As a small business, competing with large incumbents can be a daunting challenge. They have more money, more people, and more data, but they can also be inflexible and slow to adopt new technologies. In this talk, we will explore how small businesses can use the power of large language models (LLMs) to compete with large incumbents, particularly in industries like insurance. We will present two examples of how we are using LLMs at Anzen to streamline insurance underwriting and analyze employment agreements and discuss ideas for future applications. By harnessing the power of LLMs, small businesses can level the playing field and compete more effectively with larger companies.

Scalable, Serverless deployments of LangChain apps on the cloud without sacrificing the ease and convenience of local development. Streaming experiences without worrying about infrastructure

With the fast pace of innovation and the release of Large Language Models like Bard or GPT4, the role of data scientists and machine learning engineers is rapidly changing. APIs from Google, OpenAI, and other companies democratize access to machine learning but also commoditize some machine learning projects. In his talk, Hannes will explain the state of the ML world and which machine learning projects are in danger of being replaced by 3rd party APIs. He will walk the audience through a framework to determine if an API could replace your current machine-learning project and how to evaluate Machine Learning APIs in terms of data privacy and AI bias. Furthermore, Hannes will dive deep into how you can hone your machine-learning knowledge for future projects.

Adam will highlight potential negative user outcomes that can arise when adding LLM-driven capabilities to an existing product. He will also discuss strategies and best practices that can be used to ensure a high-quality user experience for customers.

Despite their advanced capabilities, Language Model Models (LLMs) are often too slow and resource-intensive for use at scale in voice applications, particularly for large-scale audio or low-latency real-time processing. SlimFast addresses this challenge by introducing Domain Specific Language Models (DSLMs) that are distilled from LLMs on specific data domains and tasks. SlimFast provides a practical solution for real-world applications, offering blazingly fast and resource-conscious models while maintaining high performance on speech intelligence tasks. We demo a new ASR-DSLM pipeline that we recently built, which performs summarization on call center audio.

One of the biggest challenges of getting LLMs in production is their sheer size and computational complexity. This talk explores how smaller specialised models can be used in most cases to produce equally good results while being significantly cheaper and easier to deploy.

Today, our personal and professional networks have reached an unprecedented level of complexity. The last two decades of tech innovation have connected us to more people, across more communication channels, spanning a wider range of contexts than ever before. Our growing networks have become intractable to manage as individuals, let alone teams.

Adept AI is developing a natural language software collaborator that utilizes LLMs to perform software tasks described in natural language. However, LLMs can suffer from overconfidence, hallucinations, and a lack of self-awareness, which can lead to incorrect actions. Jacob highlights an example of how the model can make a wrong action and emphasizes the importance of implementing safety checks such as action reversibility and content filters. By incorporating safety checks, Adept AI aims to improve the model's capabilities and ensure that it moves in the right direction.