Artificial Intelligence and Machine Learning
Machine Learning is a field within Artificial Intelligence that leverages various techniques to extract meaning from data and predict future outcomes based on that data.
So why should you be interested in something as complex as machine learning? Easy. Because it isn’t as difficult to get started as it used to be.
The growing interest in AI and machine learning and implementation of applications that leverage ML continue to accelerate due to the availability of cloud-based services that provide quick access to complex functionality.
For example, most cloud providers have services available for image recognition, neural network implementation, and deployment of custom models. These services take the complexity out of deploying models across large compute clusters. The bulk of the work is now focused on your data and how you want to use it to extract information.
Artificial Intelligence Applications
The vast quantity of data that is collected by most organizations can be put to work, in many cases. Machine Learning can help you:
- Discover patterns in data very quickly
- Apply Predictive Techniques to help guide a customer journey or fulfillment path
- Recognize sentiment from text input such as reviews and emails
- Locate objects in images or video streams
- Natural language Processing and speech generation
AI and Machine Learning in Industries
Media and Entertainment
The first example that usually comes to mind is Netflix. Anyone who has used Netflix has seen recommendations pop up. And if you’ve been a long-time subscriber, you’ve probably noticed that the recommendations become more accurate with time. Other uses in entertainment run from audio signature recognition to matching artists with the most appropriate venues.
Machine learning techniques are starting to gain serious momentum in the healthcare world due to the large number of sensors and wearables that are available. An Apple Watch, for example, can track steps, heartbeats, workouts, and other physical data that can be used to predict health outcomes in near real-time. Convolutional Neural Networks can be used to help radiologists flag images that may contain an anomaly that could be missed by the naked eye.
The ubiquitous use case in financial services has been fraud detection. Credit card companies utilize your purchase history to spot a purchase that seems out of character and send you an alert. Another popular use is to employ neural networks to determine which investments are likely to pay off.
Online Commerce and Retail
The ability to spot purchasing trends, suggest add-on products, and use predictive analytics to help improve site merchandising. Techniques used by retailers, whether range from simple regression algorithms to random forests to Recurrent Neural Networks and beyond. Understanding the right technique to use and how to properly analyze and prepare the data is critical to success.
Internet of Things
The number of devices connected to the internet that perform a discrete function is rising exponentially. IoT devices that control machinery, track movement, log vital health information — to name a few — generate reams of detailed data that can be mined for intelligence. Coupled with machine learning techniques, this data can help companies determine the best time to perform preventive maintenance on equipment, help optimize transportation and logistics activity based on road and weather conditions, or keep a patient advised of their health status.
Types of Machine Learning
Supervised Learning techniques start with historical data outcomes. Take the Netflix example from earlier: A person’s review of a movie can be compared with attributes such as movie genre, running time, actors, director, format and other data to learn what types of movie attributes result in a person giving a movie a positive review. That data, called the training set, can be used to construct a model that predicts what type of review a person would give a film when run against the attributes of movie that they haven’t watched.
This type of machine learning algorithm is used to spot trends or commonalities in data where there is no historical label as in the supervised learning approach. In retail, a machine learning algorithm called k-Means can be used to group consumers into classifications that help identify those with common traits. For example, k-Means can be used to find your most valuable customers, your VIPs, from existing data without the need to provide a training set.
This machine learning approach is centered around multi-layer neural networks that can operate on massive data sets, learning outcomes based on training data. Training these deep learning models requires knowledge of the data, statistical expertise, and knowledge of deep learning frameworks such as TensorFlow. Knowing how to build a multi-layer neural network and configuring its hyper-parameters is challenging and TechCXO’s experience can help. Deep Learning models are highly empirical and can require multiple iterations of model construction and testing before the most effective model can be deployed.
Machine Learning Services
Data Analysis and Preparation
Leverage our decades of collective experience in machine learning to examine and uncover the most impactful features in a data set. We know how to dig into your data and help drive enormous value for your business and your customers.
Machine Learning Development and Programming
Our partners can work with your team to determine the best possible algorithms and models to extract value from your data. In addition, we can codify those algorithms and assist you in deploying them for use in your enterprise’s applications.
Deep Learning and Neural Network Design and Deployment
Whether an RNN, CNN or other type of neural network, we can help you design the most accurate and performant model to assist with your deep learning needs.