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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.

The Changelog The Changelog #380

Productionising real-world ML data pipelines

Yetunde Dada from QuantumBlack joins Jerod for a deep dive on Kedro, a workflow tool that helps structure reproducible, scaleable, deployable, robust, and versioned data pipelines. They discuss what Kedro’s all about and how it’s “changing the landscape of data pipelines in Python”, the ins/outs of open sourcing Kedro, and how they found early success by sweating the details. Finally, Jerod asks Yetunde about her passion project: a virtual reality film which debuted at the Sundance Film Festival in January.

Practical AI Practical AI #72

How the U.S. military thinks about AI

Chris and Daniel talk with Greg Allen, Chief of Strategy and Communications at the U.S. Department of Defense (DoD) Joint Artificial Intelligence Center (JAIC). The mission of the JAIC is “to seize upon the transformative potential of artificial intelligence technology for the benefit of America’s national security… The JAIC is the official focal point of the DoD AI Strategy.” So if you want to understand how the U.S. military thinks about artificial intelligence, then this is the episode for you!

Practical AI Practical AI #59

Flying high with AI drone racing at AlphaPilot

Chris and Daniel talk with Keith Lynn, AlphaPilot Program Manager at Lockheed Martin. AlphaPilot is an open innovation challenge, developing artificial intelligence for high-speed racing drones, created through a partnership between Lockheed Martin and The Drone Racing League (DRL).

AlphaPilot challenged university teams from around the world to design AI capable of flying a drone without any human intervention or navigational pre-programming. Autonomous drones will race head-to-head through complex, three-dimensional tracks in DRL’s new Artificial Intelligence Robotic Racing (AIRR) Circuit. The winning team could win up to $2 million in prizes.

Keith shares the incredible story of how AlphaPilot got started, just prior to its debut race in Orlando, which will be broadcast on NBC Sports.

StackShare Icon StackShare

Cultivating your data lake

This post by Lauren Reeder of Segment goes over the different layers to consider when working with a data lake. What’s a data lake, you ask?

A data lake is a centralized repository that stores both structured and unstructured data and allows you to store massive amounts of data in a flexible, cost effective storage layer.

Her article explains what tools are needed and provides code & SQL statements to get started. 🤟

Andrew Ste cvcompiler.com

The most in-demand data science skills of 2019

Since data science has a huge impact on today’s businesses, the demand for DS experts is growing. At the moment I’m writing this, there are 144,527 data science jobs on LinkedIn alone. But still, it’s important to keep your finger on the pulse of the industry to be aware of the fastest and most efficient data science solutions.

Click through for key takeaways and trend analysis.

The most in-demand data science skills of 2019

Practical AI Practical AI #50

Celebrating episode 50 and the neural net!

Woo hoo! As we celebrate reaching episode 50, we come full circle to discuss the basics of neural networks. If you are just jumping into AI, then this is a great primer discussion with which to take that leap.

Our commitment to making artificial intelligence practical, productive, and accessible to everyone has never been stronger, so we invite you to join us for the next 50 episodes!

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