- Biweekly Data & Analytics Digest
- Posts
- AI Infrastructure Challenges, GPT Scaling Debates, and Advances in Data Management
AI Infrastructure Challenges, GPT Scaling Debates, and Advances in Data Management
Cliffside Chronicle: Your Biweekly Data & Analytics Digest
Josh Miramant
20 Nov • Estimated Reading Time: 5 minutes
Highlight 1: Databricks Report Highlights Enterprise Challenges in Supporting AI
Some Key Findings:
Widespread Adoption with Infrastructure Concerns: While 85% of surveyed organizations are using generative AI in at least one business function, only 22% feel that their current IT infrastructure can support new AI applications effectively. This indicates a significant gap between AI adoption and the readiness of enterprise infrastructures to support these technologies.
Challenges in Data Integration and Management: Many organizations face issues with data fragmentation and the challenge of integrating AI into their existing data systems. About 48% of data engineers spend most of their time resolving issues related to data source connections, highlighting the need for robust data management strategies to support AI initiatives.
Potential for AI-driven Transformation: Despite the challenges, there is a clear recognition of AI’s potential to transform business operations. The report suggests that enterprises are keen to integrate AI to enhance decision-making and operational efficiencies, but they must first overcome substantial hurdles in infrastructure and data management to fully leverage AI capabilities.
Highlight 2: Experts take - Has GPT hit a scaling limit?
There has been significant discussion among OpenAI’s founders regarding the scaling limits of GPT. Sam Altman expresses confidence in “a clear path” to achieving AGI, while Ilya Sutskever predicts a deceleration in progress.
Below are some expert papers offering insights on this topic.
"Scaling Laws for Neural Language Models" (OpenAI Kaplan et al., 2020):
"Training Compute-Optimal Large Language Models" (Google DeepMind Hoffmann et al., 2022): https://arxiv.org/pdf/2203.15556
"Mixture of Experts with Expert Choice" (Google, Zhou et al., 2022): https://arxiv.org/pdf/2202.09368
"Training Language Models to Follow Instructions" (OpenAI, Ouyang et al): https://arxiv.org/pdf/2203.02155
"Learning to Summarize from Human Feedback" (OpenAI Stiennon et al., 2020): https://arxiv.org/abs/2009.01325
Highlight 3: Joe Reis discusses the importance of Quality in data models
In this week’s Five-Minute Friday, Joe Reis highlighted some excellent points about the ongoing challenge of balancing speed and quality in software and data team projects. He emphasized that true quality in software development and data management stems from taking the time to do things correctly. This approach reduces errors and, paradoxically, results in faster progress over time.
It’s worth a read/listen!
Highlight 4: LLM’s Visualized - Interactive Transformer Explainer tool
The Interactive Transformer Explainer tool is an impressive resource that provides a dynamic visualization of how GPT models predict tokens and generate attention maps. It offers an intuitive, real-time glimpse into the complex mechanisms of transformer models, making it easier for users to understand and analyze how these models process and respond to text inputs. This accessibility is invaluable for both educational purposes and for developers looking to delve deeper into AI model behaviors.
Highlight 5: The advent of the Open Data Lake
This one is well worth a read. Julien Le Dem explores the evolution of data lakes into open, interoperable platforms. He highlights the role of open-source technologies like Apache Iceberg and LakeFS in enhancing data management and version control. It also discusses industry movements, such as Databricks’ acquisition of Tabular, aimed at improving data accessibility and integration. The piece underscores the importance of open data lakes in fostering innovation and collaboration within the data engineering community.
What topics interest you most in AI & Data?We’d love your input to help us better understand your needs and prioritize the topics that matter most to you in future newsletters. |
“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”