What If People Use Autonomous Vehicles To Do Bad Things?

There’s a fairly large flaw in the way that programmers are currently addressing ethical concerns related to artificial intelligence and autonomous vehicles (AVs). Namely, existing approaches don’t account for the fact that people might try to use the AVs to do something bad.

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CIO Amber Boehnlein Takes Computing up a Notch

Computer scientists, software developers and system administrators are coming together under one roof in the newly established Computational Sciences and Technology Division at the Department of Energy’s Thomas Jefferson National Accelerator Facility. Amber Boehnlein, Jefferson Lab’s chief information officer, has been promoted to associate director for computational sciences and technology, heading up the new division.

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Silicon ‘neurons’ may add a new dimension to computer processors

When it fires, a neuron consumes significantly more energy than an equivalent computer operation. And yet, a network of coupled neurons can continuously learn, sense and perform complex tasks at energy levels that are currently unattainable for even state-of-the-art processors.What does a neuron do to save energy that a contemporary computer processing unit doesn’t?Computer modelling by researchers at Washington University in St.

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‘Artificial Chemist’ Combines AI, Robotics to Conduct Autonomous R&D

Researchers have developed a technology called “Artificial Chemist,” which incorporates artificial intelligence and an automated system for performing chemical reactions to accelerate R&D and manufacturing of commercially desirable materials.

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Calibrated approach to AI and deep learning models could more reliably diagnose and treat disease

In a recent preprint (available through Cornell University’s open access website arXiv), a team led by a Lawrence Livermore National Laboratory computer scientist proposes a novel deep learning approach aimed at improving the reliability of classifier models designed for predicting disease types from diagnostic images, with an additional goal of enabling interpretability by a medical expert without sacrificing accuracy. The approach uses a concept called confidence calibration, which systematically adjusts the model’s predictions to match the human expert’s expectations in the real world.

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