “fire on all cylinders”.  If nothing else, data is probably even more front and center in 2018, in both business and personal conversations.  Some of the reasons, however, have changed.

On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world.   It is no accident that one of the key themes in the corporate world in 2018 so far has been “digital transformation”.  The term may feel quaint to some (“isn’t that what’s been happening for the last 25 years?”), but it reflects that many of the more traditional industries and companies are now fully engaged into their journey to become truly data-driven.   

On the other hand, a much broader cross-section of the public has become aware of the pitfalls of data. Whether it is through the very public debate over the risks of AI, the Cambridge Analytica scandal, the massive Equifax data breach, GDPR-related privacy discussions or reports of growing government surveillance in China, the data world has started revealing some darker, scarier undertones.

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Frontier AI: How far are we from artificial “general” intelligence, really?

Some call it “strong” AI, others “real” AI, “true” AI or artificial “general” intelligence (AGI)… whatever the term (and important nuances), there are few questions of greater importance than whether we are collectively in the process of developing generalized AI that can truly think like a human — possibly even at a superhuman intelligence level, with unpredictable, uncontrollable consequences.

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Growing Pains: The 2018 Internet of Things Landscape

For proponents of the Internet of Things, the last 12-18 months have been often frustrating. The Internet of Things (IoT) was supposed to be huge by now.  Instead, the industry news has been dominated by a string of startup failures, as well as alarming security issues.  Cisco estimated in a (controversial) study that almost 75% of IoT projects fail.  And the Internet of Things certainly lost a part of its luster as a buzzword, easily supplanted in 2017 by AI and bitcoin.

Interestingly, however, the Internet of Things continues its inexorable march towards massive scale.  2017 was most likely the year when the total number of IoT devices (wearables, connected cars, machines, etc.) surpassed mobile phones.  Global spending in the space continues to accelerate – IDC was forecasting it to hit $800 billion in 2017, a 16.7% increase over the previous year’s number.  

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Interview with Machine Learnings

A few days ago, I sat down Sam DeBrule of Machine Learnings for a broad conversation about AI and startups.  We got into a number of topics including creative data acquisition tactics, data network effects, and what makes AI startups different.

The interview is here:  Why AI Companies Can’t Be Lean Startups – A Conversation with Matt Turck of FirstMark Capital.

 

Firing on All Cylinders: The 2017 Big Data Landscape

 

It feels good to be a data geek in 2017.

Last year, we asked “Is Big Data Still a Thing?”, observing that since Big Data is largely “plumbing”, it has been subject to enterprise adoption cycles that are much slower than the hype cycle.  As a result, it took several years for Big Data to evolve from cool new technologies to core enterprise systems actually deployed in production.

In 2017, we’re now well into this deployment phase.  The term “Big Data” continues to gradually fade away, but the Big Data space itself is booming.  We’re seeing everywhere anecdotal evidence pointing to more mature products, more substantial adoption in Fortune 1000 companies, and rapid revenue growth for many startups.

Meanwhile, the froth has indisputably moved to the machine learning and artificial intelligence side of the ecosystem. AI experienced in the last few months a “Big Bang” in collective consciousness not entirely dissimilar to the excitement around Big Data a few years ago, except with even more velocity.

2017 is also shaping up to be an exciting year from another perspective: long-awaited IPOs.  The first few months of this year have seen a burst of activity for Big Data startups on that front, with warm reception from the public markets.

All in all, in 2017 the data ecosystem is firing on all cylinders.  As every year, we’ll use the annual revision of our Big Data Landscape to do a long-form, “State of the Union” roundup of the key trends we’re seeing in the industry.

Let’s dig in.

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Debunking the “No Human” Myth in AI

 

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What goes up must go down, and the hype around AI will inevitably deflate sooner or later.
 
One unfortunate consequence of the hype is that it created the widely-shared perception that AI reached seemingly overnight a stage where it can be fully automated, leading both to endless possibilities, as well as concerns about its impact on jobs and society.
 
However, this is not the reality just yet and, both in private conversations and on social media, I’m starting to increasingly sense a backlash – the general theme being that “so many humans are involved behind the scenes” in various AI products or companies.  This is sometimes ushered in Theranos-Like tones, as if the horrible underbelly of the beast was about to be exposed.
 
So let’s make it clear: today, scores of humans are involved just about everywhere in AI, whether in tiny startups or massive tech companies.  In fact, most AI products are very much NOT fully automated, at least not in an end-to-end, 100% bulletproof way.   It is probably ok for the general press to get a bit carried away with AI.  However, we in the tech industry should probably better understand this reality, and acknowledge it as a necessary step in the process of building a major new wave of technology products.
 

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