AI

Consumers use more AI than they realize. While only 33% think they use AI-enabled technology, 77% actually use an AI-powered service or device, according to Pega

Convolutional Neural Network

Taking what humans do and scaling it up millions of times in neural networks has enormous implications for your organization. You could route all your customer service calls to the right location. You could have machines look through millions of patient x-rays, listen to your customer requests, or even find new business opportunities.

Deep Learning

Deep learning uses artificial neural networks which have been around for a while. However, it uses some new ways of constructing the networks and utilizes advances in hardware to gain some very impressive results. Google, for example, is using deep learning in its speech-to-text system. If you're curious, head over to Google and try it out.

Natural Language Processing

Natural language processing is a field concerned with the ability of a computer to understand, analyze, manipulate, and potentially generate human language. This can be English, Spanish, French, anything like that. E.g. Spam filter, determining whether an incoming email is spam or not, based on the content of the body, the subject, and maybe the email domain.

Machine Learning

Machine learning is different. Here you're not creating detailed instructions. Instead, you're giving the computer the data and tools it needs to study the problem and solve it without being told what to do. Then you're giving the computer the ability to remember what it did so it can adapt, evolve, and learn. That's not that much different from how humans learn.

Recommendation System

Recommendation systems are everywhere.

If you've ever looked for books on Amazon or browsed through posts on Facebook, you've used the recommendation system without even knowing it. With online shopping, consumers have nearly infinite choices.

No one has enough time to try every product for sale. Recommendation systems play an important role in helping users find products and content they care about without having to spend all their time digging through things they won't like.

Behind the scenes, these systems are powered by a recommender function. A recommender function takes in information about the user and predicts the rating the user would give the product.

If you can predict the user's rating for a product before the user even sees the product, that's very powerful. That means you can show the user only the things they would like the best and not waste their time with products they won't care about.

Facial Recognition

50 years ago, facial recognition would have been impossible. The technology and algorithms for facial recognition were just being created. Computers were barely powerful enough to run the algorithms, and it could take hours to recognize one face. Today, we have real-time performance and use it as a security device on some mobile computers.

Robotics

One of the best ways to connect up with humans is to join us in the physical world. Robotics is about having machines work on physical tasks. It's been around for thousands of years. Inventors have long been fascinated with finding ways to make machines behave like living objects. Up until recently, robotics has been limited to creating highly specialized machines.

AI