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.

Daniel and Chris explore Semantic Scholar with Doug Raymond of the Allen Institute for Artificial Intelligence. Semantic Scholar is an AI-backed search engine that uses machine learning, natural language processing, and machine vision to surface relevant information from scientific papers.

Daniel and Chris do a deep dive into The AI Index 2019 Annual Report, which provides unbiased rigorously-vetted data that one can use “to develop intuitions about the complex field of AI”. Analyzing everything from R&D and technical advancements to education, the economy, and societal considerations, Chris and Daniel lay out this comprehensive report’s key insights about artificial intelligence.

PyTorch3d is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data. For this reason, all operators in PyTorch3d:

• Are implemented using PyTorch tensors
• Can handle minibatches of hetereogenous data
• Can be differentiated
• Can utilize GPUs for acceleration

Get started with tutorials on deforming a sphere mesh into a dolphin, rendering textured meshes, camera position optimization, and more.

Production ML systems include more than just the model. In these complicated systems, how do you ensure quality over time, especially when you are constantly updating your infrastructure, data and models? Tania Allard joins us to discuss the ins and outs of testing ML systems. Among other things, she presents a simple formula that helps you score your progress towards a robust system and identify problem areas.

Here’s a new acronym for you: Generative Teaching Networks (GTN)

GTNs are deep neural networks that generate data and/or training environments on which a learner (e.g., a freshly initialized neural network) trains before being tested on a target task (e.g., recognizing objects in images). One advantage of this approach is that GTNs can produce synthetic data that enables other neural networks to learn faster than when training on real data. That allowed us to search for new neural network architectures nine times faster than when using real data.

Fake data, real results? Sounds pretty slick.

One of the things people most associate with AI is automation, but how is AI actually shaping automation in manufacturing? Costas Boulis from Bright Machines joins us to talk about how they are using AI in various manufacturing processes and in their “microfactories.” He also discusses the unique challenges of developing AI models based on manufacturing data.

Show us humans a picture of someone in uniform on a mound of dirt throwing a ball and we will quickly tell you we’re looking at baseball. But how do you make a computer come to the same conclusion?

Visual Question Answering

In this post, we’ll explore basic methods for performing VQA and build our own simple implementation in Python

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!

We have all used web and product search technologies for quite some time, but how do they actually work and how is AI impacting search? Andrew Stanton from Etsy joins us to dive into AI-based search methods and to talk about neuroevolution. He also gives us an introduction to Rust for production ML/AI and explains how that community is developing.

Evan Sparks, from Determined AI, helps us understand why many are still stuck in the “dark ages” of AI infrastructure. He then discusses how we can build better systems by leveraging things like fault tolerant training and AutoML. Finally, Evan explains his optimistic outlook on AI’s economic and environmental health impact.

NanoNeuron is an over-simplified version of the Neuron concept from Neural Networks. NanoNeuron is trained to convert temperature values from Celsius to Fahrenheit.

The NanoNeuron.js code example contains 7 simple JavaScript functions (which touches on model prediction, cost calculation, forward/backwards propagation, and training) that will give you a feeling of how machines can actually “learn”. No 3rd-party libraries, no external data-sets or dependencies, only pure and simple JavaScript functions.

This is not a complete guide to machine learning. Just a primer.

WIRED’s business unit interviewed Jerome Pesenti, VP of artificial intelligence at Facebook. The major takeaway:

[he] is encouraged by progress in artificial intelligence, but sees the limits of the current approach to deep learning.

Could this be the beginning of the end for this particular AI hype cycle?

GANs are at the center of AI hype. However, they are also starting to be extremely practical and be used to develop solutions to real problems. Jakub Langr and Vladimir Bok join us for a deep dive into GANs and their application. We discuss the basics of GANs, their various flavors, and open research problems.

This booklet covers four main steps of designing a machine learning system:

1. Project setup
2. Data pipeline
3. Modeling: selecting, training, and debugging
4. Serving: testing, deploying, and maintaining

It comes with links to practical resources that explain each aspect in more details. It also suggests case studies written by machine learning engineers at major tech companies who have deployed machine learning systems to solve real-world problems.

Streamlit recently burst onto the scene with their intuitive, open source solution for building custom ML/AI tools. It allows data scientists and ML engineers to rapidly build internal or external UIs without spending time on frontend development. In this episode, Adrien Treuille joins us to discuss ML/AI app development in general and Streamlit. We talk about the practicalities of working with Streamlit along with its seemingly instant adoption by AI2, Stripe, Stitch Fix, Uber, and Twitter.

We’ve mentioned ML/AI in the browser and in JS a bunch on this show, but we haven’t done a deep dive on the subject… until now! Victor Dibia helps us understand why people are interested in porting models to the browser and how people are using the functionality. We discuss TensorFlow.js and some applications built using TensorFlow.js

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.

Streamlit lets you create apps for your machine learning projects with deciptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser.

Coming soon to a Practical AI podcast near you…

Folks have been talking about TensorFlow 2 for some time now (See Practical AI #42 for one excellent example), but now it’s finally here. The bulleted list:

• Easy model building with Keras and eager execution.
• Robust model deployment in production on any platform.
• Powerful experimentation for research.
• API simplification by reducing duplication and removing deprecated endpoints.

This is a huge release. Check out the highlights list in the changelog to see for yourself.

We’re talking with Sherol Chen, a machine learning developer, about AI at Google and AutoML methods. Sherol explains how the various AI groups within Google work together and how AutoML fits into that puzzle. She also explains how to get started with AutoML step-by-step (this is “practical” AI after all).

Ported from David Sandberg’s TensorFlow facenet repo.

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!

This week we bend reality to expose the deceptions of deepfake videos. We talk about what they are, why they are so dangerous, and what you can do to detect and resist their insidious influence. In a political environment rife with distrust, disinformation, and conspiracy theories, deepfakes are being weaponized and proliferated as the latest form of state-sponsored information warfare. Join us for an episode scarier than your favorite horror movie, because this AI bogeyman is real!

Never heard of algorithmic differentiation? It’s the tool that “underlies modern machine learning”. I learned that from reading the intro to this handbook, btw.

It begins with a calculus-101 style understanding and gradually extends this to build toy implementations of systems similar to PyTorch and TensorFlow.