Welcome to the wiki for the program "INTEGRAT-reading-the-mind-of-the-AI-robot"

WHY INTEGRAT?

It used to be that only human experts examined data and made decisions. Now Artificial Intelligence (AI) is enabling robotic decision making in an ever-widening variety of applications. As society allows this to happen, there is a greater likelihood that these robot decisions can affect people’s lives. It makes sense, therefore, to understand the capabilities and societal implications of AI robots.

Big data is a term used to describe both the opportunities and the problems associated with so much information now available for decision making. With the advent of the Internet of Things (IoT) the impact of this huge amount of data is only growing. Actionable decisions need to be distilled from big data and AI can only go so far based on linear extrapolation. Because of this, many non-linear deep learning algorithms are being developed.

What exactly have these AI robots learned so deeply from all of this big data?

This question is very reasonable for society to ask. It is not enough to train and create a great AI robot. Many researchers are realizing that before their systems can be deployed, they must be able to prove to human experts that the robots learned the right things from the right data. This is difficult because human expert decision makers are not necessarily the same people who are good at creating robot AI. These two teams must work together in a user friendly way.

If only we could read the robot mind.

The reason INTEGRAT was created was to teach researchers how to read the robot mind, using a particular AI problem set most suited to to the task. Beyond this problem set, INTEGRAT allows researchers to learn these methods and perform hands-on laboratory experiments that are extensible to many AI applications. INTEGRAT is a teaching tool.

INTEGRAT teaches researchers how to answer the following critical "robot mind-reading" questions:

Is the data selected and are the extracted features any good?

Is the training set as a whole any good?

Will it work in the field?

What if an ambiguous example can have more than one correct classification?

What is the robot doing at this moment?

Additional topics in this Wiki include:

The INTEGRAT Training Example - Phoneme Recognition

The INTEGRAT Software

The INTEGRAT Patents (now expired)

I hope these methods will help to improve the understanding by individuals and society of the capabilities and societal implications of conventional and emerging technologies, including intelligent systems.

Best Regards, Paul Alton Nussbaum, Ph.D.