AI & Machine Learning

How hard is hiring? With one sheet of paper and maybe a few hours of conversation, a company is supposed to assess how well someone could contribute hopefully thousands of hours of work. The hiring manager has to assess how well a prospect’s skills match the job requirements, how quickly they could learn new skills,

Why do we need a data warehouse in the first place? As a data professional, what do you need to know about data warehouse at the bare minimum?

Artificial intelligence is gradually changing almost every aspect of peoples’ lives, including in their workplaces. Most people think that Artificial Intelligence has come to replace Human Intelligence, but it has not.

Language models in NLP(Natural Language Processing) Systems are Machine Learning based models which are trained to learn and understand Natural Languages like how humans do. A simple example of a trained language model will predict the occurrence of the next word in a sentence Mainframe Modernization entails the process of migrating or improving the IT

No more confusion about what confusion matrix is and which evaluation metrics you should focus on for your next binary classification challenge.

In any scenario of AI in automobiles, it has a huge impact on the industry, production technology, and related areas (insurance, marketing, financial valuation, credit, and so on).

The EU is leading the charge to regulate and manage the vast emerging AI market. The implications for the entire AI industry are huge, and it is best to get out in front of and prepare for these regulations now by implementing the right tools and processes.

OpenAI has already outdone itself with GPT-3, a new generative language model that is bigger than GPT-2 by orders of magnitude. The largest version of the GPT-3 model has 175 billion parameters, more than 100 times the 1.5 billion parameters of GPT-2.

This blog is a metaphorical journey that many of us are taking toward the concept of building large enterprise-scale knowledge graphs (EKGs).

Artificial neural networks have evolved through the ages. In this post, we cover two key anatomies that have emerged: fully-connected versus convolutional.

If your analytics and data science organization has not yet put in place a Chief Data Scientist to help lead the execution and implementation of your analytics strategy, it is time to think about how creating such a role can be an important step in guiding your organization to the next level.