Advice for insurers and health practitioners focuses on promoting cost-effective treatments and dissuading the use of ineffective procedures; and policymakers will find ideas for increasing organ and other medical donations and improving end-of-life care.
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These offer particularly promising opportunities to discourage ineffective use of antibiotics and encourage the use of equivalent but cheaper treatments, like the use of generics. Simon University Professor of Economics and Psychology and co-founder of the behavioral economics field. In fact, many commonly performed and costly surgeries can make patients worse off. Researchers have tested behaviorally-inspired interventions, including incentive programs, but the results have often been short-lived.
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Neural networks mapped in both sexes of the worm
In this case, Hollenstein tested whether data gleaned from the physical act of reading could help a neural network work better. Last fall, Hollenstein and collaborators at the University of Copenhagen used her dataset to guide a neural network to the most important parts of a sentence it was trying to understand.
In deep learning, researchers typically rely on so-called attention mechanisms to do this, but they require large amounts of data to work well. By adding data around how long our eyes linger on a word, the researchers helped the neural networks focus on critical parts of a sentence as a human would.
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Gaze, the researchers found, was useful for a range of tasks, including identifying hate speech, analyzing sentiment, and detecting grammatical errors. In subsequent work Hollenstein found that adding more information about gaze, such as when eyes flit between words to confirm a relationship, helped a neural network better identify entities, like places and people.
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The hope, Hollenstein says, is that gaze data could help reduce the manual labeling required to use machine learning in rare languages, and in reading tasks where labeled data is especially limited, like text summaries. Ideally, she adds, gaze would be just the starting point, eventually complemented by the EEG data she gathered as researchers find more relevant signals in the noise of brain activity.
Still, eye tracking is unlikely to change how computer scientists build their algorithms, says Jacob Andreas, a researcher at Microsoft-owned Semantic Machines. Gaze data is difficult to gather, requiring specialized lab equipment that needs constant recalibration, and EEGs are messier still, involving sticky probes that need to be wet every 30 minutes. But Hollenstein sees improvements on the horizon, with better webcams and smartphone cameras, for example, that could passively collect eye-tracking data as participants read in the leisure of their homes.
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In any case, some of what they learn by improving machines might help us understand that other black box, our brains. As Andreas notes, researchers are constantly scouring neural networks for signs that they make use of humanlike intuitions—rather than relying on pattern matching based on reams of data.
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Perhaps by observing what aspects of eye tracking and EEG signals improve the performance of a neural network, researchers might begin to shed light on what our brain signals mean. A neural network might become a kind of model organism for the human mind. Getty Images. Related Video. Artificial Intelligence neural networks.
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