Machine Learning technology landscape
Introduced here is a brief description of the technologies used in the case studies presented in the Gallery page.
Procedures that enable computer programs to learn, change, and modify their knowledge by themselves. Underpins predictive analytics, complex pattern recognition, generating new designs and data-mining.
Examples: Recommender systems that recommend movies and products on pages like Netflix and Amazon.
Computing system that is modeled on interconnected nodes like those found in the human brain. Multiple layers of these artificial neurons learn from the data / examples provided to it.
Deep Learning has demonstrated capability to identify persons by face, or by their typing, identify forged signatures, recognize speech, and many other tasks hitherto considered beyond the scope of computer science. Deep learning applications are great at pattern recognition, and enable things like speech recognition, image recognition and natural language processing.
Example: Amazon‟s voice assistant Alexa is built on deep learning.
Natural language processing
Technology that enables computer to interpret text and/or speech.
The application can understand meaning of sentences and paragraphs either written. Sentiment of the writer / speaker can be identified. This ability also facilitates translation to multiple languages
A direct extension of NLP. Software bots that interacts with humans online, receiving and sending texts with the aim of emulating the way a human communicates.
Examples: Chatbots from banks,
AI system with the ability to interpret images and videos like a human does.
The advances in computing power and deep learning have made Computer Vision one of the most exciting AIsub fields.
Examples: Facial Recognition and Object tracking in in images and videos.
The application of computer vision that gives the ability to identify people and faces from images and videos.
Examples: Automatic tagging of people in images uploaded on Facebook through facial recognition.
Predictive & Prescriptive analytics
Machine learning system that predicts future outcomes (and also suggests action in case of prescriptive) based on patterns in past data.
Example: Weather forecasts are based on complex prediction models.
Large data sets collected over the years. They can be plain records/files (unstructured) or structured records in a database.
All Machine Learning and AI models use big data to train themselves on. The rise in big data technologies has raised machines to near humanlevel understanding.
Our Technology Stack
We provide an integrated set of technologies, encompassing
• Knowledge based systems (representation and inference)
• Machine learning
• Data mining and
• Deep learning
Fig: Our AI / ML engine
Using these technologies, we provide a platform of
• Computer Vision
• Natural Language Processing (NLP) and
• Predictive Analytics.