Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone. Join co-hosts Adel Nehme and Richie Cotton as they delve into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.
The episode discusses the development of large language models, improving model training, judging and developing new skills, limitations and future trends, specialized models and model serving, as well as breakthroughs and future expectations in the field of generative AI.
This episode explores the importance of Excel in data science, including its practicality, relevance, and role in communicating complex data processes. It discusses features like Power Query, dynamic arrays, and Excel tables that enhance data analysis and visualization. The episode also emphasizes the significance of Excel in gaining executive buy-in, communicating technical concepts, and driving impact through data storytelling. Additionally, it highlights the need for a data mindset, effective communication, and teamwork in building successful data projects.
The episode covers various topics related to AI, data analysis, data engineering, decision intelligence, advanced data technologies, centralized vs decentralized data teams, trends in data and analytics, emotional intelligence in data work, communication and collaboration skills, and preparing for Gen AI.
This episode covers the transition to RAG LLMs, customizing LLMs for different audiences and domains, creating LLMs with mixture of experts and knowledge graphs, improving efficiency and speed of models, considerations for custom LLMs, legalities and optimization of custom LLMs, and skills and outlook in generative AI.
This episode covers various aspects of data science and machine learning, including the evolving role of data scientists, the importance of full-stack skills, the role of Metaflow in simplifying data science workflows, scalability considerations, ensuring reliability in ML infrastructure, and optimizing machine learning model quality and iteration speed.
Opinions about AI from non-experts, historical lessons, and political decisions shape the future of AI. The decision to focus on space exploration over other scientific pursuits was driven by geopolitical goals. Building AI with positive impact and considering potential threats is crucial. Thoughtful regulation and societal acceptance are important for AI development. Politicians face challenges in regulating technology, and understanding between technology and politics communities is necessary. The government played a crucial role in shaping the internet, and lessons from its decentralization can be applied to AI development. Democratic nations should use AI for positive purposes, and active participation in shaping the future of AI is encouraged.
The episode discusses the future of generative AI and its impact on society. It explores different perspectives from experts in investing, building infrastructure, and developing AI tools. The adoption landscape for AI is examined, along with challenges hindering enterprise adoption. The investment in training models and improvements is analyzed, highlighting the division between large language models and purpose-built smaller models. Challenges in deploying Gen AI models are discussed, including technical and business process challenges. The evolving landscape of work is explored, focusing on automation and the need for adaptation. Finally, a debate on brute force innovation and human creativity is presented.
The episode explores the challenges of maintaining high-quality data in the age of generative AI. It emphasizes the importance of bridging the gap between data producers and consumers to ensure trustworthy and usable data. Managing complex data landscapes, collaboration, and cultural alignment are identified as key factors in enhancing data quality. The impact of bad data in generative AI and the changing perspectives on data quality and governance are also discussed.
The episode discusses the growing AI job market, the impact of AI on different roles and organizations, required skills for AI positions, the importance of communication skills in data and AI fields, articulating needs for AI applications, breaking into the AI job market, and the future trends of AI in job roles.
The episode discusses the importance of AI training, the State Department's efforts in providing AI training to employees, promoting AI training programs within organizations, the success of AI training programs, setting up AI training programs, and effective training approaches. Key insights include the need for comprehensive training, the role of champions in promoting training, the benefits of AI training programs, and the importance of tailoring training content to meet learners' needs.