SALAD is an AI-driven language-learning application designed to help foreigners learn Japanese. It offers translations in Kanji-Kana-Romaji, speech recognition, translated audio, vocabulary tracking, grammar explanations, and songs generated from newly learned words. The app targets beginners and intermediate learners, aiming to make language acquisition more accessible and enjoyable. SALAD uses daily translations to enhance fluency and comfort in communication with native speakers. The primary objectives include effective Japanese language learning, user engagement, and progress tracking. A survey by us found that 39% of foreigners in Japan face discomfort in conversations with Japanese speakers. Over 60% of foreigners expressed confidence in SALAD's ability to enhance their Japanese language skills. The app uses large language models, speech recognition, and diffusion models to bridge the language gap and foster a more inclusive community in Japan.

At its core, feather was a tool that allowed model developers to build shareable user interfaces for their models in under 20 lines of code. Using the Python SDK, developers specified visual components that users would interact with. (e.g. a FileUpload component to allow users to upload a file). Our service then provided 1) a URL that allowed others to access and use the model visually via a user interface; 2) an API endpoint to allow programmatic requests to a model. In this paper, we discuss feather's motivations and the value we intended to offer AI researchers and developers. For example, the SDK can support multi-step models and can be extended to run automatic evaluation against held out datasets. We additionally provide comprehensive technical and implementation details. N.B. feather is presently a dormant project. We have open sourced our code for research purposes: https://github.com/feather-ai/

The model that we're going to use for this experiment may not be the exact same that OpenAI used for ChatGPT, but it's definitely a good baseline. This is the closest you can get, so let's go and grab the keys. Prerequisites: you'd need an OpenAI API key, and if you sign up for free, you'd get some credits, but afterwards you would have to pay. Second, we will be using Gradio, which is a platform for creating Machine Learning application for creating interface for the Chatbot. Go to the OpenAI's playground, make sure you click on text-davinci-003 and go to the'View Code' and click on copy the code: That piece of code should look exactly like this: I use a VSCode for this occasion.

Machine Learning is a fast-growing field, and its applications have become ubiquitous in our day-to-day lives. As the demand for ML models increases, so makes the demand for user-friendly interfaces to interact with these models. This blog is a tutorial for building intuitive frontend interfaces for Machine Learning models using two popular open-source libraries – Streamlit vs. Gradio. Streamlit is a python library for building data-driven applications specifically designed for machine learning and data science. It makes it easy to create a frontend UI in just a short amount of time with multiple features. On the other hand, Gradio is a library for Machine Learning models that makes it possible to quickly and easily create web-based interfaces for your models.

In this article I illustrate how to identify and anonymise places in Python, without the usage of NLP techniques, such as Named Entity Recognition. Places identification is based on a gazetteer, which is built from the Geonames Database. Geonames is a Web service, containing (almost) all the places in the world. The Geonames database can be downloaded for free at at this link. The idea behind this article is to build a gazetteer from the Geonames Database and exploit it to recognise places in a sentence.

In the field of technology, machine learning is nothing new. The capacity to automate channels and increase company process flexibility brought about a revolutionary change for numerous industries. The machine learning lifecycle governs many aspects of developing and deploying trained model APIs in the production environment. Model deployment, which differs from the creation of ML models in that it has a steeper learning curve for beginners, has proven to be one of the most significant challenges in data science. Model deployment refers to integrating a machine learning model that accepts an input and delivers an output to make helpful business decisions based on data into an already-existing production environment.

Some days ago, I discovered a very interesting Python package, named Gradio. According to its authors, Gradio permits to build demos for Machine Learning. The package is exploited by machine learning teams at Google, Facebook, and Amazon. Thus, I decided to study this package and build a little demo. While reading the documentation, I was very pleased to discover an interesting feature, that other similar packages, such as streamlit do not provide.

You've built your Machine Learning model with 99% accuracy and now you are ecstatic. Then you paused and you were like – now what? Well first, you might have thought of uploading your code to GitHub and showing people your Jupyter notebook file. It comprises those gorgeous-looking visualizations you created using Seaborn, those extremely powerful ensemble models, and how they are able to pass their evaluation metrics and so on. But then you noticed that no one is interacting with it.

Do you remember the first time you started to build some SQL queries to analyse your data? I'm sure most of the time you just wanted to see the "Top selling products" or "Count of product visits by weekly". Why write SQL queries instead of just asking what you have in your mind in natural language? This is now possible thanks to the recent advancements in NLP. You can now not just use the LLM (Large Language Model) but also teach them new skills.

This is a write-up for my old project ClothingGAN. The project generates clothing design with AI using StyleGAN and semantically edits it with attributes such as sleeve, size, dress, jacket, etc. You can also do style transfer as shown in the image above by first generating 2 different clothing designs (output 1) with different seed numbers. Then, it will generate a third design (output 2) that mixes the previous 2 designs. You can then adjust how much style or structure you want it to inherit from the two original designs.