​ In his book The Book of Why, Judea Pearl advocates for teaching cause and effect principles to machines in order to enhance their intelligence. The accomplishments of deep learning are essentially just a type of curve fitting, whereas causality could be used to uncover… Read More »Generate a counterfactual analysis of corn response to nitrogen with Amazon SageMaker JumpStart solutions Paul Barna AWS Machine Learning Blog

​ The size and complexity of large language models (LLMs) have exploded in the last few years. LLMs have demonstrated remarkable capabilities in learning the semantics of natural language and producing human-like responses. Many recent LLMs are fine-tuned with a powerful technique called instruction tuning,… Read More »Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart Vivek Gangasani AWS Machine Learning Blog

​ If you have been on the internet recently, it is very likely that you might have heard about large language models or the applications built around them. The most well-known example is OpenAI’s ChatGPT, which employs the GPT-Turbo-3.5 large language model. Large language models,… Read More »Meet GPT4All: A 7B Parameter Language Model Fine-Tuned from a Curated Set of 400k GPT-Turbo-3.5 Assistant-Style Generation Khushboo Gupta Artificial Intelligence Category – MarkTechPost

​

In this post, we introduce Koala, a chatbot trained by fine-tuning Meta’s LLaMA on dialogue data gathered from the web. We describe the dataset curation and training process of our model, and also present the results of a user study that compares our model to ChatGPT and Stanford’s Alpaca. Our results show that Koala can effectively respond to a variety of user queries, generating responses that are often preferred over Alpaca, and at least tied with ChatGPT in over half of the cases.

We hope that these results contribute further to the discourse around the relative performance of large closed-source models to smaller public models. In particular, it suggests that models that are small enough to be run locally can capture much of the performance of their larger cousins if trained on carefully sourced data. This might imply, for example, that the community should put more effort into curating high-quality datasets, as this might do more to enable safer, more factual, and more capable models than simply increasing the size of existing systems. We emphasize that Koala is a research prototype, and while we hope that its release will provide a valuable community resource, it still has major shortcomings in terms of content, safety, and reliability, and should not be used outside of research.

Online interactive demo
Open source training and serving framework

Read More »Koala: A Dialogue Model for Academic Research The Berkeley Artificial Intelligence Research Blog

​ Home Table of Contents Deploying a Custom Image Classifier on an OAK-D Introduction Configuring Your Development Environment Having Problems Configuring Your Development Environment? Project Structure Deploying the Model on OAK Configuring the Prerequisites Defining the Utilities Creating the Images Pipeline Creating the Camera Pipeline… Read More »Deploying a Custom Image Classifier on an OAK-D Aditya Sharma PyImageSearch