Artificial Intelligence Research and Development
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The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources.
Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Advances in AI will require a strong community of AI researchers.
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Learn how to build better products with on-device data and privacy by default in a new online comic from Google AI. An outline of key areas where government can work with civil society and AI practitioners to provide important guidance on responsible AI development and use. Machine learning-based forecasts may one day help deploy emergency services and inform evacuation plans for areas at risk of an aftershock.
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A tool that enables scientists, data journalists, data geeks, or anyone else to easily find datasets stored in thousands of repositories across the web. We released a research framework for fast prototyping of reinforcement learning algorithms, which we hope will empower researchers to explore new ideas. Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum-classical algorithms are popular for applications in quantum simulation, optimization, and machine learning.
Due to its simplicity and hardware efficiency, random circuits are often proposed as initial guesses for exploring the space of quantum states. We show that the exponential dimension of Hilbert space and the gradient estimation complexity make this choice unsuitable for hybrid quantum-classical algorithms run on more than a few qubits.
Specifically, we show tha Nature Communications , vol. Purpose Use adjudication to quantify errors in diabetic retinopathy DR grading based on individual graders and majority decision, and to train an improved automated algorithm for DR grading. Design Retrospective analysis. Participants Retinal fundus images from DR screening programs.
Will China Overtake the U.S. in Artificial Intelligence Research?
Methods Images were each graded by the algorithm, U. The adjudicated consensus of the retinal specialists served as the reference standard. Main Outcome Measures For agreement between different graders as well as between the graders and the algorithm, we measured the quadratic-weight Guidelines for human-AI interaction design.
Careers See all open research positions. Solving some of the toughest challenges in AI.
Meet our research community. Eric Horvitz. Making the most of micro-moments with Dr.