Intelligence Explosion

 

This graph represents a conceptual scenario called the "Intelligence Explosion," which outlines the potential trajectory of artificial intelligence (AI) progress. It uses "effective compute" (measured logarithmically and normalized to GPT-4) as a proxy for AI capability over time.

Key Elements:

1. Axes:

   • Y-axis: The "Effective Compute" scale is logarithmic, ranging from $10^{-8}$ to $10^{15}$, normalized to GPT-4's compute level.
   • X-axis: The timeline spans from 2018 to 2030, showing the progression of AI development over time.

2. Markers of AI Progress:

   • GPT-2: Equivalent to a preschooler's cognitive capability.
   • GPT-3: Compared to an elementary schooler's cognitive capability.
   • GPT-4: Analogous to a "smart high schooler."
   • "Automated Alec Radford?": Hypothetical level of compute where AI might fully automate its own development processes (possibly named as a nod to an AI researcher or key figure).
   • Superintelligence: Theoretical level where AI surpasses human intelligence by orders of magnitude.

3. The Intelligence Explosion:

   • The graph suggests a rapid acceleration in AI capability due to "Automated AI Research." This concept implies that once AI reaches a certain threshold of intelligence, it could take over its own development, exponentially improving itself.

4. Shaded Region:

   • Represents uncertainty or variability in the timeline and speed of this hypothetical intelligence explosion. This shaded area spans the late 2020s, showing the range of possibilities for achieving superintelligence.

Interpretation:

• The graph conveys a hypothetical trajectory where advancements in AI (driven by self-optimization) lead to an "explosion" in capability within a short timeframe.
• It underscores the potential risks and opportunities of reaching a point where AI systems are capable of autonomous research and improvement.

ChatGPT Limitation or Flaw?

I found an interesting limitation/flaw in ChatGPT when i present it with different questions in a single prompt.

First I present ChatGPT with two questions in one prompt, one after another. The second question in the same prompt uses the answer from the first question. 

Diagram 1: ChatGPT returns a wrong value.

Diagram 1 shows that ChatGPT generated a result for the prompt that includes 2 questions in the same prompt. If I asked ChaGPT separately, the result is shown in Diagram 3 which is not the same result as in Diagram 1.

Diagram 2: Different result from ChatGPT is question is asked separately.

If I ask a non mathematical questions to ChatGPT as shown in Diagram 3, the result tends to be consistent with questions that are asked separately and answered individually

Diagram 3: Non-mathematical question

As a matter of fact Blue + Yellow = Green, and Green + Red = Brown.

Another Example:

Diagram 4: Chat-GPT 4 seems to use different ways to interpret the 2 similar questions.

I have not gotten the time to find out further in what extend that ChatGPT become less reliable based on how I structure the prompts. Just be mindful that ChatGPT is not bullet proof and it's best that we do our verification on works that we rely on ChatGPT.