artificial intelligence researchers strive to simulate human intelligence, but examples from the animal kingdom reveal that intelligence may be far more diverse and complex than we think, underscoring the importance of “natural versus artificial intelligence”.
article contents:
- introduction
- why does natural intelligence matter?
- the fruit fly example
- bees and collective thinking
- squirrel memory
- the problem of defining intelligence
- why do we confuse intelligence with data training?
- conclusion: the multiplicity of forms of intelligence
- frequently asked questions
introduction
sam altman recently sparked a widespread debate about how close we are to achieving artificial general intelligence—close to human-level intelligence. although the idea of reaching human-like intelligence is ambitious, many academic and scientific voices point out that we still lack a clear definition of intelligence itself, whether natural or artificial.
why does natural intelligence matter?
researchers believe that studying the intelligence of living creatures—from flies and bees to squirrels and others—reveals vital aspects of cognitive capabilities that current artificial models struggle to emulate. we often focus on large language models and consider them an “intellectual” success simply because they produce formatted text. but we forget that these models do not possess a true understanding of the physical or social world.
the fruit fly example
fruit flies are a striking example of rapid decision-making in a complex environment. despite the small size of their brain, they possess an amazing ability to sense threats and make decisions to escape or maneuver within fractions of a second. this represents environmental intelligence that requires direct interaction with the real world.
bees and collective thinking
bees possess a superior ability to make collective decisions without a centralized “leader.” bees communicate via the “waggle dance” to exchange information about nectar locations or potential nest sites. this type of collective intelligence proves that cooperation can generate a highly efficient system that sometimes outperforms decentralized artificial intelligence systems.
squirrel memory
animal brilliance is not limited to flight or cooperation behaviors alone, but extends to memory skills, as seen in the case of squirrels. they have the ability to remember where nuts are buried for long periods, and even to deceive by making fake burials that trick other animals. this practical intelligence is completely far removed from the “hallucinations” that appear in some generative artificial models.
the problem of defining intelligence
psychologists point out that human intelligence itself remains a mystery, touching on concepts such as “fluid intelligence” that helps us solve new problems. researchers have not been able to formulate a comprehensive theory explaining human intelligence, so how can we then assume that we are on an inevitable path toward human-like intelligence in machines?
why do we confuse intelligence with data training?
most current artificial models rely on analyzing massive amounts of data and generating responses based on statistical patterns. they are adept at predicting what might follow a certain phrase, but they lack physical or bodily knowledge of the world. on the other hand, animals’ mental capacities evolve through living interaction with their environment, giving them an innate ability to handle novel situations.
conclusion: the multiplicity of forms of intelligence
intelligence cannot be considered a straight line connecting human intelligence to artificial intelligence. living organisms provide us with multifaceted models, showing that intelligence is linked to environment, physical interaction, and emotion. therefore, it may be useful to support artificial intelligence research with studies on the intelligence of living organisms, in hopes of arriving at fairer and deeper models of intelligence.
frequently asked questions
1. why the focus on fruit flies, bees, and squirrels?
because they are diverse examples of natural intelligence: rapid response in fruit flies, collective intelligence in bees, and complex memory in squirrels.
2. can artificial intelligence reach the level of animal intelligence?
despite significant progress in deep learning, animal intelligence still outperforms in aspects such as physical interaction and environmental adaptation.
3. what are “hallucinations” in artificial models?
they are errors or fabricated information resulting from inaccurate statistical analysis, as the models lack an actual connection to reality.
4. why is human intelligence a mystery?
because it remains difficult for scientists to agree on a comprehensive definition, and because human mental processes are complex and tied to social and emotional elements.
5. how does natural intelligence differ from artificial intelligence?
natural intelligence is formed through direct interaction with the environment and evolutionary pressures, whereas current artificial intelligence relies on mathematical models and massive data training.
6. can insights from the animal kingdom be gathered to develop artificial intelligence?
yes, studying survival and decision-making strategies in animals can give us insight to develop more effective artificial intelligence systems.
