This article provides an introduction to forecasting insurance claims payouts. We focus specifically on the claims arising from weather events (events) that result in large scale destruction such as hurricanes, wildfires, floods, etc. We first provide a general overview of the traditional methodology and then discuss potential use of machine learning (ML) techniques to enhance the forecasting process. While the examples and website references provided in this article are US-centric, the ideas presented herein are general and can be applied to all locations. In other regions and countries, the analyst will need to substitute the appropriate data sources for event data.

Beyond these virtual assistants, voice technology and conversational AI have increased in popularity over the last decade and are used in many applications. One use of Natural Language Processing (NLP) technology is to analyse and gain insight from the written transcripts of audio-- whether from voice assistants or from other scenarios like meetings, interviews, call centres, lectures or TV shows. Yet when we speak, things are more complicated than a simple text transcription suggests. This post talks about some of the differences between written and spoken language, especially in the context of conversation. To understand conversation, we need data.

AI and machine learning (ML) have become critical technologies in information security, as they are able to quickly analyze millions of events and identify many different types of threats – from malware exploiting zero-day vulnerabilities to identifying risky behavior that might lead to a phishing attack or download of malicious code. These technologies learn over time, drawing from the past to identify new types of attacks now. Histories of behavior build profiles on users, assets, and networks, allowing AI to detect and respond to deviations from established norms.

An online app called Amica is now using artificial intelligence to help separating couples make parenting arrangements and divide their assets. For many people, the coronavirus pandemic has put even the strongest of relationships to the test. A May survey conducted by Relationships Australia found 42% of 739 respondents experienced a negative change in their relationship with their partner under lockdown restrictions. There has also been a surge in the number of couples seeking separation advice. The Australian government has backed the use of Amica for those in such circumstances.

The current knowledge system of macroeconomics is built on interactions among a small number of variables, since traditional macroeconomic models can mostly handle a handful of inputs. Recent work using big data suggests that a much larger number of variables are active in driving the dynamics of the aggregate economy. In this paper, we introduce a knowledge graph (KG) that consists of not only linkages between traditional economic variables but also new alternative big data variables. We extract these new variables and the linkages by applying advanced natural language processing (NLP) tools on the massive textual data of academic literature and research reports. As one example of the potential applications, we use it as the prior knowledge to select variables for economic forecasting models in macroeconomics. Compared to statistical variable selection methods, KG-based methods achieve significantly higher forecasting accuracy, especially for long run forecasts.

The efficient treatment of long-range interactions for point clouds is a challenging problem in many scientific machine learning applications. To extract global information, one usually needs a large window size, a large number of layers, and/or a large number of channels. This can often significantly increase the computational cost. In this work, we present a novel neural network layer that directly incorporates long-range information for a point cloud. This layer, dubbed the long-range convolutional (LRC)-layer, leverages the convolutional theorem coupled with the non-uniform Fourier transform. In a nutshell, the LRC-layer mollifies the point cloud to an adequately sized regular grid, computes its Fourier transform, multiplies the result by a set of trainable Fourier multipliers, computes the inverse Fourier transform, and finally interpolates the result back to the point cloud. The resulting global all-to-all convolution operation can be performed in nearly-linear time asymptotically with respect to the number of input points. The LRC-layer is a particularly powerful tool when combined with local convolution as together they offer efficient and seamless treatment of both short and long range interactions. We showcase this framework by introducing a neural network architecture that combines LRC-layers with short-range convolutional layers to accurately learn the energy and force associated with a $N$-body potential. We also exploit the induced two-level decomposition and propose an efficient strategy to train the combined architecture with a reduced number of samples.

Incorporating fairness constructs into machine learning algorithms is a topic of much societal importance and recent interest. Clustering, a fundamental task in unsupervised learning that manifests across a number of web data scenarios, has also been subject of attention within fair ML research. In this paper, we develop a novel notion of fairness in clustering, called representativity fairness. Representativity fairness is motivated by the need to alleviate disparity across objects' proximity to their assigned cluster representatives, to aid fairer decision making. We illustrate the importance of representativity fairness in real-world decision making scenarios involving clustering and provide ways of quantifying objects' representativity and fairness over it. We develop a new clustering formulation, RFKM, that targets to optimize for representativity fairness along with clustering quality. Inspired by the $K$-Means framework, RFKM incorporates novel loss terms to formulate an objective function. The RFKM objective and optimization approach guides it towards clustering configurations that yield higher representativity fairness. Through an empirical evaluation over a variety of public datasets, we establish the effectiveness of our method. We illustrate that we are able to significantly improve representativity fairness at only marginal impact to clustering quality.

Interpreting how persuasive language influences audiences has implications across many domains like advertising, argumentation, and propaganda. Persuasion relies on more than a message's content. Arranging the order of the message itself (i.e., ordering specific rhetorical strategies) also plays an important role. To examine how strategy orderings contribute to persuasiveness, we first utilize a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus. We then visualize interplay between content and strategy through an attentional LSTM that predicts the success of textual requests. We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request.

Some of the largest digital consultancies across the globe have come together to assess the state of the global artificial intelligence (AI), revealing that Japanese businesses lead the way when it comes to AI adoption. The study was conducted by US-based research firm ESI ThoughtLab in collaboration with a consortium of digital services and consulting firms operating at the cutting edge of AI. Deloitte, Publicis Sapient, Cognizant, Appen, Dataiku and DataRobot were all involved in the study, which surveyed more than 1,000 companies across 15 countries. The goal was to understand the size and scale of AI initiatives across the world. According to the report, Japan emerges as a surprise leader in AI adoption.

There's this meme out there to make people who care about artificial intelligence safety look crazy. If AI ever starts doing something that might destroy humanity, we'll just shut it off. AI requires power and computers to function. So, if machines start building nuclear weapons of their own free will or turning everything into paper clips, we'll have no problem. Some prominent voices on the topic try to refute this argument by saying we don't know the types of extremely strong arguments and persuasion a hyper-evolved machine can produce.