Tapping into the world's unstructured data with an AI semantic layer

Marc Andreessen famously said, "Software is eating the world," in his 2011 blog, and explained how businesses were running on software and displacing the old value chain of industries. Thanks to the internet!

Fast forward to 2022, Artificial Intelligence makes the same software industry very nervous. Since 2011 many developments have taken place to make this happen. Computing power has increased, the advent of data and a fully connected IoT world is exploding with training data for AI algorithms and a decrease in cloud storage costs.

Artificial Intelligence & Machine Learning

Artificial Intelligence is a technology that empowers a machine to simulate human behavior. To enable this, we need machine learning which allows a device to learn from past data without explicitly programming. Machine learning teams have dealt with a lot of data while training a model.

Structured VS Unstructured data?

Data can be further classified into structured and unstructured. Structured data is also called tabular data, which can be arranged in rows and columns. For example, an excel sheet report in which you will have columns and rows which contain identifiable data. Unstructured data don't have an arranged data model and hence cannot be stored in traditional form. Today, more than 80% of world data is unstructured, available in text, image, and speech; hence, this data has been untapped for machine learning. Converting into structured data is a gold mine for business.

Computer Vision- Subset of Artificial Intelligence

As humans, we can identify, process, and classify images instantaneously, often without conscious knowledge. However, categorizing and processing a vast amount of data is possible only with machine intervention. Computers are fed with colossal data to activate the identification process quickly. Thus, an AI developed which gives machines a vision to identify and process information from image and video data, called machine vision or computer vision.

Labellerr takes analytics to the next level.

As we enter the data-centric age of machine learning, the ML team needs operationalized data pipeline to train their model. This is why we are placing our bets on Labellerr, creating a complete computer vision stack that will propel advancements in AI by simulating real-world data to train computer vision-based machine learning models rapidly.?

Finding and fixing data errors is one of the biggest impediments to effective ML across the enterprise. The founder of Labellerr (Puneet Jindal) felt this pain while working as a data scientist in leading tech start-ups. Labellerr has built an incredible team, made product innovations across the stack, and created a first-of-its-kind ML data intelligence platform.?

Founders have also understood the gravity of partnership. So, they built a partnership with US-based software companies like GoodData, Ascend USA, and Yash technologies. They are gaining traction by cracking a fantastic deal with Wadhwani AI, Scientia mobile, and Perceptly.

Author:

Rahul Sharma?