Podcast Summary
Answer engines and language models: Answer engines aim to combine language models with knowledge graphs and vector search to provide accurate and coherent answers to users' queries, making the search process more efficient and user-friendly
The future of answering questions and discovering knowledge lies in the intersection of generative AI, language models, and search. This is an evolution from traditional web search, where users have to sift through documents to find accurate answers. Generative AI, such as language models, promises to provide answers directly, but it's not perfect yet. There have been attempts to create answer engines, but the accuracy and coherence of the answers have been a challenge. However, recent advancements in language models, like GPT 2 and GPT 3, have made it clear that there's potential for a breakthrough. Perplexity, a company founded by researchers with a background in language modeling and reinforcement learning, aims to build an answer engine from the ground up. Their approach is to pair language models with knowledge graphs and vector search to retrieve reliable and contextually relevant information. By doing so, they hope to provide accurate and coherent answers to users' queries, making the search process more efficient and user-friendly.
Proplex evolution: Proplex evolved from a bot to an answer engine, providing quick and accurate answers through advanced language models, differentiating itself from search engines with its focus on accuracy and speed.
The use of advanced language models like Proplex, which started as a Slack or Discord bot, evolved into an answer engine, providing quick and accurate answers, significantly improving over time. The founder's experience of using it to answer questions about hiring and insurance demonstrated its usefulness. The release of more advanced models, such as DaVinci 3 and ChatGPT, further showcased the potential of these technologies. Proplex differentiates itself from search engines by taking the first step of retrieving relevant documents and synthesizing them into human-readable answers, providing a simpler and faster experience. The focus on accuracy and speed sets Proplex apart from other search engines that do not generate answers themselves.
Integration of LLMs and external data retrieval: The future of information retrieval and generation lies in the synergy between large language models and external data retrieval, allowing models to generate informational text based on prompts while also grounding responses with accurate information from external sources.
The future of information retrieval and generation lies in the synergy between large language models (LLMs) and external data retrieval. LLMs, like ChargeGPT, can generate informational text based on prompts and have a vast internal knowledge base. However, they have limitations, such as inability to perform complex computations or reason with untrained data. To overcome these limitations, integrating external data retrieval is crucial. This can be done on the fly and can ground the model's responses with accurate information. The future also holds the development of agents that can perform actions and utilize various tools to reason about multiple sources of information. Perplexity, a team that has been exploring this field deeply, is focusing primarily on the information retrieval aspect using web data as a main component. They are also working on integrating various data sources and developing methods to reason with untrained data. The ultimate goal is to have a top-level powerful model that can reason behind multiple things and utilize tools effectively.
Balancing LLMs and specialized models: Companies like Perplexity use a combination of LLMs and specialized models to provide quick answers to complex questions, balancing speed, cost, and capabilities. However, managing multiple models can be challenging and requires careful consideration to avoid 'AI model debt'.
The use of large language models (LLMs) and smaller, specialized models in answer engines is a balancing act. LLMs, while powerful, can be slower and more expensive. Smaller, specialized models, on the other hand, are faster and more cost-effective but limited in their capabilities. Companies like Perplexity use a combination of both models to provide quick answers to complex questions. This approach allows for rapid product development and iteration, but managing multiple models can be challenging. The key is to not go overboard with customized models and to avoid relying solely on one model for all tasks. The rapid advancement of AI technology means that models can change frequently, leading to a potential issue of "AI model debt" where adjustments to prompts or the introduction of new models require significant effort to update and manage existing systems.
AI infrastructure design: Design infrastructure that supports model-agnostic systems, anticipates new features, and adapts to new modalities for efficient AI technology implementation.
In the field of AI model development, it's essential to design infrastructure and systems that are model-agnostic to accommodate various models and their advancements. Anticipating new features and their potential benefits for the product is crucial. It's not always necessary to incorporate every new feature, but only those that make sense for the product. Building a system that can adapt to new modalities and support them efficiently is vital. Additionally, the UI and user experience are other crucial aspects that require exploration and consideration when implementing new AI technologies. Staying informed and anticipating new developments while making strategic product decisions is key to keeping up with the ever-evolving AI landscape.
Advanced Gen AI interfaces: As AI technology advances, new and more intuitive interfaces like generative UIs and voice technology will become increasingly important for advanced Gen AI products.
While chat interfaces have their place in the current stage of AI technology, they may not be the most suitable UI for advanced Gen AI products as people begin to focus more on the capabilities and user experience of the products themselves. The speakers express a belief that as AI technology advances, new and more intuitive interfaces, such as generative UIs and voice technology, will become increasingly important. They also discuss the potential for seamless integration of AI capabilities into everyday activities, such as walking the dog or driving a car. Perplexity, the company mentioned in the discussion, is already exploring these possibilities by improving their mobile app for voice queries and generating voice content. Ultimately, the goal is to create interfaces that are more convenient and effective for users in various contexts, beyond just text-based chat interfaces.
Data Poisoning and Generated Content: As technology advances, we must be aware of data poisoning and generated content, which require constant efforts to filter out, but historically, we've been successful in doing so. The future includes not only retrieving accurate information but also making decisions based on it and performing actions, making interactions with AI systems more seamless and productive.
As we continue to advance in technology, particularly in the realm of AI and information retrieval systems, we must be mindful of the potential issue of data poisoning and the increasing presence of generated content on the web. This technological challenge is reminiscent of spam filters from the past, requiring a constant battle between generators and discriminators. However, the good news is that historically, we've been more successful in detecting and filtering out unwanted content. As we move forward, the vision for the future includes not only retrieving accurate and useful information but also making decisions based on that information and performing actions on your behalf. This opens up a new dimension of complexity versus quality, where you can ask simple or complex questions and receive answers or suggestions, ultimately leading to time-saving and efficient decision-making. The future lies in mastering information retrieval, decision-making, and action execution, making our interactions with AI systems more seamless and productive.
Effective communication: Active listening, clear expression, respectful dialogue, empathy, open-mindedness, patience, and a willingness to learn are essential for effective communication in any interaction.
Effective communication is key in any interaction, be it personal or professional. During our discussion, we emphasized the importance of active listening, clear expression of ideas, and respectful dialogue. We also touched upon the value of empathy and understanding in fostering strong relationships. Overall, the conversation underscored the significance of open-mindedness, patience, and a willingness to learn from one another. By practicing these skills, we can build meaningful connections and make a positive impact on those around us. So, let's continue to engage in thoughtful dialogue and strive for better understanding in all our interactions.