Deep Convolutional Neural Networks (DCNNs) are used extensively in biomedical image segmentation. However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to semantic image segmentation. Atrous convolution is an alternative for the down sampling layer. It increases the receptive field whilst maintains the spatial dimension of feature maps. In this paper, a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers. Furthermore, different atrous blocks, shortcut connections and normalization methods are explored to select the optimal network structure setting. These lead to a new and full-scale DCNN - Atrous Convolutional Neural Network (ACNN), which incorporates cascaded atrous II-blocks, residual learning and Fine Group Normalization (FGN). Application results of the proposed ACNN to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image segmentation demonstrate that the proposed ACNN can achieve comparable segmentation Dice Similarity Coefficients (DSCs) to U-Net, optimized U-Net and hybrid network, but with significantly reduced trainable parameters due to the use of full-scale feature maps and therefore computationally is much more efficient for both the training and inference.

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future may have a possible dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers may need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Many learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, many of these workers are shooting themselves in the feet. In this paper, we propose Human-in-the-loop Artificial Intelligence (HitAI) as a fairer paradigm for AI systems. Recognizing that any AI system has humans in the loop, HitAI will reward these aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Merry Men, HitAI researchers should fight for a fairer Robin Hood Artificial Intelligence that gives back what it steals. This article is part of the special track on AI and Society.

We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale. EvalAI is built to provide a scalable solution to the research community to fulfill the critical need of evaluating machine learning models and agents acting in an environment against annotations or with a human-in-the-loop. This will help researchers, students, and data scientists to create, collaborate, and participate in AI challenges organized around the globe. By simplifying and standardizing the process of benchmarking these models, EvalAI seeks to lower the barrier to entry for participating in the global scientific effort to push the frontiers of machine learning and artificial intelligence, thereby increasing the rate of measurable progress in this domain. Our code is available here.

"Everyone knows" the cloud is the least expensive way to host AI development and production, right? Well, it turns out that the best solution may depend on where you are on your AI journey, how intensively you will be building out your AI capabilities, and what your end-game looks like. Today I wanted to share some of my learnings and observations. For those looking for more details, you can read the paper Moor Insights & Strategy published on the topic here. Cloud service providers (CSPs) have extensive portfolios of development tools and pre-trained deep neural networks for voice, text, image, and translation processing.

Researchers at DeepMind, owned by Google's parent company, are applying their powerful artificial intelligence systems to drug discovery research. Researchers at DeepMind, owned by Google's parent company, are applying their powerful artificial intelligence systems to drug discovery research. Researchers at DeepMind, owned by Google's parent company, are applying their powerful artificial intelligence systems to drug discovery research. Researchers at DeepMind, owned by Google's parent company, are applying their powerful artificial intelligence systems to drug discovery research. You can think of it as a World Cup of biochemical research.

Can the minds of machines teach us something new about what it means to be human? When it comes to the intricate story of our species' complex origins and evolution, it appears that they can. A recent study used machine learning technology to analyze eight leading models of human origins and evolution, and the program identified evidence in the human genome of a "ghost population" of human ancestors. The analysis suggests that a previously unknown and long-extinct group of hominins interbred with Homo sapiens in Asia and Oceania somewhere along the long, winding road of human evolutionary history, leaving behind only fragmented traces in modern human DNA. The study, published in Nature Communications, is one of the first examples of how machine learning can help reveal clues to our own origins.

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Microsoft has developed open-source software for crafting artificial intelligence models. But in recent months, the company has changed course, opting to work more closely with Facebook and contribute to the development of Facebook's own flavor of free AI software. Microsoft hasn't made a big deal about the change. But it reflects a willingness to back software that comes from other major technology companies, rather than focusing only on its own platforms. Google is the company behind the most popular open-source AI software, TensorFlow, which became available in late 2015.

Could deep learning help paleontologists and geneticists hunt for ghosts? When modern humans first migrated out of Africa 70,000 years ago, at least two related species, now extinct, were already waiting for them on the Eurasian landmass. These were the Neanderthals and Denisovans, archaic humans who interbred with those early moderns, leaving bits of their DNA behind today in the genomes of people of non-African descent. But there have been growing hints of an even more convoluted and colorful history: A team of researchers reported in Nature last summer, for instance, that a bone fragment found in a Siberian cave belonged to the daughter of a Neanderthal mother and a Denisovan father. The finding marked the first fossil evidence of a first-generation human hybrid.

ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models. ML.NET allows you to create and use machine learning models targeting common tasks such as classification, regression, clustering, ranking, recommendations and anomaly detection. It also supports the broader open source ecosystem by proving integration with popular deep-learning frameworks like TensorFlow and interoperability through ONNX. Some common use cases of ML.NET are scenarios like Sentiment Analysis, Recommendations, Image Classification, Sales Forecast, etc. Please see our samples for more scenarios.