• 1 - Introduction:
• 2 - The Turing Test?
• 3 - Brave New World?
• 4 - Progress:
• 5 - The problem of Consciousness:
• 6 - Conclusion:

1 - Introduction:

1.1 - Request 1:

1.2 - Request 2:

1.3 - Request 3:

1.4 - Request 4:

2 - The Turing Test?

2.1 - Cultural Knowledge:

2.1.1 - Request 5:

2.2 - Reasoning Ability:

2.2.1 - Request 6:

2.2.2 - Request 7:

2.3 - Imagination:

2.3.1 - Request 8:

3 - Brave New World?

3.1 - Mastery of Natural Languages and their subtleties:

3.1.1 - Request 9:

3.1.2 - Request 10:

3.2 - Self-references:

3.2.1 - Request 11:

3.2.2 - Request 12:

"Cette phrase contient 32 caractères".
 ----- ------ -------- -- ----------
 12345 678901 23456789 01 2345678901

3.3 - Logic:

3.3.1 - Request 13:

3.3.2 - Request 14:

3.3.3 - Request 15:

• The knowledge base of ChatGPT 3.5 appears to have been "frozen" in 2021, while at that time the record for the number of calculated digits had been broken several times:
  
  
  • On 03/14/2019, Emma Harula Iwao calculated 31,415,926,535,897 digits,
  • On 01/29/2020, Timothy Mullican reached 50 trillion of digits,
  • On 08/2021, the University of Applied Sciences of the Grisons (HES) computed 62.8 trillion digits.
  
  Thus, more than a billion (1,000,000,000) digits were known by the end of 2021.


• Furthermore, ChatGPT 3.5 contradicts itself by stating that 31.4 trillion digits (which is much higher than a billion) were known while the billionth digit was not.


• Lastly, claiming that the calculations took several years is false, even if they are undoubtedly very lengthy.

3.3.4 - Request 16:

def       is_prime(n):
          if        n< 2:
                    return    False
          for       i in range(2, int(n ** 0.5) + 1):
                    if        n % i == 0:
                              return    False
          return    True
 
def       even_primes(limit):
          primes = []
          for       i in range(2, limit+1, 2):
                    if        is_prime(i):
                              primes.append(i)
          return    primes
 
                                                  # Example usage
 
print(even_primes(100))
 

3.3.5 - Request 17:

def       is_prime(n):
          if        n<= 1:
                    return    False
          if        n<= 3:
                    return    True
          if        n % 2 == 0 or n % 3 == 0:
                    return    False
          i = 5
          while     i * i<= n:
                    if        n % i == 0 or n % (i + 2) == 0:
                              return    False
                    i += 6
          return    True
 
def       generate_prime_pairs():
          pairs = []
          for       i in range(2, 1000):  # Adjust the range as needed
                    if        is_prime(i) and is_prime(i + 3):
                              pairs.append((i, i + 3))
          return    pairs
 
prime_pairs = generate_prime_pairs()
if        prime_pairs:
          print("Pairs of prime numbers with a difference of 3 are:")
          for       pair in prime_pairs:
                    print(pair)
else:
          print("No prime pairs found with a difference of 3 in the specified range.")
 

3.3.6 - Request 18:

def       trouver_solutions_diophantiennes(max_val):
          solutions = []
          for       x in range(1, max_val):
                    for       y in range(x, max_val):
                              for       z in range(y, max_val):
                                        if        x ** 3 + y ** 3 == z ** 3:
                                                  solutions.append((x, y, z))
          return    solutions
 
max_val = 100                                     # Vous pouvez ajuster cette valeur en fonction de la plage que vous souhaitez explorer
solutions = trouver_solutions_diophantiennes(max_val)
if        solutions:
          print("Les solutions pour x^3 + y^3 = z^3 sont :")
          for       solution in solutions:
                    print(solution)
else:
          print("Aucune solution trouvee dans la plage specifiee.")
 

3.3.7 - Request 19:

3.4 - Elementary Knowledge:

3.4.1 - Request 20:

3.4.2 - Request 21:

3.4.4 - Request 22:

3.4.4 - Request 23:

3.4.5 - Request 24:

3.4.6 - Request 25:

3.4.7 - Request 26:

3.5 - Advanced Knowledge:

3.5.1 - Request 27:

3.6 - Hallucinations:

3.6.1 - Request 28:

4 - Progress:

• Through "occasional" human interventions. However, in this case, how can we guarantee that a modification in these complex neural networks does not spread and disrupt other correct information?
• By expanding the knowledge base and validating its content. In this scenario, real-time access to the Internet would be absolutely essential.
• By changing the model. Indeed, if ChatGPT 3.5 is solely based on GPT [01], certain problems cannot be solved by a "simple" natural language model.
• In use cases such as "encyclopedic consultation," where it is essential to distinguish between what is TRUE and what is FALSE, it might be possible to subject ChatGPT 3.5's outputs to an "adversarial" AI (as done in the field of picture synthesis or games) that would ensure their accuracy. However, this poses a significant challenge: How can we establish, validate, and update a reliable knowledge base for such an AI? But for us, beings with consciousness, doesn't it play the role of that "antagonistic" AI? Indeed, our subconscious constantly generates information (in a broad sense). When we sleep, our consciousness is no longer active, and our dreams often have the incoherence of certain outputs from ChatGPT 3.5. However, when we are awake, consciousness then plays the role, if necessary, of a coherence filter, particularly sorting between the TRUE and the FALSE.

• And what about environmental issues regarding the consumption of eletricity and water necessary for the proprer functioning of these systems? We shall note in passing that the human brain (and of all living beings...) does not have the same needs, fortunately far from it!

5 - The problem of Consciousness:

6 - Conclusion: