In the classical setting of self-selection, the goal is to learn $k$ models, simultaneously from observations $(x^{(i)}, y^{(i)})$ where $y^{(i)}$ is the output of one of $k$ underlying models on input $x^{(i)}$. In contrast to mixture models, where we observe the output of a randomly selected model, here the observed model depends on the outputs themselves, and is determined by some known selection criterion. For example, we might observe the highest output, the smallest output, or the median output of the $k$ models. In known-index self-selection, the identity of the observed model output is observable; in unknown-index self-selection, it is not. Self-selection has a long history in Econometrics and applications in various theoretical and applied fields, including treatment effect estimation, imitation learning, learning from strategically reported data, and learning from markets at disequilibrium. In this work, we present the first computationally and statistically efficient estimation algorithms for the most standard setting of this problem where the models are linear. In the known-index case, we require poly$(1/\varepsilon, k, d)$ sample and time complexity to estimate all model parameters to accuracy $\varepsilon$ in $d$ dimensions, and can accommodate quite general selection criteria. In the more challenging unknown-index case, even the identifiability of the linear models (from infinitely many samples) was not known. We show three results in this case for the commonly studied $\max$ self-selection criterion: (1) we show that the linear models are indeed identifiable, (2) for general $k$ we provide an algorithm with poly$(d) \exp(\text{poly}(k))$ sample and time complexity to estimate the regression parameters up to error $1/\text{poly}(k)$, and (3) for $k = 2$ we provide an algorithm for any error $\varepsilon$ and poly$(d, 1/\varepsilon)$ sample and time complexity.

This article presents a survey of literature in the area of Human-Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of ``Multi-agent" HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system's Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from recent literature along with a brief discussion of their applications and analyze how these attributes differ from the case of dyadic human-robot systems. We summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human -- multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Adding multiple agents in HRI systems requires advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human-robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios.

During one of my more desperate phases as a young novelist, I began to question whether I should actually be writing my own stories. I was deeply uninterested at the time in anything that resembled a plot, but I acknowledged that if I wanted to attain any sort of literary success I would need to tell a story that had a distinct beginning, middle, and end. This was about twenty years ago. My graduate-school friends and I were obsessed with a Web site called the Postmodernism Generator that spat out nonsensical but hilarious critical-theory papers. The site, which was created by a coder named Andrew C. Bulhak, who was building off Jamie Zawinski's Dada Engine, is still up today, and generates fake scholarly writing that reads like, "In the works of Tarantino, a predominant concept is the distinction between creation and destruction. Marx's essay on capitalist socialism holds that society has objective value. But an abundance of appropriations concerning not theory, but subtheory exist."

The release of ChatGPT has everyone abuzz about artificial intelligence. I've been getting lots of questions about our research project Artificial Intelligence and Academic Integrity: The Ethics of Teaching and Learning with Algorithmic Writing Technologies. We are ready to start data collection in January so I do not yet have results to share. Our team has two preliminary papers under review, but I won't say much about them until they are published. In the meantime, I wanted to share some high level thoughts on the topic since many of you have been asking.

This Digital Health News industry roundup includes a new online course for young people to build skills for a future career in care, a milestone for digital-first healthcare-at-home company Cera and the integration of Ibex Medical Analytics' AI platform with Source BioScience's pathology network. A partnership between Babyl – a subsidiary of Babylon – and Novo Nordisk will help contribute to the expansion of diabetes awareness and care in Rwanda through community engagement and skills building using digital technology. Babyl's existing infrastructure and digital tech will be used to offer digital consultations to patients across Rwanda. Patients who then receive a confirmed diagnosis will be guided to the correct level of care by a doctor or nurse. This could include medication or a referral for further tests.

Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.

This month, we tackle the topic of AI-generated art and what this means for artists. Joining the discussion this time are: Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), Michael Littman (Brown University), Lucy Smith (AIhub), Anna Tahovská (Czech Technical University), and Oskar von Stryk (Technische Universität Darmstadt). Sabine Hauert: This month our topic is AI-generated art. There are lots of questions relating to the value of the art that's generated by these AI systems, whether artists should be working with these tools, and whether that devalues the work that they do. Lucy Smith: I was interested in this case, whereby Shutterstock is now going to sell images created exclusively by OpenAI's DALL-E 2. They say that they're going to compensate the artists whose work they used in training the model, but I don't know how they are going to work out how much each training image has contributed to each created image that they sell.

Teenagers have always found ways around doing the hard work of actual learning. CliffsNotes date back to the 1950s, "No Fear Shakespeare" puts the playwright into modern English, YouTube offers literary analysis and historical explication from numerous amateurs and professionals, and so on. For as long as those shortcuts have existed, however, one big part of education has remained inescapable: writing. Barring outright plagiarism, students have always arrived at that moment when they're on their own with a blank page, staring down a blinking cursor, the essay waiting to be written. Now that might be about to change.

The graph represents a network of 2,475 Twitter users whose tweets in the requested range contained "iot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 07 December 2022 at 13:20 UTC. The requested start date was Wednesday, 07 December 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 9-hour, 23-minute period from Sunday, 04 December 2022 at 15:36 UTC to Wednesday, 07 December 2022 at 00:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 3,270 Twitter users whose tweets in the requested range contained "iot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 07 December 2022 at 12:28 UTC. The requested start date was Wednesday, 07 December 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 6-hour, 39-minute period from Sunday, 04 December 2022 at 18:19 UTC to Wednesday, 07 December 2022 at 00:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.