Core Insights - The article discusses the limitations of AI models in recognizing images, particularly focusing on the example of a six-fingered hand, illustrating how models rely on training data and probability rather than true visual understanding [38][41]. Group 1: Multimodal Models - The term "multimodal" refers to models that can process different types of data, such as audio and visual inputs, but many claimed multimodal models have not undergone proper training [7][8]. - True multimodal capabilities involve integrating various sensory inputs, while current models often struggle with complex visual data due to the inherent limitations of their training datasets [8][30]. Group 2: Image Recognition Challenges - AI models do not "see" images in the human sense; they process images as numerical data, which requires extensive preprocessing to convert into high-dimensional vectors for recognition [10][11]. - The recognition process relies heavily on labeled training data, where the model learns to associate images with descriptions, leading to biases based on the prevalence of certain features in the training set [14][15]. Group 3: Data Limitations - The training data used for AI models often does not encompass the full spectrum of real-world scenarios, leading to challenges in recognizing outlier cases, such as a six-fingered hand [29][30]. - Models are typically trained on common patterns, which means they may fail to identify rare or unusual features unless specifically trained on those cases [30][41]. Group 4: Task-Specific Limitations - The ability of a model to recognize specific features, like the number of fingers on a hand, is contingent upon the task it is designed to perform; recognizing a hand may not require identifying the number of fingers [18][36]. - The article emphasizes that while models can be trained to recognize specific features, they still operate within the constraints of their training data and the defined tasks [36][39]. Group 5: Conclusion and Future Opportunities - The discussion concludes that AI models are fundamentally probability-driven systems that require continuous calibration with real-world data to improve their accuracy and reduce hallucinations [41][42]. - Recognizing the limitations of current models and embracing the need for diverse training data may present new opportunities for industries looking to leverage AI technology effectively [42].