Software Engineering Insights

Artificial Intelligence (AI) and data engineering are closely interlinked. On one hand, making sense of unstructured data is the process known as data science or data engineering. On the other side of the same coin, AI-programmed computers have the ability to learn as they go, getting better at solving particular sorts of problems as they accumulate more data. So one cannot exist without the other.

Machine Learning (ML) is an application of Artificial Intelligence that has the maximum number of use cases, in almost every industry. Healthcare, automobile, marketing, finance, agriculture, retail- all of them are leveraging the power of ML to automate tasks and bring agility to operations. 

Speech recognition is a technology that has been going through continuous innovation and improvements for almost half a century. It has led to several successful use cases in the form of voice assistants such as Alexa, Siri, etc., voice biometrics, official transcription software, and the list goes on. So what really is Automatic Speech Recognition and what are the underlying technologies that enable it?

A majority of healthcare data today, about 80% of it, happens to be unstructured. Most sources used for aggregating Electronic Health Records (EHR) require a considerable amount of pre-processing because most of them are not machine-readable. Natural Language Processing (NLP) and various Machine Learning (ML) techniques can, however, unlock all the potential enmeshed in unstructured data.

Artificial Intelligence, in practice, is complex. There are several technologies and training methodologies involved. There is a high risk involved in algorithm failure. With this,  businesses want nothing but the best of AI practitioners when it comes to executing the use of AI technology. That’s when the Artificial Intelligence (AI) Center of Excellence (CoE) comes into the picture. 

Over the last half-decade, Artificial Intelligence solutions has made incredible advances in pivotal areas such as vision, video generation, linguistic processing, trend prediction, and so on. While AI attempts to make giant leaps toward matching human intelligence, industry insiders forecast the emergence of Artificial General Intelligence (AGI) as the next obvious step.

The global technology ecosystem has an insatiable demand for AI today but without the right training data and efficient AI model development, this domain cannot be utilized to its full potential. With fully actualized AI models that arise from high-quality build lifecycles, continuous innovation in AI fields such as Natural Language Processing (NLP) or predictive analytics can be ensured.

Taking a business to a global scale involves navigation, not just through untraversed markets but also across language-based hurdles. Natural Language Processing (NLP) offers the utility of Machine Translation, wherein a source language is converted to a target language automatically with significant accuracy. It is the most viable solution to a fast and effective translation process without human intervention.

In today's advanced competitive world, the most valuable commodity is data. Data has many forms, from pictures of people and places to patient records and banking transactions. Machine Learning is a branch of Artificial Intelligence that can fully leverage this data to learn from it and enhance a business' bottom line.

The latest innovation in AI, known as deep learning, helps AI models learn from a fairly large dataset of examples. This type of learning, however, does not generalize for conditions that were left out or missed out during the training process. The alternative to this is Transfer Learning, which is extensively witnessing its application in the field of Natural Language Processing (NLP).