Microsoft has banned its staff from taking part in hoaxes and lies on April Fools' Day. Making things up and tricking people into believing them has become a staple of the tech industry at the beginning of April in recent years. Google, particularly, has led the way – but much of the technology industry has undertaken increasingly overwrought and convincing pranks as time has gone on. Many of those hoaxes and lies have accidentally led to negative coverage, as well as the spreading of the kind of misinformation that companies like Facebook and Google have repeatedly been accused of spreading. We'll tell you what's true.

LG has announced that the G8 will arrive on April 11th, with pre-orders starting March 29th at major carriers including AT&T, Sprint, T-Mobile and Verizon (Engadget's parent company). It'll undercut its South Korean rival's price by a fair margin -- pricing starts at $820 up front versus Samsung's $900, and that's before the usual promos that knock as much as $150 off the price. Whether or not it's worth the savings over the S10 will likely depend on just how much you like LG's rather unusual priorities. You don't get a telephoto lens or 8GB of RAM ('just' 6GB) for the money, but you do get party tricks like in-the-air hand gestures, an OLED display that doubles as the speaker and a more secure, depth-based face unlock. That's not including more familiar staples like the quad DAC and a dedicated Google Assistant button.

President Donald Trump and his top U.S. military adviser met with Google's CEO about concerns that Silicon Valley's AI collaborations in China may benefit the Chinese military. Such worries reflect awareness of how certain technologies developed for civilian purposes can also provide military advantages in the strategic competition playing out between the United States and China. The meeting comes after General Joseph Dunford, chairman of the Joint Chiefs of Staff, leveled pointed criticism at Google for pursuing technological collaborations with Chinese partners, during his testimony before the Senate Armed Services Committee on 14 March. The spotlight's glare on Google grew harsher when President Trump followed up on Twitter: "Google is helping China and their military, but not the U.S. Terrible!" But beyond the focus on Google, the Pentagon seems more broadly concerned about U.S. tech companies inadvertently giving China a leg up in developing AI applications with military and national security implications.

"If you program a machine, you know what it's capable of. If the machine is programming itself, who knows what it might do?" ― Garry Kasparov Artificial Intelligence is a complex subject. However, reading and acquiring knowledge through books written on Artificial Intelligence, Machine Learning, Data Science and other related topics can help technology enthusiasts to a great extent. Here is a list of ten books on AI and Machine Learning that provide the information on basics of technology, its present, the future paradigm and the most rabid fictionalized set-ups that are expected to arrive in the coming future. We have rated the books on a scale of 1-5, considering their depth, research, uniqueness, reader's review, and the AiThority News Quotient.

Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transformations of original images; however, those transformed images intrinsically have a similar distribution to the original ones, leading to limited performance improvement. To fill the data lack in the real image distribution, we synthesize brain contrast-enhanced Magnetic Resonance (MR) images---realistic but completely different from the original ones---using Generative Adversarial Networks (GANs). This study exploits Progressive Growing of GANs (PGGANs), a multi-stage generative training method, to generate original-sized 256 X 256 MR images for Convolutional Neural Network-based brain tumor detection, which is challenging via conventional GANs; difficulties arise due to unstable GAN training with high resolution and a variety of tumors in size, location, shape, and contrast. Our preliminary results show that this novel PGGAN-based DA method can achieve promising performance improvement, when combined with classical DA, in tumor detection and also in other medical imaging tasks.

Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric Magnetic Resonance Imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the Central Gland (CG) and Peripheral Zone (PZ) can guide towards differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on Deep Learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of Convolutional Neural Networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets.

There has been a strong demand for algorithms that can execute machine learning as faster as possible and the speed of deep learning has accelerated by 30 times only in the past two years. Distributed deep learning using the large mini-batch is a key technology to address the demand and is a great challenge as it is difficult to achieve high scalability on large clusters without compromising accuracy. In this paper, we introduce optimization methods which we applied to this challenge. We achieved the training time of 74.7 seconds using 2,048 GPUs on ABCI cluster applying these methods. The training throughput is over 1.73 million images/sec and the top-1 validation accuracy is 75.08%.

This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-noising by designing neural networks with intra-bag attentions. In this paper, both intra-bag and inter-bag attentions are considered in order to deal with the noise at sentence-level and bag-level respectively. First, relation-aware bag representations are calculated by weighting sentence embeddings using intra-bag attentions. Here, each possible relation is utilized as the query for attention calculation instead of only using the target relation in conventional methods. Furthermore, the representation of a group of bags in the training set which share the same relation label is calculated by weighting bag representations using a similarity-based inter-bag attention module. Finally, a bag group is utilized as a training sample when building our relation extractor. Experimental results on the New York Times dataset demonstrate the effectiveness of our proposed intra-bag and inter-bag attention modules. Our method also achieves better relation extraction accuracy than state-of-the-art methods on this dataset.

Continuous monitoring of cardiac health under free living condition is crucial to provide effective care for patients undergoing post operative recovery and individuals with high cardiac risk like the elderly. Capacitive Electrocardiogram (cECG) is one such technology which allows comfortable and long term monitoring through its ability to measure biopotential in conditions without having skin contact. cECG monitoring can be done using many household objects like chairs, beds and even car seats allowing for seamless monitoring of individuals. This method is unfortunately highly susceptible to motion artifacts which greatly limits its usage in clinical practice. The current use of cECG systems has been limited to performing rhythmic analysis. In this paper we propose a novel end-to-end deep learning architecture to perform the task of denoising capacitive ECG. The proposed network is trained using motion corrupted three channel cECG and a reference LEAD I ECG collected on individuals while driving a car. Further, we also propose a novel joint loss function to apply loss on both signal and frequency domain. We conduct extensive rhythmic analysis on the model predictions and the ground truth. We further evaluate the signal denoising using Mean Square Error(MSE) and Cross Correlation between model predictions and ground truth. We report MSE of 0.167 and Cross Correlation of 0.476. The reported results highlight the feasibility of performing morphological analysis using the filtered cECG. The proposed approach can allow for continuous and comprehensive monitoring of the individuals in free living conditions.

Multiplex networks have become increasingly more prevalent in many fields, and have emerged as a powerful tool for modeling the complexity of real networks. There is a critical need for developing inference models for multiplex networks that can take into account potential dependencies across different layers, particularly when the aim is community detection. We add to a limited literature by proposing a novel and efficient Bayesian model for community detection in multiplex networks. A key feature of our approach is the ability to model varying communities at different network layers. In contrast, many existing models assume the same communities for all layers. Moreover, our model automatically picks up the necessary number of communities at each layer (as validated by real data examples). This is appealing, since deciding the number of communities is a challenging aspect of community detection, and especially so in the multiplex setting, if one allows the communities to change across layers. Borrowing ideas from hierarchical Bayesian modeling, we use a hierarchical Dirichlet prior to model community labels across layers, allowing dependency in their structure. Given the community labels, a stochastic block model (SBM) is assumed for each layer. We develop an efficient slice sampler for sampling the posterior distribution of the community labels as well as the link probabilities between communities. In doing so, we address some unique challenges posed by coupling the complex likelihood of SBM with the hierarchical nature of the prior on the labels. An extensive empirical validation is performed on simulated and real data, demonstrating the superior performance of the model over single-layer alternatives, as well as the ability to uncover interesting structures in real networks.