About: ARTIFICIAL INTELLIGENCE (AI)
& MACHINE LEARNING
For many of us, being asked to list examples of Artificial Intelligence (AI) conjures the illusory whispers of Siri and Alexa. Today’s tech enthusiasts might also cite AI applications as ingenious as self-driving cars, immersive gaming, and new modes for treating cancer. The truth is, most of the time we don't even realize when AI is at work. From Netflix’s brilliant “top picks for you” to the facial recognition software that tags our friends in Facebook photos, AI is working to make life easier.
No matter the application, at the heart of AI is the following capability: AI perceives its environment and takes action to achieve a goal. And although AI technology has yet to generate humanoid friends like C3PO of Star Wars, or even foes like Ava of Ex Machina… one thing is for sure, AI can perform certain tasks better than humans can.
What if you had an AI virtual assistance that could help:
Manage data and inform mission-critical decisions
Reduce uncertainty about the future of your business
Determine market fit of products
Improve team talent and performance
AI received some traction in the 1940s but was often considered a “harebrained scheme” publicly until the last few decades. Why? Major improvements in computing technology and mathematical applications have empowered AI in the 21st century. Advancements in hardware are enabling us to store and manage an infinite stream of information. Take the gaming industry for example. Early AI could play amateur level checkers, and the programming was hand-coded and based on simple mathematical probabilities. Fast forward to today’s AI that can win Go, a strategic game that requires much more than math. It requires complex pattern recognition and a human-like “intuitive sense.”
To beat world-class Go players, AlphaGo AI draws on an artificial neural network where millions of parameters are continually adjusting to improve the computer’s knowledge. In this sense, the program is mimicking the patterns of a human brain and learning from its experiences. This sort of complex thought process includes two subsets of AI: machine learning and deep learning.
“Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”
Machine Learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. So rather than using old-school software that follows a set of predetermined rules to accomplish a certain task, the machine reviews data and learns on its own how best to perform the task. Machines are able to find patterns in complex data sets that would otherwise be missed by human eyes. Thus, they are extremely useful for solving problems and making decisions.
Example Data Representations:
Deep Learning goes even further by parsing layers upon layers of data representations and assessing the information with multiple levels of computing approaches. Deep Learning is often the driving process in the most human-like AI applications.
AI for Business
AI driven by deep/machine learning is adept at interpreting complex data and making decisions. Consider what this means for how we run business. AI can drive a more intelligent approach for every activity while assessing potential clients and target markets. The ways in which AI can already impact your business are significant, and the field of AI is growing dramatically.
Synigy is the first to apply AI and Deep Machine Learning
across the entire sales cycle and at all strategic and tactical levels.
Video: Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
What is Machine Learning?
Why does it matter that Google's DeepMind computer has beaten a human at Go?
AI and Deep Learning in 2017 – A Year in Review