Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, and more). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!
Production data labeling workflows
It’s one thing to gather some labels for your data. It’s another thing to integrate data labeling into your workflows and infrastructure in a scalable, secure, and useful way. Mark from Xelex joins us to talk through some of what he has learned after helping companies scale their data annotation efforts. We get into workflow management, labeling instructions, team dynamics, and quality assessment. This is a super practical episode!
Evaluating models without test data
WeightWatcher, created by Charles Martin, is an open source diagnostic tool for analyzing Neural Networks without training or even test data! Charles joins us in this episode to discuss the tool and how it fills certain gaps in current model evaluation workflows. Along the way, we discuss statistical methods from physics and a variety of practical ways to modify your training runs.
The new stable diffusion model is everywhere! Of course you can use this model to quickly and easily create amazing, dream-like images to post on twitter, reddit, discord, etc., but this technology is also poised to be used in very pragmatic ways across industry. In this episode, Chris and Daniel take a deep dive into all things stable diffusion. They discuss the motivations for the work, the model architecture, and the differences between this model and other related releases (e.g., DALL·E 2). (Image from stability.ai)
Licensing & automating creativity
AI is increasingly being applied in creative and artistic ways, especially with recent tools integrating models like Stable Diffusion. This is making some artists mad. How should we be thinking about these trends more generally, and how can we as practitioners release and license models anticipating human impacts? We explore this along with other topics (like AI models detecting swimming pools 😊) in this fully connected episode.
Privacy in the age of AI
In this Fully-Connected episode, Daniel and Chris discuss concerns of privacy in the face of ever-improving AI / ML technologies. Evaluating AI’s impact on privacy from various angles, they note that ethical AI practitioners and data scientists have an enormous burden, given that much of the general population may not understand the implications of the data privacy decisions of everyday life. This intentionally thought-provoking conversation advocates consideration and action from each listener when it comes to evaluating how their own activities either protect or violate the privacy of those whom they impact.
Practical, positive uses for deep fakes
Differentiating between what is real versus what is fake on the internet can be challenging. Historically, AI deepfakes have only added to the confusion and chaos, but when labeled and intended for good, deepfakes can be extremely helpful. But with all of the misinformation surrounding deepfakes, it can be hard to see the benefits they bring. Lior Hakim, CTO at Hour One, joins Chris and Daniel to shed some light on the practical uses of deepfakes. He addresses the AI technology behind deepfakes, how to make positive use of deep fakes such as breaking down communications barriers, and shares how Hour One specializes in the development of virtual humans for use in professional video communications.
With so many podcasts out there when you decide to listen, this is honestly one of the most interesting find. My new Addiction ! Thanks for sharing knowledge not only on practical AI but on other channels as well.
Learnt a lot from this podcast
[tl;dr - I like this podcast] I started listening to Practical AI on my commute. The hosts make it their job to be inclusive of beginners (like me) and break down complex topics into simple to understand conversations. The topics are open-ended. There is a wide variety of guests. Guests I particularly liked to hear from were spaCy, HuggingFace and NVIDIA because we actually use that stuff. I like listening to the Fully Connected episodes as well. This podcast does not get into discussing things like inner working details of GANs, etc because a podcast is neither meant to nor is suitable for deep technical explanations. They instead treat complex topics in a conversational manner. I use this podcast to not only learn but also with catch up industry developments without wasting time on sifting through hundreds of look-alike sites
Nothing “Practical” going on here
I heard the first episode and found the proposition to be enticing to stick around - practical advice with software devs angle in mind. But over a few episodes it turned out to be just a high level discussion with companies in the space with very little to offer on practical specifics of going from zero to building ML stack. The interview area is already saturated with TWiML&AI, DataFramed, O’Reilly Data and many others.