Podcast Summary
Pairing LLMs with knowledge graphs: Pairing large language models with knowledge graphs and vector search can enhance retrieval methods, prompt engineering, and overall performance.
While large language models (LLMs) like ChatGPT offer impressive capabilities, they require reliable data and context to function effectively. The team at Neo4j is exploring how to enhance LLMs by pairing them with knowledge graphs and vector search. This approach can help improve retrieval methods, prompt engineering, and overall performance. The adoption of AI in enterprises is ongoing, with a growing trend towards using multiple model providers and open models. The landscape is still evolving, and companies are navigating the complexities of implementing and integrating various AI solutions. Data science teams are shifting towards more specialized roles, reflecting the growing complexity of the field. Overall, the AI community is continuing to explore new ways to harness the power of LLMs and other advanced AI technologies.
AI industry maturation: The AI industry is shifting from hype and marketing to practical applications, recognizing the strengths and limitations of different models, integrating software and AI teams, and focusing on unique value propositions.
The software industry is experiencing a maturing phase, where there's a shift from hype and marketing to a focus on practical applications and combinations of various models and technologies. Data science teams still exist and are expanding, but there's a recognition of the limitations and strengths of different AI models. Companies are also starting to integrate software and AI teams operationally, leading to more efficient and effective use of resources. The trend of data science as a function continues to grow, with a focus on unique value propositions for organizations. The idea of full stack data science or AI agent development is materializing, with more integration of software and data science roles in larger organizations. Overall, it's an exciting time as the industry moves towards a more holistic and efficient approach to AI and software development.
Apple's approach to AI: Apple positions AI as a feature enhancement, integrates it into devices and tasks, addresses privacy concerns, and provides tools for building advanced AI features.
Apple's approach to AI differentiates them from other tech companies by positioning AI as a feature enhancement rather than the product itself. This was evident in their WWDC 2023 announcements, where they focused on integrating AI into devices and tasks for users. This shift in focus received a positive response, especially from those skeptical of the hype surrounding AI products. Furthermore, Apple addressed privacy concerns regarding their use of external AI models by giving users control over whether or not to use these models on a per-use basis. This choice empowers users and addresses the trade-offs between closed model providers and open models, allowing organizations to navigate the use of these models based on their specific needs and resources. Early adoption of Plumb, a tool for building advanced AI features, is enabling product teams to transform data and create reliable, high-quality structured output, leading to faster idea validation. Overall, Apple's approach to AI and the availability of tools like Plumb demonstrate the ongoing evolution of AI integration in technology and its potential impact on various industries and applications.
Internal hosting vs Third-party APIs: Organizations prioritize data control and privacy by increasingly turning to internally hosted models, despite the added effort and resources required. This allows them to maintain full control over their data and AI features, while third-party APIs offer convenience and advanced functionality but pose significant risks with data leaving the network and potential for biased processing.
Organizations are increasingly turning to internally hosted models due to privacy and data control concerns when using third-party APIs. While third-party APIs offer convenience and advanced functionality, there are significant risks associated with data leaving an organization's network and the potential for biased or opinionated processing of user inputs. Internal hosting allows organizations to maintain full control over their data and the models they use, even if it requires more effort and resources. Additionally, there is a growing recognition that open models may not provide the same level of performance as closed, productized systems, but the trade-off is worth it for many organizations to maintain control over their data and AI features. It's important to note that both internal hosting and third-party APIs have their pros and cons, and the choice between the two depends on an organization's specific needs and priorities. The control element is a developing mindset among organizations, and it's essential to consider the implications of using closed, productized systems, which can offer impressive functionality but also make decisions about how to process user data without the user's full knowledge or control.
Retrieval Augmented Generation: RAG techniques have evolved beyond direct response generation, using hypothetical documents and query transformation for better query regeneration
Retrieval Augmented Generation (RAG) techniques in the field of data science have evolved beyond the naive approach of directly generating responses based on input queries. Instead, more advanced methods involve the use of hypothetical documents and query transformation. These techniques allow for the regeneration of queries to better suit the retrieval task at hand. This wider picture of RAG was discussed, although not all aspects were covered in detail. It's important for practitioners to recognize that the typical approach may be sufficient in some cases, but advanced tools are available to move beyond this baseline. However, many individuals seem to be getting stuck at this stage, so it's crucial to be aware of these advancements. Overall, the conversation underscored the importance of staying informed about the latest developments in RAG and considering the potential benefits of more sophisticated methods.