Chris Benson is Principal Artificial Intelligence Strategist at Lockheed Martin. He came to Lockheed Martin from Honeywell SPS, where he was Chief Scientist for Artificial Intelligence & Machine Learning. Chris built and operationalized Honeywell’s first dedicated AI team from the ground up. Before that he was on the AI Team at Accenture.
As a strategist and thought leader, Chris is among the world’s most in-demand professional keynote speakers on artificial intelligence, machine learning, emerging technologies, and visionary futurism. His inspirational keynotes are known for their passion, energy, and clarity. He is a seasoned storyteller who delights in captivating his audiences with inspiring narratives and insightful analysis at conferences, broadcasts, interviews, forums, and corporate events around the world.
Chris is an innovative hands-on solutions architect for artificial intelligence and machine learning - and the emerging technologies they intersect - robotics, IoT, augmented reality, blockchain, mobile, edge, and cloud.
He is Co-Host of the Practical AI podcast, which reaches thousands of AI enthusiasts each week, and is also the Founder & Organizer of the Atlanta Deep Learning Meetup - one of the largest AI communities in the world.
Chris and his family are committed animal advocates who are active in animal rescue, and strive to make strategic improvements on specific animal welfare issues through advocacy for non-partisan, no-kill, and vegan legislation and regulation.
Chris Benson’s opinions are his own.
https://chrisbenson.com
Daniel and Chris get you Fully-Connected with AI questions from listeners and online forums:
Daniel and Chris have a fascinating discussion with Anna Goldie and Azalia Mirhoseini from Google Brain about the use of reinforcement learning for chip floor planning - or placement - in which many new designs are generated, and then evaluated, to find an optimal component layout. Anna and Azalia also describe the use of graph convolutional neural networks in their approach.
In the midst of the COVID-19 pandemic, Daniel and Chris have a timely conversation with Lucy Lu Wang of the Allen Institute for Artificial Intelligence about COVID-19 Open Research Dataset (CORD-19). She relates how CORD-19 was created and organized, and how researchers around the world are currently using the data to answer important COVID-19 questions that will help the world through this ongoing crisis.
AI legend Stuart Russell, the Berkeley professor who leads the Center for Human-Compatible AI, joins Chris to share his insights into the future of artificial intelligence. Stuart is the author of Human Compatible, and the upcoming 4th edition of his perennial classic Artificial Intelligence: A Modern Approach, which is widely regarded as the standard text on AI. After exposing the shortcomings inherent in deep learning, Stuart goes on to propose a new practitioner approach to creating AI that avoids harmful unintended consequences, and offers a path forward towards a future in which humans can safely rely of provably beneficial AI.
So many AI developers are coming up with creative, useful COVID-19 applications during this time of crisis. Among those are Timo from Deepset-AI and Tony from Intel. They are working on a question answering system for pandemic-related questions called COVID-QA. In this episode, they describe the system, related annotation of the CORD-19 data set, and ways that you can contribute!
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.
Practical AI is a weekly podcast that’s marking artificial intelligence practical, productive, and accessible to everyone. If world of AI affects your daily life, this show is for you.
From the practitioner wanting to keep up with the latest tools & trends…
(clip from episode #68)
To the AI curious trying to understand the concepts at play and their implications on our lives…
(clip from episode #39)
Expert hosts Chris Benson and Daniel Whitenack are here to keep you fully-connected with the world of machine learning and data science.
Please listen to a recent episode that interests you and subscribe today. We’d love to have you as a listener!
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.
