Text & Generative AI Business applications

Finetuning LLMs Efficiently with AdaptersWhy Finetuning LLMs? Large language models (LLMs) like BERT, GPT-3, GPT-4, LLaMA, and others are trained on a large corpus of data and have general knowledge. However, they may not perform as well on specific tasks without finetuning. For example, if you want to use a pretrained LLM for analyzing legal or medical documents, finetuning it on a corpus of legal documents can significantly improve the model's performance. (Interested readers can find an overview of different LLM finetuning methods in my previous article,

What is Auto-GPT and why does it matter? | TechCrunchAuto-GPT is the latest craze sweeping the AI space. But what is it, exactly, and why should anyone care? Here's everything you need to know.

Unfortunately, OpenAI and Google have moatsWhile everyone went crazy over a leaked memo, no one took the time to think through how companies have worked in the internet era.

What is a large language model? How can it be used to enhance your business? In this conversation, Ali Rowghani, Managing Director of YC Continuity, talks with Raza Habib, CEO of Humanloop, about the cutting-edge AI powering innovations today—and what the future may hold.

They discuss how large language models like Open AI's GPT-3 work, why fine-tuning is important for customizing models to specific use cases, and the challenges involved with building apps using these models. If you're curious about the ethical implications of AI, Raza shares his predictions about the impact of this quickly developing technology on the industry and the world at large.

Chapters (Powered by https://bit.ly/chapterme-yc) -

00:00 - Intro

01:30 - Large Language Models (LLM)

04:32 - What is fine-tuning a model?

07:38 - Build Apps using LLM

09:46 - Future of the Developer Job

11:32 - Breakthroughs

15:17 - OpenAI Mission

17:30 - LLM for Startups

18:51 - Hiring at HumanLoop

Anthropic begins supplying its text-generating AI models to select startupsAnthropic, a startup developing safety-oriented AI models, has quietly begun making its tech available to commercial ventures.

Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal StandardsArtificial Intelligence (AI) is taking on increasingly autonomous roles, e.g., browsing the web as a research assistant and managing money. But specifying goals and restrictions for AI behavior is difficult. Similar to how parties to a legal contract cannot foresee every potential "if-then" contingency of their future relationship, we cannot specify desired AI behavior for all circumstances. Legal standards facilitate the robust communication of inherently vague and underspecified goals. Instructions (in the case of language models, "prompts") that employ legal standards will allow AI agents to develop shared understandings of the spirit of a directive that can adapt to novel situations, and generalize expectations regarding acceptable actions to take in unspecified states of the world. Standards have built-in context that is lacking from other goal specification languages, such as plain language and programming languages. Through an empirical study on thousands of evaluation labels we constructed from U.S. court opinions, we demonstrate that large language models (LLMs) are beginning to exhibit an "understanding" of one of the most relevant legal standards for AI agents: fiduciary obligations. Performance comparisons across models suggest that, as LLMs continue to exhibit improved core capabilities, their legal standards understanding will also continue to improve. OpenAI's latest LLM has 78% accuracy on our data, their previous release has 73% accuracy, and a model from their 2020 GPT-3 paper has 27% accuracy (worse than random). Our research is an initial step toward a framework for evaluating AI understanding of legal standards more broadly, and for conducting reinforcement learning with legal feedback (RLLF).

The $72M ARR Google Suite of generative AIHey everyone! 👋 Sign up to our email list for more weekly private market insights. [the-google-docs-of-generative-ai] TL;DR: Generative AI copywriting apps Jasper ($72M ARR) and Copy.ai ($11M ARR) grew fast by productizing GPT-3 for marketers, but now they have competition from fast-follower startups renting the same foundational models and web 2.0 behemoths like Google and Microsoft with massive distribution. Check out our coverage of Jasper (dataset) and Copy.ai (dataset), and read our interviews with Dave Rogenmoser, CEO and co-founder of Jasper and Chris Lu, co-founder of Copy.ai for more.

Notion is using AI to automatically write your blog posts, job descriptions, and poetryIn the future, your notes write themselves — all you have to do is ask.

Google’s text-to-image AI model Imagen is getting its first (very limited) public outingGoogle adds Imagen to its AI Test Kitchen app.

www.analyticsinsight.net/introducing-originality-ai-the-worlds-first-chatgpt-and-gpt-3-ai-content-detector/

Automate your SEO Tasks with Free Guides & ToolsLearn how to Automate your SEO Process Tasks with reliable free guides, resources and tools.

"Generative AI stands to change how work happens in one industry after another. But software engineering’s transformation isn’t done yet. There’s a lot more to do for developers. Copilot, which leverages www.sequoiacap.com/article/ai-powered-developer-tools/ OpenAI’s model Codex, may be just the opening salvo in AI’s transformation of how software engineers work. Andrej Karpathy predicted in 2017 that neural networks would create a new generation of software, “Software 2.0,” and we may see the same reinvention of the tooling that helps people make software — a “Developer Tools 2.0.” Sequoia, https://www.sequoiacap.com/article/ai-powered-developer-tools/

What Are Word and Sentence Embeddings?Sentence and word embeddings are the bread and butter of language models. Here is a very simple introduction to what they are.

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