Japan officially takes ChatGPT mainstream
Plus: Google Bard now helps you code
Hello, Engineering Leaders and AI enthusiasts,
Welcome to the fourth edition of The AI Edge newsletter. In today’s edition we cover another big update from Google. Thank you everyone who is reading this.
In today’s edition:
🤖 Yokosuka, first Japanese city to use ChatGPT in municipal offices.
💻 Google Bard will now help in software development
📖 Monday Learnings: Fine-tuning Large Language Models by
Let’s go
Yokosuka, Japan makes history as the first to employ AI-powered Chatbot in its municipal offices
Yokosuka becomes the first Japanese municipality to use OpenAI’s ChatGPT in municipal offices, as part of a one-month trial involving 4000 employees aimed at improving administrative tasks. With a declining population and limited employees, the city is hoping to use AI tools to free up human resources for tasks that require personal attention.
The hope is to use ChatGPT to assist in tasks like summarization, marketing copy ideation, drafting administrative documents, and perfecting easy-to-understand language. The move comes as the Japanese government is exploring AI's potential use for government administrative tasks.
Why does this matter?
The use of ChatGPT in Yokosuka's municipal offices marks a significant step towards incorporating artificial intelligence into government administrative tasks in Japan. With the country's population declining and fewer employees available to take on administrative tasks, Yokosuka hopes to free up human resources by using ChatGPT. As other municipalities across Japan may also explore the potential of ChatGPT, this development highlights the growing role of AI in transforming traditional work processes and has the potential to significantly impact the future of government services.
Google Bard goes beyond writing: Now assisting you with programming and debugging tasks!
Google Research has updated its generative AI experiment, Bard, to include programming and software development tasks, such as code generation, debugging, and explanation. With the new capabilities, Bard can help explain code snippets, generate code, and debug code, and it supports over 20 programming languages, including C++, Go, Java, Javascript, Python, and Typescript.
However, since Bard is an early experiment, users should carefully test and review the code for errors, bugs, and vulnerabilities before relying on it. The new coding capabilities of Bard will enable generative AI to accelerate software development, inspire innovation, and help people solve complex engineering challenges.
Why does this matter?
It has the potential to make coding more accessible to a wider audience, including beginners and non-technical users. The ability to generate code, explain code, and debug errors through an AI-powered chatbot could greatly enhance the speed and efficiency of software development. While Bard is still an early experiment and may not always provide optimal or accurate code, it could serve as a valuable tool for learning and collaboration in programming.
Monday Learnings: Ways to fine tune LLMs
writes a detailed and well researched article on fine tuning large language models (LLMs). It discusses the different ways in which large language LLMs can be utilized for new tasks, mainly through in-context learning and fine-tuning. In-context learning enables a model to learn a new task without any additional training, making it useful when direct access to the model is limited. Fine-tuning, on the other hand, involves adapting and training a model on a specific task, usually leading to superior results. The article outlines the following three conventional feature-based and fine-tuning approaches for LLM with practical examples:
Feature-based approach
Finetuning I – Updating The Output Layers
Finetuning II – Updating All Layers
Additionally, the article discusses the concept of prompt tuning and indexing, which offer more resource-efficient alternatives to fine-tuning but may have limitations in adaptability to task-specific nuances.
Why does this matter?
The ability to fine-tune large language models (LLMs) has revolutionized natural language processing (NLP) and opened up new opportunities for applications such as language translation, sentiment analysis, and chatbots. It matters because the fine-tuning process enables developers and researchers to train LLMs on specific tasks with smaller datasets and achieve state-of-the-art performance.
This approach reduces the need for massive datasets, which is often a limiting factor in NLP research, and allows for faster experimentation and development. Fine-tuning LLMs also enables transfer learning, where a pre-trained model can be fine-tuned on a specific task and then re-used for other similar tasks, saving time and computational resources. Overall, fine-tuning LLMs has the potential to accelerate advancements in NLP and lead to new breakthroughs in the field.
What Else Is Happening
🔊 Amazon introduces Dialogue Boost an AI-powered tool for Prime Video that enhances dialogue volume in movies and TV shows. (Link)
💰 After Reddit, Stack Overflow will start charging AI giants for training data. (Link)
👮 Homeland Security Plans to Utilize AI to Tackle Critical Missions. (Link)
Trending Tools
Wonder Studio: AI tool that animates, lights and composes CG characters into live-action scenes.
BloopAi: Fast code search engine written in Rust.
Aimbly: Quick and accurate data insights and summaries for busy professionals.
Altermind: Create personalized AI entities with your data for specific tasks.
Cantrip: Use AI to generate websites easily without code or complex design.
Codenull ai: Build any AI model without writing code for various applications.
SafeGPT: Collaborative & Open-Source Quality Assurance for all AI models
Letsask: Dynamic AI chatbot builder for ChatGPT! No coding or training required. Train with documents or websites.
That's all for now!
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