While artificial intelligence (AI) continues to linger in popular imagination in the form of humanoid robots, in real life AI more often exists as a process enabler. Over the past several years, as costs democratized the technology, AI and related emerging technologies like machine learning (ML) and deep learning (DL) became more accessible to mid-market companies. Today, most businesses use AI in one capacity or another — streamlining work, minimizing risk, and gaining competitive insights.
These innovations are more than buzzwords. They have powerful potential to revolutionize the way your business collects, processes, and acts on data to solve the real problems facing your business.
AI, ML, and DL in the business context
To find the right AI applications for your business, it helps to understand your options.
|Machines programmed to be “smart”
|Machines that learn from experience provided by data and algorithms
|ML applied to larger data sets and using multi-layered artificial neural networks
|Smartphones, chatbots, virtual assistants
|Spam filters, online purchasing recommendations
|Alexa, Google translate, facial recognition, self-driving cars
|Example Use Case
|Configuring a CMS to deliver personalized website experiences using available data points
|Discovering patterns in data such as “customers who buy X also buy Y,” purchasing cart analysis
|Processing a large volume of unstructured data, such as images or voice recordings, to generate insights
|Machine can only act on specific rules provided
|Humans must input data parameters as a starting point
|Requires very powerful – and expensive – computational resources
How machine learning differs from AI
“ML is the science of getting computers to act without being explicitly programmed.”Stanford University
Machine learning takes a different approach to developing artificial intelligence. Instead of hand-coding a specific set of rules to accomplish a particular task, ML trains the machine using large amounts of data and algorithms that give it the ability to learn how to perform a task.
Over the years, algorithmic approaches within ML evolved from decision tree learning, inductive logic programming, linear or logistic regressions, clustering, reinforcement learning, and Bayesian networks. Currently, machine learning uses three general models:
- Supervised learning: Humans supply factors until the machine can accurately apply the distinctions (for example, defining what counts as spam to a filter).
- Unsupervised learning: The system trains itself on provided data, which is used to surface unknown patterns, as in clustering and association.
- Clustering looks for patterns of demographics in data and how they predict one another, as in targeting groups of customers with products they will likely need. Association uncovers rules that describe data, as in online book or movie recommendations based on previous purchases and purchasing-cart predictions.
- Reinforcement learning: Using complex algorithms, the system learns through trial and error toward a defined “reward” of success. Cycling quickly through mistakes or near mistakes, the machine adjusts the weight of the previous results against the desired outcome.
How deep learning works
As another method of statistical learning that extracts features or attributes from raw data sets, deep learning builds on ML frameworks. While ML requires humans to provide desired features manually, DL uses even more complex algorithms and achieves more sophisticated results without human input.
Deep learning algorithms automatically extract features for classification. This ability requires a huge amount of data to train the algorithms and ensure accurate results. To process this volume of data, DL requires specially designed, usually cloud-based computers with high-performance CPUs or GPUs.
Using multi-layered artificial neural networks inspired by the biology of the human brain — specifically the organic interconnections between neurons — deep learning trains artificial neurons to identify patterns in information to produce the desired output. Unlike the human brain, artificial neural networks operate via discrete layers, connections, and directions of data propagation.
Three common types of artificial neural networks and DL processing applications are:
- Convolutional neural networks (CNN) are deep artificial neural networks that are used to classify images, cluster them by similarity, and perform object recognition. These algorithms navigate self-driving cars and enable facial recognition, but are also used in leading-edge medical applications such as identifying tumor types.
- Generative adversarial networks (GAN) are composed of two neural networks: a generative network and a discriminative network. While GANs can be used negatively as in the creation of “deep fake” photos and video, organizations can also use GANs to create privacy-safe data pools for ML.
- Natural language processing (NLP) is the ability to analyze, understand, and generate human language, whether text or speech. Alexa, Siri, Cortana, and Google Assistant all use NLP engines, and many businesses are exploring ways to incorporate voice into their proprietary applications and digital solutions.
Make smart decisions about AI
New Era Technology provides cloud infrastructure and emerging technology solutions that accelerate your digital transformation. Our teams help businesses across a wide variety of industries uncover the best use cases for AI, and the right emerging technology solutions to meet your goals. We can help you source, clean, and integrate your data, build and train machine learning models, and iteratively test and improve your solution to maximize results.
Not sure how this might work for your business? Check out these real-world examples: