Join us in episode eight of Practically Intelligent as we welcome Jacob Solawetz, CTO at arcee.ai and former founding engineer at Roboflow. Jacob, a trailblazer in computer vision and AI, discusses the evolution and impact of multimodal AI technologies. Delving into the challenges of developing large-scale vision applications, he offers insights from his rich experience in both vision models and domain-adapting LLMs - and what that means for the future of multimodal AI. Jacob also sheds light on his work at arcee.ai, focusing on specialized language models and the art of model distillation.

In this enlightening seventh episode of Practically Intelligent, we take a look at the pivotal role of benchmarking in advancing AI with Praveen Paritosh, a leading figure in AI research. Discover why shared benchmarks are not just important, but critical in pushing the boundaries of AI technology. Praveen enlightens us on the legacy benchmarks like SQuAD, instrumental in testing early question-answer systems, and how they paved the way for early leaderboards in AI. We discuss the concept of shared benchmarks as a mechanism for the research community to collectively tackle and progress in specific challenges, drawing parallels between NLP and image recognition benchmarks like ImageNet. However, it's not all straightforward – benchmarks, while guiding us in the right direction, are merely proxies. We discuss the challenges of differentiating between conceptual learning driven by reasoning and rote learning based on memorization. Join us for a deep dive into the intricacies and nuances of AI benchmarking, a critical yet complex tool in the evolution of artificial intelligence.

Giada Pistilli, Principal Ethicist at Huggingface, discusses AI ethics and data governance. Topics include training data inconsistencies, potential biases in AI, ethical training guidelines, 'moral charter' in AI models, and hazards of 'Ethics Shopping'.

Adam Azzam, AI Product Lead at Prefect, discusses challenges of merging traditional software engineering with AI engineering. They explore marrying large language models with strongly typed semantic interfaces, suitable problems for Marvin AI, and the benefits of using LLMs in software engineering.

Sinan and Akshay chat with Nathan Lambert, a prominent Machine Learning researcher and analyst. They discuss evaluating language models, the Open LLM leaderboard, Luther's test harness, and the challenges of evaluating large language models and low data quality.

Former Engineering Lead behind Deep Learning at AWS, Leo Dirac, shares a walk through history and key takeaways for builders in the AI/ML space. They discuss the importance of vector databases, comparing different options, and the challenges of computer vision. Leo also talks about his new venture, Groundlight.AI, and its role in simplifying computer vision for engineering leaders.

In this episode, Sinan and YC W23 founders discuss how they're building with LLMs, the architectural tradeoffs they've made, and navigating discussions with customers. They also cover the release of open source models, the blistering pace of news in the AI ecosystem, and licensing and IP questions. The podcast explores topics like using AI to generate instructions, Y Combinator companies using LMs, building and fine-tuning AI models for enterprise clients, data security and privacy concerns when integrating LOMs, the importance of data in the era of AI, language learning journey, and the correctness of machine translation in multiple languages.

The first episode of this podcast discusses the release of the ChatGPT API and its impact on AI business models. They explore the concept of reinforcement learning from human feedback and its importance in improving AI models. They also touch on the future of cloud providers and AI models, alignment in language models, and the possibility of discussing the singularity and AI regulation with future guests.