If not the most deadly, the novel coronavirus (COVID-19) is one of the most contagious diseases to have hit our green planet in the past decades. In little over three months since the virus was first spotted in mainland China, it has spread to more than 90 countries, infected more than 185,000 people, and taken more than 3,500 lives. As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence. Though current AI technologies are far from replicating human intelligence, they are proving to be very helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19. Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak.

Leveraging the best of breed techologies such as TensorFlow, Keras, openCV and PyTorch for our integration libraries and Amazon AWS and Google Cloud for our REST APIs, the software we design and create are pragmatic, perform amazingly well under constraints and most importantly "just work". From 1-to-1 coaching workshops to periodic code reviews we enhance the skills of your data science and engineering teams. Architect & design a solution that solves your key business challenges with AI. Implement – Best-in-class AI solutions built by experts from the Dhakma network. Are you a start-up or SME looking to implement a AI prototype?

Image registration and segmentation are the two most studied problems in medical image analysis. Deep learning algorithms have recently gained a lot of attention due to their success and state-of-the-art results in variety of problems and communities. In this paper, we propose a novel, efficient, and multi-task algorithm that addresses the problems of image registration and brain tumor segmentation jointly. Our method exploits the dependencies between these tasks through a natural coupling of their interdependencies during inference. In particular, the similarity constraints are relaxed within the tumor regions using an efficient and relatively simple formulation.

In a time of public health emergency such as the COVID-19 pandemic, one may wonder why bother discussing Artificial Intelligence (AI). This article is not about pushing the relevance of AI but to outline how it is making a meaningful contribution to the fight against the pandemic. The role of AI goes back to the very beginning of the outbreak of SARS-CoV-2, the virus that causes COVID-19. BlueDot, a Canadian AI platform, was one of the first entities in the world to identify an unusual cluster of pneumonia cases in Wuhan and signal to the world the possibility of an outbreak. This alert came way before any international health authorities issued a warning.

Socionext Inc. has developed a prototype chip that incorporates newly-developed quantized Deep Neural Network (DNN) technology, enabling highly-advanced AI processing for small and low-power edge computing devices. The prototype is a part of a research project on "Updatable and Low Power AI-Edge LSI Technology Development" commissioned by the New Energy and Industrial Technology Development Organization (NEDO) of Japan. The chip features a "quantized DNN engine" optimized for deep learning inference processing at high speeds with low power consumption. Today's edge computing devices are based on conventional, general-purpose GPUs. These processors are not generally capable of supporting the growing demand for AI-based processing requirements, such as image recognition and analysis, which need larger devices at higher cost due to increases in power consumption and heat generation.

Rubik's Code is a boutique data science and software service company with more than 10 years of experience in Machine Learning, Artificial Intelligence & Software development. Check out the services we provide. Eager to learn how to build Deep Learning systems using Tensorflow 2 and Python? Get our'Deep Learning for Programmers' ebook here!

A fundamental problem in AI is it cannot learn as efficient as a human. Many deep learning classifiers demonstrate superhuman performance but it requires millions of training samples. Each task are trained independently from others. In this article, we will look into the problems and then check out some of the proposed solutions. We try to define it as "learn how to learn".

If not the most deadly, the novel coronavirus (COVID-19) is one of the most contagious diseases to have hit our green planet in the past decades. In little over three months since the virus was first spotted in mainland China, it has spread to more than 90 countries, infected more than 185,000 people, and taken more than 3,500 lives. As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence. Though current AI technologies are far from replicating human intelligence, they are proving to be very helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19. Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak.

Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented. There exists extensive literature on this subject in the context of natural images that can potentially be built upon. However, it cannot be assumed that conclusions about robustness will transfer from natural images to mammogram images, due to significant differences between the two image modalities. In order to determine whether conclusions will transfer, we measure the sensitivity of a radiologist-level screening mammogram image classifier to four commonly studied input perturbations that natural image classifiers are sensitive to. We find that mammogram image classifiers are also sensitive to these perturbations, which suggests that we can build on the existing literature. We also perform a detailed analysis on the effects of low-pass filtering, and find that it degrades the visibility of clinically meaningful features called microcalcifications. Since low-pass filtering removes semantically meaningful information that is predictive of breast cancer, we argue that it is undesirable for mammogram image classifiers to be invariant to it. This is in contrast to natural images, where we do not want DNNs to be sensitive to low-pass filtering due to its tendency to remove information that is human-incomprehensible.

Automatic Speech Recognition (ASR) has increased in popularity in recent years. The evolution of processor and storage technologies has enabled more advanced ASR mechanisms, fueling the development of virtual assistants such as Amazon Alexa, Apple Siri, Microsoft Cortana, and Google Home. The interest in such assistants, in turn, has amplified the novel developments in ASR research. However, despite this popularity, there has not been a detailed training efficiency analysis of modern ASR systems. This mainly stems from: the proprietary nature of many modern applications that depend on ASR, like the ones listed above; the relatively expensive co-processor hardware that is used to accelerate ASR by big vendors to enable such applications; and the absence of well-established benchmarks. The goal of this paper is to address the latter two of these challenges. The paper first describes an ASR model, based on a deep neural network inspired by recent work in this domain, and our experiences building it. Then we evaluate this model on three CPU-GPU co-processor platforms that represent different budget categories. Our results demonstrate that utilizing hardware acceleration yields good results even without high-end equipment. While the most expensive platform (10X price of the least expensive one) converges to the initial accuracy target 10-30% and 60-70% faster than the other two, the differences among the platforms almost disappear at slightly higher accuracy targets. In addition, our results further highlight both the difficulty of evaluating ASR systems due to the complex, long, and resource intensive nature of the model training in this domain, and the importance of establishing benchmarks for ASR.