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• The speaker discusses how Cody, a natural language interface, can explain complex code to non-technical stakeholders. |
• The speaker mentions a pitch meeting with a Fortune 500 company where Cody was used to explain open-source libraries, impressing even a 30-year-out-of-touch programmer. |
• The speaker predicts that in a few years, almost every human will be empowered to create software in some way. |
• The speaker discusses the potential for language models to become the primary interface for coding, reducing the need for traditional coding skills. |
• The speaker contrasts the strengths and weaknesses of language models, citing examples of their limitations. |
• The speaker proposes a future where systems combine the strengths of language models and traditional coding, creating hybrid systems that are more powerful than either on their own. |
• Acquisition of Code Cove by Sentry |
• Role of Code Cove in developer lifecycle (before deploy time) |
• Role of Sentry in developer lifecycle (after deploy time) |
• Integration of Code Cove and Sentry |
• Benefits of integrating Code Cove and Sentry (de-risking code changes and software) |
• Onboarding process for teams to use Code Cove with Sentry |
• Code Cove's analysis and reporting of code coverage |
• CodeCov's ability to ensure code coverage across an entire team |
• Promotion of Sentry.io with a free team plan for three months using code "changelog" |
• Mention of CodeCov.io for code coverage |
• Databricks discussion, specifically Dolly 2 |
• Author Denny Lee's presentation preparation habits and procrastination/efficiency debate |
• The speaker and a colleague from Denmark gave a presentation together, but they had to put together their slides just 30 minutes before the session. |
• The speaker has been able to get away with this approach so far, but acknowledges it may eventually "bite" them. |
• The topic of the presentation is Dolly, a model that allows for fine-tuning an older model with good data to get good results, saving millions of dollars in training costs. |
• Dolly 1.0 was successful in achieving good results with a minimal amount of data, leading the team to generate their own data. |
• The team generated their own data by having employees contribute to a Q&A format, which was used to train the model. |
• The data generated was used to train Dolly, and the team was able to share the weights and model with others, but not the data itself due to its proprietary nature. |
• The conversation discusses the Dolly 2.0 model, a chatbot that can answer questions |
• Dolly 2.0 is a clone of Dolly, the first sheep to be cloned |
• The model is used to generate 15,000 Q&A pairs, which are then used to train the model |
• The model is trained using a old model from two years ago and costs $100 to train |
• The model is compared to ChatGBT4.0, which is found to be more verbose but equally correct |
• The conversation discusses the use of the model, including how to download and run it using Databricks or Hugging Face |
• The conversation also touches on the limitations of the model, including its tendency to provide long answers and the need for users to be able to control its verbosity. |
• Optimizing a model for M1 Mac performance |
• Bug in model causing garbage answers |
• Fixing the bug |
• Collecting and formatting data for Dolly to understand |
• Asking specific, detailed questions for Dolly to provide accurate answers |
• Example of asking questions to gather information about making great espresso |
• Addressing bias in the data and answers provided by Dolly |
• Importance of verbosity in providing context and proof for training models |
• Limitations of training large models with vast amounts of data |
• Dolly1.0's surprising performance with minimal training data |
• Business benefits of using open-source models to maintain data ownership and privacy |
• Fine-tuning large language models for specific use cases |
• Availability of various open-source models from companies like Hugging Face |
• Cost-effectiveness of using open-source models with smaller training data sets |
• Concerns about data ownership and privacy when using third-party services |
• Positive mention of Microsoft and OpenAI's contributions to the field |
• Discussion about paying more for data and its perceived value |
• Mention of the "no moat" concept and how it relates to the overemphasis on collecting large amounts of data |
• Criticism of the idea that companies must collect massive amounts of data to train models |
• Suggestion that foundational models can be fine-tuned with a smaller amount of data and be effective |
• Idea that companies trying to build a moat around themselves are actually giving away their competitive advantage |
• Discussion of the benefits of building one's own model and not relying on third-party services |
• Mention of using ChatDB and other services for convenience and value |
• Discussion of the future of AI and data processing with Databricks' angle being the importance of ETL and data processing. |
• Databricks' purpose is to make it easy for users to process and access large amounts of data, regardless of the technology used. |
• The company believes in open-source systems and services, and users should own their data. |
• Data should be a competitive advantage, and services like OpenAI can be useful, but users should know what they're doing. |
• Databricks provides a platform that makes it easy to use services like OpenAI within a database platform. |
• The company suggests that users should understand when to use a service and when to build their own model. |
• The use of Dolly 2.0, an open model, can be leveraged for personal betterment, such as generating blogs based on transcripts. |
• Databricks makes it easy for users to build, maintain, train, and infer against their own models. |
• The company's goal is to simplify the process of synthesizing key points from conversations. |
• Reviewing and validating AI model output |
• The potential of open-source models like Dolly to simplify processes |
• Using AI models to generate content based on user style and transcripts |
• The importance of choosing the right foundational model for specific tasks |
• Nat Friedman and the Nat.dev playground for testing and comparing different models |
• The value of experimentation and testing to determine the best model for a particular use case |
• The CentOS project was pivoted due to business agenda and commercial needs, causing a significant pain point in the industry. |
• The Rocky Linux and RESF (Rocky Enterprise Software Foundation) were created to ensure that similar issues do not happen again. |
• The RESF is a community-run organization that governs the management of Rocky Linux and other projects. |
• Eleuther AI, a non-profit organization, has trained several large open-source language models, including Pythia. |
• Eleuther AI is seeking help from the open-source community to address issues related to maintainability, licensing, regulation, and sustainability in the AI ecosystem. |
• The organization is promoting foundation models and large language models, and encouraging people to get involved and contribute to the open-source work needed to build a robust and enduring ecosystem. |
• Donations from companies such as Google, Stability AI, and Hugging Face |
• Applying for grants from the U.S. government |
• Computing resources for training large language models |
• Cost of training large language models, with examples of specific models and costs |
• The TensorFlow Research Cloud and its role in providing free computing resources |
• The development and deployment of open-source language models, including GTP NeoX and Pythia |
• The political will and sponsorship required to train large language models |
• Open source AI research in general |
• Large-scale AI, language models, and protein interactions (AlphaFold) |
• Creating an open source infrastructure for training large language models |
• Developing the Pile dataset and evaluation suite for language models |
• Training large language models (up to 20 billion parameters) |
• Investigating model interpretability, ethics, and alignment |
• Designing a model suite for scientific research (DALI-2) |
• Focusing on tracing the behavior of language models back to their training data |
• Intermediary checkpoints for model performance evaluation |
• Understanding memorization in language models |
• Investigating the effect of training data on model behavior |
• Reverse engineering the interaction between models and data |
• Improving model interpretability by studying model development |
• Forecasting memorization and designing transparent models |
• Addressing the reproducibility issue in model training |
• Enabling model designability and predictability |
• Understanding how AI models change over the course of training |
• Importance of being prepared for future paradigm shifts in AI |
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