For ex: some kind of augmented drum could allow you to create and play more intricate rhythms than would normally be possible, perhaps by having ML extrapolate and combine patterns in ways that are too complex to even write (I used to play drums). It seems like the ear can hear patterns that are beyond what can be played or even created by a person.
Facebook uses machine learning and ranking models to deliver the best experiences across many different parts of the app, such as which notifications to send, which stories you see in News Feed, or which recommendations you get for Pages you might want to follow. To surface the most relevant content, it's important to have high-quality machine learning models. We look at a number of real-time signals to determine optimal ranking; for example, in the notifications filtering use case, we look at whether someone has already clicked on similar notifications or how many likes the story corresponding to a notification has gotten. Because we perform this every time a new notification is generated, we want to return the decision for sending notifications as quickly as possible.
K-Means procedure - which is a vector quantization method often used as a clustering method - does not explicitly use pairwise distances b/w data points at all (in contrast to hierarchical and some other clusterings which allow for arbitrary proximity measure). It amounts to repeatedly assigning points to the closest centroid thereby using Euclidean distance from data points to a centroid. However, K-Means is implicitly based on pairwise Euclidean distances b/w data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry. It is multivariate mean in euclidean space. Euclidean space is about euclidean distances.
One of the major issues with artificial neural networks is that the models are quite complicated. For example, let's consider a neural network that's pulling data from an image from the MNIST database (28 by 28 pixels), feeds into two hidden layers with 30 neurons, and finally reaches a soft-max layer of 10 neurons. The total number of parameters in the network is nearly 25,000. This can be quite problematic, and to understand why, let's take a look at the example data in the figure below.
Artificial intelligence (AI), machine learning (ML), and robots are the sights and sounds of science fiction books and movies. Isaac Asimov's Three Laws of Robotics, first introduced in the 1942 short story "Runaround," became the backbone for his novel I, Robot and its film adaptation (Fig. 1). Although we are still far away from achieving what movie producers and sci-fi writers have envisioned, the state of AI and ML has progressed significantly. AI software has also been in use for decades but advances in ML, including the use of deep neural networks (DNNs), are making headlines in application areas like self-driving cars.