Data Science Icon

Data Science

73 Stories
All Topics

Practical AI Practical AI #114

The world's largest open library dataset

Unsplash has released the world’s largest open library dataset, which includes 2M+ high-quality Unsplash photos, 5M keywords, and over 250M searches. They have big ideas about how the dataset might be used by ML/AI folks, and there have already been some interesting applications. In this episode, Luke and Tim discuss why they released this data and what it take to maintain a dataset of this size.

Practical AI Practical AI #113

A casual conversation concerning causal inference

Lucy D’Agostino McGowan, cohost of the Casual Inference Podcast and a professor at Wake Forest University, joins Daniel and Chris for a deep dive into causal inference. Referring to current events (e.g. misreporting of COVID-19 data in Georgia) as examples, they explore how we interact with, analyze, trust, and interpret data - addressing underlying assumptions, counterfactual frameworks, and unmeasured confounders (Chris’s next Halloween costume).

Peter Wang anaconda.com

Anaconda's dividend program helps sustain the open source DS/ML community

Anaconda CEO (and Practical AI guest) Peter Wang:

I am excited to announce the Anaconda Dividend Program, which formalizes our commitment to direct a portion of our revenue to open-source projects that help advance innovation in data science. We are launching the program in partnership with NumFOCUS, and will kick off with a seed donation of $10,000, as well as an additional 10% of single-user Commercial Edition subscription revenue through the end of this year. Going forward, we will fund the dividend with at least 1% of our revenue in 2021, with a minimum of $25,000 committed for the year.

We’ve been beating the successful-businesses-that-thrive-in-large-part-due-to-open-source-software-should-set-aside-revenues-to-support-those-projects drum for years now, so it’s exciting to see forward-looking companies like Anaconda step up and do just that. More like this! 🙏

Practical AI Practical AI #109

When data leakage turns into a flood of trouble

Rajiv Shah teaches Daniel and Chris about data leakage, and its major impact upon machine learning models. It’s the kind of topic that we don’t often think about, but which can ruin our results. Raj discusses how to use activation maps and image embedding to find leakage, so that leaking information in our test set does not find its way into our training set.

Practical AI Practical AI #106

Learning about (Deep) Learning

In anticipation of the upcoming NVIDIA GPU Technology Conference (GTC), Will Ramey joins Daniel and Chris to talk about education for artificial intelligence practitioners, and specifically the role that the NVIDIA Deep Learning Institute plays in the industry. Will’s insights from long experience are shaping how we all stay on top of AI, so don’t miss this ‘must learn’ episode.

Practical AI Practical AI #105

When AI goes wrong

So, you trained a great AI model and deployed it in your app? It’s smooth sailing from there right? Well, not in most people’s experience. Sometimes things goes wrong, and you need to know how to respond to a real life AI incident. In this episode, Andrew and Patrick from BNH.ai join us to discuss an AI incident response plan along with some general discussion of debugging models, discrimination, privacy, and security.

Practical AI Practical AI #103

Getting Waymo into autonomous driving

Waymo’s mission is to make it safe and easy for people and things to get where they’re going.
After describing the state of the industry, Drago Anguelov - Principal Scientist and Head of Research at Waymo - takes us on a deep dive into the world of AI-powered autonomous driving. Starting with Waymo’s approach to autonomous driving, Drago then delights Daniel and Chris with a tour of the algorithmic tools in the autonomy toolbox.

Practical AI Practical AI #102

Hidden Door and so much more

Hilary Mason is building a new way for kids and families to create stories with AI. It’s called Hidden Door, and in her first interview since founding it, Hilary reveals to Chris and Daniel what the experience will be like for kids. It’s the first Practical AI episode in which some of the questions came from Chris’s 8yo daughter Athena.

Hilary also shares her insights into various topics, like how to build data science communities during the COVID-19 Pandemic, reasons why data science goes wrong, and how to build great data-based products. Don’t miss this episode packed with hard-won wisdom!

The Changelog The Changelog #409

Celebrating Practical AI turning 100!! 🎉

We’re so excited to see Chris and Daniel take this show to 100 episodes, and that’s exactly why we’re rebroadcasting Practical AI #100 here on The Changelog. They’ve had so many great guests and discussions about everything from AGI to GPUs to AI for good. In this episode, we circle back to the beginning when Jerod and I joined the first episode to help kick off the podcast. We discuss how our perspectives have changed over time, what it has been like to host an AI podcast, and what the future of AI might look like. (GIVEAWAY!)

Practical AI Practical AI #100

Practical AI turns 100!!! 🎉

We made it to 100 episodes of Practical AI! It has been a privilege to have had so many great guests and discussions about everything from AGI to GPUs to AI for good. In this episode, we circle back to the beginning when Jerod and Adam from The Changelog helped us kick off the podcast. We discuss how our perspectives have changed over time, what it has been like to host an AI podcast, and what the future of AI might look like. (GIVEAWAY!)

Practical AI Practical AI #96

Practical AI Ethics

The multidisciplinary field of AI Ethics is brand new, and is currently being pioneered by a relatively small number of leading AI organizations and academic institutions around the world. AI Ethics focuses on ensuring that unexpected outcomes from AI technology implementations occur as rarely as possible. Daniel and Chris discuss strategies for how to arrive at AI ethical principles suitable for your own organization, and what is involved in implementing those strategies in the real world. Tune in for a practical AI primer on AI Ethics!

Go github.com

Go+ is like Go if it were built for data scientists

This new data-science-focused language is fully compatible with Go*, but streamlines things for data science use. It simplifies common scripting tasks. This in Go:

package main

func main() {
    a := []float64{1, 2, 3.4}
    println(a)
}

Becomes this in Go+:

a := [1, 2, 3.4]
println(a)

And adds features like list comprehensions for easier data processing:

a := [1, 3, 5, 7, 11]
b := [x*x for x <- a, x > 3]
println(b) // output: [25 49 121]

mapData := {"Hi": 1, "Hello": 2, "Go+": 3}
reversedMap := {v: k for k, v <- mapData}
println(reversedMap) // output: map[1:Hi 2:Hello 3:Go+]

It can be compiled directly to bytecode or transpiled into Go code. Give it a go on the playground.

*I almost described it as a “superset” of Go, but I’m not 💯 if that’s true.

Practical AI Practical AI #92

The long road to AGI

Daniel and Chris go beyond the current state of the art in deep learning to explore the next evolutions in artificial intelligence. From Yoshua Bengio’s NeurIPS keynote, which urges us forward towards System 2 deep learning, to DARPA’s vision of a 3rd Wave of AI, Chris and Daniel investigate the incremental steps between today’s AI and possible future manifestations of artificial general intelligence (AGI).

Practical AI Practical AI #89

AI for Good: clean water access in Africa

Chandler McCann tells Daniel and Chris about how DataRobot engaged in a project to develop sustainable water solutions with the Global Water Challenge (GWC). They analyzed over 500,000 data points to predict future water point breaks. This enabled African governments to make data-driven decisions related to budgeting, preventative maintenance, and policy in order to promote and protect people’s access to safe water for drinking and washing. From this effort sprang DataRobot’s larger AI for Good initiative.

Practical AI Practical AI #86

Exploring the COVID-19 Open Research Dataset

In the midst of the COVID-19 pandemic, Daniel and Chris have a timely conversation with Lucy Lu Wang of the Allen Institute for Artificial Intelligence about COVID-19 Open Research Dataset (CORD-19). She relates how CORD-19 was created and organized, and how researchers around the world are currently using the data to answer important COVID-19 questions that will help the world through this ongoing crisis.

Career dfrieds.com

Data Science: reality doesn't meet expectations

After taking a 12-week data science bootcamp and in 2016 and then launching into industry, Dan Friedman’s expectations weren’t remotely met:

Over the past few years, I’ve worked as a Data Scientist, a Data Engineer, and as an industry consultant. I’ve also learned from the stories of dozens of data scientists and similar professions, actively read articles on data science and followed data science thought leaders on Twitter.

Across these diverse data experiences, I have noticed common themes.

Below are seven most common (and at times flagrant) ways that data science has failed to meet expectations in industry. Throughout each section, I’ll propose solutions to these shortcomings.

Maybe I’ve been listening to Practical AI too much, but I am not surprised that one of his seven shortcomings is that most of the job is spent cleaning data. That being said, there’s a lot here that is surprising to me and worthy of consideration for anyone thinking about entering the industry.

Practical AI Practical AI #85

Achieving provably beneficial, human-compatible AI

AI legend Stuart Russell, the Berkeley professor who leads the Center for Human-Compatible AI, joins Chris to share his insights into the future of artificial intelligence. Stuart is the author of Human Compatible, and the upcoming 4th edition of his perennial classic Artificial Intelligence: A Modern Approach, which is widely regarded as the standard text on AI. After exposing the shortcomings inherent in deep learning, Stuart goes on to propose a new practitioner approach to creating AI that avoids harmful unintended consequences, and offers a path forward towards a future in which humans can safely rely of provably beneficial AI.

Practical AI Practical AI #83

Mapping the intersection of AI and GIS

Daniel Wilson and Rob Fletcher of ESRI hang with Chris and Daniel to chat about how AI powered modern geographic information systems (GIS) and location intelligence. They illuminate the various models used for GIS, spatial analysis, remote sensing, real-time visualization, and 3D analytics. You don’t want to miss the part about their work for the DoD’s Joint AI Center in humanitarian assistance / disaster relief.

Practical AI Practical AI

Welcome to Practical AI

Practical AI is a weekly podcast that’s marking artificial intelligence practical, productive, and accessible to everyone. If world of AI affects your daily life, this show is for you.

From the practitioner wanting to keep up with the latest tools & trends…

(clip from episode #68)

To the AI curious trying to understand the concepts at play and their implications on our lives…

(clip from episode #39)

Expert hosts Chris Benson and Daniel Whitenack are here to keep you fully-connected with the world of machine learning and data science.

Please listen to a recent episode that interests you and subscribe today. We’d love to have you as a listener!

Practical AI Practical AI #81

Building a career in Data Science

Emily Robinson, co-author of the book Build a Career in Data Science, gives us the inside scoop about optimizing the data science job search. From creating one’s resume, cover letter, and portfolio to knowing how to recognize the right job at a fair compensation rate.

Emily’s expert guidance takes us from the beginning of the process to conclusion, including being successful during your early days in that fantastic new data science position.

0:00 / 0:00