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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:
The INTEGRAT Training Example - Phoneme Recognition
INTEGRAT Patents (now expired)
US Patent 5864803
Question: HOW CAN WE ASSIGN MORE THAN ONE CORRECT CLASSIFICATION WHEN EXPERTS CANNOT AGREE?
Because data sets can be very large and continuously streaming in from many sources, AI robot creators will chop up the data into segments that can be presented to identify a decision. Sometimes different human experts will see the same data segment and identify different decisions. Similarly, sometimes a human expert will examine one data segment and come up with two possible decisions, each of which are valid. INTEGRAT accounts for this situation in all stages of the creation and testing of the data and the AI robot.
Independent Claims 1 and 6 - Provide a friendly user interface to present data segments to the human expert and let them change the segmentation algorithm, or manually change a segment, if it could yield more than one decision.
Independent Claims 13, 17, and 20 – Provide a mechanism whereby multiple “correct” decisions can be assigned to one data segment, as well as AI training algorithms to support this ambiguity. Finally, allow the AI robot to come up with a single “best” decision when presented with ambiguous data, while also alerting users to “second best” decisions, and so on.
US Patent 5867816
Question: WILL THE ROBOT FUNCTION JUST AS WELL WHEN THE DATA SEGMENTATION, FEATURE EXTRACTION, AND DECISION IDENTIFIER ARE FULLY AUTOMATED?
The AI Robot is not useful if it requires a human expert to accompany the device when it is deployed in the field for day to day use. The robot must work on its own. Nevertheless, a quality assurance mechanism is required that makes the robot demonstrate it is working in “fully automatic” mode while a human expert grades it on the decisions the AI has made.
Independent Claims 1, 11, and 29 – The human expert examines and modifies the segmentation, features, and decision identifications to improve automated functioning.
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.