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!
Wow, 2019 was an amazing year for AI! In this fully connected episode, Chris and Daniel discuss their list of top 5 notable AI things from 2019. They also discuss the “state of AI” at the end of 2019, and they make some predictions for 2020.
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.
Congrats to Clément and the Hugging Face team on this milestone!
The company first built a mobile app that let you chat with an artificial BFF, a sort of chatbot for bored teenagers. More recently, the startup released an open-source library for natural language processing applications. And that library has been massively successful.
The library mentioned is called Transformers, which is dubbed as ‘state-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.’
If any of these things ring a bell to you, it may be because Practical AI co-host Daniel Whitenack has been a huge supporter of Hugging Face for a long time and mentions them often on the show. We even had Clément on the show back in March of this year.
Style-based GAN architecture produces impressive image generation results, but it’s not without its limitations. NVidia’s research team has been hard at work fixing some of the problems with StyleGAN (artifacts).
In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably detect if an image is generated by a particular network.
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.
I love everything about this: the creativity, the engineering, the relentless desire to be as lazy as humanly possible. Chris automated 100% of this process, from content creation to social interactions to the sales pitch. A must-read.
Imagine an infinitely generated world that you could explore endlessly, continually finding entirely new content and adventures. What if you could also choose any action you can think of instead of being limited by the imagination of the developers who created the game?
SpaCy is awesome for NLP! It’s easy to use, has widespread adoption, is open source, and integrates the latest language models. Ines Montani and Matthew Honnibal (core developers of spaCy and co-founders of Explosion) join us to discuss the history of the project, its capabilities, and the latest trends in NLP. We also dig into the practicalities of taking NLP workflows to production. You don’t want to miss this episode!
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.
There’s a lot of hype about knowledge graphs and AI-methods for building or using them, but what exactly is a knowledge graph? How is it different from a database or other data store? How can I build my own knowledge graph? James Fletcher from Grakn Labs helps us understand knowledge graphs in general and some practical steps towards creating your own. He also discusses graph neural networks and the future of graph-augmented methods.
This is a short introduction on methods that use neural networks in an offensive manner (bug hunting, shellcode obfuscation, etc.) and how to exploit neural networks found in the wild (information extraction, malware injection, backdooring, etc.).
Everyone is talking about it. OpenAI trained a pair of neural nets that enable a robot hand to solve a Rubik’s cube. That is super dope! The results have also generated a lot of commentary and controversy, mainly related to the way in which the results were represented on OpenAI’s blog. We dig into all of this in on today’s Fully Connected episode, and we point you to a few places where you can learn more about reinforcement learning.
What’s the most practical of practical AI things? Data labeling of course! It’s also one of the most time consuming and error prone processes that we deal with in AI development. Michael Malyuk of Heartex and Label Studio joins us to discuss various data labeling challenges and open source tooling to help us overcome those challenges.
Times series data is everywhere! I mean, seriously, try to think of some data that isn’t a time series. You have stock prices and weather data, which are the classics, but you also have a time series of images on your phone, time series log data coming off of your servers, and much more. In this episode, Anais from InfluxData helps us understand the range of methods and problems related to time series data. She also gives her perspective on when statistical methods might perform better than neural nets or at least be a more reasonable choice.
Winner of Mozilla’s $50,000 award for art and advocacy exploring AI.
Stealing Ur Feelings is an augmented reality experience that reveals how your favorite apps can use facial emotion recognition technology to make decisions about your life, promote inequalities, and even destabilize American democracy. Using the AI techniques described in corporate patents, Stealing Ur Feelings learns your deepest secrets just by analyzing your face.
If you haven’t tried this yet, drop what you’re doing and give it a go. Top notch production.
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
The United States has blacklisted several Chinese AI companies working in facial recognition and surveillance. Why? What are these companies doing exactly, and how does this fit into the international politics of AI? We dig into these questions and attempt to do some live fact finding in this episode.
Colin Lecher reporting for The Verge:
Last week, Gov. Gavin Newsom signed into law AB 730, which makes it a crime to distribute audio or video that gives a false, damaging impression of a politician’s words or actions.
While the word “deepfake” doesn’t appear in the legislation, the bill clearly takes aim at doctored works. Lawmakers have raised concerns recently that distorted deepfake videos, like a slowed video of House Speaker Nancy Pelosi that appeared over the summer, could be used to influence elections in the future.
This is the first (but likely not the last) piece of legislation aimed at fighting the potential impact of GANs Gone Wild.
It’ll be interesting to watch this game play out. I think the only long-term, sustainable solution will emerge from the same arena where the problem began: technological advances.
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.