Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D.

We discuss the objectives of any endeavor in creating artificial intelligence, AI, and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. This suggests that our attempts at AI could have been misguided; what we actually need to strive for can be termed artificial curiosity, AC, and intelligence happens as a consequence of those efforts. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. We discuss what these essential doctrines might be and why their establishment is required to form connections, possibly growing, between a knowledge store that has been built up and new pieces of information that curiosity will bring back. As more findings are acquired and more bonds are fermented, we need a way to, periodically, reduce the amount of data; in the sense, it is important to capture the critical characteristics of what has been accumulated or produce a summary of what has been gathered. We start with the intuition for this line of reasoning and formalize it with a series of models (and iterative improvements) that will be necessary to make the incubation of intelligence a reality. Our discussion provides conceptual modifications to the Turing Test and to Searle's Chinese room argument. We discuss the future implications for society as AI becomes an integral part of life.

There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples. Semi-supervised learning algorithms can work with both types of data, combining them to obtain better performance for both clustering and classification. Also, these datasets commonly have a high number of dimensions. This article presents a new semi-supervised method based on self-organizing maps (SOMs) for clustering and classification, called Semi-Supervised Self-Organizing Map (SS-SOM). The method can dynamically switch between supervised and unsupervised learning during the training according to the availability of the class labels for each pattern. Our results show that the SS-SOM outperforms other semi-supervised methods in conditions in which there is a low amount of labeled samples, also achieving good results when all samples are labeled.

In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Hence, Semi-Supervised learning models can benefit from both types of data to improve the obtained performance. Also, it is important to develop methods that are easy to parameterize in a way that is robust to the different characteristics of the data at hand. This article presents a new method based on Self-Organizing Map (SOM) for clustering and classification, called Adaptive Local Thresholds Semi-Supervised Self-Organizing Map (ALTSS-SOM). It can dynamically switch between two forms of learning at training time, according to the availability of labels, as in previous models, and can automatically adjust itself to the local variance observed in each data cluster. The results show that the ALTSS-SOM surpass the performance of other semi-supervised methods in terms of classification, and other pure clustering methods when there are no labels available, being also less sensitive than previous methods to the parameters values.

Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex. We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project (Cichy et al., 2019). In contrast to previous findings in the literature (Khaligh-Razavi & Kriegeskorte, 2014), we report empirical data suggesting that unsupervised models trained to predict frames of videos may outperform supervised image classification baselines in terms of correlation to spatial (fMRI) data. Our best submission achieves an average noise normalized correlation score of 16.67% and 27.67% on the fMRI and MEG tracks of the Algonauts Challenge.

The OECD Principles on Artificial Intelligence promote artificial intelligence (AI) that is innovative and trustworthy and that respects human rights and democratic values. They were adopted on 22 May 2019 by OECD member countries when they approved the OECD Council Recommendation on Artificial Intelligence. The OECD AI Principles are the first such principles signed up to by governments. Beyond OECD members, other countries including Argentina, Brazil, Colombia, Costa Rica, Peru and Romania have already adhered to the AI Principles, with further adherents welcomed. The OECD AI Principles set standards for AI that are practical and flexible enough to stand the test of time in a rapidly evolving field.

The weekend felt like an eternity for the 13-year-old. So when she handed back her "robot baby" it was with great relief. She had taken part in a program launched by the Caldas municipality in Colombia to try to tackle the problem of teenage pregnancies. "This experience was pretty tough, it's not easy being a mommy or a daddy," Ortegon said. The baby's cries were so loud that they even bothered her parents.

We will work together to foster global economic growth, while harnessing the power of technological innovation, in particular digitalization, and its application for the benefit of all. We are resolved to build a society capable of seizing opportunities, and tackling economic, social and environmental challenges, presented today and in the future, including those of demographic change. This recovery is supported by the continuation of accommodative financial conditions and stimulus measures taking effect in some countries. However, growth remains low and risks remain tilted to the downside. Most importantly, trade and geopolitical tensions have intensified. We will continue to address these risks, and stand ready to take further action. Fiscal policy should be flexible and growth-friendly while rebuilding buffers where needed and ensuring debt as a share of GDP is on a sustainable path. Monetary policy will continue to support economic activity and ensure price stability, consistent with central banks' mandates. Central bank decisions need to remain well communicated.

Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses. To address this, we propose a signed graph regularized deep neural network with adversarial augmentation, named \textsc{DiagNet}. Firstly, we use adversarial learning to generate positive and negative mass-contained mammograms for each mass class. After that, a signed similarity graph is built upon the expanded data to further highlight the discrimination. Finally, a deep convolutional neural network is trained by jointly optimizing the signed graph regularization and classification loss. Experiments show that the \textsc{DiagNet} framework outperforms the state-of-the-art in breast mass diagnosis in mammography.

Rapid advances in computing and robotics have led to calls for government controls. Before acting, we need to distinguish among the many meanings and applications of the technology. New technologies often spur public anxiety, but the intensity of concern about the implications of advances in artificial intelligence (AI) is particularly noteworthy. Several respected scholars and technology leaders warn that AI is on the path to turning robots into a master class that will subjugate humanity, if not destroy it. Others fear that AI is enabling governments to mass produce autonomous weapons--"killing machines"--that will choose their own targets, including innocent civilians. Renowned economists point out that AI, unlike previous technologies, is destroying many more jobs than it creates, leading to major economic disruptions. There seems to be widespread agreement that AI growth is accelerating.