Text offers intuitive access to information. This can, in particular, complement the density of numerical time series, thereby allowing improved interactions with time series models to enhance accessibility and decision-making. While the creation of question-answering datasets and models has recently seen remarkable growth, most research focuses on question answering (QA) on vision and text, with time series receiving minute attention. To bridge this gap, we propose a challenging novel time series QA (TSQA) dataset, QuAnTS, for Question Answering on Time Series data. Specifically, we pose a wide variety of questions and answers about human motion in the form of tracked skeleton trajectories. We verify that the large-scale QuAnTS dataset is well-formed and comprehensive through extensive experiments. Thoroughly evaluating existing and newly proposed baselines then lays the groundwork for a deeper exploration of TSQA using QuAnTS. Additionally, we provide human performances as a key reference for gauging the practical usability of such models. We hope to encourage future research on interacting with time series models through text, enabling better decision-making and more transparent systems.

In recent years, agents have become capable of communicating seamlessly via natural language and navigating in environments that involve cooperation and competition, a fact that can introduce social dilemmas. Due to the interleaving of cooperation and competition, understanding agents' decision-making in such environments is challenging, and humans can benefit from obtaining explanations. However, such environments and scenarios have rarely been explored in the context of explainable AI. While some explanation methods for cooperative environments can be applied in mixed-motive setups, they do not address inter-agent competition, cheap-talk, or implicit communication by actions. In this work, we design explanation methods to address these issues. Then, we proceed to establish generality and demonstrate the applicability of the methods to three games with vastly different properties. Lastly, we demonstrate the effectiveness and usefulness of the methods for humans in two mixed-motive games. The first is a challenging 7-player game called no-press Diplomacy. The second is a 3-player game inspired by the prisoner's dilemma, featuring communication in natural language.

The winners of three IJCAI awards have been announced. These three distinctions are: the Award for Research Excellence, the John McCarthy Award and the Computers and Thought Award. The Research Excellence award is given to a scientist who has carried out a program of research of consistently high quality throughout an entire career yielding several substantial results. The winner of the 2023 Award for Research Excellence is Sarit Kraus, Professor of Computer Science, Bar-Ilan University, Israel. Professor Kraus is recognized for her pioneering work on the study of interactions among self-interested agents, creating the field of automated negotiation, and developing methods for coalition formation and teamwork, both as formal models and real-world implementations.

'Tucker Carlson Tonight' host makes the case for why Kyle Rittenhouse is not receiving a fair trial The judge in the Kyle Rittenhouse trial has just sent the jurors home for the night to think about the trial for yet another day. So far, deliberations, in this case, have lasted about 20 hours. In a normal proceeding, we'd have the jury's decision in about 20 minutes. The essential question, in this case, is really clear did Kyle Rittenhouse have good reason to believe dangerous men were trying to murder him? And the answer is also clear and unequivocal?

Call centers, in which human operators attend clients using textual chat, are very common in modern e-commerce. Training enough skilled operators who are able to provide good service is a challenge. We suggest an algorithm and a method to train and implement an assisting agent that provides on-line advice to operators while they attend clients. The agent is domain-independent and can be introduced to new domains without major efforts in design, training and organizing structured knowledge of the professional discipline. We demonstrate the applicability of the system in an experiment that realizes its full life-cycle on a specific domain and analyze its capabilities.

Among the five new tools are one that uses artificial intelligence to help agents answer questions almost instantaneously and one that aggregates information about the customer from disparate data sources. Another makes it easier for customers to authenticate themselves when talking to an agent. The new features will help customer-service agents who are working from home while managing an increased volume of calls about everything from changes to travel plans to inquiries about unemployment benefits, said Larry Augustin, vice president of business applications for Amazon Web Services. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. "The idea is to increase productivity for the agent and increase customer satisfaction for people calling in," Mr. Augustin said.

Since the start of the 20th century, multiple psychological models have emerged to provide a scientific explanation for human motivation and a better understanding of why people do what they do. From the five-factor model, measuring traits like extraversion, agreeableness and neuroticism, or the HEXACO personality analysis of honesty, humility, and emotionality, among other traits, these models have stood the test of time in explaining the complexities of the convoluted human behavior and its mysterious makeup. But as the world has increasingly digitized, there's a new source of behavioural activity we leave behind that provides a better reflection of ourselves than previously thought. Enter INFI, a game-changer in the space of Artificial Intelligence (AI) and human-machine interaction, and the first to crack the code of converting validated psychological models into algorithms, enabling a new level of individually-based insights. Their technology promises to reinvent a lost art of personal communication and change the way people are engaged in the digital world.

Despite some hiccups, the use of artificial intelligence (AI) and big data in marketing will continue to flourish as companies look for new ways to influence consumer behavior, said the head of a Morristown-based digital marketing firm at a recent tech conference. Nick Kraus, founder and CEO of Kraus Marketing, said that today's marketing strategies, dominated by the likes of digital giants Google, Facebook, Instagram and Twitter, have become smarter and more pervasive as the result of AI and data analytics providing deeper insights into consumers' buying habits. Kraus was one of several speakers who presented their views on the rapid growth of digital marketing at the DigiCON 2019 conference, held on October 11 at the County College of Morris, in Randolph. The Morris County Chamber of Commerce hosted the event. The use of AI and big data for marketing purposes has been around for awhile, but many businesses, especially smaller ones, may not know how to apply these technologies to various marketing tools, such as social media, email, search engine optimization and websites.

Bessemer Venture Partners is launching a $10 million early-stage seed program to back new startups in the healthcare industry. According to TechCrunch, the company's celebrity healthcare investor, Steve Kraus, and its head of investments in Israel, Adam Fisher, will lead the Deep Health Seed Program to invest anywhere between $100,000 and $2 million into startups using machine learning to solve problems in healthcare. The firm has already invested in Qventus, a hospital management service, as well as healthcare companies such as Allena Pharmaceuticals, Docent Health, OvaScience, Verastem and Flex Pharma. Bessemer will specifically be looking for investment opportunities in workflow automation startups like Qventus; digital diagnostics companies that augment or replace human diagnosticians with algorithms; and predictive and programmatic tools to measure and monitor population health. In addition to announcing the new seed funding program, Bessemer also revealed its first investment in a company called Subtle Medical, which is focused on stroke and neurological disorders, improving the quality and speed of medical imaging exams by enhancing the use of lower-quality scans, which will remove the need for repeat imaging procedures.

Seeing a tremendous opportunity to leverage machine learning technologies in the healthcare industry, Bessemer Venture Partners is launching a $10 million early-stage seed program to back new startups. Led by the firm's celebrity healthcare investor, Steve Kraus (seriously: the guy has his own podcast), and its head of investments in Israel, Adam Fisher, the Deep Health Seed Program will place bets of anywhere between $100,000 and $2 million into early-stage companies using machine learning to solve problems in healthcare. It's no exaggeration to say that machine learning can transform the healthcare industry entirely. The proliferation of data brought on by the increasing digitization of workflows in hospitals, patient information, the popularization of wearables and mapping of the human genome means that everything from the health of populations to the genetic composition of our cells can be monitored -- and potentially managed through the application of data. Bessemer has already placed several bets on this hypothesis, including its investment in Qventus (a hospital management service that we'd covered last month).