The advent of big data, cloud computing, and machine learning are revolutionizing how many professionals approach their work. These technologies offer exciting new ways for engineers to tackle real-world challenges. But with little exposure to these new computational methods, engineers lacking data science or experience in modern computational methods might feel left behind.
Artificial Intelligence and Machine Learning
While AI is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You’ll see how these two technologies work, with examples and a few funny asides.
Machine learning and deep learning are subfields of AI
As a whole, artificial intelligence contains many subfields, including:
- Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude.
- A neural network is a kind of machine learning inspired by the workings of the human brain. It’s a computing system made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
- Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
- Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
- Natural language processing is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.
While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks “smartly.”
Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems.
How big data plus AI produced smart applications
Remember the big data hoopla a few years ago? What was that about? Advancements in computer processing and data storage made it possible to ingest and analyze more data than ever before. Around the same time, we started producing more and more data by connecting more devices and machines to the internet and streaming large amounts of data from those devices.
With more language and image inputs into our devices, computer speech and image recognition improved. Likewise, machine learning had much more information to learn from.
All of these advancements brought artificial intelligence closer to its original goal of creating intelligent machines, which we’re starting to see more and more in our everyday lives. From recommendations on our favorite retail sites to auto generated photo tags on social media, many common online conveniences are powered by artificial intelligence.
Where are we today with AI?
With AI, you can ask a machine questions – out loud – and get answers about sales, inventory, customer retention, fraud detection and much more. The computer can also discover information that you never thought to ask. It will offer a narrative summary of your data and suggest other ways to analyze it. It will also share information related to previous questions from you or anyone else who asked similar questions. You’ll get the answers on a screen or just conversationally.
How will this play out in the real world? In health care, treatment effectiveness can be more quickly determined. In retail, add-on items can be more quickly suggested. In finance, fraud can be prevented instead of just detected. And so much more.
In each of these examples, the machine understands what information is needed, looks at relationships between all the variables, formulates an answer – and automatically communicates it to you with options for follow-up queries.
We have decades of artificial intelligence research to thank for where we are today. And we have decades of intelligent human-to-machine interactions to come.