Human Involvement Helps Researchers Perfect New Algorithms to Train Robots

Many underestimate the role of humans in successful deployment of AI solutions. Alegion engine produces AI training data and enables content moderation, sentiment analysis, data enrichment, tagging, categorization, and more.

By Alegion. Sponsored Post.

Human + Robot There are plenty of misconceptions in the business community about the role that humans play when it comes to the successful deployment of an artificial intelligence initiative. Most underestimate that role despite mounting evidence to the contrary.

For example, consider the recent news that researchers at the U.S. Army Research Laboratory (ARL) and the University of Texas at Austin (UT) have developed new techniques for robots or computer programs to learn how to perform tasks by interacting with a human instructor.

Together, they considered a specific case where a human provides real-time feedback in the form of critique, spawning a new algorithm called Deep TAMER.

It is an extension of TAMER that uses deep learning -- a class of machine learning algorithms that are loosely inspired by the brain to provide a robot the ability to learn how to perform tasks by viewing video streams in a short amount of time with a human trainer.

According to Army researcher Dr. Garrett Warnell, the team considered situations where a human teaches an agent how to behave by observing it and providing critique, for example, "good job" or "bad job" -similar to the way a person might train a dog to do a trick. Warnell said the researchers extended earlier work in this field to enable this type of training for robots or computer programs that currently see the world through images, which is an important first step in designing learning agents that can operate in the real world.

Many current techniques in artificial intelligence require robots to interact with their environment for extended periods of time to learn how to optimally perform a task. During this process, the agent might perform actions that may not only be wrong, like a robot running into a wall for example, but catastrophic like a robot running off the side of a cliff. Warnell said help from humans will speed things up for the agents, and help them avoid potential pitfalls.

About Alegion
Alegion is an Austin-based technology company with a software platform that generates optimal training data for AI and Machine Learning (ML) initiatives.

Alegion accomplishes this with a proprietary software platform, which produces "ground truth," or exemplary AI Training Data, and digital content. Alegion's engine also enables other services, such as content moderation, sentiment analysis, data enrichment, tagging, and categorization. In addition, the company helps companies during and after deployment of the AI solution by validating algorithms and developing rules for the handling of exceptions.

These capabilities lead to greater quality control of AI and ML initiatives at the outset as well as speedier, more efficient deployments.

The company's leadership team is comprised of entrepreneurs, technology veterans, and community leaders, who bring decades of experience at Fortune 100 and emerging startups. They have seen the rise of cloud and the on-demand marketplace and understand its impact on the traditional labor paradigm.

Founded in 2011, Alegion has provided solutions to dozens of Fortune 500 companies and public-sector entities, including Charles Schwab, Conde Nast, UBS and the State of Texas, to name a few.

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