Serial cybersecurity entrepreneur Shlomo Kramer said in a 2005 interview that cybersecurity is "a bit like Alice in Wonderland" where you run as fast as you can only to stay in place. In 2020, to paraphrase the second part of the Red Queen's observation (actually from Through the Looking Glass), if you wish to stay ahead of cyber criminals, you must run twice--or ten times--as fast as that. The 141 predictions listed here reveal the state-of-mind of key participants in the cybersecurity defense industry and highlight all that's hot today. The future is murky, but we know for sure that on January 1, 2020, the California Consumer Privacy Act (CCPA) will go into effect; that the U.S. presidential election will take place on November 3, 2020; and that on October 1, 2020, if you "wish to fly on commercial aircrafts or access federal facilities" in the U.S., you must have a REAL ID compliant card. Other than these known events, the crystal balls of the participants in this survey warn us ...

Sparse generalized additive models (GAMs) are an extension of sparse generalized linear models which allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modeling (Yu et al. 2019), we propose a multi-stage algorithm, called $\textit{reluctant additive modeling (RAM)}$, that can fit sparse generalized additive models at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.

This article presents a summary graph to show the relationships between Information Retrieval (IR) and other related disciplines. The figure tells the key differences between them and the conditions under which one would transition into another. When I studied Machine Learning (ML), my favorite figure among all was "The Table of Common Distributions" in Casella and Berger's 2002 book "Statistical Inference". It appeared in the book's appendix. Every time when I saw this figure, I was in awe.

Machine learning techniques are currently used extensively for automating various cybersecurity tasks. Most of these techniques utilize supervised learning algorithms that rely on training the algorithm to classify incoming data into different categories, using data encountered in the relevant domain. A critical vulnerability of these algorithms is that they are susceptible to adversarial attacks where a malicious entity called an adversary deliberately alters the training data to misguide the learning algorithm into making classification errors. Adversarial attacks could render the learning algorithm unsuitable to use and leave critical systems vulnerable to cybersecurity attacks. Our paper provides a detailed survey of the state-of-the-art techniques that are used to make a machine learning algorithm robust against adversarial attacks using the computational framework of game theory. We also discuss open problems and challenges and possible directions for further research that would make deep machine learning-based systems more robust and reliable for cybersecurity tasks.

The growth of ecommerce in the recent past can only be described as explosive and sweeping across the planet. According to a 2016 study, half of all dollars spent online in America belong to Amazon. And consider this, Recommendation Engines alone drive 35% of that revenue. But it is not ecommerce alone that's reaping the huge benefits that recommendation engines have to offer. Direct to device streaming services such as Netflix, Spotify among others, analyze user behavior almost to a micro moment level, then gather data surrounding similar users who are likely to buy the same items based on their browsing history, and provide that much needed nudge to move on to the next purchase on the platform.

In the last few years, numerous developments have led to a growing awareness of the maturity of Artificial Intelligence. Self-driving cars and personal assistants like Alexa and Siri are some of the consumer-facing technologies that have helped to fuel this awareness. This knowledge can also bring with it a certain dystopian fear about robots and technology "taking over". While we should always strive to be cautious with new technologies, our concerns should also be tempered by understanding the long curve of development that typically precedes these seemingly overnight maturings of technology. I've been reading Artificial Intelligence in Education, a 2019 publication by Wayne Holmes, Maya Bialik, and Charles Fadel, that explores implications of AI in the realm of teaching and learning.

Serial cybersecurity entrepreneur Shlomo Kramer said in a 2005 interview that cybersecurity is "a bit like Alice in Wonderland" where you run as fast as you can only to stay in place. In 2020, to paraphrase the second part of the Red Queen's observation (actually from Through the Looking Glass), if you wish to stay ahead of cyber criminals, you must run twice--or ten times--as fast as that. The 141 predictions listed here reveal the state-of-mind of key participants in the cybersecurity defense industry and highlight all that's hot today. The future is murky, but we know for sure that on January 1, 2020, the California Consumer Privacy Act (CCPA) will go into effect; that the U.S. presidential election will take place on November 3, 2020; and that on October 1, 2020, if you "wish to fly on commercial aircrafts or access federal facilities" in the U.S., you must have a REAL ID compliant card. Other than these known events, the crystal balls of the participants in this survey warn us ...

Risk managers are grappling with a fear of the unknown. AI hasn't been adopted at a large scale and the unintended consequences aren't fully documented, according to Steve Culp, senior managing director for finance and risk at management consulting firm Accenture PLC. "Before there were ships," he said, "we never had shipwrecks." Eleven percent of risk managers in banking, capital markets and insurance say they aren't fully capable of assessing AI-related risks, according to a survey of 683 risk managers in nine countries released this week by Accenture. Respondents expressed a similar comfort level with assessing the possible downsides associated with blockchain technology, quantum computing and other emerging areas of technology.

Medical imaging and radiology are facing a major crisis with an ever-increasing complexity and volume of data along an immense economic pressure. Machine learning has emerged as a key technology for developing novel tools in computer aided diagnosis, therapy and intervention. Still, progress is slow compared to other fields of visual recognition, which is mainly due to the domain complexity and constraints in clinical applications, i.e., robustness, high accuracy and reliability. "Medical Imaging meets NeurIPS" aims to bring researchers together from the medical imaging and machine learning communities to discuss the major challenges in the field and opportunities for research and novel applications. The proposed event will be the continuation of a successful workshop organized in NeurIPS 2017 and 2018 (https://sites.google.com/view/med-nips-2018).

The two areas of Artificial Intelligence, namely machine learning and deep learning, raise more questions than an entire field combined, mainly because these two areas are often mixed up and used interchangeably when referring to statistical modeling of data; however, the techniques used in each are different and you need to understand the distinctions between these data modeling paradigms in order to refer to them by their corresponding name. In this article, we'll explain the definitions of artificial intelligence, machine learning, deep learning, and neural networks, briefly overview each of those categories, explain how they work, and finish with an explicit comparison of machine learning vs deep learning. Artificial Intelligence (hereafter referred to as AI) is the intelligence demonstrated by machines as opposed to the natural intelligence of humans. AI can be further classified into three different systems: analytical, human-inspired, and humanized artificial intelligence. Analytical AI generates the cognitive representation of the world through learning that's based on past experiences to predict future decisions.