In recent years, Artificial Intelligence (AI) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB. We can think of blockchains as "blue ocean" databases: they escape the "bloody red ocean" of sharks competing in an existing market, opting instead to be in a blue ocean of uncontested market space. Famous blue ocean examples are Wii for video game consoles (compromise raw performance, but have new mode of interaction), or Yellow Tail for wines (ignore the pretentious specs for wine lovers; make wine more accessible to beer lovers). By traditional database standards, traditional blockchains like Bitcoin are terrible: low throughput, low capacity, high latency, poor query support, and so on.

Radio signal recognition in dense and complex multi-user spectrum environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, enforcing spectrum policy, and implementing effective radio sensing and coordination systems. Classical approaches to the problem focus on energy detection and the use of expert features and decision criteria to identify and categorize specific modulation types [2] [1]. These approaches rely on prior knowledge of signal properties, features, and decision statistics to separate known modulations and are typically derived under simplified analytic hardware, propagation, radio environment models. We recently demonstrated the viability of naive feature learning for supervised radio classification systems [14] which allows for joint feature and classifier learning given labeled datasets and examples. In this case we were able to outperform traditional expert decision statistic based classification in sensitivity and accuracy by a significant margin. This was a powerful result, providing significant performance improvements against current day solutions, but it still relied entirely on supervised learning and well curated training data. In the real world, and especially in the radio domain, we are faced with vast amounts of unlabeled example data available to our sensor and incomplete knowledge of class labels comprising ground truth. To address this problem we investigate alternative strategies for radio identification learning which rely less heavily on labeled training data and are capable of making sense of radio signals with either no or less labeled examples, potentially drastically reducing the burden of data curation on such a machine learning system for developers and maintainers, and allowing systems to recognize new signals and scale to to understand new environments over time.

Kaggle, the nearly ten year old startup that hosts competitions for data science aficionados, is hosting a competition with a $1 million purse to improve the classification of potentially cancerous lesions in the lungs. The funds are being provided by the Laura and John Arnold Foundation as part of the 2017 Data Science Bowl, hosted by Booz Allen Hamilton and Kaggle. This isn't the first time that major prize money has been given away to accelerate research in targeted areas. The Data Science Bowl featured a competition last year to identify signs of heart failure with a $200,000 purse and the year before it tasked data scientists to assess ocean health. The $1 million going towards this year's competition will be the most ever given out as a prize on the site.

Abstract: One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods and their thirteen variants based on extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed classifiers mainly lie in two categories: reconstruction based and boundary based, where three proposed classifiers belong to reconstruction based and three belong to boundary based. We are presenting both types of learning viz., online and offline learning for OCC. Out of six methods, four are offline and remaining two are online methods. Out of four offline methods, two methods perform random feature mapping and two methods perform kernel feature mapping. We present a comprehensive discussion on these methods and their comparison to each other. Kernel feature mapping based approaches have been tested with RBF kernel and online version of one-class classifiers are tested with both types of nodes viz., additive and RBF. It is well known fact that threshold decision is a crucial factor in case of OCC, so, three different threshold deciding criteria have been employed so far and analyses the effectiveness of one threshold deciding criteria over another. Further, these methods are tested on two artificial datasets to check there boundary construction capability and on eight benchmark datasets from different discipline to evaluate the performance of the classifiers. Our proposed classifiers exhibit better performance compared to ten traditional one-class classifiers and ELM based two one-class classifiers. Through proposed one-class classifiers, we intend to expand the functionality of the most used toolbox for OCC i.e.

Object detection using Haar feature-based cascade classifiers is at least a decade old. OpenCV framework provides a default pre-built haar and lbp based cascade classifiers for face and eye detection which are very good quality detectors. However, I had never measured the accuracy of these face and eye detectors. I recently discovered that pre-built haar/lbp cascades have a relatively higher false positive rates which might make them unsuitable for many use-cases. It's possible to build an eye detector with very high accuracy and low false positive rates for many cases with OpenCV.

Kaggle, the nearly ten year old startup that hosts competitions for data science aficionados, is hosting a competition with a $1 million purse to improve the classification of potentially cancerous lesions in the lungs. The funds are being provided by the Laura and John Arnold Foundation and Booz Allen Hamilton as part of the 2017 Data Science Bowl. This isn't the first time that major prize money has been given away to accelerate research in targeted areas. The Data Science Bowl featured a competition last year to identify signs of heart failure with a $200,000 purse and the year before it tasked data scientists to assess ocean health. The $1 million going towards this year's competition will be the most ever given out as a prize on the site.

Two out of every five people in the US will be diagnosed with cancer during their lifetimes, according to the National Cancer Institute . Now the same technology behind improved voice assistants and credit card fraud detection--artificial intelligence--is now being implemented into lung cancer screenings for this year's Data Science Bowl. Booz Allen Hamilton and Kaggle are hoping to inspire data scientists and medical communities around the world to use artificial intelligence to improve lung cancer screening technology at this year's Data Science Bowl. The 90-day Data Science Bowl competition will award winners with $1 million in prizes. Funds for the prize purse will be provided by the Laura and John Arnold Foundation.

In the U.S., cancer will strike two in every five people in their lifetimes. But it affects all of us. That's why, in 2015, the office of the Vice President announced the Cancer Moonshot. It's an audacious effort to make a decade's worth of progress in cancer prevention, diagnosis, and treatment in just five years. Beginning today, the 2017 Data Science Bowl will pursue one of the Cancer Moonshot's key goals: unleashing the power of data against this deadly disease. Presented by Booz Allen and Kaggle, the competition will convene the data science and medical communities to develop cancer detection algorithms, and help end the disease as we know it.

Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant features. However, a linear model with just several parameters normally is not demanding in terms of training data. In this paper, we propose an elegant linear model to untangle confounding factors in challenging realistic multichannel signals such as 2D face videos. The simple yet powerful model does not rely on huge training data and is natural for recognizing facial actions without explicitly disentangling the identity. Base on well-understood intuitive linear models such as Sparse Representation based Classification (SRC), previous attempts require a prepossessing of explicit decoupling which is practically inexact. Instead, we exploit the low-rank property across frames to subtract the underlying neutral faces which are modeled jointly with sparse representation on the action components with group sparsity enforced. On the extended Cohn-Kanade dataset (CK+), our one-shot automatic method on raw face videos performs as competitive as SRC applied on manually prepared action components and performs even better than SRC in terms of true positive rate. We apply the model to the even more challenging task of facial action unit recognition, verified on the MPI Face Video Database (MPI-VDB) achieving a decent performance. All the programs and data have been made publicly available.

In recent years, Artificial Intelligence (AI) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models. Some appear almost unreasonable, like AI that can own itself -- AI DAOs. All of them are opportunities. This article will explore these applications. Before we discuss applications, let's first review what's different about blockchains compared to traditional big-data distributed databases like MongoDB. We can think of blockchains as "blue ocean" databases: they escape the "bloody red ocean" of sharks competing in an existing market, opting instead to be in a blue ocean of uncontested market space. Famous blue ocean examples are Wii for video game consoles (compromise raw performance, but have new mode of interaction), or Yellow Tail for wines (ignore the pretentious specs for wine lovers; make wine more accessible to beer lovers). By traditional database standards, traditional blockchains like Bitcoin are terrible: low throughput, low capacity, high latency, poor query support, and so on.