The performance of deep neural networks is often attributed to their automated, task-related feature construction. It remains an open question, though, why this leads to solutions with good generalization, even in cases where the number of parameters is larger than the number of samples. Back in the 90s, Hochreiter and Schmidhuber observed that flatness of the loss surface around a local minimum correlates with low generalization error. For several flatness measures, this correlation has been empirically validated. However, it has recently been shown that existing measures of flatness cannot theoretically be related to generalization: if a network uses ReLU activations, the network function can be reparameterized without changing its output in such a way that flatness is changed almost arbitrarily. This paper proposes a natural modification of existing flatness measures that results in invariance to reparameterization. The proposed measures imply a robustness of the network to changes in the input and the hidden layers. Connecting this feature robustness to generalization leads to a generalized definition of the representativeness of data. With this, the generalization error of a model trained on representative data can be bounded by its feature robustness which depends on our novel flatness measure.

The wide adoption of machine learning in the critical domains such as medical diagnosis, law, education had propelled the need for interpretable techniques due to the need for end users to understand the reasoning behind decisions due to learning systems. The computational intractability of interpretable learning led practitioners to design heuristic techniques, which fail to provide sound handles to tradeoff accuracy and interpretability. Motivated by the success of MaxSA T solvers over the past decade, recently MaxSA T -based approach, called MLIC, was proposed that seeks to reduce the problem of learning interpretable rules expressed in Conjunctive Normal Form (CNF) to a MaxSA T query. While MLIC was shown to achieve accuracy similar to that of other state of the art black-box classifiers while generating small interpretable CNF formulas, the runtime performance of MLIC is significantly lagging and renders approach unusable in practice. In this context, authors raised the question: Is it possible to achieve the best of both worlds, i.e., a sound framework for interpretable learning that can take advantage of MaxSAT solvers while scaling to real-world instances? In this paper, we take a step towards answering the above question in affirmation. We propose IMLI: an incremental approach to MaxSA T based framework that achieves scalable runtime performance via partition-based training methodology. Extensive experiments on benchmarks arising from UCI repository demonstrate that IMLI achieves up to three orders of magnitude runtime improvement without loss of accuracy and interpretability.

We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer learning). MNMT is more promising and interesting than its statistical machine translation counterpart because end-to-end modeling and distributed representations open new avenues for research on machine translation. Many approaches have been proposed in order to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and hence deserve further exploration. In this paper, we present an in-depth survey of existing literature on MNMT. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Wherever possible we address the strengths and weaknesses of several techniques by comparing them with each other. We also discuss the future directions that MNMT research might take. This paper is aimed towards both, beginners and experts in NMT. We hope this paper will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.

In December 2018, Canada and France announced plans for a new international body to study and steer the effects of artificial intelligence on the world's people and economies. Canadian prime minister Justin Trudeau said the International Panel on Artificial Intelligence would be established by the Group of Seven leading western economies and play a role in "addressing some of the ethical concerns we will face in this area." It was to be modeled on the UN's Intergovernmental Panel on Climate Change, which helped establish consensus on the world's climate crisis and recommends possible responses. Just over a year later, the IPAI has been renamed the Global Partnership on AI, but it still hasn't quite gotten off the ground. Six of the G7 are on board--with the United States the lone holdout.

When Microsoft CEO Satya Nadella visited New Zealand three years ago, it was an event on the tech calendar. Friday's Translator announcement made his latest visit a cultural milestone. More than 1000 industry leaders and media filled the room at Auckland's Eden Park where Satya announced the addition of te reo Māori to Microsoft Translator, alongside the 60 languages already supported by the free application. The move will enable anyone around the world to translate text to and from te reo Māori. At a time when just 3 per cent of Kiwis speak te reo Māori, yet Prime Minister Jacinda Ardern is calling for 1 million additional speakers by 2040, it's crucial that people have the tools to engage with each other in te reo Māori in their everyday lives.

The United States government says it will limit the export of certain types of artificial intelligence software that's used to analyze images from satellites in order to keep it out of the hands of foreign rivals such as China. Reuters said the ban, which goes into effect Monday, relates to a 2018 law known as the Export Control Reform Act that requires the government look into how it can restrict the export of new technologies it deems "essential to the national security" of the U.S. The scope of the ban is rather narrow, at least for now. It applies specifically to software that uses neural networks, a component of machine learning, to discover "points of interest" in geospatial images created by satellites. For example, software that can identify houses or vehicles. Furthermore, the ban only applies to software that has a graphical user interface.

The "LegalTech Artificial Intelligence Market" is evolving at an exciting pace driven by changing dynamics and risk ecosystem, an analysis of which forms the crux of the report. The study on the global LegalTech Artificial Intelligence Market takes a closer look at several regional trends and the emerging regulatory landscape to assess its prospects. The critical evaluation of the various growth factors and opportunities in the global LegalTech Artificial Intelligence Market offered in the analyses helps in assessing the lucrativeness of its key segments. Summary of Market: The global LegalTech Artificial Intelligence market is valued at xx million US$ in 2019 is expected to reach xx million US$ by the end of 2025, growing at a CAGR of xx% during 2019-2025. Legal technology, also known asLegal Tech, refers to the use oftechnologyandsoftwareto providelegal services.

Three police officers were assaulted and six juveniles were arrested after multiple fights broke out at a Mesa skate rink on Saturday night, authorities said. Weekend wrap-up: Here are the biggest Arizona stories from Jan. 3-5 Blowback: Iran abandons nuclear limits after US killing Luxury garage storage company bringing 2 new locations to the Valley Weinstein's reckoning: Trial looms 2 years after #MeToo wave Valley eye surgeon faces multiple charges for alleged billing scheme Weekend wrap-up: Here are the biggest Arizona stories from Jan. 3-5 Counting whales from space pitched as key to saving them Iraq's Parliament calls for expulsion of U.S. troops Tips on how to create, manage your budget in the new year Iraq's Parliament calls for expulsion of U.S. troops Iraq's Parliament calls for expulsion of U.S. troops Arizona ex-fire chief pleads guilty to theft charges A former fire chief accused of embezzling $40,000 from his Arizona district pleaded guilty to felony charges of theft. Valley doctor says soot from candles can be harmful to your health Candles smell good but they're not all that great for your health for one specific reason, according to one Valley doctor. Phoenix lab uses artificial intelligence to slow, manage Alzheimer's disease Arizona is projected to have one of the fastest growing rates of Alzheimer's disease in the country over the next few years, and a clinical lab testing company in the Valley is trying to reverse that. '1917,' 'Once Upon a Time ...in Hollywood' win Golden Globes See winners from the 2020 Golden Globes, hosted by Ricky Gervais.

For the past few years, we've shared predictions each December on what we believe will be the top ten technology policy issues for the year ahead. As this year draws to a close, we are looking out a bit further. It gives us all an opportunity to reflect upon the past ten years and consider what the 2020s may bring. As we concluded in our book, Tools and Weapons: The Promise and the Peril of the Digital Age, "Technology innovation is not going to slow down. The work to manage it needs to speed up." Digital technology has gone longer with less regulation than virtually any major technology before it. This dynamic is no longer sustainable, and the tech sector will need to step up and exercise more responsibility while governments catch up by modernizing tech policies. In short, the 2020s will bring sweeping regulatory changes to the world of technology. Tech is at a crossroads, and to consider why, it helps to start with the changes in technology itself. The 2010s saw four trends intersect, collectively transforming how we work, live and learn. Continuing advances in computational power made more ambitious technical scenarios possible both for devices and servers, while cloud computing made these advances more accessible to the world. Like the invention of the personal computer itself, cloud computing was as important economically as it was technically. The cloud allows organizations of any size to tap into massive computing and storage capacity on demand, paying for the computing they need without the outlay of capital expenses. More powerful computers and cloud economics combined to create the third trend, the explosion of digital data.

The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wise perturbation to fool the classifier. An alternative approach is to have perturbations in the latent space. However, such perturbations are hard to control due to the lack of interpretability and disentanglement. In this paper, we propose a more practical adversarial attack by designing structured perturbation with semantic meanings. Our proposed technique manipulates the semantic attributes of images via the disentangled latent codes. The intuition behind our technique is that images in similar domains have some commonly shared but theme-independent semantic attributes, e.g. thickness of lines in handwritten digits, that can be bidirectionally mapped to disentangled latent codes. We generate adversarial perturbation by manipulating a single or a combination of these latent codes and propose two unsupervised semantic manipulation approaches: vector-based disentangled representation and feature map-based disentangled representation, in terms of the complexity of the latent codes and smoothness of the reconstructed images. We conduct extensive experimental evaluations on real-world image data to demonstrate the power of our attacks for black-box classifiers. We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.