How Machine Learning is Already Shaping the Nuclear World

This post was written by Katharina Brown, a FSI Global Policy Intern on NTI’s Scientific and Technical Affairs team. Katharina is a senior at Stanford University studying Computer Science with a focus on Artificial Intelligence.

Today’s popular discussion of artificial intelligence (AI) issues reflects the need to debate the implications of new AI-driven technologies, like lethal autonomous weapons systems or self-driving cars, before they become widely adopted. Although there hasn’t been as much public debate on AI and machine learning (ML) in the nuclear world, new ML research has the potential to disrupt the nuclear field, and the security implications deserve discussion.

  •       In July 2018, researchers at the University of Tokyo published a tool for predicting the direction of radioactive material dispersion. Using datasets of near-surface wind conditions labeled with correct directions determined by a meteorological simulation, they trained a support vector machine (SVM) to classify examples into four discrete directions.[1] A SVM extrapolates data into higher dimensions until it finds a clear boundary between two categories, making it a useful tool for classification tasks like this one. This approach had an average success rate of 85%, and was accurate in predicting conditions up to 33 hours in advance. The researchers cited the government’s struggle to respond to the 2011 Fukushima Daiichi Nuclear Power Plant disaster as motivation for their work and expressed their hope that Japan would adopt a similar model to inform crisis management, in order to make better decisions on when and how to evacuate areas affected by a future release of radiation.
  •       In 2017, researchers at Purdue University published a new pipeline for detection of tiny cracks in underwater surfaces at nuclear power plants. Using a set of videos of underwater components, the approach aggregates data from multiple frames of the video, uses a neural network[2] to identify potential cracks, and finally applies a probabilistic test to filter out false positives caused by smudges, welds, or other normal features of the equipment. This paper is only one example of the various machine learning approaches to fault detection in nuclear reactors published over the last two years.

These innovations are currently academic projects, not yet solutions that have been adopted by industry or government entities. That’s why now is the time to consider the security concerns of ML approaches—before we ask technologies like these to inspect a nuclear power plant or protect people from radiation dispersion.

ML security is distinct from computer security because the fundamental characteristic of ML—using what’s already been seen to deal with new problems—makes it a target of some unique forms of exploitation. Attacking an ML system doesn’t require cracking passwords or stealing data; it’s possible to generate malicious input perfectly tailored to trick an ML system, even when the target’s algorithm and training data are unknown.[3]  Even models that achieve high rates of accuracy on a representative test data set can produce unexpected results when they encounter data that’s deliberately manipulated or simply unusual. The more we entrust critical issues to ML systems, the more serious the possible safety or security consequences of mistakes by the system could be.

The offensive-defensive race to control ML systems does not yet have a clear winner. Innovative techniques are being developed to generate attacks automatically, as well as to improve robustness against these threats. Even more importantly for critical applications, researchers are investigating ways to verify ML models, or guarantee a certain level of performance even against adversarial inputs.  This dynamic research area deserves attention and support, especially from industries that have an interest in adopting ML for safety- or security-sensitive work. 

Rather than wait for the world to be transformed by AI in the distant future, we should pay careful attention to shaping the field’s progress in a more secure direction, especially when it comes to applications as vitally important as nuclear. 

[1] There were six SVM’s, each one tasked with discriminating between only two of the labels (a “one-versus-one” approach). An SVM works by finding the largest possible margin separating two categories, so to account for more than two categories as in this case, multiple SVMs are needed.

[2] The type of neural network used, a convolutional neural network (CNN), is commonly used in image processing. It works by repeatedly applying mathematical transformations (“convolutions”) to the input in order to extract features that can be used to identify similar images.

[3] Of course, ML systems can also be vulnerable to the same threats as any other system – if the attacker does manage to steal the model’s training data, for example, they have a significant advantage in crafting an attack.

September 24, 2018

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