An artificial intelligence-powered method for detecting tumor DNA in blood has the potential to improve cancer care with the very early detection of recurrence and close monitoring of tumor response during therapy.
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AI speech-to-text can hallucinate violent language
Speak a little too haltingly and with long pauses, and OpenAI’s speech-to-text transcriber might put harmful, violent words in your mouth, Cornell researchers have discovered.
Female AI ‘teammate’ engenders more participation from women
A new study suggests that the gender of an AI’s voice can positively tweak the dynamics of gender-imbalanced teams and could help inform the design of bots used for human-AI teamwork.
Successful Artificial Intelligence Event Inspires Large Audience on May 29
The Emerging Tech Dialogues event on May 29, 2024 — the first in a new series — drew more than 750 registrations from Cornell, Weill Cornell Medicine, and Cornell Tech faculty, staff, students, and researchers — all interested in exploring Artificial Intelligence in Higher Education, the symposium’s theme.
Twin Bowers CIS Entrepreneurs Bring AI to Academica
Twins Alsa Khan and Muhammad Jee explain how their AI platform, Mr. EzPz, could help to make artificial intelligence more reliable for students as well as educators.
Quantum AI framework targets energy intensive data centers
A new quantum computing-based optimization framework developed at Cornell could reduce energy consumption in large data centers handling artificial intelligence (AI) workloads by as much as 12.5% and reduce their carbon emissions by as much as 9.8%.
Through research and education, Bowers CIS is shaping fairer, ethical AI
In its world-class research and teaching, Cornell Bowers CIS is uniquely positioned to guide tomorrow’s innovators as they dive into issues of ethics, fairness and privacy, while weighing the policy implications of technological advances.
AI may improve doctor-patient interactions for older adults with cancer
Researchers have developed an AI tool that uses machine learning and large language models to identify treatment options based on patients’ diagnoses, demographic information and priorities.