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AI research is fueled by the pursuit of ever-greater sophistication, which includes training systems to think and behave like humans. The end goal? Who knows. The goal for now? To create autonomous, generalized AI agents capable of performing a wide range of tasks. This concept is usually termed artificial general intelligence (AGI) or superintelligence. It’s challenging to pinpoint precisely what AGI entails because there’s virtually zero consensus on what ‘intelligence’ is, or indeed, when or how artificial systems might achieve it. Skeptics even believe that AI, in its current state, can never truly obtain general intelligence. Professor Tony Prescott and
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Schematic of the FNI-RL framework: (a) Brain-inspired RL systems. (b) Adversarial imagination module simulating amygdala function. (c) Fear-constrained actor-critic mechanism. (d) Agent-environment interaction loop. Source: ResearchGate.
To put this system through its paces, the researchers tested it in a series of high-fidelity driving simulations featuring challenging, safety-critical scenarios:
- Sudden cut-ins and swerves by aggressive drivers
- Erratic pedestrians jaywalking into traffic
- Sharp turns and blind corners with limited visibility
- Slick roads and poor weather conditions
Across these tests, the FNI-RL-equipped vehicles demonstrated remarkable safety performance, consistently outperforming human drivers and traditional reinforcement learning (RL) techniques to avoid collisions and practice defensive driving skills.
In one striking example, the FNI-RL system successfully navigated a sudden, high-speed traffic merger with a 90% success rate, compared to just 60% for a state-of-the-art RL baseline.
It even achieved safety gains without sacrificing driving performance or passenger comfort.
In other tests, the researchers probed the FNI-RL system’s ability to learn and generalize defensive strategies across driving environments.
In a simulation of a busy city intersection, the AI learned in just a few trials to recognize the telltale signs of a reckless driver sudden lane changes, aggressive acceleration – and pre-emptively adjust its own behavior to give a wider berth.
Remarkably, the system was then able to transfer this learned wariness to a novel highway driving scenario, automatically registering dangerous cut-in maneuvers and responding with evasive action.
This demonstrates the potential of neurally-inspired emotional intelligence to enhance the safety and robustness of autonomous driving systems.
By endowing vehicles with a “digital amygdala” tuned to the visceral cues of road risk, we may be able to create self-driving cars that can navigate the challenges of the open road with a fluid, proactive defensive awareness.
Towards a science of emotionally-aware robotics
While recent AI advancements have relied on brute-force computational power, researchers are now drawing inspiration from human emotional responses to create smarter and more adaptive artificial systems.
This paradigm, named “bio-inspired AI,” extends beyond self-driving cars to fields like manufacturing, healthcare, and space exploration.
There are many exciting angles to explore. For example, robotic hands are being developed with “digital nociceptors” that mimic pain receptors, enabling swift reactions to potential damage.
In terms of hardware, IBM’s bio-inspired analog chips use “memristors” to store varying numerical values, reducing data transmission between memory and processor.
Similarly, researchers at the Indian Institute of Technology, Bombay, have designed a chip for Spiking Neural Networks (SNNs), which closely mimic biological neuron function.
Professor Udayan Ganguly reports this chip achieves “5,000 times lower energy per spike at a similar area and 10 times lower standby power” compared to conventional designs.
These advancements in neuromorphic computing bring us closer to what Ganguly describes as “an extremely low-power neurosynaptic core and real-time on-chip learning mechanism,” key elements for autonomous, biologically inspired neural networks.
Combining nature-inspired AI technology with architectures informed by natural emotional states like fear or curiosity could thrust AI into an entirely new state of being.
As researchers push those boundaries, they’re not just creating more efficient machines they’re potentially birthing a new form of intelligence.
As this line of research evolves, autonomous machines might roam the world among us, reacting to unpredictable environmental cues with curiosity, fear, and other emotions considered distinctly human.
The impacts? That’s another story altogether.
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Published: 2024-07-20T19:20:23
