Large-scale trends in urban crime and global terrorism are well-predicted by socio-economic drivers, but focused, event-level predictions have had limited success. Standard machine learning approaches are promising, but lack interpretability, are generally interpolative, and ineffective for precise future interventions with costly and wasteful false positives. Here, we are introducing Granger Network inference as a new forecasting approach for individual infractions with demonstrated performance far surpassing past results, yet transparent enough to validate and extend social theory. Considering the problem of predicting crime in the City of Chicago, we achieve an average AUC of ~90\% for events predicted a week in advance within spatial tiles approximately $1000$ ft across. Instead of pre-supposing that crimes unfold across contiguous spaces akin to diffusive systems, we learn the local transport rules from data. As our key insights, we uncover indications of suburban bias -- how law-enforcement response is modulated by socio-economic contexts with disproportionately negative impacts in the inner city -- and how the dynamics of violent and property crimes co-evolve and constrain each other -- lending quantitative support to controversial pro-active policing policies. To demonstrate broad applicability to spatio-temporal phenomena, we analyze terror attacks in the middle-east in the recent past, and achieve an AUC of ~80% for predictions made a week in advance, and within spatial tiles measuring approximately 120 miles across. We conclude that while crime operates near an equilibrium quickly dissipating perturbations, terrorism does not. Indeed terrorism aims to destabilize social order, as shown by its dynamics being susceptible to run-away increases in event rates under small perturbations.

Recurrent Neural Networks (RNNs) can be seriously impacted by the initial parameters assignment, which may result in poor generalization performances on new unseen data. With the objective to tackle this crucial issue, in the context of RNN based classification, we propose a new supervised layer-wise pretraining strategy to initialize network parameters. The proposed approach leverages a data-aware strategy that sets up a taxonomy of classification problems automatically derived by the model behavior. To the best of our knowledge, despite the great interest in RNN-based classification, this is the first data-aware strategy dealing with the initialization of such models. The proposed strategy has been tested on four benchmarks coming from two different domains, i.e., Speech Recognition and Remote Sensing. Results underline the significance of our approach and point out that data-aware strategies positively support the initialization of Recurrent Neural Network based classification models.

How do groups of individuals achieve consensus in movement decisions? Do individuals follow their friends, the one predetermined leader, or whomever just happens to be nearby? To address these questions computationally, we formalize Coordination Strategy Inference Problem. In this setting, a group of multiple individuals moves in a coordinated manner towards a target path. Each individual uses a specific strategy to follow others (e.g. nearest neighbors, pre-defined leaders, preferred friends). Given a set of time series that includes coordinated movement and a set of candidate strategies as inputs, we provide the first methodology (to the best of our knowledge) to infer the set of strategies that each individual uses to achieve movement coordination at the group level. We evaluate and demonstrate the performance of the proposed framework by predicting the direction of movement of an individual in a group in both simulated datasets as well as two real-world datasets: a school of fish and a troop of baboons. Moreover, since there is no prior methodology for inferring individual-level strategies, we compare our framework with the state-of-the-art approach for the task of classification of group-level-coordination models. The results show that our approach is highly accurate in inferring the correct strategy in simulated datasets even in complicated mixed strategy settings, which no existing method can infer. In the task of classification of group-level-coordination models, our framework performs better than the state-of-the-art approach in all datasets. Animal data experiments show that fish, as expected, follow their neighbors, while baboons have a preference to follow specific individuals. Our methodology generalizes to arbitrary time series data of real numbers, beyond movement data.

Social bias in machine learning has drawn significant attention, with work ranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for different contexts, to developing algorithms to mitigate bias. In natural language processing, gender bias has been shown to exist in context-free word embeddings. Recently, contextual word representations have outperformed word embeddings in several downstream NLP tasks. These word representations are conditioned on their context within a sentence, and can also be used to encode the entire sentence. In this paper, we analyze the extent to which state-of-the-art models for contextual word representations, such as BERT and GPT-2, encode biases with respect to gender, race, and intersectional identities. Towards this, we propose assessing bias at the contextual word level. This novel approach captures the contextual effects of bias missing in context-free word embeddings, yet avoids confounding effects that underestimate bias at the sentence encoding level. We demonstrate evidence of bias at the corpus level, find varying evidence of bias in embedding association tests, show in particular that racial bias is strongly encoded in contextual word models, and observe that bias effects for intersectional minorities are exacerbated beyond their constituent minority identities. Further, evaluating bias effects at the contextual word level captures biases that are not captured at the sentence level, confirming the need for our novel approach.

A recent market intelligence report that is published by Data Insights Partner on the global Artificial Intelligence in Healthcare Application market makes an offering of in-depth analysis of segments and sub-segments in the regional and international Artificial Intelligence in Healthcare Application market. The research also emphasizes on the impact of restraints, drivers, and macro indicators on the regional and global Artificial Intelligence in Healthcare Application market over the short as well as long period of time. A detailed presentation of forecast, trends, and dollar values of global Artificial Intelligence in Healthcare Application market is offered. In accordance with the report, the global Artificial Intelligence in Healthcare Application market is projected to expand at a CAGR of 35% over the period of forecast. One of the greatest challenges in the healthcare arena is huge healthcare data and it is impossible to handle all of these data for medical personnel. Here comes the opportunity of artificial intelligence (AI) in healthcare field.

A few years ago, Paul Allen, the co-founder of Microsoft, published the results of something called the Great Elephant Census, which counted all the savanna elephants in Africa. What it found rocked the conservation world: In the seven years between 2007 and 2014, Africa's savanna Elephant population decreased by about a third and was on track to disappear completely from some African countries in as few as 10 years. To reverse that trend, researchers landed on a technology that is rewriting the rules for everything from our household appliances to our cars: artificial intelligence. AI's ability to find patterns in enormous volumes of information is demystifying not just Elephant behavior but human behavior -- specifically poacher behavior -- too. "AI can process huge amounts of information to tell us where the elephants are, how many there are," said Cornell University researcher Peter Wrege.

Babylon, a UK start-up, plans to "put an accessible and affordable health service in the hands of every person on earth" by putting artificial intelligence (AI) tools to work. Currently, the company has operations in the UK and Rwanda and hopes to expand to the Middle East, the United States, and China. The company's strategy is to combine the power of AI with the medical expertise of humans to deliver unparalleled access to healthcare. The Amazing Ways Babylon Health Is Using Artificial Intelligence To Make Healthcare Universally ... [ ] Accessible Babylon's engineers, doctors, and scientists developed an AI system that can receive data about the symptoms someone is suffering from, compare the information to a database of known conditions and illnesses to find possible matches, and then identify a course of action and related risk factors. People can use the "Ask Babylon" feature to inquire about their medical concerns to get an initial understanding of what they might be dealing with, but this service is not intended to replace the expertise of a doctor or be used in a medical emergency.

The recruitment process has begun for the world's very first university dedicated to artificial intelligence, here in the UAE. More than 3,000 people have started the process to attend The Mohamed bin Zayed University of Artificial Intelligence, which will open for students from September 2020. World's first artificial intelligence university to open in Abu Dhabi The university, based in Abu Dhabi's Masdar City, received the majority of applications from the UAE, Saudi Arabia, Algeria, Egypt, India and China. More than 230 eager students have completed applications to attend next year's course - less than two weeks after the launch of the university was announced. "It is gratifying that there has already been such a strong expression of interest so quickly after the announcement," said Prof Sir Michael Brady, interim president of MBZUAI.

From elephants and rhinos to sea turtles and lemurs, poaching is quickly driving many endangered species to the brink of extinction. Often, governments and activists struggle to effectively monitor vast expanses of land for handfuls of poachers who travel at night. So what if artificial intelligence did it for them? In South Africa, conservationists were making no headway on preventing rampant rhino poaching. Hluhluwe–iMfolozi Park, the "birthplace of rhinos," was a particular hotspot, logging hundreds of dead rhinos in a single year.

Artificial intelligence developer DeepMind has just announced its latest milestone: a bot called AlphaStar that plays the popular real-time strategy game StarCraft II at Grandmaster level. This isn't the first time a bot has outplayed humans in a strategy war game. In 1981, a program called Eurisko, developed by artificial intelligence (AI) pioneer Doug Lenat, won the US championship of Traveller, a highly complex strategy war game in which players design a fleet of 100 ships. Eurisko was consequently made an honorary Admiral in the Traveller navy. The following year, the tournament rules were overhauled in an attempt to thwart computers.