Mind Games: Are Imperfect Simulations Useful for Testing Artificial General Intelligence?

As artificial intelligence (AI) systems become more capable, there is growing interest in developing tests to measure progress towards artificial general intelligence (AGI). One approach is to create simulated environments, or “mind games”, where an AI can demonstrate human-level intelligence across a variety of tasks. However, developing simulations that fully capture the complexity of the real world remains an immense challenge. This raises the question – can imperfect simulations still provide useful benchmarks for AGI?

The Promise and Limitations of AI Simulations

In recent years, simulations and games have emerged as popular platforms for training and testing AI systems. Environments like OpenAI Gym provide spaces where algorithms can learn to play games like chess or Go, control robotic hands, or navigate mazes. Other tools like AI Safety Gridworlds and PsychLab focus on modeling aspects of human psychology and capabilities.

The appeal of simulations is clear. They provide:

• Simplified environments to isolate and test specific capabilities.
• Faster iteration compared to experiments in the physical world.
• Safe testing spaces where AI strategies can be evaluated without real-world risks.
• Interactive frameworks where algorithms can learn through trial-and-error experience.

However, even the most advanced simulations fall far short of replicating the messiness and complexity of the real world. Key limitations include:

• Narrow focus areas: Most simulations only model small slices of the world.
• Simplified physics: Realistic physics is computationally expensive to simulate.
• Limited sensory inputs: Vision, sound, touch – our rich sensory experience is hard to model.
• Scripted interactions: Real-world ambiguity and variability is hard to capture.
• Reliance on human design: Building creative, unbiased tests for general intelligence is hugely challenging.

While striving for perfect simulations may be unrealistic, the key question is – can we create useful approximations to measure and drive progress?

Benchmarking AI Using Imperfect Games

Despite their limitations, imperfect games and simulations can still provide insight into AI capabilities:

Modelling Distinct Cognitive Abilities

Narrow simulations allow us to benchmark AI systems on specific facets of intelligence like:

• Memory – e.g. remembering lengthy stories or complex rules.
• Reasoning – e.g. solving math problems or navigating social dilemmas.
• Communication – e.g. comprehending questions and responding appropriately.

Though narrow, focused benchmarks are practically valuable and pushing state-of-the-art in these areas brings us closer to AGI.

Providing Interactive Environments

Unlike static benchmarks, interactive simulations allow:

• Open-ended exploration – AI agents can invent new strategies and behaviors.
• Repeated practice – agents can acquire skills and knowledge through experience.
• Closed feedback loops – simulations provide feedback to guide learning.

The opportunity for experiential learning is key for developing general intelligence.

Modelling Emergent Complexity

Interesting complex dynamics can emerge even from simple game mechanics. For example, Go has relatively simple rules but the game still confounds the world’s best players.

Likewise, seemingly simple physics simulations exhibit rich emergent complexity. AI researchers are actively exploring using such simulations as challenging benchmarks.

Stepping Stones to More Complex Tests

Mastering current game benchmarks allows researchers to progress to more complex domains, e.g.:

• From board games to multiplayer videogames.
• From controlling robot hands to full-body planning.
• From static images to interactive visual environments.

Like curriculum learning for humans, staged benchmarks allow gradual progress towards more advanced tests of intelligence.

Key Criteria for Useful AI Benchmarks

Useful simulations for benchmarking AGI progress should:

• Isolate distinct cognitive abilities – Focus benchmarks on specific facets of intelligence.
• Provide interactive environments – Allow open-ended learning through experience.
• Exhibit emergent complexity – Simple mechanics can lead to rich dynamics.
• Enable incremental scaling – Mastering current benchmarks unlocks more complex ones.

Additionally, ideal simulations should:

• Model key real-world dynamics – Physics, objects, agent interactions, etc.
• Capture diverse situations and tasks – Test generalizability beyond narrow use cases.
• Require explainable actions – Assess whether behavior is reasonable/ethical.
• Measure human-like performance – Provide reference points to compare AI capabilities.

No single benchmark will fully capture general intelligence. However, suites of complementary games and simulations offer stepping stones towards evaluating progress in AI research.

Promising Avenues for Developing AI Benchmarks

Many research avenues hold promise for yielding useful benchmarks on the path towards AGI:

Physics Simulators

Advances in simulating real-world physics will enable testing manipulation, construction, and mechanics in increasingly diverse scenarios. Examples:

• MuJoCo – Multi-joint dynamics with contact.
• AI2-THOR – Indoor scenes with interactive objects.
• NVIDIA Omniverse – High-fidelity multi-GPU simulation.

Multi-Agent Environments

Testing social intelligence requires simulations with multiple interacting agents. Examples:

• NetHack Learning Environment – ASCII game with diverse creatures.
• Social Physics – Crowd simulations using psychological models.
• Hide&Seek – Environment for modeling theory of mind.

Human-in-the-Loop Testing

AI assistants that interact with real humans pose new challenges. Platforms like Anthropic’s Claude enable natural language interaction at scale.

Procedurally Generated Content

Algorithmically generating new game content (levels, puzzles, characters, dialogue, etc) provides endless novelty to test generalizability. Examples:

• Elder Scrolls Arena – Generated dungeons from 1993.
• No Man’s Sky – Quadrillion+ planets generated procedurally.
• GPT-3 – Text/code generation with deep learning.

Adversarial Testing Environments

Testing AI agents against adversarial opponents can reveal blindspots. Examples:

• AlphaStar League – Pitting AI vs human pros.
• Debate platforms – Argument self-play to stress test reasoning.
• Red teaming – Practice anticipating worst-case scenarios.

Competitions and adversarial peer testing will grow increasingly important for benchmarking AGI safety.

Key Roles for Imperfect Simulations in AGI Research

While we are still far from simulations that can comprehensively test general intelligence, imperfect games and virtual environments can already accelerate AGI progress in crucial ways:

• Training AI assistants – Interactive simulations provide critical practice on real-world skills like communication and problem-solving.
• Stress testing systems – Simulations enable safe experimentation to probe the limits of current capabilities.
• Benchmarking progress – Games benchmark specific facets of intelligence, while more open environments test generalizability.
• Developing theory – Virtual experiences inform theories and model development of cognition and learning.
• Evaluating safety – Simulations provide crucial tools for testing AI alignment, interpretability, and robustness.
• Engaging the public – Experiences like chatting with AI assistants make cutting-edge research accessible.

Though a perfect facsimile of the world may never exist, well-designed simulations will provide invaluable environments for unlocking artificial general intelligence. Steady advances across enough complementary simulations and benchmarks hold the key to measuring and achieving AGI in the years ahead.

Frequently Asked Questions

Are current AI benchmarks like AlphaGo useless for testing general intelligence?

No – games like chess and Go still provide measurable milestones for progress. Mastering them shows concrete improvements in specialized reasoning capabilities. The key is to sustain momentum beyond any single benchmark, since none fully tests general intelligence.

Can’t we just test AI directly in the complex real world?

Testing AI safety in real-world environments is extremely risky and limiting. Simulations allow extensive testing with less risk. They provide complete observability and control to safely stress test systems in a wide variety of scenarios.

What level of simulation fidelity is needed to properly test artificial general intelligence?

No consensus threshold exists. Useful approximations likely range from simple 2D games to near-photorealistic 3D environments – and perhaps eventually blending synthetic and real data. The key is filling gaps with complementary benchmarks while steadily increasing complexity.

Can simulations encourage development of artificial general intelligence that is detached from reality?

It’s a valid concern – but the flexibility of simulations also enables studying how virtual experience transfers to real physical environments. DevelopingTests that require realistic reasoning and teach human values are important to keep AGI grounded.

How expensive and time-consuming is it to develop high-fidelity simulations?

The costs are dropping rapidly. Procedural generation and commodified cloud simulation services enable quickly iterating complex environments. But developing rigorous benchmarks and metrics still requires ample research. Collaboration between tech companies, researchers and regulators is key.

If an AI excels in a wide variety of games and simulations, does that truly indicate general intelligence?

Not conclusively. Successful benchmarks increase confidence and provide stepping stones, but reasonable doubts remain until systems can robustly handle the open-ended complexity of the real world. Safely empirical testing in reality will be the ultimate proof. But we’re not there yet.

Conclusion

Games and simulations are invaluable tools for guiding and measuring progress towards artificial general intelligence. Even imperfect virtual environments can isolate cognitive abilities, test emergent dynamics, and provide critical interactive experiences. Truly comprehensive tests of AGI remain beyond current capabilities, but steady advancement across suites of complementary benchmarks offers a pragmatic path forward. Imperfect simulations will enable breakthroughs in AI safety research and accelerate our journey towards beneficial artificial general intelligence.