Massachusetts Institute of Technology (MIT) researchers have discovered a powerful antibiotic compound using their machine-learning algorithm to counter many of the world's deadliest bacteria, including some strains that are immune to all known antibiotics. It prevented infections in two different mouse models, according to MIT official release. An advanced computer model that can screen more than a hundred million chemical compounds was used to design potential antibiotics that can kill dangerous bacteria. Speaking about the discovery, James Collins, the Termeer Professor of Medical Engineering and Science at MIT stated in a press release: "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery. He added that the researchers at MIT revealed this "amazing" molecule which is arguably one of the most potent antibiotics that has ever been discovered.

Are your enterprise's partners, customers, and employees growing weary of unfulfilled technology and innovation promises? Maybe there's a sense of resentment about technologies that don't work as promised. Or perhaps there's some concern about what's being sacrificed to pave the way for this technological progress. Fueled by headlines about the questionable ethics of big tech platforms such as Facebook, Google, and Amazon, this "techlash," or backlash, is directed against Silicon Valley tech firms and innovation. The general public has become more suspicious of technologies that seem creepy in terms of invading privacy and maybe even acting in a manner that is ethically questionable.

People's Google searches, social media posts and even chatbot questions are being used by artificial intelligence to try and predict where the novel coronavirus is going to pop up next. The technology, which has been fine-tuned over the last 15 years, is already feeding information to major health agencies like the World Health Organisation to help them decide where they should focus their efforts. One system, called HealthMap, uses publicly available data from across the internet as well as user-submitted information, according to one of its developers, John Brownstein, a professor at Harvard Medical School. "We work in this hybrid of data mining as well as crowdsourcing," he told the ABC's news podcast The Signal. "What's really phenomenal here is we're seeing incredible international collaboration and a huge amount of data sharing."

Luddy School of Informatics, Computing, and Engineering dean Raj Acharya poses for a headshot. Acharya will step down mid-March to participate in an artificial intelligence research initiative at IU. Courtesy of Indiana University Dean of the Luddy School of Informatics, Computing, and Engineering Raj Acharya will step down mid-March to participate in an artificial intelligence research initiative. Acharya said the school will hire an acting dean to replace him and then conduct a national search to find a permanent dean. Acharya launched the Department of Intelligent Systems Engineering in 2016 and has been dean since July 2016. He will now be associate vice president for research with the specific task of promoting artificial intelligence.

Speculations are rife that the EU might soon be bringing in a new set of rules and regulations aimed at artificial intelligence developers. FREMONT, CA: After rolling out comprehensive data privacy regulations, the EU is now considering norms to regulate the development of Artificial Intelligence (AI) solutions. The technology of artificial intelligence has been a revelation and is one of the most-used among all the advanced technologies. Although beneficial, there have been apprehensions regarding the potential misuse of the technology, owing to its over-reaching capabilities. These apprehensions might have been the factors driving the EU towards getting a new AI policy on the cards.

In this work, we propose a hybrid approach to synchronize large scale networks. In particular, we draw on Kalman Filtering (KF) along with time-stamps generated by the Precision Time Protocol (PTP) for pairwise node synchronization. Furthermore, we investigate the merit of Factor Graphs (FGs) along with Belief Propagation (BP) algorithm in achieving high precision end-to-end network synchronization. Finally, we present the idea of dividing the large-scale network into local synchronization domains, for each of which a suitable sync algorithm is utilized. The simulation results indicate that, despite the simplifications in the hybrid approach, the error in the offset estimation remains below 5 ns.

In this paper, we consider the highly ill-posed problem of jointly recovering two real-valued signals from the phaseless measurements of their circular convolution. The problem arises in various imaging modalities such as Fourier ptychography, X-ray crystallography, and in visible light communication. We propose to solve this inverse problem using alternating gradient descent algorithm under two pretrained deep generative networks as priors; one is trained on sharp images and the other on blur kernels. The proposed recovery algorithm strives to find a sharp image and a blur kernel in the range of the respective pre-generators that \textit{best} explain the forward measurement model. In doing so, we are able to reconstruct quality image estimates. Moreover, the numerics show that the proposed approach performs well on the challenging measurement models that reflect the physically realizable imaging systems and is also robust to noise

Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Nonnegative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with nonnegative RESCAL, we propose a latent dimension determination method which is based on clustering of the solutions of multiple realizations of nonnegative RESCAL decompositions. We demonstrate the performance of our model selection method on synthetic data and then we apply our method to decompose a network of international trade flows data from International Monetary Fund and validate the resulting features against empirical facts from economic literature.

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.

Data augmentations are an important ingredient in the recipe for training robust neural networks, especially in computer vision. A fundamental question is whether neural network features explicitly encode data augmentation transformations. To answer this question, we introduce a systematic approach to investigate which layers of neural networks are the most predictive of augmentation transformations. Our approach uses layer features in pre-trained vision models with minimal additional processing to predict common properties transformed by augmentation (scale, aspect ratio, hue, saturation, contrast, brightness). Surprisingly, neural network features not only predict data augmentation transformations, but they predict many transformations with high accuracy. After validating that neural networks encode features corresponding to augmentation transformations, we show that these features are primarily encoded in the early layers of modern CNNs.