Posts in tag

Machine Learning


If you’ve seen photos of a teapot shaped like an avocado or read a well-written article that veers off on slightly weird tangents, you may have been exposed to a new trend in artificial intelligence (AI). Machine learning systems called DALL-E, GPT and PaLM are making a splash with their incredible ability to generate creative work. These systems are known as “foundation models” and are not …

Artificial Intelligence (AI) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make …

A simple algorithm that revolutionized how neural networks approach language is now taking on vision as well. It may not stop there. Imagine going to your local hardware store and seeing a new kind of hammer on the shelf. You’ve heard about this hammer: It pounds faster and more accurately than others, and in the …

Researchers surveyed 100 high-performing companies to determine how they successfully use machine learning technologies. The survey studied companies across sectors, looking at their data analytics and tech usage. Those seeing the biggest gains from digital technologies have strong governance, partnerships and data availability, as well as employees trained in machine intelligence. Which companies deploy machine …

There was no shortage of cybersecurity headlines in 2021. From REvil’s attacks, disappearance and resurgence to a brewing “cyber cold war” sweeping the world, 2021 was one of the most hectic years yet for the cybersecurity industry. And 2022 looks like it is going to be just as challenging, if not more so. A complex mix of people-centric training …

In this tutorial, you’ll learn about imbalanced data and how to handle them in machine learning classification in Python. Imbalanced data occurs when the classes of the dataset are distributed unequally. It is common for machine learning classification prediction problems. An extreme example could be when 99.9% of your data set is class A (majority class). At the same …

AS ALGORITHMS ANALYZE MAMMOGRAMS AND SMARTPHONES CAPTURE LIVED EXPERIENCES, RESEARCHERS ARE DEBATING THE USE OF AI IN PUBLIC HEALTH. John Quackenbush was frustrated with Google. It was January 2020, and a team led by researchers from Google Health had just published a study in Nature about an artificial intelligence (AI) system they had developed to analyze mammograms for …

Researchers from the National University of Singapore have concluded that the more explainable AI becomes, the easier it will become to circumvent vital privacy features in machine learning systems. They also found that even when a model is not explainable, it’s possible to use explanations of similar models to ‘decode’ sensitive data in the non-explainable model. The research, …

In this talk, Aparna Dhinakaran, Co-Founder and CPO of Arize AI, covered the challenges organizations face in checking for model fairness, such as the lack of access to protected class information to check for bias and diffuse organizational responsibility of ensuring model fairness. Aparna also dived into the approaches organizations can take to start addressing ML fairness …

In this tutorial, we’ll explain the random forest algorithm in machine learning. Random forests are powerful, popular, and easy to use algorithms for predictive modeling. As the name suggests, the model is an ensemble of many decision trees, with better performance than an individual tree alone. The algorithm can be used for both supervised classification and regression problems. Following …