DataFramed
#145 Why AI will Change Everything—with Former Snowflake CEO, Bob Muglia
Mon Jul 10 2023Description
The episode covers topics such as the emergence of artificial general intelligence, Microsoft's role in democratizing data and business computing, the convergence of modern data stack platforms, AI tools and vector databases, adopting general AI products and ensuring data governance, data governance and fine-tuning models, the potential of powerful AI and English language understanding, the impact of powerful AI and the future of data analysis, and opportunities for entrepreneurship in the field of data science.
Insights
Artificial general intelligence (AGI) may be achieved within 10 years
The episode highlights the expectation that AGI will be available in the near future, which has significant implications for various industries and society as a whole.
Modern data platforms are converging to combine machine learning and data warehousing
The convergence of modern data stack platforms like Snowflake, Databricks, Microsoft Azure Fabric, Amazon's data solution, and Google's BigQuery is enabling the integration of machine learning capabilities into data warehousing systems, opening up new possibilities for end-to-end machine learning applications.
Vector databases play a crucial role in the era of large language models
With the rise of large language models and embeddings, vector databases have become important tools for storing and retrieving text with semantic associations. They enable vector searches and similarity searches, enhancing the accuracy and effectiveness of data analysis.
Data governance is essential for adopting AI products
To successfully adopt general AI products, organizations need to prioritize data governance. This includes centralizing and securing data, establishing clear access control policies, and making informed decisions about data privacy.
Powerful AI has the potential to revolutionize various fields
Powerful AIs can accelerate progress in medical science, drug discovery, diagnosis, and business processes. However, it is crucial to ensure that AI is directed towards benefiting mankind and that potential risks are carefully managed.
English language understanding in computers is advancing data analysis
The increasing capability of computers to understand English language queries is lowering barriers between humans and machines in the field of data analysis. This development simplifies the process of expressing queries and enables more efficient data analysis.
Entrepreneurship opportunities abound in the field of data science
With advancements in technology and the availability of new tools, aspiring entrepreneurs have numerous opportunities to pursue their ideas in the field of data science. Leveraging strengths and solving problems in innovative ways can lead to success.
Chapters
- The Emergence of Artificial General Intelligence
- Microsoft's Role in Democratizing Data and Business Computing
- The Convergence of Modern Data Stack Platforms
- AI Tools and Vector Databases in Modern Data Platforms
- Adopting General AI Products and Ensuring Data Governance
- Data Governance and Fine-Tuning Models
- The Potential of Powerful AI and English Language Understanding
- The Impact of Powerful AI and the Future of Data Analysis
- Opportunities for Entrepreneurship in the Field of Data Science
The Emergence of Artificial General Intelligence
00:00 - 06:51
- Artificial general intelligence is expected to be available within 10 years.
- Entrepreneurialism is important in the world of data as it drives innovation and learning.
- Bots and AI are becoming more prevalent in answering questions and providing information.
- Bob shares a story about installing a data center in his house to test software while working on SQL Server at Microsoft.
Microsoft's Role in Democratizing Data and Business Computing
06:30 - 13:22
- Microsoft's software and third-party applications changed the way small businesses operated in the early 90s.
- Small Business Server consolidated everything onto one server, benefiting small businesses.
- Microsoft played a significant role in democratizing data and business computing, making it accessible to millions of companies worldwide.
- Windows Server was instrumental in bringing business computing to a broad audience.
- The modern data stack emerged around 2015-2016 with the cloud as its foundation.
- Data analytics became available as a service, eliminating the need for companies to run it themselves.
- Cloud technology enabled databases to scale beyond previous limitations, handling large amounts of data and users simultaneously.
- Snowflake was an example of cloud-built technology that revolutionized working with data by eliminating silos and ensuring consistency across systems.
- SQL databases are at the core of the modern data stack, providing powerful tools for slicing, dicing, aggregating, and analyzing data.
The Convergence of Modern Data Stack Platforms
12:53 - 20:05
- There are five distinct modern data stack platforms: Snowflake, Databricks, Microsoft Azure Fabric, Amazon's data solution, and Google's BigQuery.
- Each platform has its own strengths and starting point, but they are all converging to build similar systems that combine machine learning and data warehousing.
- Data lakes have become the preferred approach for large companies to store all their data, with two main technologies being used: iceberg (preferred by Snowflake, Google, and Amazon) and Delta (supported by Databricks and Microsoft).
- It is likely that major cloud providers will start supporting both formats in the future.
- The trend of injecting machine learning solutions into data stacks is making it easier to build end-to-end machine learning applications.
- Organizations should be exploring these new technologies and experimenting with them to find solutions that work for them.
- The emergence of large language models and artificial intelligence technology allows for independent intelligence in computer programs, which can greatly enhance application effectiveness.
- Prototyping with tools like GPT-3 is relatively straightforward, but productizing and ensuring alignment with company values requires more work.
- Tools for enterprises to do this will emerge in the next three to twelve months.
AI Tools and Vector Databases in Modern Data Platforms
19:49 - 27:14
- Modern data platforms like Snowflake, Databricks, and Microsoft are making it easier to build AI tools.
- Commercial models like GPT-3.5 or GPT-4 are powerful but expensive to run, while open source models are less capable but more affordable.
- AI factories and vector databases are emerging technologies that simplify the incorporation of AI into applications.
- Vector databases store text with semantic associations, allowing for vector searches and similarity searches.
- Combining knowledge from organizations with large language models can generate accurate answers from raw data.
- Vector databases have become important due to the rise of large language models and embeddings.
- The future of search involves vectorizing information and using natural language questions augmented by knowledge from vector databases.
Adopting General AI Products and Ensuring Data Governance
26:48 - 33:27
- To adopt general GIV AI products, organizations should start by getting their data assets in order and transitioning to the modern data stack.
- Data should be centralized and secured to ensure proper access control.
- Organizations can work with their modern data stack provider and utilize the tools and partners they offer to build intelligent data applications.
- It is important to understand what applications are most valuable and where to focus efforts.
- Companies need to prioritize data privacy and establish policies for managing customer data.
- Enforcing privacy requires making business decisions about who gets access to what information.
- Common mistakes include not centralizing and securing data, as well as neglecting to establish clear business policies around data access.
Data Governance and Fine-Tuning Models
33:09 - 39:58
- Data governance is crucial when using AI.
- Fine tuning models should only be done with relevant information.
- Contaminating models with irrelevant data can cause problems.
- DocuGami uses open source models and additional training to maintain privacy.
- Training on general information and specific customer data should be isolated.
- Tools are needed to facilitate incorporating AI into business applications.
- Isaac Asimov's science fiction influenced opinions on AI and robotics.
- Asimov saw intelligent machines as tools created by humans to help people.
- He defined the laws of robotics, which focused on protecting humans and robots' existence.
The Potential of Powerful AI and English Language Understanding
39:37 - 46:32
- Asimov's laws of robotics are useful guidelines for creating intelligence
- The zeroth law was added to ensure robots do not harm humanity
- Rules and regulations should be put in place to ensure AI works on our behalf
- Science fiction can help us figure out how the future ought to be
- The arc of data innovation is an acceleration of progress in technological innovation
- Artificial general intelligence (AGI) may be achieved within 10 years
- Technological singularity represents progress beyond human speed
- AI systems will accelerate progress and potentially quantum computing as well
- Ensuring AI's direction is beneficial to mankind is important
- Powerful AIs can help in medical science, drug discovery, diagnosis, and business processes
The Impact of Powerful AI and the Future of Data Analysis
46:07 - 53:09
- Powerful AIs can help accelerate medical science, drug discovery, and diagnosis of medical issues.
- AIs can automate business processes and make them more efficient.
- AI and quantum computing will lead to fascinating discoveries and solve previously unsolvable problems.
- There are potential risks with powerful AI, but they also have countless productive uses.
- Deep fakes created by AI pose both positive and negative consequences.
- English language understanding in computers will lower barriers between humans and machines.
- English is becoming the primary language for data analysis, making it easier for users to express queries.
- Open source models capable of natural language data analysis may be available in the near future.
- Intelligence in the form of AI can reinvent various application spaces, but incumbents may still succeed if they incorporate intelligence effectively.
- Creating a semantic model of a business using knowledge graphs is an emerging area with great potential.
- It's a good time for aspiring entrepreneurs to pursue their ideas in the field of data science.
Opportunities for Entrepreneurship in the Field of Data Science
52:43 - 53:36
- New technologies can help you out in pursuing your ideas and dreams.
- It's a great time to be entrepreneurial with the advancements in technology.
- There are many opportunities to work on new things.
- Leverage your strengths when solving problems in a different way for people.