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.
Catherine Breslin of Cobalt joins Daniel and Chris to do a deep dive on speech recognition. She also discusses how the technology is integrated into virtual assistants (like Alexa) and is used in other non-assistant contexts (like transcription and captioning). Along the way, she teaches us how to assemble a lexicon, acoustic model, and language model to bring speech recognition to life.
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.
Matt Brems from General Assembly joins us to explain what “data science” actually means these days and how that has changed over time. He also gives us some insight into how people are going about data science education, how AI fits into the data science workflow, and how to differentiate yourself career-wise.
Craig Wiley, from Google Cloud, joins us to discuss various pieces of the TensorFlow ecosystem along with TensorFlow Enterprise. He sheds light on how enterprises are utilizing AI and supporting AI-driven applications in the Cloud. He also clarifies Google’s relationship to TensorFlow and explains how TensorFlow development is impacting Google Cloud Platform.
Expanding AI technology to the local languages of emerging markets presents huge challenges. Good data is scarce or non-existent. Users often have bandwidth or connectivity issues. Existing platforms target only a small number of high-resource languages.
Our own Daniel Whitenack (data scientist at SIL International) and Dan Jeffries (from Pachyderm) discuss how these and related problems will only be solved when AI technology and resources from industry are combined with linguistic expertise from those on the ground working with local language communities. They have illustrated this approach as they work on pushing voice technology into emerging markets.
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.
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.
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.
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.
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.
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!
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.
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.
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.
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.
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.
Chris and Daniel take some time to cover recent trends in AI and some noteworthy publications. In particular, they discuss the increasing AI momentum in the majority world (Africa, Asia, South and Central America and the Caribbean), and they dig into Hugging Face’s recent model distillation results.
The All Things Open conference is happening soon, and we snagged one of their speakers to discuss open source and AI. Samuel Taylor talks about the essential role that open source is playing in AI development and research, and he gives us some tips on choosing AI-related side projects.
In this very special fully-connected episode of Practical AI, Daniel interviews Chris. They discuss High Performance Computing (HPC) and how it is colliding with the world of AI. Chris explains how HPC differs from cloud/on-prem infrastructure, and he highlights some of the challenges of an HPC-based AI strategy.
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).