The Path to AGI
We stand at a precipice, gazing into a fog-blanketed valley. I see an interesting path across it that I am personally developing AI systems to evaluate. More on that in a sec, let’s finish painting the picture.
The vibe, if you will.
The valley in front of us represents Artificial General Intelligence – a machine intelligence capable of understanding, learning, and applying knowledge across any domain, just like a human being.
We've sent probes into the mists, mostly in the form of increasingly sophisticated, narrow AI, but the true peaks of general intelligence remain elusive. We have a map, sketched with the lines of mathematical principles and dotted with the successes of specific algorithms, but the route remains unclear.
Heck, we aren’t exactly sure what even constitutes intelligence, artificial or otherwise, in the first place!
One thing is becoming increasingly certain, however: no single, isolated approach will get us there.
AGI isn't about mastering one game, one language, or one sensory input. It's about the synthesis of all of these, the ability to learn and adapt to a constantly shifting, unpredictable world. And that synthesis, I believe, will require a powerful fusion of approaches, specifically, the pairing of advanced versions of Generative Adversarial Networks (GANs) that leverage Reinforcement Learning (RL), augmented by a deep understanding of spatial and temporal relationships.
Why these specific technologies?
Because they represent two fundamental aspects of intelligence: understanding the world and interacting with it.
GANs: The Artists of Internal Representation
Think of GANs as internal artists.
They consist of two competing neural networks: a Generator and a Discriminator. The Generator is the creative force, attempting to create synthetic data – images, sounds, text – that resemble real-world data. The Discriminator is the critic, constantly evaluating the Generator's output, trying to distinguish between the real and the fake.
This adversarial dance, this constant push and pull, forces the Generator to become increasingly adept at capturing the underlying structure and distribution of the real world. It's not just memorizing; it's learning the rules that govern how things look, sound, and behave. The Discriminator, in turn, becomes an incredibly discerning judge, capable of identifying subtle nuances and deviations from reality.
Now, extend this concept beyond static images. Imagine Spatio-Temporal GANs. These are GANs designed not just to generate a single snapshot, but a sequence of events, a story unfolding over time. Think of generating a video of a ball bouncing realistically, or predicting the movement of clouds in the sky, or even simulating the complex interactions of molecules in a chemical reaction.
This is where things get truly interesting. Spatio-Temporal GANs force the Generator to internalize not just the static appearance of things, but also the dynamics of the world – the laws of physics, the patterns of motion, the cause-and-effect relationships that govern how things change over time. They are learning, in a sense, a compressed, internal model of reality.
By the way, time is a sequence… ST-GANs provide extremely important capabilities which give AI systems the ability to reason through time. You're absolutely right. The explicit incorporation of time as a fundamental dimension is crucial, and Spatio-Temporal GANs offer a powerful framework for doing so. Traditional GANs, often focused on static images, are like snapshots of a single moment. ST-GANs, on the other hand, deal with sequences, with the unfolding of events over time. This is not merely a cosmetic difference; it fundamentally alters the kind of knowledge the AI can acquire and the types of reasoning it can perform.
Think of it this way: a single image of a bouncing ball tells you very little. You see a sphere at a particular location, and that's it. But a sequence of images, showing the ball's trajectory, reveals much more: its velocity, acceleration, the force of gravity acting upon it, and even predictions about its future position. Time, in this context, isn't just an extra dimension; it's the key to unlocking a deeper understanding of the underlying dynamics.
ST-GANs achieve this by incorporating temporal dependencies into both the Generator and the Discriminator. The Generator isn't just trying to create realistic individual frames; it's trying to create believable sequences that adhere to the laws of physics and the patterns of the real world. The Discriminator, in turn, isn't just judging the realism of individual frames; it's evaluating the coherence and consistency of the entire sequence over time.
This ability to reason through time provided by ST-GANs unlocks several critical capabilities for AI systems:
Predictive Modeling: ST-GANs are inherently predictive. By learning the temporal dynamics of a system, they can forecast future states. This is essential for any agent that needs to plan ahead, anticipate consequences, or react to changing circumstances. Imagine an autonomous vehicle predicting the future trajectories of pedestrians and other vehicles, or a robot anticipating the movement of an object it's trying to grasp. This goes beyond simple extrapolation; it's about understanding the underlying causes of motion and change.
Causal Reasoning: Time provides a crucial clue for inferring causality. If event A consistently precedes event B, and manipulating A affects B, it suggests a causal link. ST-GANs, by learning to generate realistic sequences, can implicitly capture these causal relationships. They can learn, for example, that pushing a button (event A) causes a light to turn on (event B), not just that the two events often occur together. This is a crucial step towards building AI systems that can understand why things happen, not just what happens.
Anomaly Detection: If an ST-GAN has learned the "normal" patterns of a system over time, it can easily detect anomalies – events that deviate significantly from the expected sequence. This is valuable for monitoring complex systems, identifying potential problems, and flagging unusual behavior. Imagine a security system using an ST-GAN to analyze video footage and detect suspicious activity, or a medical monitoring system using an ST-GAN to detect subtle changes in a patient's vital signs that might indicate a developing problem.
Understanding Actions and Intentions: Human actions are inherently temporal. We understand actions by observing their unfolding over time, inferring the goals and intentions of the actor. ST-GANs can be used to model these action sequences, allowing AI systems to recognize and understand human actions, and even predict their future steps. This is crucial for human-robot interaction, collaborative robotics, and any system that needs to interpret human behavior.
Counterfactual reasoning: ST-Gans, by being able to model reality, can also simulate counterfactuals. What would have happened if a different action was taken at a particular point in time? The ability to simulate "what if" scenarios is critical for true understanding.
In essence, ST-GANs move beyond static representations of the world to embrace the dynamic, ever-changing nature of reality. They provide a framework for learning the "rules of the game" – the underlying principles that govern how things change over time.
This ability to reason through time is not just a nice-to-have feature; it's a fundamental requirement for building truly intelligent systems that can understand, interact with, and adapt to the complexities of the real world. It is a massive step closer to bridging the gap between narrow AI and the flexible, general-purpose intelligence of humans.
Reinforcement Learning: The Explorer Navigating the World
Reinforcement Learning, on the other hand, is the explorer. It's about learning through trial and error, through interaction with an environment. An RL agent receives feedback in the form of rewards or penalties, and it gradually learns to take actions that maximize its cumulative reward. It's like a child learning to walk – taking clumsy steps, falling down, and gradually figuring out the intricate balance and coordination required for locomotion.
RL excels at solving complex problems with delayed rewards, where the consequences of actions may not be immediately apparent. It's the driving force behind breakthroughs in game playing (like AlphaGo), robotics, and autonomous systems.
But traditional RL often struggles with the "curse of dimensionality" The real world is incredibly complex, with an infinite number of possible states and actions. Exploring this vast space purely through random trial and error is hopelessly inefficient. Simply optimizing for a specific reward function can lead to narrow, brittle solutions that don't generalize well to new situations.
The Synthesis: Internal Models Meet External Action
This is where the magic happens. I know from firsthand experience.
In my machine learning engineering work related to wealth systems and wealth building I combined the power of Spatio-Temporal GANs with Reinforcement Learning. I did this to build a machine to “read” the charts with me, a trading copilot. For understandable reasons I am not currently planning on publishing a roadmap to recreate Fortuna, but for the Paid Subscribers of
we are going to dive deeper into how GANs and RL coordinate together to improve system performance dramatically.