From: mk_thisisit
In the context of very large artificial intelligence systems, “temperature” is a parameter that directly regulates their creativity [00:00:45]. This parameter determines the extent to which these systems will deviate from the patterns they have already learned [00:04:16].
Temperature as a Creativity Regulator
When the “temperature” parameter is set higher, it allows the AI system’s predictions, such as how a sentence might look, to differ more significantly from the data it was trained on [00:04:28]. This deviation fosters creativity in the system [00:04:37]. Conversely, a lower temperature would result in output that more closely reproduces learned patterns [00:04:19].
This concept relates to how a fragment of a vast neural network with billions of parameters is stimulated [00:06:11]. If the stimulation is strong, it spreads to other parameters encoding different concepts [00:06:18]. This enables the system to create associations not directly related to the initial query, allowing it to “fly away completely and start fantasizing about some side topic” due to high variation [00:06:26].
Analogy to Human Brains
The concept of “temperature” in AI can be compared to “neuronal noise” in human brains [00:04:41]. This noise causes human neurons to work in a “stochastic” or random way, meaning they don’t function identically every time [00:04:45]. For example, in a normal discussion, humans usually answer slightly differently even when asked the same question twice [00:05:00]. Similarly, AI systems, when asked repeatedly, provide slightly different answers due to this inherent “noise” [00:05:11]. This allows the system to “hallucinate” or “confabulate,” producing information that might not always be true upon verification, much like humans sometimes confabulate [00:05:20].
Distinction from Physical Temperature
It’s crucial to distinguish this “temperature” parameter from the physical temperature of server rooms [00:05:41]. While a broken air conditioning system could affect a neural network’s functioning due to heat, the “temperature” parameter in AI systems is a conceptual setting, not a measure of heat [00:05:54].
The name “temperature” originates from its use in statistical physics, where it defines the average energy of particles [00:06:42]. If this average energy is high, particles can jump further, evoking distant associations [00:07:03]. Therefore, interpreting concepts in different contexts requires care [00:07:14].
In essence, “temperature” in AI is a parameter that dictates how much variation or “blind” deviation the system can afford around a given topic [00:05:57].