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?
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:
- Project setup
- Data pipeline
- Modeling: selecting, training, and debugging
- 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.
Daniel and Chris explore three potentially confusing topics - generative adversarial networks (GANs), deep reinforcement learning (DRL), and transfer learning. Are these types of neural network architectures? Are they something different? How are they used? Well, If you have ever wondered how AI can be creative, wished you understood how robots get their smarts, or were impressed at how some AI practitioners conquer big challenges quickly, then this is your episode!
A simple guide to what CNNs are, how they work, and how to build one from scratch in Python.
This buils on Victor’s original intro to Neural Networks that we linked up previously.
The latest machine learning research from my friends at Fast Forward Labs. Shiou Lin Sam and Nisha Muktewar teach us what meta-learners are and how they learn.
This wraps up a pre-trained model for SQLova. Here are some examples using the ‘bridges’ dataset. 👇
Chris and Daniel take you on a tour of local and global AI events, and discuss how to get the most out of your experiences. From access to experts to developing new industry relationships, learn how to get your foot in the door and make connections that help you grow as an AI practitioner.
Then drawing from their own wealth of experience as speakers, they dive into what it takes to give a memorable world-class talk that your audience will love. They break down how to select the topic, write the abstract, put the presentation together, and deliver the narrative with impact!
At the recent O’Reilly AI Conference in New York City, Chris met up with O’Reilly Chief Data Scientist Ben Lorica, the Program Chair for Strata Data, the AI Conference, and TensorFlow World.
O’Reilly’s ‘AI Adoption in the Enterprise’ report had just been released, so naturally Ben and Chris wanted to do a deep dive into enterprise AI adoption to discuss strategy, execution, and implications.
