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Using wallets as a reward and penalty mechanism

I’m creating an AI moderator for social media using reinforcement learning (RL). My goal is to reduce toxicity/outrage and increase civility in online interaction. Background In my previous post, I demonstrated how an AI agent can learn to behave like a stereotypical social media user. This baseline RL environment suggests that behavior on social media is driven by the incentive structure of…

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#AI #behavioral science #reinforcement learning #social media

Slides for "Achieving Practical Reproducibility with Transparency and Accessibility" (DSSV 2020)

Slides for “Achieving Practical Reproducibility with Transparency and Accessibility” (DSSV 2020)

I was invited to speak at the ASA’s Symposium on Data Science and Statistics as well as the SAMSI/IASC conference on Data Science, Statistics, and Visualization thanks to Jim Harner at WVU. Both talks were on my approach to reproducible science based on my forthcoming book Introduction to Reproducible Science in R.

The talks make two key points:

The scientific method can be deconstructed…

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Request for comments on planned features for futile.logger 1.5

I will be pushing a new version of futile.logger (version 1.5) to CRAN in January. This version introduces a number of enhancements and fixes some bugs. It will also contain at least one breaking change. I am making the release process public, since the package is now used in a number of other packages. If you use futile.logger, this is your opportunity to influence the direction of the package…

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#logging #programming #system design

Preview my new book: Introduction to Reproducible Science in R

I’m pleased to share Part I of my new book “Introduction to Reproducible Science in R“. The purpose of this book is to approach model development and software development holistically to help make science and research more reproducible. The need for such a book arose from observing some of the challenges that I’ve seen teaching graduate courses in natural language processing and machine learning,…

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#reading #stem

Introducing the AI Workplace

My startup, Pez.AI, is launching a publication on Medium called the AI Workplace. We’re exploring a radically different way of working, one that is sustainable and healthy for all those involved. This publication is meant to be a springboard for discussion and collaboration on different ways of working, and how to leverage artificial intelligence (AI) as a catalyst for change.

A Canadian frog…

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#artificial intelligence #future of work #leadership

How to call bullshit on AI companies (aka a short lesson on recall)

Now that software ate the world, what’s for dessert? Those in the know know that it’s AI. It seems everyone took Kevin Kelly’s recommendationto “take X and add AI” to heart. Fast forward to 2018 and all startups tout adding AI to X. There’s a fine line between hype and bullshit, so how do you separate the substance from the snake oil (or to be fair the dream)? Knowing how to do this is important…

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#bias #crash course #hiring #startups

Data-driven unit testing for data scientists and quant developers alike

Often overlooked, testing is a critical process that saves time over the long term and enables building complex systems. Unit tests for model (descriptive, predictive, or prescriptive analytics) systems differ from standard software. Effective unit testing of computational systems requires a particular emphasis on the data used in the tests. Below are some guidelines to follow to streamline your…

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#advice #best practices #testing

Fermi Poker - Gambling for quants and data scientists

Fermi Poker – Gambling for quants and data scientists

Fermi problemsare well known for honing your ability to estimate quantities that are difficult (or impossible) to measure. Named after physicist Enrico Fermi, these eponymous problems can be used for more than estimating the strength of an atomic bomb and are great for any quantitative field, including business. These problems teach you to think carefully about your assumptions, resulting in…

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Modeling data with functional programming, Part I

The latest draft of my book is available. This will be my last pre-publication update, as I’m in the process of editing the remainder of the book. That said, the first part is packed with examples and provides a solid foundation on its own. I’m including the preface below to whet people’s appetite for the complete book when it is published.

Preface

This book is about programming. Not just any…

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What you need to know about data augmentation for machine learning

Plentiful high-quality data is the key to great machine learning models. But good data doesn’t grow on trees, and that scarcity can impede the development of a model. One way to get around a lack of data is to augment your dataset. Smart approaches to programmatic data augmentation can increase the size of your training set 10-fold or more. Even better, your model will often be more robust (and…

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A simple workflow for deep learning

As a follow-up to my Primer On Universal Function Approximation with Deep Learning, I’ve created a project on Github that provides a working example of building, training, and evaluating a neural network. Included are helper functions in Lua that I wrote to simplify creating the data and using some functional programming techniques.

The basic workflow for the example is this:

Create/acquire a…

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A primer on universal function approximation with deep learning (in Torch and R)

Arthur C. Clarke famously stated that “any sufficiently advanced technology is indistinguishable from magic.” No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people’s hearts. This primer sheds some light on how neural networks work, hopefully adding to the wonder while…

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Lead your own data science projects with the 3 Ps

There’s a lot of literature on learning the technical aspect of data science: statistics, machine learning, data munging, big data. This material will serve you well when starting out or working under a lead. But what about when you are ready to spread your wings and lead a project yourself or embark on a project independently? Here you need a different sort of storytelling – the type that…

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#advice #learning

Best practices for logging computational systems in R and Python

As is the case with most quant software, it’s a bit different from run-of-the-mill software. The somewhat prosaic world of logging is one such place where there are some differences. What’s different about quant systems? First, they have multiple run modes. Particularly in finance, models often run in real-time but also historically. Models may also run incrementally or in batch. These scenarios…

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#best practices #logging #software development

How to write good tests in R

Testing is an often overlooked yet critical component of any software system. In some ways this is more true of models than traditional software. The reason is that computational systems must function correctly at both the system level and the model level. This article provides some guidelines and tips to increase the certainty around the correctness of your models.

Testing is a critical part of…

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#programming #testing

Modeling data with functional programming - State based systems

Modeling data with functional programming – State based systems

I’m pleased to announce the availability of my latest chapter on state based systems for my book “Modeling data with functional programming in R”. This chapter is the culmination of the ideas presented in the preceding chapters and presents numerous examples.

The chapter initially discusses the idea of state and how to manage it within closures. From this kernel we start to build some…

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Rosie the Robot vs Roomba: Two Views on AI

This site generously supported by Datacamp. Datacamp offers a free interactive introduction to R coding tutorial as an additional resource. Already over 100,000 people took this free tutorial to sharpen their R coding skills.

A while back Gary Marcus gave a talk Making AI More Human, which I previously dissected here. In this presentation Marcus presented the notion that “what we wanted was Rosie…

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