After months spent traveling to packed rallies, refusing to wear masks in public, and throwing parties while the rest of the country was coping with the spread of the coronavirus, Donald Trump and the GOP are dealing personally with an outbreak that almost anybody could've seen coming. Trump tested positive and has been hospitalized with the virus, and many of those closest to him, including first lady Melania, campaign manager Bill Stepien, press secretary Kayleigh McEnany, former adviser Kellyanne Conway, and close aide Hope Hicks, have all tested positive. And yet, even though the aggressive flouting of public health guidelines is precisely what led to this coronavirus cluster at the highest levels of government, Vice President Mike Pence and Sen. Kamala Harris (D-Calif.) After Trump tested positive, one would have thought that both sides would make an effort to make the venue safer. Finally, after some haggling about how far apart the two candidates would stand--with the Democrats arguing for more safety--the debate commission agreed to place the podiums 12 feet apart and set plexiglass between the candidates.

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders. Companies and cities all over world are experimenting with using artificial intelligence to reduce and prevent crime, and to more quickly respond to crimes in progress. The ideas behind many of these projects is that crimes are relatively predictable; it just requires being able to sort through a massive volume of data to find patterns that are useful to law enforcement. This kind of data analysis was technologically impossible a few decades ago, but the hope is that recent developments in machine learning are up to the task.

A team of planetary scientists and AI researchers at NASA's Jet Propulsion Laboratory in Southern California tapped artificial intelligence to identify fresh craters on Mars. The High-Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter (MRO) spotted the craters. AI technology first discovered the craters in images taken the orbiter's Context Camera, then scientists followed up with the HiRISE image to confirm the craters. The accomplishment offers hope for both saving times and accelerating the volume of findings, as noted by NASA's Jet Propulsion Laboratory. According to the laboratory, scientists typically spend hours each day studying images captured by NASA's MRO, looking for changing surface phenomena like dust devils, avalanches, and shifting dunes.

The world is moving in the direction of hottest technology trends to maneuver enterprises with intelligent solutions and lead revenue generation. Artificial Intelligence aka AI is one such technology through which powerful and intelligent solutions are developed that are capable of enhancing operational efficiency and fueling business growth. AI enables computers to replicate the human intelligence and provide reliable results for enhanced business understanding and growth through automation of business processes and operations. Common use technology like voice powered assistants including Siri & Alexa, humanoid robots like Sophia, and car driving bots are premier results of AI. Due to its dynamism to perform narrow and general tasks, it is also sometimes known as narrow AI and general AI.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. If artificial intelligence will destroy humanity, it probably won't be through killer robots and the incarnation--it will be through a thousand paper cuts. In the shadow of the immense benefits of advances in technology, the dark effects of AI algorithms are slowly creeping into different aspects of our lives, causing divide, unintentionally marginalizing groups of people, stealing our attention, and widening the gap between the wealthy and the poor. While we're already seeing and discussing many of the negative aspects of AI, not enough is being done to address them. And the reason is that we're looking in the wrong place, as futurist and Amy Webb discusses in her book The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity. Many are quick to blame large tech companies for the problems caused by artificial intelligence.

Pruning and quantization are widely applied techniques for compressing deep neural networks, often driven by the resource constraints of deploying models to mobile phones or embedded devices (Esteva et al., 2017; Lane & Warden, 2018). To-date, discussion around the relative merits of different compression methods has centered on the tradeoff between level of compression and top-line metrics such as top-1 and top-5 accuracy (Blalock et al., 2020). Along this dimension, compression techniques are remarkably successful. It is possible to prune the majority of weights (Gale et al., 2019; Evci et al., 2019) or heavily quantize the bit representation (Jacob et al., 2017) with negligible decreases to test-set accuracy. However, recent work by Hooker et al. (2019a) has found that the minimal changes to top-line metrics obscure critical differences in generalization between pruned and non-pruned networks. The authors establish that pruning disproportionately impacts predictive performance on a small subset of the dataset. We build upon this work and focus on the implications of these findings for a dataset with sensitive protected attributes such as gender and age. Our work addresses the question: Does compression amplify existing algorithmic bias?

We present a new framework for recycling independent variational approximations to Gaussian processes. The main contribution is the construction of variational ensembles given a dictionary of fitted Gaussian processes without revisiting any subset of observations. Our framework allows for regression, classification and heterogeneous tasks, i.e. mix of continuous and discrete variables over the same input domain. We exploit infinite-dimensional integral operators based on the Kullback-Leibler divergence between stochastic processes to re-combine arbitrary amounts of variational sparse approximations with different complexity, likelihood model and location of the pseudo-inputs. Extensive results illustrate the usability of our framework in large-scale distributed experiments, also compared with the exact inference models in the literature.

Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches. However, current neural solutions are arguably limited in how they model context. In this paper, we enhance a segmenter based on a hierarchical attention BiLSTM network to better model context, by adding a coherence-related auxiliary task and restricted self-attention. Our optimized segmenter outperforms SOTA approaches when trained and tested on three datasets. We also the robustness of our proposed model in domain transfer setting by training a model on a large-scale dataset and testing it on four challenging real-world benchmarks. Furthermore, we apply our proposed strategy to two other languages (German and Chinese), and show its effectiveness in multilingual scenarios.

Cognitive communications have emerged as a promising solution to enhance, adapt, and invent new tools and capabilities that transcend conventional wireless networks. Deep learning (DL) is critical in enabling essential features of cognitive systems because of its fast prediction performance, adaptive behavior, and model-free structure. These features are especially significant for multi-antenna wireless communications systems, which generate and handle massive data. Multiple antennas may provide multiplexing, diversity, or antenna gains that, respectively, improve the capacity, bit error rate, or the signal-to-interference-plus-noise ratio. In practice, multi-antenna cognitive communications encounter challenges in terms of data complexity and diversity, hardware complexity, and wireless channel dynamics. The DL-based solutions tackle these problems at the various stages of communications processing such as channel estimation, hybrid beamforming, user localization, and sparse array design. There are research opportunities to address significant design challenges arising from insufficient data coverage, learning model complexity, and data transmission overheads. This article provides synopses of various DL-based methods to impart cognitive behavior to multi-antenna wireless communications.

In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text. However, the tasks of evaluating quality differences between NLG systems and understanding how humans perceive the generated text remain both crucial and difficult. In this system demonstration, we present Real or Fake Text (RoFT), a website that tackles both of these challenges by inviting users to try their hand at detecting machine-generated text in a variety of domains. We introduce a novel evaluation task based on detecting the boundary at which a text passage that starts off human-written transitions to being machine-generated. We show preliminary results of using RoFT to evaluate detection of machine-generated news articles.