Introducing Artificial Intelligence and Machine Learning

Disrupting the Intelligence World
October 9, 2017
22nd Annual SCIP European Summit in Cascais, Portugal
October 18, 2017- Business Translator
- Business Translator
- Business Translator
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- Cognitive Computing
- Information Discovery
- Information Discovery
- Information Discovery
- Information Discovery
- Information Discovery
- Information Discovery
- Information Discovery
- Information Discovery
- Knowledge Management
- Knowledge Management
- Knowledge Management
- Knowledge Management
- Knowledge Management
- Knowledge Management
- Knowledge Management
- Knowledge Management
We all know and feel that the volume of available information has grown exponentially in recent years and will continue to do so. However, at the same time the computational power and storage capabilities of machines are increasing fast and there are more sophisticated algorithms created to sift through all this information.
We have arrived at a point where we are starting to ask ourselves if machines can actually think for us. While the concept of Artificial Intelligence (AI) has been around for a long time (see image below), these recent advances in algorithms, processing power and exponential growth in available information are suddenly enabling the creation of machines with unprecedented capabilities.
Image: Machine Learning can be seen as a subset of Artificial Intelligence
While these technologies might not actually “think” in the way we mean by thinking, machines are beginning to perform tasks that have always been thought to be the sole domain of humans – and sometimes even being superior to humans at performing some of the tasks.
Suddenly, meaningful AI does not seem so far away. Artificial Intelligence is a broad field in computer science that simulates aspects of human intelligence, while Machine Learning refers to a more specific process of using algorithms to accomplish a specific task.
Machine Learning is often expressed in different ways (e.g. Artificial Intelligence, predictive analytics, data-mining, deep learning, forecasting, Natural Language Processing and Simulations), but basically it is all about algorithms that analyze data to find models that can be used to predict outcomes or understand context with significant accuracy and improve or “learn” as more information is made available — in short, to train the system rather than program it.
This was part three in our new blog series, based on the article “Machine Learning Implications for Intelligence and Insights”, written by Jesper Martell, Comintelli, and Paul Santilli, Hewlett Packard Enterprise.