The podcast features Ali Ghodsi, CEO and cofounder of Databricks, and Ben Horowitz, cofounder of a16z, discussing the challenges and intricacies of AI adoption in enterprises.
They delve into the ongoing data wars, the potential of Large Language Models (LLMs), and the future of AI in businesses.
Challenges in AI Adoption
Enterprises face significant hurdles in AI adoption due to concerns about data privacy and security, internal politics over AI ownership, and the realization of their data’s value.
However, once an enterprise has successfully integrated AI, it’s difficult for them to be replaced.
The Future of AI
The race between open-source and proprietary AI will continue, with significant innovation expected in making AI more efficient and accessible.
However, there may be diminishing returns and potential scaling walls.
Ethics of AI
Job losses due to automation and the misuse of technology are significant ethical concerns in AI.
However, it’s noted that nations with the highest GDP are the ones that have automated the most.
Super AGI Threat
While a super Artificial General Intelligence (AGI) could cause significant harm if left unchecked, the scenario is unlikely in the near future due to the high costs and complexities involved in training large models.
The Growing Value of Data
Enterprises are recognizing the potential of their data and the competitive edge it could provide with AI.
This has led to a reluctance to share data with third parties and a growing interest in developing and owning their AI IP.
Building Own AI Models
Despite the complexity and cost, there are potential benefits for enterprises in building their own AI models.
Databricks’ acquisition of Mosaic, which aids enterprises in building AI models from scratch, has proven successful for several large enterprises.
Large vs Small Models
For specific use cases, enterprises can train smaller models that are faster, cheaper, and potentially more accurate for their needs.
However, large models, if fine-tuned correctly, can be more intelligent and versatile.
Fine-tuning AI Models
Fine-tuning existing AI models for specific tasks is a current focus in the AI research community.
However, it is a challenging and costly process, as it involves modifying the entire model and serving multiple versions can be expensive.
The Future of AI in Enterprises
As companies continue to understand the value of their data and the potential of AI, more will start to build their own models.
However, the challenge of fine-tuning these models for specific tasks remains a significant hurdle.
Importance of Large Models
Despite their challenges, large models hold significant importance.
Larger models, if fine-tuned correctly, can be more intelligent.
Mastering the fine-tuning process to modify large models for specific tasks without altering the entire model is a significant challenge in the field.
Universities and AI Development
Universities feel left out of the current AI landscape due to their lack of resources compared to large tech companies.
However, they are expected to continue to innovate and find ways to contribute to the field.
Open Source AI’s Role
Open source has played a crucial role in advancing AI and will continue to do so.
However, companies that train large, effective AI models typically don’t have an incentive to release them to the open-source community.
