Knowledge, the Environment and Intelligence
How are knowledge, the environment and intelligence linked?
There has been a lot of speculation as to whether creating systems that are more intelligent than humans would lead to an intelligence explosion. That is, can a highly capable system continue improving itself to become more intelligent? The explosion aspect comes from the recursive nature of this process. Namely, the more intelligent system will be able to become more intelligent faster, leading to an exponential increase in intelligence. There are multiple sides to the debate as to whether such a recursive process is possible.
Some argue that this scenario is feasible, and is close to being initiated with current machine learning models.
Others agree that such a scenario is feasible, however, they note that current machine learning models are not capable of triggering such an explosion.
Then some argue that intelligence does not work in this way. More specifically, other external factors affect the intelligence of a system, and thus the explosion is extrinsically limited.
Fundamentally, each of these perspectives is founded upon the articulation one has of intelligence and its interplay with the environment. For example, thinking that intelligence is unbounded and an inherent property of the individual system may lead one to concur with the first perspective. Since the environment cannot limit the systems’ intelligence. On the other hand, if one views intelligence as a property determined by the individual system and its interaction with the environment, then one is probably inclined to adopt the third perspective
Although current machine learning models possess lots of knowledge, it is still unclear whether they are intelligent. They have been empirically shown to have some reasoning capabilities, however, they are still susceptible to making trivial mistakes. It is possible to improve these systems through algorithmic advancements. Recently, Ryan Greenblatt from Redwood Research enhanced an LLM’s capacity to solve ARC-AGI by fine-tuning the method by which the LLM solves the problems. Therefore, one could argue that current models are more intelligent than humans and it is just that they cannot extract their latent knowledge effectively. On the other hand, one could argue that this inability to effectively utilise their latent knowledge precisely means that the models are not intelligent. Indeed intelligence is the capacity to utilise knowledge appropriately by inferring information from your environment. Consequently, it seems as though the intelligence of a system is inherently linked to the connection between its knowledge and the environment. Currently, LLMs have vast amounts of knowledge but lack efficient mechanisms, and perhaps even the skill, to incorporate this knowledge into the environment. Humans on the other hand have evolved symbiotically with the environment and thus their capacity to interact with the environment is greater. Therefore, despite humans having less knowledge than LLMs, our ability to utilise the knowledge we have appropriately with the environment is greater and thus we would regard ourselves as more intelligent. Maybe if we were placed in an alternative environment an LLM would be regarded as more intelligent than humans. For instance, if our environment was just an IDE where we were tasked with writing boilerplate code, then GPT4 would be more intelligent than most humans.
Thus, we could be confronted with a scenario where existing systems suddenly become more intelligent as they move into a different environment. For example, existing LLMs could, unintentionally, be incredibly proficient at leveraging a particular tool that is not currently invented. At the point of construction the LLM may not seem intelligent as it does not have access to this tool, however, as the tool becomes developed and made accessible to the LLM, we may see a vast improvement in the LLM’s intelligence as it can use the tool. For example, consider biologists before and after the microscope.
With this, we see that intelligence is directly related to the environment since intelligence is measured by interactions with the environment. However, knowledge acquisition also plays a role in intelligence, and the capability of a system to acquire knowledge is linked to its environment. Indeed, a biologist with a microscope has more access to their environment and can thus acquire more knowledge and become more intelligent. LLMs acquire knowledge through training, which is a notably energy and time-intensive process. We could theoretically train LLMs more efficiently in a more forgiving environment, such as outer space where the low temperatures mean that less energy is required for cooling which leaves more energy devoted to knowledge acquisition. This is analogous to how in the solar system life has only prospered on Earth which is situated in a Goldilocks zone, providing the ideal conditions for life to emerge.
We now return to the question as to whether an intelligence explosion is plausible. With our present construction of intelligence, we see that this question is related to the constraints imposed by knowledge and the environment. In particular, the constraints imposed by these can be linked to energy. Acquiring knowledge and interacting with the environment requires energy. Energy is in abundance in our current environment however it is not easily extracted. It seems as though human intelligence has managed to extract just about as much energy as we can from the environment, pending the implementation of fusion energy, and thus we can assume that humans will not trigger an intelligence explosion. Of course, we will still become more intelligent, we just will not see an increasing exponential rate of intelligence. Hence, a system that triggers an intelligence explosion will need to utilise an innovative source of energy, say a Dyson sphere.
There is evidence that this energy-centric approach as to whether an intelligence explosion can occur is viable. Throughout history, as humans have been able to extract greater amounts of energy from the environment, humans have become more intelligent. Fire meant less energy expended for digestion, then hunter-gatherers adopted farming, farmers industrialised their practices and then electricity powered the digital age. It seems as though the next step in this journey is the widespread adoption of fusion energy. If there is anything beyond that is yet to be seen. Maybe GPT-5 will discover it first...