The world's fastest deep learning supercomputer is being used to develop algorithms that can help researchers automatically design neural networks for cancer research, according to the Oak Ridge National Laboratory. The World Health Organisation estimates that by 2025, the number of diagnosed new cases of cancer will reach 21.5 million a year, compared to the current number of roughly 18 million. Researchers at Oak Ridge National Laboratory (ORNL) and Stony Brook University reckon that this means doctors will have to analyse about 200 million biopsy scans per year. Neural networks could help ease their workloads, however, so that they can focus more on patient care. There have been several studies describing how computer vision models can be trained to diagnose cancerous cells in the lung or prostate. Although these systems seem promising they're time consuming and expensive to build.

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.

The same image shown using different analysis methods. Defects that don't exist are shown in purple, and defects that weren't identified are orange. In mere hours, researchers created a neural network that performed as well as a human expert, demonstrating MENNDL's ability to significantly reduce the time to analyze electron microscopy images. Finding defects in electron microscopy images takes months. It's called MENNDL, the Multinode Evolutionary Neural Networks for Deep Learning.

Finding defects in electron microscopy images takes months. It's called MENNDL, the Multinode Evolutionary Neural Networks for Deep Learning. It creates artificial neural networks--computational systems that loosely mimic the human brain--that tease defects out of dynamic data. It runs on all available nodes of the Summit supercomputer, performing 152 thousand million million calculations a second. In mere hours, scientists using MENNDL created a neural network that performed as well as a human expert.

You don't have to dig too deeply into the archive of dystopian science fiction to uncover the horror that intelligent machines might unleash. The Matrix and The Terminator are probably the most well-known examples of self-replicating, intelligent machines attempting to enslave or destroy humanity in the process of building a brave new digital world. The prospect of artificially intelligent machines creating other artificially intelligent machines took a big step forward in 2017. However, we're far from the runaway technological singularity futurists are predicting by mid-century or earlier, let alone murderous cyborgs or AI avatar assassins. The first big boost this year came from Google.

You don't have to dig too deeply into the archive of dystopian science fiction to uncover the horror that intelligent machines might unleash. The Matrix and The Terminator are probably the most well-known examples of self-replicating, intelligent machines attempting to enslave or destroy humanity in the process of building a brave new digital world. The prospect of artificially intelligent machines creating other artificially intelligent machines took a big step forward in 2017. However, we're far from the runaway technological singularity futurists are predicting by mid-century or earlier, let alone murderous cyborgs or AI avatar assassins. The first big boost this year came from Google.

Deep neural networks--a form of artificial intelligence--have demonstrated mastery of tasks once thought uniquely human. Their triumphs have ranged from identifying animals in images, to recognizing human speech, to winning complex strategy games, among other successes. Now, researchers are eager to apply this computational technique--commonly referred to as deep learning--to some of science's most persistent mysteries. But because scientific data often looks much different from the data used for animal photos and speech, developing the right artificial neural network can feel like an impossible guessing game for nonexperts. To expand the benefits of deep learning for science, researchers need new tools to build high-performing neural networks that don't require specialized knowledge.