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Linear Digressions

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Kullback Leibler divergence, or KL divergence, is a measure of information loss when you try to approximate one distribution with another distribution.  It comes to us originally from information theory, but today underpins other, more machine-learning-focused algorithms like t-SNE.  And boy oh boy can it be tough to explain.  But we're trying our hardest in this episode!

KL Divergence

Even as we rely more and more on machine learning algorithms to help with everyday decision-making, we're learning more and more about how they're frighteningly easy to fool sometimes.  Today we have a roundup of a few successful efforts to create robust adversarial examples, including what it means for an adversarial example to be robust and what this might mean for machine learning in the future.

Adversarial Examples

In this episode of the O’Reilly Media Podcast, I spoke with Gayle Sheppard, vice president and general manager of Saffron AI Group at Intel, and David Thomas, chief analytics officer for Bank of New Zealand (BNZ). Our conversations centered around the utility of artificial intelligence in the financial services industry.

Associative Memory AI: Enabling Machines to Fight Financial Crime With Human-Like Reasoning

In this episode of the SuperDataScience Podcast, I chat with the the founders of Cigen, Leigh and Daniel Pullen. You will listen to an overview of RPA or the Robotics Process Automation, talk what it’s all about, how it works, how business gets value from it and also how people can learn this and integrate to their career. You will learn the different RPA tools or the global tools that any business can use across any applications. Also, you will know the concept of Robots-for-Hire and how it is valuable for entrepreneurs.

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/173

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 173: Understanding Robotics Process Automation (RPA) to Disrupt Your Business

Convex optimization is one of the keys to data science, both because some problems straight-up call for optimization solutions and because popular algorithms like a gradient descent solution to ordinary least squares are supported by optimization techniques. But there are all kinds of subtleties, starting with convex and non-convex functions, why gradient descent is really an optimization problem, and what that means for your average data scientist or statistician.

Convex (and non-convex) Optimization

with James J. Collins, Vijay Pande (@vijaypande), and Hanne Tidnam (@omnivorousread)

The idea of 'designing biology' -- once science fiction -- has over the last 20 years become just... science. In this episode, a16z bio general partner Vijay Pande with Hanne Tidnam talk all about the field of synthetic biology with James J. Collins, professor of bioengineering at MIT. Collins, whose work in synthetic biology and systems biology pioneered the field, has also launched a number of companies and received numerous awards and honors (including a MacArthur "Genius" Award, an NIH Director's Pioneer Award, and Sanofi-Institut Pasteur Award).

This wide-ranging conversation about the birth of synthetic biology covers everything from the founding story of the discipline to what "engineering and designing" biology really looks like in action -- when instead of engineering electrons, you are engineering toggle switches for genes -- to the disciplinary differences (and synergies) between how biologists and engineers see the world. What are the engineering and design principles, techniques, approaches that work best when applied to science? How does building a company in this new space look different, in terms of platforms and products? And how is this new field changing education in science, all the way down to kits that allow you to play with the machinery of a cell... at home... and even in middle school?

a16z Podcast: All About Synthetic Biology

with Susannah Fox (@susannahfox), Anil Sethi (@anilsethiusa / @ciitizencorp), Vijay Pande (@vijaypande), and Sonal Chokshi (@smc90) 

The problem of "dark data" in healthcare isn't just a feel-good empowerment thing, but a structural issue that leads to miscommunication and extra friction, different players in the entire healthcare system not being able to collaborate with each other, and just major missed opportunities all round. And yes, it also leads to lack of empowerment for patients, not to mention doctors too (who often have less than 30 minutes on site to do their jobs).

But we already know all that. What's not clear is WHY and HOW is this the case, when the very point of HIPAA -- the Health Insurance Portability and Accountability Act (of 1996!) -- is to make data portable, not private. That is, IF patients know to ask for it... and can easily get it. So what if we could have a sort of permissioned "permissionless innovation" for healthcare data, not only bringing all that dark data to light, but more importantly -- borrowing from the history of internet innovation -- letting all sorts of expected and unexpected uses be built on top as a result? What happens when data and entities can talk to each other (à la APIs) through patients at the center of the circle of data?

From the Dr. Google problem (or opportunity!) to clinical trials and even the opioid crisis, we -- Susannah Fox (former CTO of the U.S. Department of Health and Human Services); Anil Sethi (CEO and founder of Ciitizen); and a16z bio general partner Vijay Pande; in conversation with Sonal Chokshi -- explore all this and more in this episode of the a16z Podcast. Let there be light!

a16z Podcast: Dark Data in Healthcare

Veterinary oncology can inform human oncology, and vice versa -- providing a better model for looking at drug performance, interrelationships, and more. Especially when you add in data (there's no "doggy HIPAA!") and networks to get a "living laboratory at scale".

Or so argues Amy Abernethy (Chief Medical and Chief Scientific Officer at Flatiron Health and advisor to One Health), who was recently named the new Principal Deputy Commissioner of the FDA, pending ethics clearance; and Christina Lopes, CEO and co-founder of One Health; in conversation with a16z bio general partner Jorge Conde. Dogs -- as a species, as pets, as companions, as family members -- evolved alongside humans, so are actually more similar to us... not just genetically and in terms of the biologic pathways that may cause cancer, but also in exposure to similar environmental factors as well.

But what does this all mean when it comes to thinking about real-world evidence in science, human clinical trials, and more broadly, building a bio company? How can product designers -- of all kinds -- backwards-architect their product roadmap for data network effects? And how can bio founders keep both a big-picture roadmap in mind while also focusing on specific milestones, and while working across unconnected disciplines as well? We cover all this and more in this special episode of the a16z Podcast, recorded during the recent J.P.M. healthcare conference in San Francisco.

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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.
 
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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: Connecting Hearts, Bodies, and Networks to Cure Cancer

with George Church (@geochurch) and Jorge Conde (@JorgeCondeBio)

Renowned scientist George Church is known for his groundbreaking work and methods used for the first genome sequence, and for his work in genome editing, writing & recoding -- in fact, Church’s innovations have become an essential building block for most of the DNA sequencing methods and companies we see today. In this conversation, a16z bio general partner Jorge Conde -- who also founded a company with Church out of the George Church Lab -- take us on a wild journey into the scientist’s mind and work, starting with what the leading pioneer in the space makes of where we are today with CRISPR (especially given recent news about CRISPR babies in China), to the broader implications of all of this on a cultural level, and finally to what it really takes to go from science fiction, to lab, to reality.

a16z Podcast: What's in the Water at the George Church Lab?

In this episode of the a16z Podcast -- which originally aired as a video on YouTube -- general partner Alex Rampell (and former fintech entrepreneur as the CEO and co-founder of TrialPay) talks with operating partner Frank Chen about the quickly changing fintech landscape and, even more importantly, why the landscape is changing now.

Should the incumbents be nervous? About what, exactly? And most importantly, what should big companies do about all of this change? But the conversation from both sides of the table begins from the perspective of the hungry and fast fintech startup sharing lessons learned, and then moves to more concrete advice for the execs in the hot seat at established companies.

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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.

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 investor or prospective investor, 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 which should be read in their entirety.)

Past performance is not indicative of future results. Any charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. 

Please see https://a16z.com/disclosures for additional important information.

a16z Podcast: Fintech for Startups and Incumbents

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2

The Data Science Open Source Ecosystem

This is a re-release of an episode that originally ran in October 2019.

If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Procella: YouTube's super-system for analytics data storage

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

A Data Science Take on Open Policing Data

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.

A Reality Check on AI-Driven Medical Assistants

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night!

—Katie and Ben