Interesting list of jobs and industries that could be affected/are being affected by the possibilities of ChatGPT and LLMs.
Hmm...kinda poor summary from ChatGPT 3.5...
The web page discusses the Harper.ai plugin, which allows users to use their chat jibiri to browse and extract data from documents, summarize web pages and YouTube videos, generate outlines, create comparison tables, and extract data in table format. Users can also select different models to use, depending on their needs. The plugin has a wide range of applications, and the possibilities are endless.
HARPA AI is a Chrome extension for web automation that includes AI-powered features such as summarizing and rewriting content, extracting and monitoring web pages and data, and integrating with Google Search to provide AI-assisted search results. It also includes an AI writer, page-aware GPT prompts, and the ability to track competitor websites. The tool can be used for various purposes such as marketing, copywriting, productivity, HR, product development, and spreadsheets. HARPA AI uses a hybrid AI engine built on top of ChatGPT and its own machine learning to automate tasks on the web. The tool is safe and does not store data on remote servers or show ads, and requests made to ChatGPT from HARPA AI are sent to OpenAI servers for processing.
The CEO of OpenAI, Sam Altman, has confirmed that GPT-5 is not in training and will not be for some time. This is in response to an open letter calling for a pause on the training of AI systems more powerful than GPT-4. Altman emphasizes the importance of safety issues and caution in releasing GPT-5, as emergent abilities can arise spontaneously from the interactions and complexities within a system. OpenAI is testing and making GPT-4 even safer, building it on years of alignment research that they pursued in anticipation of models like GPT-4. Altman also agrees that OpenAI should release its alignment data set and alignment evaluations to other companies to ensure AI safety.
The article discusses prompt engineering, which is the process of creating inputs that determine the output generated by an AI language model. High-quality prompts can result in better output while poorly defined prompts can lead to inaccurate or negative responses. The article explains that AI language models rely on deep learning algorithms and natural language processing (NLP) to fully understand human language. There are two main learning methods for language models: supervised and unsupervised learning. The article also covers prompt categories, which include information-seeking prompts and instruction-based prompts