[…] AI learns primarily through massive datasets and extensive simulations, regardless of the application.
Now, researchers from Duke University and the Army Research Laboratory have developed a platform to help AI learn to perform complex tasks more like humans. Nicknamed GUIDE for short
[…]
“It remains a challenge for AI to handle tasks that require fast decision making based on limited learning information,” […]
“Existing training methods are often constrained by their reliance on extensive pre-existing datasets while also struggling with the limited adaptability of traditional feedback approaches,” Chen said. “We aimed to bridge this gap by incorporating real-time continuous human feedback.”
GUIDE functions by allowing humans to observe AI’s actions in real-time and provide ongoing, nuanced feedback. It’s like how a skilled driving coach wouldn’t just shout “left” or “right,” but instead offer detailed guidance that fosters incremental improvements and deeper understanding.
In its debut study, GUIDE helps AI learn how best to play hide-and-seek. The game involves two beetle-shaped players, one red and one green. While both are controlled by computers, only the red player is working to advance its AI controller.
The game takes places on a square playing field with a C-shaped barrier in the center. Most of the playing field remains black and unknown until the red seeker enters new areas to reveal what they contain.
As the red AI player chases the other, a human trainer provides feedback on its searching strategy. While previous attempts at this sort of training strategy have only allowed for three human inputs — good, bad or neutral — GUIDE has humans hover a mouse cursor over a gradient scale to provide real-time feedback.
The experiment involved 50 adult participants with no prior training or specialized knowledge, which is by far the largest-scale study of its kind. The researchers found that just 10 minutes of human feedback led to a significant improvement in the AI’s performance. GUIDE achieved up to a 30% increase in success rates compared to current state-of-the-art human-guided reinforcement learning methods.
[…]
Another fascinating direction for GUIDE lies in exploring the individual differences among human trainers. Cognitive tests given to all 50 participants revealed that certain abilities, such as spatial reasoning and rapid decision-making, significantly influenced how effectively a person could guide an AI. These results highlight intriguing possibilities such as enhancing these abilities through targeted training and discovering other factors that might contribute to successful AI guidance.
[…]
The team envisions future research that incorporates diverse communication signals using language, facial expressions, hand gestures and more to create a more comprehensive and intuitive framework for AI to learn from human interactions. Their work is part of the lab’s mission toward building the next-level intelligent systems that team up with humans to tackle tasks that neither AI nor humans alone could solve.
Source: Training AI through human interactions instead of datasets | ScienceDaily
In 2020 something like this was done as well: Researchers taught a robot to suture by showing it surgery videos
Robin Edgar
Organisational Structures | Technology and Science | Military, IT and Lifestyle consultancy | Social, Broadcast & Cross Media | Flying aircraft