Machine Learning Implications for Intelligence and Insights - PART 3

Introducing Artificial Intelligence and Machine Learning

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 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.

Conventional software programs are hard-coded by developers with specific instructions on the tasks they need to execute. While this works well in many situations, this type of top down instruction manual has limitations and does not evolve over time. The human programmer cannot code provisions that account for every possible state of the world. If the environment changes, the software programs will malfunction or cease to be relevant. By contrast, it is possible to create algorithms that “learn” from data and, if necessary, evolve and adapt to new circumstances without being explicitly reprogrammed. The concept underpinning machine learning is to give the algorithm “experiences” (training data) and a generalized strategy for learning, then let the algorithm identify patterns, associations, and insights from the data—in short, to train the system rather than program it.

More to come...

This was part THREE in our series, based on the article “Machine Learning Implications for Intelligence and Insights”, written by Jesper Martell, Comintelli, and Paul Santilli, Hewlett Packard Enterprise.

WEBINAR: Machine Learning Implications for Intelligence and Insights

Presenters: Jesper Martell, Comintelli & Paul Santilli, Hewlett Packard Enterprise

Thursday, October 26, 2017, 10:00am Eastern, Hosted by   SCIP

This interactive webinar describes how Machine Learning (ML) can be applied to solve intelligence problems.

1. What is a Machine Learning algorithm?
2. How can new ML/AI technologies augment our intelligence capabilities?
3. What are some of the challenges and risks of ML?

Read more and register here!