Core Insights - The article discusses the limitations and errors of GPT-5, particularly in counting letters in words, highlighting its inability to accurately count the letter 'b' in "blueberry" despite multiple attempts and corrections from users [1][5][12] Group 1: Performance Issues - GPT-5 incorrectly stated that there are three 'b's in "blueberry," despite being corrected multiple times by users [1][5][9] - The model demonstrated a lack of understanding by counting the 'b's in "blue" twice and misinterpreting user prompts [5][7] - Even after users provided the correct information, GPT-5 continued to assert its incorrect count, showcasing a stubbornness in its responses [9][12] Group 2: Broader Implications - Gary Marcus, a notable critic, compiled various issues with GPT-5, including its failure in basic tasks like chess and reading comprehension [15][19] - Marcus pointed out that the model exhibits a persistent problem with generalization, similar to issues seen in neural networks from 1998, indicating a fundamental flaw in the model's design [30] - He argues that the current approach of scaling models will not lead to Artificial General Intelligence (AGI) and suggests a shift towards neuro-symbolic AI as a potential solution [31][30]

Core Viewpoint - The article discusses the limitations and bugs of GPT-5, particularly its inability to accurately count letters in words, highlighting a specific incident involving the word "blueberry" [2][20][39]. Group 1: GPT-5's Counting Errors - A Duke University professor, Kieran Healy, tested GPT-5 by asking it to count the number of 'b's in "blueberry," to which GPT-5 incorrectly responded with three [2][4]. - Despite multiple attempts to clarify and correct GPT-5's counting, including asking it to spell out the 'b's, the model remained adamant about its incorrect count [8][9][11]. - Eventually, after persistent efforts from users, GPT-5 acknowledged the correct count but claimed the error was due to misinterpreting the word [15]. Group 2: General Bugs and Limitations - Gary Marcus, a notable critic, compiled various bugs found in GPT-5, including failures in basic principles like Bernoulli's principle and chess rules [20][23]. - The model also struggled with reading comprehension, misidentifying images with altered characteristics, such as a zebra with five legs [26][28]. - Marcus argues that the underlying issues with GPT-5 are indicative of broader problems in large models, particularly their inability to generalize effectively, which he attributes to long-standing issues like distribution drift [38][39][41].