The recent developments in the world of artificial intelligence (AI) have sparked renewed interest in the potential and risks of artificial general intelligence (AGI). AGI has the capacity to learn and perform intellectual tasks at a level comparable to humans. Rapid advancements in AI, particularly in deep learning, have generated both excitement and concern about the emergence of AGI. Several companies are actively working on developing AGI, raising the question of whether current AI developments are leading toward AGI.
Deep learning, which is a machine learning method based on artificial neural networks, is widely used in contemporary AI, including in models such as ChatGPT. It has become popular due to its ability to handle different data types and its reduced need for pre-processing, among other benefits. Many experts believe that deep learning will continue to advance and play a crucial role in achieving AGI.
However, deep learning has its limitations. Creating models that reflect training data requires large datasets and expensive computational resources. These models derive statistical rules that mirror real-world phenomena and apply these rules to current real-world data to generate responses. The sensitivity of these rules to the uncertainty of the natural world makes them less suitable for realizing AGI. For example, the crash of a cruise Robotaxi in June 2022 could be attributed to the vehicle encountering a new situation for which it lacked training, rendering it incapable of making decisions with certainty.
Humans, who are the models for AGI, do not create exhaustive rules for real-world occurrences. Instead, humans typically engage with the world by perceiving it in real-time, relying on existing representations to understand the situation, the context, and any other incidental factors that may influence decisions. Rather than construct rules for each new phenomenon, humans repurpose existing rules and modify them as necessary for effective decision-making.
For example, when faced with a new object on a forest trail, a human would likely begin to assess the object from a distance, update information continuously, and opt for a robust decision drawn from a “distribution” of actions that proved effective in previous analogous situations. This approach focuses on characterizing alternative actions in respect to desired outcomes rather than predicting the future — a subtle but distinctive difference. Achieving AGI might require diverging from predictive deductions to enhancing an inductive “what if..?” capacity when prediction is not feasible.
Decision-making under deep uncertainty (DMDU) methods such as Robust Decision-Making may provide a conceptual framework to realize AGI reasoning over choices. DMDU methods analyze the vulnerability of potential alternative decisions across various future scenarios without requiring constant retraining on new data. They evaluate decisions by pinpointing critical factors common among those actions that fail to meet predetermined outcome criteria. The goal is to identify decisions that demonstrate robustness — the ability to perform well across diverse futures. While many deep learning approaches prioritize optimized solutions that may fail when faced with unforeseen challenges, DMDU methods prize robust alternatives that may trade optimality for the ability to achieve acceptable outcomes across many environments. DMDU methods offer a valuable conceptual framework for developing AI that can navigate real-world uncertainties.
Developing a fully autonomous vehicle (AV) could demonstrate the application of the proposed methodology. The challenge lies in navigating diverse and unpredictable real-world conditions, thus emulating human decision-making skills while driving. Despite substantial investments by automotive companies in leveraging deep learning for full autonomy, these models often struggle in uncertain situations. Due to the impracticality of modeling every possible scenario and accounting for failures, addressing unforeseen challenges in AV development is ongoing.
One potential solution involves adopting a robust decision approach. The AV sensors would gather real-time data to assess the appropriateness of various decisions — such as accelerating, changing lanes, braking — within a specific traffic scenario. If critical factors raise doubts about the algorithmic rote response, the system then assesses the vulnerability of alternative decisions in the given context. This would reduce the immediate need for retraining on massive datasets and foster adaptation to real-world uncertainties. Such a paradigm shift could enhance AV performance by redirecting focus from achieving perfect predictions to evaluating the limited decisions an AV must make for operation.
As AI evolves, there may be a need to depart from the deep learning paradigm and emphasize the importance of decision context to advance towards AGI. Deep learning has been successful in many applications but has drawbacks for realizing AGI. DMDU methods may provide the initial framework to pivot the contemporary AI paradigm towards robust, decision-driven AI methods that can handle uncertainties in the real world.
