Society is rife with fears about the future of AI. For some, like Richard Branson and Ray Dalio, it's AI's exacerbation of the wealth gap and the looming social crisis it could bring about. My worry for AI, however, is that we are vilifying a technology that may in fact be our single greatest resource for creating a just world--if we build it right. While there do exist serious ethical conundrums and uncertainties regarding the potential for artificial superintelligence, at the moment, what we are facing on Earth is mostly a complex assemblage of human problems. It is true that automation is contributing to the increasing inequality within most countries, with the income gap widening between each country's educated middle and upper classes and their less-educated lower classes.

A look at how Yemen's brutal civil war is creating a market for prosthetic limbs. Each is missing a vital part of their body – a hand, a leg, an arm. Inside that building is new hope for each: Prosthetic limbs are being cut, carved, melted and molded. Young patient recently outfitted with a new leg waits for his training session outside the Ma'rib prosthetics center in Yemen (Fox News/Hollie McKay) "Sometimes I go to my office to cry for each of these miserable stories," Dr. Haitham Ahmed Ali Ahmed, a Sudanese volunteer with Physicians Across Continents, told Fox News. "It isn't fair, but we do whatever we can to give them another chance."

Deep in the rainforest in a northern corner of the Republic of Congo, some of the most sophisticated monitoring of animal sounds on earth is taking place. Acoustic sensors are collecting large amounts of data around the clock for the Elephant Listening Project. These sensors capture the soundscape in Nouabalé-Ndoki National Park and adjacent logging areas: chimpanzees, gorillas, forest buffalo, endangered African grey parrots, fruit hitting the ground, blood-sucking insects, chainsaws, engines, human voices, gunshots. But researchers and local land managers who placed them there are listening for one sound in particular -- the calls of elusive forest elephants. Forest elephants are in steep decline; scientists estimate two-thirds of Africa's population has likely been lost to ivory poaching in recent decades.

When we think about job displacement our minds often go to factory workers and calls centers, but as leading artificial Intelligence expert Dr. Vivienne Ming points out in a recent interview with the Financial Times, the middle class in professional services may be the most challenged in this computed future. Ming cites a recent competition at Columbia University between human lawyers and AI counterparts reviewing agreements with loopholes. The AI found 95 per cent of them in 22 seconds, it took the humans over an hour. As a lawyer reading this you may have two conflicting emotions: concern, but the other should be joy that your life is about to get easier; free of the day to day drudgery of reviewing agreements, allowing you to concentrate on what really matters. This is what technology does – an advantage to highly complementary skills.

You could work for Amazon in your PJs. Amazon has more than 1,200 Amazon staff in the greater Boston area. BOSTON – Amazon isn't just a Seattle company anymore, and a visit to its offices in this university city explains why. Here, in an old Necco wafer candy factory in the formerly industrial neighborhood of Fort Point, Rohit Prasad oversees 1,200 workers developing Alexa, the company's digital assistant. Walls made out of shipping containers, a playful nod to Amazon's main business, and exposed brick echo the urban tech vibe of its Seattle headquarters.

Acoustic sensors, big data, machine learning and protection of threatened animal species: all these elements go hand in hand in the Elephant Listening Project that is taking place in the jungles of the Republic of the Congo. For 24 hours a day, the sensors collect large amounts of data from the acoustic environment of the Nouabalé-Ndoki National Park and neighboring forest areas. Among these data, the sounds of African elephants stand out, whose population has dropped by 30% in just 6 years, mainly due to poaching. But … what is the use of analyzing the sounds of elephants? Well to calculate the variations of its threatened population; but for this it is necessary to identify the sounds produced by the elephants from the rest of the jungle sounds, and then identify the elephants individually in order to count them, an impossible task to be performed from the air . This is possible thanks to Conservation Meetrics, a project promoted by Microsoft within its AI for Earth initiative, based on the use of machine learning to monitor wildlife and evaluate the results of conservation work.

As a result, scientist and biologists can track real-time movement patterns of wild elephants, alerting forest authorities of any poaching related activity (by flagging off gunshot sounds) and finding and blocking online ads that attempt to sell illegal ivory or elephant parts -- again thanks to image recognition based artificial intelligence which hunts down these online ads. Hopefully, due to this AI technique, wild elephants will start flourishing again.

We present an approach for reconfiguration of dynamic visual sensor networks with deep reinforcement learning (RL). Our RL agent uses a modified asynchronous advantage actor-critic framework and the recently proposed Relational Network module at the foundation of its network architecture. To address the issue of sample inefficiency in current approaches to model-free reinforcement learning, we train our system in an abstract simulation environment that represents inputs from a dynamic scene. Our system is validated using inputs from a real-world scenario and preexisting object detection and tracking algorithms.

Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges. This paper gives a selective overview on recent advance on high-dimensional factor models and their applications to statistics including Factor-Adjusted Robust Model selection (FarmSelect) and Factor-Adjusted Robust Multiple testing (FarmTest). We show that classical methods, especially principal component analysis (PCA), can be tailored to many new problems and provide powerful tools for statistical estimation and inference. We highlight PCA and its connections to matrix perturbation theory, robust statistics, random projection, false discovery rate, etc., and illustrate through several applications how insights from these fields yield solutions to modern challenges. We also present far-reaching connections between factor models and popular statistical learning problems, including network analysis and low-rank matrix recovery.

Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the interactions between modalities (cross-modal interactions). In this paper, we propose the Recurrent Multistage Fusion Network (RMFN) which decomposes the fusion problem into multiple stages, each of them focused on a subset of multimodal signals for specialized, effective fusion. Cross-modal interactions are modeled using this multistage fusion approach which builds upon intermediate representations of previous stages. Temporal and intra-modal interactions are modeled by integrating our proposed fusion approach with a system of recurrent neural networks. The RMFN displays state-of-the-art performance in modeling human multimodal language across three public datasets relating to multimodal sentiment analysis, emotion recognition, and speaker traits recognition. We provide visualizations to show that each stage of fusion focuses on a different subset of multimodal signals, learning increasingly discriminative multimodal representations.