Look around.. Search is everywhere. It’s in your phone’s contact list. It’s your gateway to the internet. Search is part of your photo library. Talking to Siri or Alexa? It’s powered by search over knowledge bases. Booking a hotel room? Ordering food? Exploring your friend network? Oh, wait! I’m sure that you’re getting compelling recommendations as well. You’re right. Search is inevitable. Honestly, the word ‘search’ is quite vague. Information Retrieval (IR) is the right word to use. With the help of Information Retrieval, we have been trying for decades to organize and generate insightful information from the ever-growing disposal of data. It doesn’t matter whatever background you’re from — it can be software development, decision making, content delivery or academic, Information Retrieval could add value to your daily life.
Today, Artificial Intelligence (AI) is disrupting every industry. And Information Retrieval is no exception. One notable achievement is, with the help of Deep learning models, you can now encode any information into a compact form — latent vector representation. It doesn’t matter what data you are encoding. It can be images, text, audio, video, knowledge graphs, DNAs, chemical compounds and what not.
Okay, okay… I get it. Idea is to encode any data into a common representation so that any system built on top of that doesn’t have to deal with the raw data and instead use the encoding itself, which contains the essence of original data.
But what does this encoding mean in the first place? And why is it important?
Technically, when you train a deep learning model, you will generalize that model to learn common patterns from the data you have exposed it to. These learned patterns in data are stored within the model itself as hidden layers. We can then peak into this trained model to collect high-level embeddings for any data on its forward pass. These collected embeddings will be closer in embedding space if the input data is semantically similar and will be far apart if the inputs are dissimilar. This similarity ranking plays a big role in Information Retrieval — by adding much more contextual information. This allows us to search for content by its meaning and is a game-changer in practice. From now on we will call this a Neural Information Retrieval System because it is backed by Neural Networks.
Even if you are a nontechnical person, you can now make use of public pre-trained models made available by Google, Microsoft, and other institutions. If you want to further customize those models to your specific use cases, it’s very easy to apply transfer learning to those pre-trained models. Once you have the desired model to use for your specific application, that’s it. You’re done! and you’re ready to deploy an IR system with one more step.
Introducing Aquila Network
Aquila Network is a drop-in solution for Decentralized Neural Information Retrieval. Start running Aquila Network in a single command. The goal of Aquila Network is to make it dead easy for a Data Scientist, ML engineer or a front-end / mobile application developer to start Neural Information Retrieval in minutes.
Have you noticed that extra topping — ‘Decentralized’? You are not wrong. Aquila Network is fully decentralized and is aiming for the next era of the web. No central point of failure, offline / edge first and eventually consistent. Awesome, right?
Aquila Network is fully open-source licensed under Apache 2.0. Get started with it now. Aquila Network is still going through active development. We have big feature additions in the queue. For that, expansion of development and testing is necessary. Our doors are fully open to all kinds of community contributions. If you’re interested to work on the futuristic stack or wanted to use it internally and give back to the community, it’s our pleasure to provide a comfy on-boarding.