Why data could be the deciding factor in Africa's agricultural transformation. The world has a palm oil problem. It's a global, billion-dollar industry and its end result is irreversible environmental damage, ranging from deforestation and fires, to the loss of species such as tigers, pygmy elephants and orangutans. Palm oil is used in 50% of the products we buy (think bread, shampoo, soaps and even chocolate) due to the fact that it is the highest-yielding vegetable oil crop. Yet, in a country like Uganda, where 80% of the population is involved in agriculture as a way of life, many Ugandans farm oil palm on small plots, barely making a living. "The use of data for purposes of precision agricultural systems is being used around the world to optimize farms, from anticipating natural disasters such as droughts and flooding, to predicting the best time to harvest crops, to anticipating outbreaks of pests and disease before they impact the produce," says AgriSA's Janse Rabie.

You know when you try to go online at a Starbucks or on an airplane, first you get a little popup that asks you to accept some terms before you can get to the internet? That popup window exists in a sort of netherworld between actual internet connection and being offline–you pick it up via Wi-Fi, but until you click a box, you're not actually online. A team of five developers realized in that gray area was potentially a huge opportunity to save lives. It's an intractable problem during natural disasters: telecommunications networks and power grids are often damaged or overwhelmed; without them, first responders struggle to help survivors, coordinate evacuations, and even count the dead. Project Owl proposes an elegant solution: an AI-powered disaster coordination platform paired with a robust communication network that can reach people even when other connections are down.

Since the concept of "machines learning" was introduced in the 1950s, the field has gone from a cryptic domain understood by a few (Turing, Markov, Legendre, Laplace or Bayes) to a technology that every company must deploy. Every day we hear how data and automation improve our shopping experiences, our online searches and enables fraud prevention and cybersecurity routines to do more, faster and better for us. Now, the amalgamates created around Artificial Intelligence, Machine Learning and Big Data are bound to confuse industry observers or investors who aren't familiar with the technical details. If you're asking yourself: "What's the difference between Big Data and Machine Learning?", then for the sake of my piece, simply think about it this way: "Big Data is Machine Learning's great uncle". Machine Learning doesn't need Big Data to exist.

Authorities in Dallas are pursuing the use of drones to assist in their duties to locate suspects and access areas that are unreachable by helicopters. Paul Stokes, Dallas assistant police chief, outlined the department's planned use of the drones during a recent city council briefing, saying the technology would allow officers to make sure a building is clear before entering, assist in fires and large protests and help identify suspects, the Dallas Morning News reported on Monday. According to FOX 4, it seems as council members approved of the idea, with Councilwoman Sandy Greyson calling drones "such cool technology." "I can see 100 different uses," she told the station. Drone use in law enforcement has been a privacy concern raised by the community, and something Stokes was quick to ensure would not be compromised.

Artificial intelligence is the latest buzzword to take hold of the cybersecurity industry. It is being touted, among other things, as the ultimate solution to the cybersecurity skills gap. But just how accurate is this belief? Will AI be the cure to all of our cybersecurity ailments, as human security analysts are replaced by robots powered with artificial intelligence (AI) technology? Or will it make the skills gap even worse by changing the landscape?

Microsoft's blog post stated that if granted the contract, the company will give differential employees an option to "work on a different project or team". Microsoft said Friday it is prepared to provide its technology to the US military, including for a massive cloud computing project, despite ethics concerns among some of its employees and others in Silicon Valley. Microsoft president Brad Smith agrees. Throughout the piece, Smith continued to walk a fine line between patriotic duty to support the US military, while carefully conceding that there will be different opinions in a large and diverse company population (some of whom aren't USA citizens). The company, he said, is already working with experts to help it do so.

An attacker may use a variety of techniques to fool an automatic speaker verification system into accepting them as a genuine user. Anti-spoofing methods meanwhile aim to make the system robust against such attacks. The ASVspoof 2017 Challenge focused specifically on replay attacks, with the intention of measuring the limits of replay attack detection as well as developing countermeasures against them. In this work, we propose our replay attacks detection system - Attentive Filtering Network, which is composed of an attention-based filtering mechanism that enhances feature representations in both the frequency and time domains, and a ResNet-based classifier. We show that the network enables us to visualize the automatically acquired feature representations that are helpful for spoofing detection. Attentive Filtering Network attains an evaluation EER of 8.99$\%$ on the ASVspoof 2017 Version 2.0 dataset. With system fusion, our best system further obtains a 30$\%$ relative improvement over the ASVspoof 2017 enhanced baseline system.

Identifying changes in model parameters is fundamental in machine learning and statistics. However, standard changepoint models are limited in expressiveness, often addressing unidimensional problems and assuming instantaneous changes. We introduce change surfaces as a multidimensional and highly expressive generalization of changepoints. We provide a model-agnostic formalization of change surfaces, illustrating how they can provide variable, heterogeneous, and non-monotonic rates of change across multiple dimensions. Additionally, we show how change surfaces can be used for counterfactual prediction. As a concrete instantiation of the change surface framework, we develop Gaussian Process Change Surfaces (GPCS). We demonstrate counterfactual prediction with Bayesian posterior mean and credible sets, as well as massive scalability by introducing novel methods for additive non-separable kernels. Using two large spatio-temporal datasets we employ GPCS to discover and characterize complex changes that can provide scientific and policy relevant insights. Specifically, we analyze twentieth century measles incidence across the United States and discover previously unknown heterogeneous changes after the introduction of the measles vaccine. Additionally, we apply the model to requests for lead testing kits in New York City, discovering distinct spatial and demographic patterns.

Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been recently shown to be effective. However, previous works either assume that intra-class variances can always be generalized to new classes, or employ naive generation methods to hallucinate finite examples without modeling their latent distributions. In this work, we propose Covariance-Preserving Adversarial Augmentation Networks to overcome existing limits of low-shot learning. Specifically, a novel Generative Adversarial Network is designed to model the latent distribution of each novel class given its related base counterparts. Since direct estimation on novel classes can be inductively biased, we explicitly preserve covariance information as the "variability" of base examples during the generation process. Empirical results show that our model can generate realistic yet diverse examples, leading to substantial improvements on the ImageNet benchmark over the state of the art.

Abstract--Advanced automation is involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal, and there may exist a responsibility gap. Understanding human responsibility is particularly important when systems can harm people, as with autonomous vehicles or, most notably, with Autonomous Weapon Systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human interaction in automated systems and demonstrate its applications on decisions involving AWS. The analysis reveals that human comparative responsibility is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in advanced automation. Our responsibility model can guide system design decisions and can aid policy and legal decisions regarding human responsibility in highly automated systems. Financial markets largely function through algorithmic trading mechanisms [1, 2], semiconductor manufacturing is almost entirely automated [3], and decision support systems and aids for diagnostic interpretation have become part of medical practice [4, 5]. Similarly, in aviation, flight management systems control almost all parts of the flight [6, 7], and in surface transportation, public transportation is increasingly automated, and the first autonomous cars appear on public roads [8, 9]. Manuscript submitted October 30, 2018; (Corresponding author: Joachim Meyer) N. Douer with the Department of Industrial Engineering at Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel (email: nirdouer@mail.tau.ac.il).