Print Join the Discussion View in the ACM Digital Library The mathematical reasoning performed by LLMs is fundamentally different from the rule-based symbolic methods in traditional formal reasoning.
Growing up multiracial in the 1990s, Gabriel "Joey" Merrin regularly encountered demographic forms that forced an impossible choice: Pick one box. Deny the others. "That act of being forced to choose, ...
Explore examples of effective crypto portfolios, including optimal allocations for Bitcoin and Ether in traditional investments ...
The new coding model released Thursday afternoon, entitled GPT-5.3-Codex, builds on OpenAI’s GPT-5.2-Codex model and combines insights from the AI company’s GPT-5.2 model, which excels on non-coding ...
If the FDA follows through with the proposed guidelines, and they are not fatally twisted by pressure from the medical ...
@hopkinskimmel researchers develop novel liquid biopsy approach to identifying early-stage cancers. › ...
The Environmental Protection Agency (EPA) cracked down on lead-based products—including lead paint and leaded gasoline—in the 1970s because of its toxic effects on human health. Scientists at the ...
Pioneering biostatistician and infectious disease expert Dr. Elizabeth Halloran recently transitioned to emerita after a ...
Abstract: Capable and highly motivated engineering students are constantly on the lookout for opportunities to engage in cutting-edge research. However, effectively translating the progress made in ...
A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.
Adopting a targeted, multi-pass reading approach to research studies can help you efficiently locate and extract the information you’re looking for while identifying potential limitations. Reading a ...
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