# Our IQ Will be Higher in the Future

## ๐ Abstract

The article discusses the past and future evolution of human intelligence, as measured by IQ (Intelligence Quotient), and the potential for artificial intelligence (AI) systems to surpass the cognitive abilities of the human population.

## ๐ Q&A

### [01] The Central Limit Theorem and Gaussian Distribution of Cognitive Abilities

**1. What is the Central Limit Theorem, and how does it relate to the distribution of cognitive abilities in the human population?**

- The Central Limit Theorem states that for a large sample of independent units, the probability distribution for a variable converges to a Gaussian (normal) distribution, characterized by a mean value and a standard deviation.
- The authors assumed that the statistical distribution of cognitive abilities within the human population follows a Gaussian distribution.

**2. How did the authors use the Gaussian distribution to calculate the deviation of the most capable human brain from the mean?**

- The authors equated the area under the tail of the Gaussian probability distribution beyond the highest value to one person out of the number of humans alive at that time to obtain the deviation of the most capable human brain from the mean.
- Based on data on the growth of the human population over time, they calculated that the most capable brain changed from 5.2 standard deviations above the mean 50,000 years B.C.E. to 6.47 standard deviations at the present time.

### [02] Scaling of Brain Parameters with Skull Volume

**1. What assumption did the authors make about the scaling of brain parameters with skull volume?**

- The authors assumed that the average number of parameters (synapses) in the human brain scales in proportion to the average volume of the evolving human skull, based on the observation that average neuron count scales linearly with average brain volume in primates.

**2. How did the authors estimate the standard deviation of brain parameters in the human population?**

- The authors assumed that the ratio between the standard deviation and the mean value of brain parameters approximates the standard deviation of human IQ as a fraction of its mean value, which is about 15%.

### [03] The Exponential Growth of AI and Comparison to the Human Brain

**1. What is the current state of AI systems in terms of the number of parameters compared to the human brain?**

- The state-of-the-art number of parameters in large language models (LLMs) is currently around 1.7 trillion, which is about 0.2% of the average number of synaptic connections in the human brain.
- The largest neuromorphic computer contains 0.128 trillion artificial synapses, or about 0.015% of the number of connections in the human brain.

**2. How does the exponential growth of AI parameters compare to the growth of the human brain?**

- The authors calculated that AI systems will potentially surpass the largest number of parameters within the human population within the next one to two decades, due to the exponential growth of AI parameters.
- The exponential growth of AI is expected to saturate due to limits in available electric power, which is similar in magnitude to the power supply limitations that constrain the growth of the human brain.