Large Language Models

This note does not have a description yet.

  • AI Is the next Great Interop Layer

    I had previously observed that humans are the great interop layer—we are the glue that fits together disparate processes and tools into usable systems. After using large language models, I’m becoming convinced that they can offload a large amount of the interop cost that currently falls to us. In a nut shell, AI can ‘do what I mean not what I say’ pretty darn well.

  • Context Is Needed for Practical LLM Applications

    One of the limitations of large language models is that it is often missing context when responding. LLMs like ChatGPT (GPT 3.5 and GPT 4 at time of writing) don’t allow fine tuning and can only take in ~4,000 tokens (roughly 3,000 words) as part of the prompt. That’s not nearly enough to provide context to be specific to your application. For example, you might want an AI coding tool to help you add a feature to your codebase, but the codebase is likely much larger than the prompt would allow.

  • Zapier Natural Language Actions API

    The Zapier NLA API solves a major problem for large language models—the ability to interact with real systems. Rather than a developer integrate with every possible service, they can integrate once with Zapier and run every “Action” the user has authorized using natural language instructions.

  • Advantages of Open Source AI

    It’s almost inevitable that, after an initial research phase, progress of AI models and tools will come from open source communities rather than a corporation. Individuals can utilize fair-use to do things businesses can not do (e.g. using leaked LLaMa weights and fine tuning it). There are more people to work on fringe usecases that do not have to be commercialized. Finally, open source increases access (running 13B LLMs on a laptop, on a Raspberry Pi) allowing more people to try it and provide more feedback.

  • Prompt Injection Attacks Are Unavoidable

    While large language models are already useful for certain text based tasks, connecting them to other systems that can interact with the outside world poses new kinds of security challenges. Because it’s all based on natural language, any text can effectively become untrusted code.

  • Natural Language User Interface

    One of the super powers of large language models is that it can “do what I mean” instead of “do what I say”. This ability to interpret prompts can drastically lower the barriers to accessing and interoperating between systems. For example, writing “Send a slack message to the Ops channel with a list of customers from HubSpot that signed up in the last week” would generate actions that query the HubSpot Contacts API, parse and extract the results, and make another API request to Slack to post to the #ops channel.

  • LLM-First Programming Language

    There are many barriers to adoption for a new programming language looking to go mainstream. You have to attract a small group of enthusiasts, build an ecosystem of high quality libraries, help new people learn, and eventually grow a talent marketplace.

  • Consciousness Is Categories

    Consciousness is an emergent property of categories. As a sufficient number of categories can be represented in a system, selfhood arises and, with it, consciousness.

  • AI Multiplies the Value of Expertise

    AI reduces the cost of certain tasks to effectively zero. In doing so, it lowers the barriers to domains that would previously take years to build skills such as writing code, data analysis, and more. This is precisely why AI also increases the value of expertise and experience.

  • Chatbots Lack Affordances

    When interacting with a chatbot, there are not indications of what to say or how to say it. Without affordances, it’s difficult to know what to do at first.

  • Legal Services Has the Highest AI Occupational Exposure

    A recent paper looking into the economic impact of large language models found that the legal industry has the most potential occupational exposure from AI including augmentation or substitution.

  • How to Decide If AI Tools Can Be Used at Work

    Advancements in AI powered tools can greatly improve productivity but many companies have taken steps to limit or outright ban the use of OpenAI’s ChatGPT, GitHub Copilot, and others. What are they concerned about and how should you decide if it can be used by your company?

  • How Langchain Works

    As it turns out, combining large language models together can create powerful agents that can respond to, and take action on, complicated prompts. This is achieved by composing models and tools with an overall language model to mediate the interaction.

  • Why Vector Databases Are Important to AI

    The recent success of large language models like ChatGPT have led to a new stage of applied AI and with it, new challenges. One of those challenges is building context with a limited amount of space to get good results.