"When we try to pick out anything by itself, we find it hitched to everything else in the universe" – John Muir Often in real-world tasks, there isn't enough data to take full advantage of deep learning. However, it is possible to leverage other datasets to reach a critical mass. Sharing knowledge across diverse datasets leads to more general knowledge, deeper insights and more well-informed decisions. This is especially true in domains like healthcare, where data for any particular task can be expensive or dangerous to collect. Modeling datasets separately wastes useful structure that could be shared between them.

Spike based communication is believed to be the primary way in which biological neutrons interact, so spiking neuron models are of interest to computational neuroscientists. Special purpose hardware (often called brain-inspired or neuromorphic hardware) can potentially deliver better power / performance numbers than deep learning hardware accelerators. With that being said there are few modern machine learning focussed libraries available to explore spiking neural networks. We are in the early stages of creating one based on PyTorch (https://github.com/norse/norse). What we've publicly published is enough to explore supervised learning on small datasets like MNIST and CIFAR-10.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5,6,7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20–30 min)2. In a multicenter, prospective clinical trial (n 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%).

Artificial Intelligence (AI) based on a combination of deep-learning algorithms and laser-imaging technology can be utilised to examine brain tissue and detect a brain tumour in near real-time according to a study published in Nature Medicine Journal on Monday. This recent AI technique can be a game-changer in intra-operative brain tumour diagnostics according to reports. The method is a combination of "Raman histology (SRH), a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion," the study said. The AI method is also much faster. The neural networks have been trained using over 2.5 million SRH images to identify brain tumours using brain tissue in under 150 seconds, according to the report.

The number of patents for inventions based on artificial intelligence, machine learning and deep learning continues to grow rapidly. Some of these inventions relate to AI technology per se, and some relate to the use of AI in specific applications, including many in healthcare, financial services and blockchain, among other industries. The USPTO has addressed various aspects of intellectual property issues with these technologies in various ways, including in an event it hosted entitled "Artificial Intelligence: Intellectual Property Policy Considerations (January 2019)." Due to some of the unique issues with these technologies, the USPTO is considering whether it should make any changes to how it handles examination of these applications. As part of this analysis, the USPTO issued a request for public comments on protection and examination of these inventions.

Artificial intelligence is at the forefront of the minds of many pharmaceutical and health care executives. We know this because, as life sciences consultants, our clients frequently ask us for advice on how best to navigate AI. But along with enthusiasm in areas as diverse as phenotypic screening, drug repositioning, and analysis of CT scans, we are also finding a growing skepticism: What is real and what is hype? An example often cited by skeptical clients are the problems surrounding IBM Watson Health, especially in the cancer treatment sphere, where reporting by STAT and the Wall Street Journal, among others, has revealed a chasm between the public relations stories and the reality as experienced by clinicians. Now is an appropriate time to ask: What is holding back artificial intelligence in health care and the life sciences?

Deep learning, a type of artificial intelligence, can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD), according to a study published in Radiology: Artificial Intelligence. Researchers said the approach could also have applications for other neurological conditions. The human brain is a complex set of networks. Advances in functional MRI, a type of imaging that measures brain activity by detecting changes in blood flow, have helped with the mapping of connections within and between brain networks. This comprehensive brain map is referred to as the connectome.

Due to the popularity of deep neural networks, many recent hardware platforms have special features that target deep neural network processing. The Intel Knights Mill CPU will feature special vector instructions for deep learning. The Nvidia PASCAL GP100 GPU features 16-b floating-point (FP16) arithmetic support to perform two FP16 operations on a single-precision core for faster deep learning computation. Systems have also been built specifically for DNN processing, such as the Nvidia DGX-1 and Facebook's Big Basin custom DNN server. DNN inference has also been demonstrated on various embedded System-on-Chips (SoCs) such as Nvidia Tegra and Samsung Exynos, as well as on field-programmable gate arrays (FPGAs).

DeepMind, Google's Artificial Intelligence subsidiary has recently made a huge step in cancer screening with its algorithm outperforming human readers. The research has received a lot of criticism since. Despite its shortcomings, the technology does have potential and it is a huge milestone for AI in medicine. Unlike most AI companies in Healthcare, DeepMind made sure to be as transparent as possible, when presenting their findings. They built trust and paved the way for adaptation by making both the limitations and the potential of their technology clear, and showed promising metrics for possible human-machine cooperation. Not only are they pioneers in AI technology, but also in its presentation. There is a lot we can learn from DeepMind, even if their solution is unable to "automate" cancer screening. After the first AI winter, AI research in the late 60s was re-branded in the form of Expert Systems: software that would mimic the decision making process of a domain expert.

What Github did for open source software, we hope platform can do for deep learning. We encourage scientists, researchers, students and hobbyist to use platform to collaboratively solve some of the most challenging vision problems.