A comedian is training an AI on sounds her fetus hears. The model's outputs, including referencing pedophilia after news exposure, show that an AI’s flaws and biases are a direct reflection of its training data—much like a child learning to swear from a parent.
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AI errors, or "hallucinations," are analogous to a child's endearing mistakes, like saying "direction" instead of "construction." This reframes flaws not as failures but as a temporary, creative part of a model's development that will disappear as the technology matures.
The term "data labeling" minimizes the complexity of AI training. A better analogy is "raising a child," as the process involves teaching values, creativity, and nuanced judgment. This reframe highlights the deep responsibility of shaping the "objective functions" for future AI.
A speaker's professional headshot was altered by an AI image expander to show her bra. This real-world example demonstrates how seemingly neutral AI tools can produce biased or inappropriate outputs, necessitating a high degree of human scrutiny, especially when dealing with images of people.
AI finds the most efficient correlation in data, even if it's logically flawed. One system learned to associate rulers in medical images with cancer, not the lesion itself, because doctors often measure suspicious spots. This highlights the profound risk of deploying opaque AI systems in critical fields.
AI's unpredictability requires more than just better models. Product teams must work with researchers on training data and specific evaluations for sensitive content. Simultaneously, the UI must clearly differentiate between original and AI-generated content to facilitate effective human oversight.
When prompted, Elon Musk's Grok chatbot acknowledged that his rival to Wikipedia, Grokipedia, will likely inherit the biases of its creators and could mirror Musk's tech-centric or libertarian-leaning narratives.
AI's occasional errors ('hallucinations') should be understood as a characteristic of a new, creative type of computer, not a simple flaw. Users must work with it as they would a talented but fallible human: leveraging its creativity while tolerating its occasional incorrectness and using its capacity for self-critique.
Based on AI expert Mo Gawdat's concept, today's AI models are like children learning from our interactions. Adopting this mindset encourages more conscious, ethical, and responsible engagement, actively influencing AI's future behavior and values.
The best AI models are trained on data that reflects deep, subjective qualities—not just simple criteria. This "taste" is a key differentiator, influencing everything from code generation to creative writing, and is shaped by the values of the frontier lab.
The Fetus GPT experiment reveals that while its model struggles with just 15MB of text, a human child learns language and complex concepts from a similarly small dataset. This highlights the incredible data and energy efficiency of the human brain compared to large language models.