If you wanted to recruit for “data science” talent at a university, where would go? Should you go to the College of Computing? Would it be in the College of Business? Is it in the Department of Mathematics? Statistics? Is there even a Department of Data Science? 

There is more variation in the housing of data science than any other academic…

Image recognition technology has transformed the way visual data is pooled and processed. It offers opportunities similar to the ones portrayed in science fiction movies that make the imagination run wild. Faster detection of objects in real-time with assured accuracy, impressive face recognition mechanics, and improved augmented reality—all are made…

Artificial Intelligence (AI) has transformed learning and development in the 21st century. With the new pandemic realities that society has been living in since 2020, Artificial Intelligence and Machine Learning contribute greatly to the world of education. Rapid learning and continuous…

Recent studies show that, ‘healthcare’, very niche and the personalized sector is now expanding its arm to embrace 'AI strategies' on priority this journey will be a roller coaster due to unawareness of where and how AI should be used,and also how to implement it on larger & wider scales. This article will talk about various areas of…

Recent studies show that, ‘healthcare’, very niche and personalized sector is now expanding its arm to embrace 'AI strategies' on priority this journey will be a roller coaster due to unawareness of where and how AI should be used ,and also how to implement it on larger & wider scales. This article will talk about various areas of AI…

Working with the audio production and engineering industry, I often wonder how the future of the voice talent market will look like with the assistance of artificial intelligence. The development of cloning technology started a while back but it did not reach today's level overnight. The debate of misusing this…

This article was written by DeLiang Wang.

My mother began to lose her hearing while I was away at college. I would return home to share what I’d learned, and she would lean in to hear. Soon it became difficult for her to hold a…

This article was written on the blog CLOUDERA Fast Forward.

Machine learning models are used for important decisions like determining who has access to bail. The aim is to increase efficiency and spot patterns in data that humans would otherwise miss. But how do we know if a machine learning model is fair? And what does fairness in machine learning…

This article was written by Damian Mingle.

Google recently started promoting automated machine learning (what they call AutoML) – something that DataRobot customers have been doing for years. So what is driving Google to now embrace this transformative approach? Guest blogger Damian Mingle explains. …

Generative Pre-trained Transformer 3 also referred to as GPT-3 is the next big revolution in artificial intelligence (AI).

In 2018, a startup, OpenAI was the first to create the autoregressive language model. GPT-3 was deemed to be the largest autoregressive language. The program has been trained regressively on approximately 45 terabytes of text data which has been processed through 175 billion parameters.…

NeoML, launched by ABBYY, the digital intelligence company is an open-source library ideal to build, train, and deploy machine learning (ML) models.

The open-source library which was earlier launched in June 2020 is now available on GitHub as well. NeoML provides huge support to both deep learning and traditional machine learning…

Being an expert at developing and understanding ML, or Machine Learning algorithms, takes time and a lot of hard work. That’s why ML (machine learning) engineers are been seen constantly learning while at the job. If the learning stops, your professional growth stops. Many of us, especially the AI aspirants, think that watching tutorial videos on AI (artificial intelligence) modeling or ML algorithm development on YouTube…

In this blog, I will introduce a R package for Heterogeneous Ensemble Learning (Classification, Regression) that is fully automated. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.…

In our previous article — 5 Challenges to be prepared for while scaling ML models, we discussed the top five challenges in productionizing scalable Machine Learning (ML) models. Our focus for this…

Machine Learning (ML) models are designed for defined business goals. ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets. ML models in production also need to be resilient and flexible for future changes and feedback. A recent study by Forrester states that improving customer experience, improving profitability & revenue growth as the key goals organizations plan to achieve specifically using ML initiatives.

Though gaining…

Enterprise software, as well as other kinds, remains a complicated endeavor, thus necessitating the use of modern means to gauge, analyze, and adapt their performance. And one of the most popular technologies in the performance engineering market right now is machine learning. Since it has demonstrated an unparalleled ability to not only help foresee performance issues and fix them. When used in the right manner — this combination can also help performance engineering teams to steer clear of…

Introduction:

Here’s a fun fact; An average human being (probably an adult) makes close to 30,000 conscious decisions every day. This isn’t entirely true though, in fact, I just made that number up. I could be right because if you think about it, how many decisions would you say you make on a day to day basis? Depending on who you are the above obviously varies widely and you know best. We all…

Introduction

Artificial neural networks are based on collection of connected nodes, and are designed to identify the patterns. They are part of deep learning, in which computer systems learn to recognize patterns and perform tasks, by analyzing training examples. For example an object recognition system can be fed to thousands of labeled images of houses, cars, traffic signals, animals etc. and would…