Garmin has some of the best proprietary fitness software around. Here's how to interpret all that meticulously gathered data. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. So, you've got a shiny new Garmin watch .

In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI's carbon footprint and other impacts. The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI "gold rush" remain difficult to pin down, let alone mitigate.

To use, or not to use, Bard? That is the Shakespearean question an Associated Press reporter sought to answer while testing out Google's artificially intelligent chatbot. The recently rolled-out bot dubbed Bard is the internet search giant's answer to the ChatGPT tool that Microsoft has been melding into its Bing search engine and other software. During several hours of interaction, the AP learned Bard is quite forthcoming about its unreliability and other shortcomings, including its potential for mischief in next year's US presidential election. Even as it occasionally warned of the problems it could unleash, Bard repeatedly emphasized its belief that it will blossom into a force for good.

AI is the digital distillation of a technological revolution that is facilitating the long-overdue evolution of the human mind. AI, as fear-inducing as anything disruptive and new is, can galvanise, turbo-charge, and trigger new avenues of intelligence in human minds. These new avenues can enable us to understand and attack society's greatest challenges today. What a layman does not know is that AI can traditionally be divided into AGI and ANI. Theorists and AI experts call this Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI). AGI is designed to be capable of performing a wide variety of intellectual tasks, while ANI is designed to perform a single or a narrow set of related tasks.

The mAP is calculated by finding Average Precision(AP) for each class and then average over a number of classes. The mAP incorporates the trade-off between precision and recall and considers both false positives (FP) and false negatives (FN). This property makes mAP a suitable metric for most detection applications. Precision-Recall curve is obtained by plotting the model's precision and recall values as a function of the model's confidence score threshold. Precision is a measure of when ""your model predicts how often does it predicts correctly?""

Since the advent of computer programming, functions have played a key role by offering advantages such as reusability, readability, modularity, error reduction, and easy modification. Reusability is considered one of the most useful traits of a function but what if I tell you there are functions that are not reusable but still useful? To find out, read along! A lambda function does not have a name and is an immediately invoked function. It can accept any number of arguments but returns only one expression, unlike regular functions.

How you can train a model to learn and predict unseen data? The reason why machine learning models in general are becoming smarter is due to their dependency on using labeled data to help them discern between two similar objects. However, without these labeled datasets, you will encounter major obstacles when creating the most effective and trustworthy machine-learning model. Deep learning has been widely used to solve tasks such as Computer vision using supervised learning. However, as with many things in life, it comes with restrictions.

The reason why machine learning models in general are becoming smarter is due to their dependency on using labeled data to help them discern between two similar objects. However, without these labeled datasets, you will encounter major obstacles when creating the most effective and trustworthy machine-learning model. Deep learning has been widely used to solve tasks such as Computer vision using supervised learning. However, as with many things in life, it comes with restrictions. Supervised classification requires a high quantity and quality of labeled training data in order to produce a robust model.

There are many posts on KDnuggets covering the explanation of key terms and concepts in the areas of Data Science, Machine Learning, Deep Learning, Big Data, etc. In fact, it's one of the tasks that KDnuggets takes quite seriously: introducing and clarifying concepts in the minds of new and seasoned practitioners alike. In many of these posts, concepts and terminology are often expounded upon and fit into The Big Picture, sometimes miring down the key concept in exchange for defining some greater notion. This is the first in a series of such posts on KDnuggets which will offer concise explanations of a related set of terms (machine learning, in this case), specifically taking a no-frills approach for those looking to isolate and define. So, let's start with a look at machine learning and related topics.

You probably already know about artificial intelligence (AI) and how it's being used more and more in our everyday lives. From the recommendation algorithms used by Netflix to the self-driving cars being developed by Tesla, AI is slowly but surely becoming a part of our world. But what you might not know is that there are many limitations in the current process of developing AI technology. Most AI systems are "dumb" in the sense that they can only be trained to do one specific task. For example, there might be a different algorithm for recognizing faces, another for understanding language, and yet another for driving a car.