Practically Intelligent

In episode 11 of Practically Intelligent, we take a look at the dynamic and intricate world of business intelligence (BI) with the esteemed Donald Farmer, a pioneer in BI and the driving force behind Power BI. This episode takes us on a journey through the evolution of BI, from its roots in decision support systems to the burgeoning era of machine learning and the transformative potential of language models.Donald shares insights on:The early days of BI and the launch of Power BI, setting the stage for today's AI advancements.The transition from traditional data warehousing to the necessity for centralization and the advent of self-service BI tools.How predictive analytics moved from a specialization to a mainstream necessity, paving the way for AI's role in strategic decision-making.The emergence of language models as key players in shaping strategic decisions, making AI not just a tool but a team member in the decision-making process.We also explore the broad spectrum of decisions influenced by BI tools, from operational to strategic, and how machine learning and AI are revolutionizing this landscape. Donald offers a critical perspective on the current hype cycle in AI, distinguishing between genuine innovation and overblown claims.

What were the biggest trends in AI in 2023 that will accelerate into 2024? What will change/what is overhyped? In this episode, we go through charts and reports from Coatue's recent AI report to dissect the key assertions made about the future of AI, from its transformative impact on technology to the burgeoning role of open-source. We'll highlight the most compelling insights, challenge hyperbole, and correct any misleading narratives we find.Whether you're an AI enthusiast or skeptic, this episode promises a nuanced exploration of AI's potential and pitfalls, offering a clear-eyed perspective on what's genuinely revolutionary and what's just hype in the world of artificial intelligence.The Report: https://www.coatue.com/blog/perspective/ai-the-coming-revolution-2023Chart 1: "Despite intense demand, AI compute costs have decreased" - Page 78Chart 2: "Problem: Model evaluation is broken today" - Page 88Chart 3: "Synthetic data can augment fine-tuning" - Page 62Chart 4: "Data quality is just as important as data quantity" - Page 61Chart 5: "Data scarcity is a potential wall to scaling models" - Page 60Chart 6: "AI regulation may be more likely than most think" - Page 26

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.

We chatted with Adam Azzam, PhD - AI Product Lead at Prefect - about the challenges of merging traditional software engineering with AI engineering. We go over why integrating LLMs present such a challenge to many developers and how Marvin AI - a new open source tool - can help.

On the newest episode of Practically Intelligent, Sinan and Akshay discuss all the hype behind vector databases and the future of how developers will work with embeddings. We also dissect how to compare different vector database options available on the market today. Congrats to Weavite, Pinecone, Qdrant and Chroma on their funding rounds!!We also have Leo Dirac, founder and CEO of Groundlight.AI - who was formerly the Engineering Lead behind Deep Learning at AWS. We take a walk through history with some key takeaways for builders and observers of the AI/ML space and talk about Leo's new - Why Deep Learning Caught the ML World by Storm - How many leading scientists and researchers underestimated the impact of novel DL on the category- How handling escalation of errors is a critical obstacle to computer vision fulfilling its potential- How Groundlight can help simplify computer vision for engineering leadersTimestamps3:30: Why are Vector Databases So Important? 09:40: How do you compare different Vector Database if you're a developer? 22:10: Guest Leo Dirac, a History of Deep Learning at AWS and AutoML39:17: Fine-Tuning as the New AutoML 43:00: What is GroundLight.AI and Human Escalation in Computer Vision

In our first episode, we chat about the ChatGPT API release, Defensibility in AI Business Models, and explores Reinforcement Learning for Human Feedback (RLHF) - an essential aspect of working with and improving AI models. This is just the beginning - we have a line-up of impressive guests and fascinating topics, so stay tuned for more insightful and practically intelligent discussions.