To stay ahead of the game and meet customer's needs, banks cannot afford to pay for costly and largely underused branches. Instead, the focus needs to shift to improving their online offerings. This past Friday was arguably the biggest day of the year for retailers, particularly online retailers. Throughout last week, you probably received emails about massive Black Friday discounts. Some of you might have put together wish lists to check out at the stroke of midnight while others used your phones, to scout whether a 30% discount was worth the still hefty price tags.

The use of facial recognition technology is continuing to expand, despite concerns about its accuracy and fairness and about how it could be used by governments to spy on people. These concerns have been heightened following a report by the Financial Times which shows that Chinese groups have a significant influence in shaping international standards regarding the technology. The report details how Chinese companies including ZTE, Dahua and China Telecom are proposing standards for facial recognition to the UN's International Telecommunication Union (ITU), the body responsible for global technical standards in the telecommunication industry. Usually, the standards set by the ITU are technical in nature, but human rights campaigners say the proposals under discussion in this case are more like policy recommendations. The standards proposed include recommendations for use cases, suggesting that facial recognition can be used by police, by employers to monitor employees, and for spotting specific targets in crowds.

The AI Organization used numerous algorithms to achieve an AI based prediction of a digital map of what the world will look like if President Trump is impeached and removed from office. The algorithms achieved a 93% predictability result on more than 1,000 simulated scenarios. The score never dropped to below an average of 93%, even after inputting digital codes built into Google and Baidu via Chinese influence and corporate mandates that were against the U.S and the Trump Administration. This digital report and the A.I. algorithms used the Geo-Political infrastructure and connected it with health, military, the intelligence community, human rights, safety of the U.S and the world at large. These algorithms involve the entire human race.

BOSTON – A few years ago, toward the end of his life, my father battled severe depression. As a physician and professor, he did not lack access to mental-health care. But he had grown up in a society that stigmatized mental illness, and he was unwilling to seek professional help. As a son, it was devastating to watch my father suffer. As a public-health researcher, I gained a new awareness of the myriad systemic failures in the provision of care.

We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Similarity,(C) Question-External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A-D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A-C.

This thesis proposal makes the following technical contributions: (i) we provide a definition of the Dynamic Influence Maximization Under Uncertainty (or DIME) problem, which models the problem faced by homeless shelters accurately; (ii) we propose a novel Partially Observable Markov Decision Process (POMDP) model for solving the DIME problem; (iii) we design two scalable POMDP algorithms (PSINET and HEALER) for solving the DIME problem, since conventional POMDP solvers fail to scale up to sizes of interest; and (iv) we test our algorithms effectiveness in the real world by conducting a pilot study with actual homeless youth in Los Angeles. The success of this pilot (as explained later) shows the promise of using influence maximization for social good on a larger scale.

The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties. This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks. These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.

Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Bayesian Networks (BNs) and investigate the factors associated with childhood diarrhoea. We make use of freeware tools to learn the graphical structure of the DHS data with score-based, constraint-based, and hybrid structure learning algorithms. We investigate the effect of missing values, sample size, and knowledge-based constraints on each of the structure learning algorithms and assess their accuracy with multiple scoring functions. Weaknesses in the survey methodology and data available, as well as the variability in the BNs generated, mean that is not possible to learn a definitive causal BN from data. However, knowledge-based constraints are found to be useful in reducing the variation in the graphs produced by the different algorithms, and produce graphs which are more reflective of the likely influential relationships in the data. Furthermore, valuable insights are gained into the performance and characteristics of the structure learning algorithms. Two score-based algorithms in particular, TABU and FGES, demonstrate many desirable qualities; a) with sufficient data, they produce a graph which is similar to the reference graph, b) they are relatively insensitive to missing values, and c) behave well with knowledge-based constraints. The results provide a basis for further investigation of the DHS data and for a deeper understanding of the behaviour of the structure learning algorithms when applied to real-world settings.

This past Friday was arguably the biggest day of the year for retailers, particularly online retailers. Throughout last week, you probably received emails about massive Black Friday discounts. Some of you might have put together wish lists to check out at the stroke of midnight while others used your phones, to scout whether a 30% discount was worth the still hefty price tags. What you may be interested to know is that artificial intelligence (AI) has tailored your online experience. AI is a technology that makes machines smart.

It is striking when you find yourself living at major inflection points of history. After all, there is no such thing as quiet times. The 1990s, for instance, seem like a distant quiet time. But in that period, ethnic cleansing in Bosnia and Rwanda were happening. O.J. Simpson's "trial of the century" has faded into the mist of history.