Arizona State University researchers, led by Subbarao Kambhampati, challenge the characterization of AI language models’ intermediate text generation as “reasoning,” arguing that this anthropomorphization fosters misconceptions about their functioning. Their analysis of models like DeepSeek’s R1 reveals that these systems can produce lengthy intermediate outputs that mimic human scratch work without genuine reasoning, and even perform better when trained on semantically meaningless data. The study cautions against interpreting these outputs as valid reasoning, as it may lead to misplaced confidence in AI capabilities and misinform users about the underlying problem-solving processes.
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'Hour of Code' Announces It's Now Evolving Into 'Hour of AI'
Microsoft has committed $4 billion towards AI education in K-12 schools and colleges, signaling a shift from traditional coding to AI-focused learning, as announced by President Brad Smith. This change is reinforced by Code.org's CEO Hadi Partovi, who revealed the Hour of Code will be renamed the Hour of AI to emphasize the importance of AI literacy in education, ensuring students understand AI's implications and applications. Want More Context? 🔎
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