Artificial Intelligence garners more frontpage headlines every day. Artificial Intelligence, or AI, is the technology enabling machines to learn from experience and perform human-like tasks.
Ping-ponging between utopian and dystopian, opinions vary wildly regarding the current and future applications, or worse, implications, of artificial intelligence. Without the proper moorings, our minds tend to drift into Hollywood-manufactured waters, teeming with robot revolutions, autonomous cars, and very little understanding of how AI actually works.
This is mostly due to the fact that AI in itself is describing different technologies, which provide machines the ability to learn in an “intelligent” way.
In our coming series of blog posts, we hope to shed light on these technologies and clarify just what it is that makes artificial intelligence, well, intelligent.
How is artificial intelligence applied?
Popular misconceptions tend to place AI on an island with robots and self-driving cars. However, this approach fails to recognize artificial intelligence’s major practical application; processing the vast amounts of data generated daily.
By strategically applying AI to certain processes, insight gathering and task automation occur at an otherwise unimaginable rate and scale.
Parsing through the mountains of data created by humans, AI systems perform intelligent searches, interpreting both text and images to discover patterns in complex data, and then act on those learnings.
What are the basic components of artificial intelligence?
Many of AI’s revolutionary technologies are common buzzwords, like “natural language processing,” “deep learning,” and “predictive analytics.” Cutting-edge technologies that enable computer systems to understand the meaning of human language, learn from experience, and make predictions, respectively.
Understanding AI jargon is the key to facilitating discussion about the real-world applications of this technology. The technologies are disruptive, revolutionizing the way humans interact with data and make decisions, and should be understood in basic terms by all of us.
Machine Learning | Learning from experience
Deep Learning | Self-educating machines
Neural Network | Making associations
Cognitive Computing | Making inferences from context
Natural Language Processing (NLP) | Understanding the language
Computer Vision | Understanding images
Additional Supporting technologies for Artificial Intelligence
- Graphical Processing Units or GPUs are a key enabler of AI, providing the massive computing power necessary to process millions of data and calculations quickly.
- The Internet of Things, or IoT, is the cumulative network of devices that are connected to the internet. The IoT is predicted to connect over 100 billion devices in the coming years.
- Intelligent data processing is being optimized using advanced algorithms for faster multi-level analysis of data. This is the solution to predict rare events, comprehending systems and unique situations.
With the integration of Application Processing Interfaces or APIs, aspects of artificial intelligence can be plugged into existing software, augmenting its normal function with AI.
Artificial Intelligence is a diverse topic
As we have learned, AI is describing a set of different technologies. Each of these technologies require detailed explanation. Staying up to date and understanding the differences of these technologies is a difficult task. Keep up with the latest changes and stay tuned for our upcoming posts.
Next, we will introduce Big Data and explore the applications of artificial intelligence solutions to structuring, connecting, and visualizing large data set to accelerate insight and empower decision-making.