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NTT, OIST signed a memorandum of understanding to collaborate in research areas to drive innovation and create a more AI-driven sustainable society.
The NUS AI Institute was launched on March 25 and will research how to address the ethical concerns of steereotypes associated with AI
ProGen-a project to design proteins using generative AI, can help find medical treatments that are more cost effective than traditional methods.
Researchers of Stanford and McMaster University have created this model.
AI and machine learning offer significant potential in pharmaceutical development and research by enabling predictive maintenance, personalized treatment, and analysis of vast data sets. However, limitations include challenges with interpreting natural language and addressing privacy and security concerns.
Undergraduate researchers at The University of Texas at Austin are actively contributing to cutting-edge AI and machine learning research, exploring areas such as human-AI interaction, continual learning, brain-machine interfaces, and AI fairness, with early exposure fostering their passion and expertise in the field.
Google Research utilizes AI to provide accurate riverine flood forecasting up to 7 days in advance in over 80 countries, aiding vulnerable populations and regions with limited data access, ultimately mitigating the impact of flooding on livelihoods and economies globally.
Info-Tech Research Group underscores the necessity of privacy impact assessments (PIAs) in AI implementations, emphasizing informed consent, data governance, and risk mitigation. Their research advocates for aligning AI objectives with privacy requirements to build trust among consumers and strengthen organizations' market position.
The Indian Army establishes the 'Signals Technology Evaluation and Adaptation Group' (STEAG) to assess and integrate advanced communication technologies like AI, 5G, and quantum computing for defense applications, aiming to enhance battlefield communication capabilities through collaboration with industry and academia.
Apple researchers are developing MM1, a multimodal AI model with up to 30 billion parameters, showcasing advanced capabilities in understanding text and image inputs, though its integration into Apple products remains uncertain pending peer review. This initiative marks a significant stride in Apple's pursuit of enhancing AI features, potentially impacting future product developments.