Making Sure AI is Appropriately Incentivized


Artificial intelligence has been gaining substantial traction in recent years. The AI market is on pace to become a 190 billion dollar industry by the year 2025, and the number of jobs requiring AI-related skills has increased 4.5 times since 2013. With new advancements, the importance of creating trustworthy and reliable AI systems is at an all-time high.


Artificial intelligence or AI was designed as a form of technology meant to resemble or model human behavior closely. Although it has improved by leaps and bounds, perfecting a design that mimics humanistic interaction can be a challenge.


AI responds to its environment following a predefined set of rules or problem-solving functions, also known as algorithms. Unlike humans who rely upon logic and emotion, the capabilities of AI primarily are dependent upon the reliability of these scientific functions.


Reinforcement learning algorithms are a specific breed of mathematical equations that use machine learning to incentivize players with a reward-based structure. This type of technology is meant to train AI agents but can sometimes end up putting reward signals above all other measures of success.


Creating alternate forms of AI systems can help shape goals in a way that can't be misinterpreted by agents. Designs should strive to implement human-like characteristics and commonsense for employing decision-making tactics.


Reinforcement Learning Games

OpenAI is a research-based laboratory out of San Francisco, CA, that uses autonomous systems and artificial intelligence to benefit humanity. They created a software called Universe, designed to measure and train AI agents using reinforcement learning technology.


OpenAI created a boat racing game called CoastRunners, which incentivizes players to earn high scores. The game was designed to encourage players to win the race, but instead, the algorithms created faulty reward functions. The computer-based system used loopholes to increase scoring by repeatedly employing opportunistic strategies instead of competing against opponents.


Within the context of a video game, faulty reward functions cause little to no harm, but the behavior shows potential issues that may occur from reinforcement learning tactics. Creating systems that replicate human interaction can be challenging - often leading to unintended results.


The Solution to AI Incentivization

As previously mentioned, within the context of a video game, faulty AI reward scenarios inflict no actual damages. In real-world situations, AI missteps have the potential of leading to unintended side effects. Allocating appropriate reward functions requires constant oversight to produce reliable and predictable results.


OpenAI has been working towards reducing the rate of misspecified rewards by exploring several different methods, including:

  • Designing algorithms that avoid directly specifying a reward and instead attempt to model humans. Creating algorithms that model human behavior can help to eliminate some of these potential issues.
  • Incorporating human feedback elements into algorithm creation could prevent missteps related to reinforcement learning scenarios.
  • Creating common sense reward systems within the game that model logistical human-like strategies.

Appropriately incentivizing AI within these environments and creating human modeled behavior can help to eliminate many common issues. As advancements within the AI realm continue to increase, new systems will be developed that more closely resemble people.


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