In this session of the Super Data Science Podcast, I chat with machine learning expert Hadelin de Ponteves about his work at Google, Canal+ and what career implications may come from the rapid growth of the Data Science field.

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/2

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

SDS 002 : Machine Learning, Recommender Systems and The Future of Data with Hadelin de Ponteves

"We live in a world where we use millions of variables to predict which ad you're going to click on. Whether or not you deserve to get a loan. What movie you might watch next. But when it comes to our bodies and even serious diseases, we want to reduce things down to just one or two variables." It's insane that we actually collect so little data about our bodies. The modern day physical is downright spartan in what it captures, not to mention that we're using 200-year old tools to capture that very limited data.

Which is why we need to borrow from other domains of science and data and apply that to our bodies, in more ways than one, argues Q founder and CEO Jeffrey Kaditz with a16z bio fund general partner Vijay Pande (in conversation with Sonal Chokshi) on this episode of the a16z Podcast. But how do we get there? What would data "rights" look like -- and could we possibly donate data much like we currently donate organs? And for catching diseases like prostate or breast cancer early, how can we use data captured over multiple points in time -- something not really done right now in medicine -- to be more predictive, sensitive, and specific beyond so-called "representative" population samples? What IS a 'diagnostic', really, anyway?

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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: Health Data -- A Feedback Loop for Humanity

As people live longer, aging is more top of mind than ever. This is especially true for the "sandwich generation" wedged between caring for aging parents as well as young children at the same time.

The fact is, the 65+ year old population (but don't you dare homogenize a multi-decade age group!) will double over just the next 15-20 years. So how does this fit into our current healthcare system? How does it fit current retail experiences, like for buying adult diapers? What are the design challenges when you're optimizing for screen-less interaction and data collection in a home environment? And finally, where do providers and payers come in?

Honor's head of design Renato Valdés Olmos and head of health system integration Kelsey Mallard join this episode of the a16z Podcast to talk about all this and more. This all goes beyond discussions about fighting age with tech though -- it's about the realities of aging and caregiving, from the very mundane (going to the bathroom, for instance) to the very profound (staying in one's home, church, and community). That's why all "healthcare is local" ... or should be.

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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 Realities of Aging / When Healthcare Is Local

In this episode of the O’Reilly Bots Podcast, I speak with Tom Coates, co-founder of Thington, a service layer for the Internet of Things. Thington provides a conversational, messaging-like interface for controlling devices like lights and thermostats, but it’s also conversational at a deeper level: its very architecture treats the interactions between different devices like a conversation, allowing devices to make announcements to any other device that cares to listen.

Tom Coates on Conversational Devices

What are those problems that you have encountered in your life which you enjoyed solving at the same time?

If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/36

SDS 036: Happiness and Problem Solving

https://www.semanticscholar.org/paper/FastQA-A-Simple-and-Efficient-Neural-Architecture-Weissenborn-Wiese/7c1576b96a1e246d77b30f7b80cec63be96fa698

03 - FastQA: A Simple and Efficient Neural Architecture for Question Answering

https://www.semanticscholar.org/paper/Design-Challenges-for-Entity-Linking-Ling-Singh/aa2a7ac7bfa9a0201d4faddd4e7bb26299a5e0be

06 - Design Challenges for Entity Linking

https://www.semanticscholar.org/paper/Learning-to-Generate-Reviews-and-Discovering-Senti-Radford-Jozefowicz/664ec878de4b7170712baae4a7821fc2602bba25

https://blog.openai.com/unsupervised-sentiment-neuron/

09 - Learning to Generate Reviews and Discovering Sentiment

https://www.semanticscholar.org/paper/Question-Answering-from-Unstructured-Text-by-Retri-Watanabe-Dhingra/89d06c2996f379c602e64b4243f026cd164400d7

13 - Question Answering from Unstructured Text by Retrieval and Comprehension

In this episode of the SuperDataScience Podcast, I chat with Data Scientist at True Motion Dan Shiebler. You will learn about the True Motion app and how this works, you will listen to great insights about how sensor data from your phone can be collected and analyzed and how data science can be applied to that, you will also understand how a behavior can change through an app, and be able to explore data science passions outside of a job. 

If you enjoyed this episode, check out show notes, resources, and more at http://www.superdatascience.com/59

SDS 059: Changing Human Behaviour Through a Driving App

https://www.semanticscholar.org/paper/Generalizing-to-Unseen-Entities-and-Entity-Pairs-w-Verga-Neelakantan/7dd8b958632b07e41979337c71d847a3f39df456

18 - Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema

with David Mack, Joseph Okpaku, and Matt Spence

How should startups engage with policymakers, build their own government relations (GR) function (whether in house or with consultants), and just begin to figure out their GR playbook? Let alone explain their moves -- not just externally, but internally too?  "We really viewed our first mission as education. Explaining what we were and, possibly more importantly, explaining what we weren't," shares Joseph Okpaku, vice president of government relations at Lyft. Think of it as a campaign, observes David Mack, senior director for public affairs at Lyft, and remember, "You can either let your impact on the community be defined, or you can work to define it yourself."

Even though it isn't a zero-sum game (and don't make it one!), you only get once chance to really get it right... not just in terms of making a first impression, but in terms of setting regulatory precedent (as well as in drawing a line). So from where and who to begin with to how they did it, the guests on this episode of the a16z Podcast share some quick lessons learned for startups engaging with local governments, in conversation with policy and regulatory affairs team partner Matt Spence.

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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 Case Study of Lyft and Local Governments

In this episode we talk about a couple of recent papers that get at the issue of training variance, and why we should not just take the max from a training distribution when reporting results.  Sadly, our current focus on performance in leaderboards only exacerbates these issues, and (in my opinion) encourages bad science.

Papers:
https://www.semanticscholar.org/paper/Reporting-Score-Distributions-Makes-a-Difference-P-Reimers-Gurevych/0eae432f7edacb262f3434ecdb2af707b5b06481
https://www.semanticscholar.org/paper/Deep-Reinforcement-Learning-that-Matters-Henderson-Islam/90dad036ab47d683080c6be63b00415492b48506

37 - On Statistical Significance, Training Variance, and Why Reporting Score Distributions Matters

In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible.

Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through large data sets and investigate business and security incidents. The company is currently focused on the security domain—where it turns out that graph representations of data are things security analysts are quite familiar with.

Graphs as the Front End for Machine Learning

In this episode of the Data Show, I spoke with Ameet Talwalkar, assistant professor of machine learning at CMU and co-founder of Determined AI. He was an early and key contributor to Spark MLlib and a member of AMPLab. Most recently, he helped conceive and organize the first edition of SysML, a new academic conference at the intersection of systems and machine learning (ML).

We discussed using and deploying deep learning at scale. This is an empirical era for machine learning, and, as I noted in an earlier article, as successful as deep learning has been, our level of understanding of why it works so well is still lacking. In practice, machine learning engineers need to explore and experiment using different architectures and hyperparameters before they settle on a model that works for their specific use case. Training a single model usually involves big (labeled) data and big models; as such, exploring the space of possible model architectures and parameters can take days, weeks, or even months. Talwalkar has spent the last few years grappling with this problem as an academic researcher and as an entrepreneur. In this episode, he describes some of his related work on hyperparameter tuning, systems, and more.

ML

Olivier Nguyen 3