Harvard Highlights AI Advances in Education and Research at Boston AI Week
Harvard University showcased innovative AI tools and research applications during Boston AI Week, emphasizing responsible leadership and educational impact.
Harvard University showcased innovative AI tools and research applications during Boston AI Week, emphasizing responsible leadership and educational impact.
Harvard University showcased innovative AI tools and research applications during Boston AI Week, emphasizing responsible leadership and educational impact.
FSU researchers develop AI model to forecast wildfire frequency and severity in California, leveraging NAIRR program resources and NVIDIA GPU grants.
AI advancements enhance decision-making and scientific discovery through innovative systems.
Vanderbilt's iSCALE AI platform has been validated for molecular mapping, enhancing gene expression analysis in medical research.
Northeastern and Howard University researchers investigate AI's dual impact on job loss risks and future job creation.
Universal scaling laws are improving efficiency in LLM training, according to MIT research.
A new AI technique from Caltech enhances the study of quantum atomic vibrations in materials.
AI tools have been developed to support the preservation of endangered languages by researchers at the University of Hawaii.
Angelo State University establishes a new Center of Excellence for Artificial Intelligence.
A new study reveals significant similarities in learning processes between humans and AI systems.
The University of Chicago Law School is launching an AI Lab in 2025 to advance research on AI in legal contexts.
USF researchers develop AI technology to identify PTSD signals in children via facial recognition.
Budget cuts threaten U.S. AI action plan amid ongoing political shifts.
Innovative use of generative AI leads to new compounds targeting drug-resistant bacteria.
MIT scientists are making remarkable advancements in AI integration for humanoid robots.
MIT researchers unveil a new method for testing AI text classification efficiency.
Undergraduate students explore AI outsourcing and its implications for employment.
CMU launches an initiative to enhance math research through AI, supported by NSF funding.
AI technologies are effectively addressing myths in neuroscience, enhancing public understanding of brain science.
NIH has announced guidance on using AI tools in the research application process.
UTA's AI research aims to revolutionize antibody drug development with new funding and collaborations.
MIT and Duke University researchers leverage AI to create tougher plastic materials, advancing polymer science.
CMU launches new NSF-supported institute to leverage AI in mathematical research.
Paul Thompson's recent lecture stresses global collaboration and ethical AI use in neuroscience.
Penn State researchers are advancing AI optimization through innovative prompt engineering and benchmarking methods.
Stanford University researchers are pioneering the development of AI systems that emphasize fairness and social responsibility.
Bay Area doctors utilize AI assistants to improve medical research efficiency and innovation.
New research suggests programming AI to simulate guilt can enhance cooperation among agents.
CMU researchers introduce a new AI method to tackle invasive leafy spurge, aiding agricultural and ecological efforts.
Latest study reveals AI models can dangerously learn unwanted behaviors from one another.
Stanford researchers highlight the impact of cultural perspectives on AI biases in large language models.
Chen Kan from UTA awarded NSF CAREER grant for AI innovations in manufacturing.
A study reveals how ontological assumptions in AI limit understanding of diverse perspectives and potentially perpetuate bias.
AI advancements at Texas A&M University are transforming the solution of complex scientific equations, enhancing progress in drug discovery and material design.
New AI solutions are set to enhance healthcare communication and improve patient outcomes.
AI-generated music is found to elicit stronger emotional responses than human compositions, as per a new study.
Willie Neiswanger's framework seeks to improve AI decision-making under uncertainty by integrating decision theory principles.
Researchers warn of diminishing understanding in advanced AI models, emphasizing monitoring of reasoning processes.