The research team at Together AI has developed a new language processing model called Llama-2-7B-32K-Instruct, which excels at understanding and responding to complex and lengthy instructions, outperforming existing models in various tasks. This advancement has significant implications for applications that require comprehensive comprehension and generation of relevant responses from intricate instructions, pushing the boundaries of natural language processing.
Over half of participants using AI at work experienced a 30% increase in productivity, and there are beginner-friendly ways to integrate generative AI into existing tools such as GrammarlyGo, Slack apps like DailyBot and Felix, and Canva's AI-powered design tools.
Artificial intelligence (AI) programmers are using the writings of authors to train AI models, but so far, the output lacks the creativity and depth of human writing.
AI engineering, ML engineering, MLOps engineering, and data engineering are projected to be the job roles that will experience significant growth due to the increased use of large language models (LLMs) and generative AI, according to a survey.
Some companies are hiring AI prompt engineers to help them optimize generative AI technology, but as the tech improves at understanding user prompts, these skills may become less necessary.
A study found that a large language model (LLM) like ChatGPT can generate appropriate responses to patient-written ophthalmology questions, showing the potential of AI in the field.
Prompt engineering and the use of Large Language Models (LLMs), such as GPT and PaLM, have gained popularity in artificial intelligence (AI). The Chain-of-Thought (CoT) method improves LLMs by providing intermediate steps of deliberation in addition to the task's description, and the recent Graph of Thoughts (GoT) framework allows LLMs to generate and handle data more flexibly, leading to improved performance across multiple tasks.
Creating a simple chatbot is a crucial step in understanding how to build NLP pipelines and harness the power of natural language processing in AI development.
The future of AI chatbots is likely to involve less generic and more specialized models, as organizations focus on training data that is relevant to specific industries or areas, but the growing costs of gathering training data for large language models pose a challenge. One potential solution is the use of synthetic data, generated by AI, although this approach comes with its own set of problems such as accuracy and bias. As a result, the AI landscape may shift towards the development of many specific little language models tailored to specific purposes, utilizing feedback from experts within organizations to improve performance.
The skill of prompt engineering, which involves refining and inputting text commands for generative AI programs, is highly valued by companies and can lead to high-paying job opportunities.
