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Chris Benson

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.

Atlanta · Twitter · GitHub · LinkedIn · Website

Practical AI Practical AI #224

Data augmentation with LlamaIndex

Large Language Models (LLMs) continue to amaze us with their capabilities. However, the utilization of LLMs in production AI applications requires the integration of private data. Join us as we have a captivating conversation with Jerry Liu from LlamaIndex, where he provides valuable insights into the process of data ingestion, indexing, and query specifically tailored for LLM applications. Delving into the topic, we uncover different query patterns and venture beyond the realm of vector databases.

Practical AI Practical AI #222

The last mile of AI app development

There are a ton of problems around building LLM apps in production and the last mile of that problem. Travis Fischer, builder of open AI projects like @ChatGPTBot, joins us to talk through these problems (and how to overcome them). He helps us understand the hierarchy of complexity from simple prompting to augmentation, agents, and fine-tuning. Along the way we discuss the frontend developer community that is rapidly adopting AI technology via Typescript (not Python).

Practical AI Practical AI #220

Causal inference

With all the LLM hype, it’s worth remembering that enterprise stakeholders want answers to “why” questions. Enter causal inference. Paul Hünermund has been doing research and writing on this topic for some time and joins us to introduce the topic. He also shares some relevant trends and some tips for getting started with methods including double machine learning, experimentation, difference-in-difference, and more.

Practical AI Practical AI #219

Capabilities of LLMs 🤯

Large Language Model (LLM) capabilities have reached new heights and are nothing short of mind-blowing! However, with so many advancements happening at once, it can be overwhelming to keep up with all the latest developments. To help us navigate through this complex terrain, we’ve invited Raj - one of the most adept at explaining State-of-the-Art (SOTA) AI in practical terms - to join us on the podcast.

Raj discusses several intriguing topics such as in-context learning, reasoning, LLM options, and related tooling. But that’s not all! We also hear from Raj about the rapidly growing data science and AI community on TikTok.

Practical AI Practical AI #218

Computer scientists as rogue art historians

What can art historians and computer scientists learn from one another? Actually, a lot! Amanda Wasielewski joins us to talk about how she discovered that computer scientists working on computer vision were actually acting like rogue art historians and how art historians have found machine learning to be a valuable tool for research, fraud detection, and cataloguing. We also discuss the rise of generative AI and how we this technology might cause us to ask new questions like: “What makes a photograph a photograph?”

Practical AI Practical AI #217

Accelerated data science with a Kaggle grandmaster

Daniel and Chris explore the intersection of Kaggle and real-world data science in this illuminating conversation with Christof Henkel, Senior Deep Learning Data Scientist at NVIDIA and Kaggle Grandmaster. Christof offers a very lucid explanation into how participation in Kaggle can positively impact a data scientist’s skill and career aspirations. He also shared some of his insights and approach to maximizing AI productivity uses GPU-accelerated tools like RAPIDS and DALI.

Practical AI Practical AI #216

Explainable AI that is accessible for all humans

We are seeing an explosion of AI apps that are (at their core) a thin UI on top of calls to OpenAI generative models. What risks are associated with this sort of approach to AI integration, and is explainability and accountability something that can be achieved in chat-based assistants?

Beth Rudden of has been thinking about this topic for some time and has developed an ontological approach to creating conversational AI. We hear more about that approach and related work in this episode.

Practical AI Practical AI #214

End-to-end cloud compute for AI/ML

We’ve all experienced pain moving from local development, to testing, and then on to production. This cycle can be long and tedious, especially as AI models and datasets are integrated. Modal is trying to make this loop of development as seamless as possible for AI practitioners, and their platform is pretty incredible!

Erik from Modal joins us in this episode to help us understand how we can run or deploy machine learning models, massively parallel compute jobs, task queues, web apps, and much more, without our own infrastructure.

Practical AI Practical AI #213

Success (and failure) in prompting

With the recent proliferation of generative AI models (from OpenAI, co:here, Anthropic, etc.), practitioners are racing to come up with best practices around prompting, grounding, and control of outputs.

Chris and Daniel take a deep dive into the kinds of behavior we are seeing with this latest wave of models (both good and bad) and what leads to that behavior. They also dig into some prompting and integration tips.

Practical AI Practical AI #212

Applied NLP solutions & AI education

We’re super excited to welcome Jay Alammar to the show. Jay is a well-known AI educator, applied NLP practitioner at co:here, and author of the popular blog, “The Illustrated Transformer.” In this episode, he shares his ideas on creating applied NLP solutions, working with large language models, and creating educational resources for state-of-the-art AI.

Practical AI Practical AI #209

3D assets & simulation at NVIDIA

What’s the current reality and practical implications of using 3D environments for simulation and synthetic data creation? In this episode, we cut right through the hype of the Metaverse, Multiverse, Omniverse, and all the “verses” to understand how 3D assets and tooling are actually helping AI developers develop industrial robots, autonomous vehicles, and more. Beau Perschall is at the center of these innovations in his work with NVIDIA, and there is no one better to help us explore the topic!

Practical AI Practical AI #207

Machine learning at small organizations

Why is ML is so poorly adopted in small organizations (hint: it’s not because they don’t have enough data)? In this episode, Kirsten Lum from Storytellers shares the patterns she has seen in small orgs that lead to a successful ML practice. We discuss how the job of a ML Engineer/Data Scientist is different in that environment and how end-to-end project management is key to adoption.

Practical AI Practical AI #202

Copilot lawsuits & Galactica "science"

There are some big AI-related controversies swirling, and it’s time we talk about them. A lawsuit has been filed against GitHub, Microsoft, and OpenAI related to Copilot code suggestions, and many people have been disturbed by the output of Meta AI’s Galactica model. Does Copilot violate open source licenses? Does Galactica output dangerous science-related content? In this episode, we dive into the controversies and risks, and we discuss the benefits of these technologies.

Practical AI Practical AI #201

Protecting us with the Database of Evil

Online platforms and their users are susceptible to a barrage of threats – from disinformation to extremism to terror. Daniel and Chris chat with Matar Haller, VP of Data at ActiveFence, a leader in identifying online harm – is using a combination of AI technology and leading subject matter experts to provide Trust & Safety teams with precise, real-time data, in-depth intelligence, and automated tools to protect users and ensure safe online experiences.

Practical AI Practical AI #200

Hybrid computing with quantum processors

It’s been a while since we’ve touched on quantum computing. It’s time for an update! This week we talk with Yonatan from Quantum Machines about real progress being made in the practical construction of hybrid computing centers with a mix of classical processors, GPUs, and quantum processors. Quantum Machines is building both hardware and software to help control, program, and integrate quantum processors within a hybrid computing environment.

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