IBM Granite tops Stanford’s list as the world’s most transparent model
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Peter Hess
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How analog in-memory computing could power the AI models of tomorrow
ResearchPeter HessA benchmark for evaluating conversational RAG
ResearchKim MartineauUsing dynamic circuits to efficiently implement quantum states with long-range entanglement
ResearchSimone Cantori, Marcel Pfaffhauser, Elisa Bäumer, Fabio Scafirimuto, and Robert DavisHow to test a quantum computer chip
ResearchMike MurphyDynamic circuits enable essential circuit cutting methods for quantum-centric supercomputing
ResearchDaniel J. Egger, Maika Takita, and Robert DavisHow to visualize modern AI research
ResearchKim MartineauA new tool for accelerating the discovery of new materials
ResearchPeter HessCharting a path to a more sustainable AI future
ResearchMike MurphyServing customized AI models at scale with LoRA
ResearchKim MartineauAdvancing quantum algorithms for large-scale simulations of quantum spin chains
ResearchSimone Cantori, Marcel Pfaffhauser, Fabio Scafirimuto, and Robert DavisNew algorithms open possibilities for training AI models on analog chips
ResearchPeter HessFor LLMs, IBM’s NorthPole chip overcomes the tradeoff between speed and efficiency
ResearchPeter HessHow memory augmentation can improve large language model efficiency and flexibility
ResearchPeter HessTeaching AI models to improve themselves
ResearchPeter HessDetecting health and science misinformation with AI
ResearchPeter HessSimulating the universe’s most extreme environments with utility-scale quantum computation
ResearchRoland C. Farrell, Marc Illa, Anthony N. Ciavarella, Martin J. Savage, and Robert Davis
