Chris Benson

Chris Benson

Appears in 275 Episodes

There's a new Llama in town

It was an amazing week in AI news. Among other things, there is a new NeRF and a new Llama in town!!! Zip-NeRF can create some amazing 3D scenes based on 2D images, an...

Legal consequences of generated content

As a technologist, coder, and lawyer, few people are better equipped to discuss the legal and practical consequences of generative AI than Damien Riehl. He demonstrate...

A developer's toolkit for SOTA AI

Chris sat down with Varun Mohan and Anshul Ramachandran, CEO / Cofounder and Lead of Enterprise and Partnership at Codeium, respectively. They discussed how to streaml...

Cambrian explosion of generative models

In this Fully Connected episode, Daniel and Chris explore recent highlights from the current model proliferation wave sweeping the world - including Stable Diffusion X...

From ML to AI to Generative AI

Chris and Daniel take a step back to look at how generative AI fits into the wider landscape of ML/AI and data science. They talk through the differences in how one ap...

Accidentally building SOTA AI

Lately.AI has been working for years on content generation systems that capture your unique “voice” and are tailored to your unique audience. At first, they didn’t kno...

Controlled and compliant AI applications

You can’t build robust systems with inconsistent, unstructured text output from LLMs. Moreover, LLM integrations scare corporate lawyers, finance departments, and secu...

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 p...

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, joi...

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 resea...

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...

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 sci...

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 Scien...

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 appro...

AI search at You.com

Neural search and chat-based search are all the rage right now. However, You.com has been innovating in these topics long before ChatGPT. In this episode, Bryan McCann...

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 datase...

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...

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 Illu...

Serverless GPUs

We’ve been hearing about “serverless” CPUs for some time, but it’s taken a while to get to serverless GPUs. In this episode, Erik from Banana explains why its taken so...

MLOps is alive and well

Worlds are colliding! This week we join forces with the hosts of the MLOps.Community podcast to discuss all things machine learning operations. We talk about how the r...

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 hy...

GPU dev environments that just work

Creating and sharing reproducible development environments for AI experiments and production systems is a huge pain. You have all sorts of weird dependencies, and then...

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 p...

ChatGPT goes prime time!

Daniel and Chris do a deep dive into OpenAI’s ChatGPT, which is the first LLM to enjoy direct mass adoption by folks outside the AI world. They discuss how it works, i...

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 Copil...

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 D...

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 mad...

The practicalities of releasing models

Recently Chris and Daniel briefly discussed the Open RAIL-M licensing and model releases on Hugging Face. In this episode, Daniel follows up on this topic based on som...

Data for All

People are starting to wake up to the fact that they have control and ownership over their data, and governments are moving quickly to legislate these rights. John K. ...

What's up, DocQuery?

Chris sits down with Ankur Goyal to talk about DocQuery, Impira’s new open source ML model. DocQuery lets you ask questions about semi-structured data (like invoices) ...

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