Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Forrester Research's recently-released predictions report for artificial intelligence highlights what most have already observed: AI adoption has evolved from an emerging, nice-to-have trend to experiment with to a legitimate, must-do priority for enterprises. Basically, get on board the AI train or be left behind. The "get on board with AI now" message has been hammered home for several years, but this year's stats do seem to point to a significant evolution: According to Forrester's Data and Analytics Survey, 2022 [subscription required], 73% of data and analytics decision-makers are building AI technologies and 74% see a positive impact on their organizations from the use of AI. No vertical industry is failing to find opportunities to implement AI, and companies at all maturity levels are transforming fundamental functions in the organization, the predictions report found, while in 2023 AI adoption will "continue to expand and be more creative, trustworthy and optimized."

The wisdom of the crowd has long become the de facto approach for eliciting information from individuals or experts in order to predict the ground truth. However, classical democratic approaches for aggregating individual \emph{votes} only work when the opinion of the majority of the crowd is relatively accurate. A clever recent approach, \emph{surprisingly popular voting}, elicits additional information from the individuals, namely their \emph{prediction} of other individuals' votes, and provably recovers the ground truth even when experts are in minority. This approach works well when the goal is to pick the correct option from a small list, but when the goal is to recover a true ranking of the alternatives, a direct application of the approach requires eliciting too much information. We explore practical techniques for extending the surprisingly popular algorithm to ranked voting by partial votes and predictions and designing robust aggregation rules. We experimentally demonstrate that even a little prediction information helps surprisingly popular voting outperform classical approaches.

"The main thing is to keep the main thing the main thing." These wise words of world-renowned business author Stephen Covey challenge each of us as we stand on the precipice of a new decade. But what's the'main thing' when navigating technology as we enter 2020? The simple answer is… Cybersecurity. As innovation explodes into every area of our lives, cybersecurity is providing the glue that can enable the good and disable the bad for implementing cutting-edge innovation as well as reducing risk from older vulnerabilities. We also see cybersecurity continue as the top priority for chief information officers (CIOs) in 2020, just as it has been for most of the past decade, with groups like the National Association of State CIOs (NASCIO). But even as cybersecurity solutions offer a way forward to ensure privacy protections are workable and effective, most people see the data breaches, ransomware, identity theft, denial-of-service attacks and other cyberattacks as proof that cybersecurity has become the Achilles Heel, not the savior, for new innovation. Even as exciting advances occur in artificial intelligence (AI), autonomous vehicles, 5G networks cloud computing, mobile devices and the Internet of Things (IoT), these same developments seem to cause negative societal disruptions that make daily news headlines. So what will happen next with cybersecurity? That's what this annual security prediction roundup will cover, from the perspective of the top cybersecurity industry companies, thought leaders, executives and journalists. Every year we catalogue the evaluators to see who has made a New Year's security prediction list and checked it twice.

As we enter 2018, there are two competing forces that are impacting the world of HR. On the one hand, the rate of social and business changes is accelerating, resulting in an ever more complex business environment. On the other hand, employees are pushing for even simpler, more engaging, and human-like experience from their enterprise applications. For HR to survive, they will need to leverage emerging technologies such as artificial intelligence (AI), machine learning, robots, and chatbots to support rapid business changes while delivering a superior employee experience. In the last few years, the creative disruption caused by technology is rampant – think what Uber did to the transportation industry, AirBnB to hospitality, and Amazon to retail.

The world of work is undergoing radical change. Business has become a real-time experience, we deal with a relentless stream of messages and communications, and we operate in a network of teams. The traditional top-down hierarchy is rapidly going away as young professionals demand more opportunity, leadership, and responsibility. Technology has become an every-day part of our lives, and we often feel a bit overwhelmed by it. Where should I post that great picture I just took?

The world of work is undergoing radical change. Business has become a real-time experience, we deal with a relentless stream of messages and communications, and we operate in a network of teams. The traditional top-down hierarchy is rapidly going away as young professionals demand more opportunity, leadership, and responsibility. Technology has become an every-day part of our lives, and we often feel a bit overwhelmed by it. Where should I post that great picture I just took?

The modern web critically depends on aggregation of information from self-interested agents, for example opinion polls, product ratings, or crowdsourcing. We consider a setting where multiple objects (questions, products, tasks) are evaluated by a group of agents. We first construct a minimal peer prediction mechanism that elicits honest evaluations from a homogeneous population of agents with different private beliefs. Second, we show that it is impossible to strictly elicit honest evaluations from a heterogeneous group of agents with different private beliefs. Nevertheless, we provide a modified version of a divergence-based Bayesian Truth Serum that incentivizes agents to report consistently, making truthful reporting a weak equilibrium of the mechanism.

Several mechanisms have been proposed for incentivizing truthful reports of a private signals owned by rational agents, among them the peer prediction method and the Bayesian truth serum. The robust Bayesian truth serum (RBTS) for small populations and binary signals is particularly interesting since it does not require a common prior to be known to the mechanism. We further analyze the problem of the common prior not known to the mechanism and give several results regarding the restrictions that need to be placed in order to have an incentive-compatible mechanism. Moreover, we construct a Bayes-Nash incentive-compatible scheme called multi-valued RBTS that generalizes RBTS to operate on both small populations and non-binary signals.

Peer prediction mechanisms allow the truthful elicitation of private signals (e.g., experiences, or opinions) in regard to a true world state when this ground truth is unobservable. The original peer prediction method is incentive compatible for any number of agents n >= 2, but relies on a common prior, shared by all agents and the mechanism. The Bayesian Truth Serum (BTS) relaxes this assumption. While BTS still assumes that agents share a common prior, this prior need not be known to the mechanism. However, BTS is only incentive compatible for a large enough number of agents, and the particular number of agents required is uncertain because it depends on this private prior. In this paper, we present a robust BTS for the elicitation of binary information which is incentive compatible for every n >= 3, taking advantage of a particularity of the quadratic scoring rule. The robust BTS is the first peer prediction mechanism to provide strict incentive compatibility for every n >= 3 without relying on knowledge of the common prior. Moreover, and in contrast to the original BTS, our mechanism is numerically robust and ex post individually rational.