Technology is embedded in every aspect of our lives. We rely on it for almost everything, especially to communicate with others. Simply, take the example of Siri or Alexa that we use in our day-to-day lives. Ever wondered how these virtual assistants understand and interpret our commands? How do they pick up terms and carry out the tasks according to our instructions? For example, when we ask Alexa to set an alarm for us.
PyTorch and TensorFlow are two of the most widely relied-upon frameworks for Deep Learning (DL) research and industry implementation. While both these DL development frameworks haven't been around too long, they have made a significant mark in AI/ML research and innovation. However, when it comes to which between PyTorch and TensorFlow is the better framework, there has not been a clear consensus.
The rapid rise of Artificial Intelligence (AI) and Machine Learning (ML) technologies has led to the growing adoption of TensorFlow, an end-to-end ML platform. TensorFlow enables AI developers to accelerate ML tasks at every step of the workflow but also has certain shortcomings and understanding both sides is very essential to fully leverage this platform.
An essential component of textual and verbal data is context and understanding the context requires some amount of sentiment analysis. To comprehend and categorize subjective feelings from communications data, Natural Language Processing (NLP) and Machine Learning (ML) methods have been used in the past. Sentiment analysis is frequently used in professional settings to comprehend customer evaluations, identify email spam, etc.
The domain of Artificial Learning (AI) known as Deep Learning (DL) is fast gaining acceptance as a go-to technology for a number of use cases. When it comes to use cases where image data makes up most of the input fed to a system, a DL technique known as semantic segmentation offers accurate implementation outcomes.
Quintillion bytes of data get generated in a day, now imagine handling those data manually! Impossible right? It would require immoderate manpower with no surety of accuracy. As we all are aware of the fact that organizations these days run on data and thus it is a vital task to manage and organize it. The data is received from multiple sources and stored at a centralized repository for usage, such as a data warehouse.
Dated or legacy tools, systems, and operational methods are not enough to deliver the quality of optimized and innovative financial services that today's digitally savvy customers expect. So, multitudes of financial organizations are leveraging Artificial Intelligence to raise the growth rate and dynamism of IT operations through AIOps.
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?