It was an emotionally dark and stormy night in 2020 when I had the urge to write a novel. I'd been having panic attacks. To work through it, I decided to write a novel about an isolated mom and a monster in the woods, along with therapy. So that November, I participated in National Novel Writing Month (NaNoWriMo), which is also a nonprofit organization that encourages creative writing through a variety of events, including its most famous and titular program where participants attempt to write a complete novel (or 50,000 words) in the month of November. I loved the "flow state" of writing that came about as a result of participating.

In recent years there has been a great deal of progress in computational methods for solving large games. Interest in the subject stems from both practical applications where AIs, such as self-driving vehicles, interact with each other and humans, and from a handful recreational games, such as chess, poker and Go, that are seen as challenging surrogates for real-world applications, while simultaneously appealing to a large population of devoted enthusiasts. In particular, work on the popular variant of poker known as Texas Hold'em has seen many years of progress culminate in a number of high-profile success stories. Poker and other card games are especially challenging, as they are games with imperfect information and a large number of game states.

When trying to solve a computational problem, we are often faced with a choice between algorithms that are guaranteed to return the right answer but differ in their runtime distributions (e.g., SAT solvers, sorting algorithms). This paper aims to lay theoretical foundations for such choices by formalizing preferences over runtime distributions. It might seem that we should simply prefer the algorithm that minimizes expected runtime. However, such preferences would be driven by exactly how slow our algorithm is on bad inputs, whereas in practice we are typically willing to cut off occasional, sufficiently long runs before they finish. We propose a principled alternative, taking a utility-theoretic approach to characterize the scoring functions that describe preferences over algorithms. These functions depend on the way our value for solving our problem decreases with time and on the distribution from which captimes are drawn. We describe examples of realistic utility functions and show how to leverage a maximum-entropy approach for modeling underspecified captime distributions. Finally, we show how to efficiently estimate an algorithm's expected utility from runtime samples.

Lecture in Remembrance of John McCarthy

The paper develops a branching-time ontology that maintains the classical restriction of forward movement through a temporal tree structure, but permits the representation of paths in which one can perform inferences about time-travel scenarios. Central to the ontology is the notion of an agent embodiment whose beliefs are equivalent to those of an agent who has time-traveled from the future.

Excelling at a test often does not translate into excelling at the skills the test purports to measure. This is true not only of humans but also of AI systems, and the more so the greater the claims of the test's significance. This became evident less than a decade after the introduction of the Winograd Schema Challenge (WSC),3 a test designed to measure an AI system's commonsense reasoning (CSR) ability by answering simple questions. An example would be, given the information: The sculpture rolled off the shelf because it wasn't anchored, answering: What wasn't anchored? There are multiple AI systems2 that achieve human performance on the WSC but are not capable of performing CSR.

Although artificial intelligence (AI) has made great strides in recent years, it still struggles to provide useful guidance about unstructured events in the physical or social world. In short, computer programs lack common sense. "Think of it as the tens of millions of rules of thumb about how the world works that are almost never explicitly communicated," said Doug Lenat of Cycorp, in Austin, TX. Beyond these implicit rules, though, commonsense systems need to make proper deductions from them and from other, explicit statements, he said. "If you are unable to do logical reasoning, then you don't have common sense."

Nuance is no longer sponsoring the competition, and the $25,000 prize mentioned below is no longer offered. The challenge lives on in the many research groups, at Microsoft Research, Facebook, and the Allen Institute, among other places, that are currently (as of 2019) working on aspects of the problem. Commonsense Reasoning is keen to promote the Winograd Schema Challenge and Nuance Communications' competition to successfully pass an alternative to the Turing Test. Background: The Turing Test is intended to serve as a test of whether a machine has achieved human-level intelligence. In one of its best-known versions, a person attempts to determine whether he or she is conversing (via text) with a human or a machine.

We consider the multi-armed bandit setting with a twist. Rather than having just one decision maker deciding which arm to pull in each round, we have $n$ different decision makers (agents). In the simple stochastic setting we show that one of the agents (called the free rider), who has access to the history of other agents playing some zero regret algorithm can achieve just $O(1)$ regret, as opposed to the regret lower bound of $\Omega (\log T)$ when one decision maker is playing in isolation. In the linear contextual setting, we show that if the other agents play a particular, popular zero regret algorithm (UCB), then the free rider can again achieve $O(1)$ regret. In order to prove this result, we give a deterministic lower bound on the number of times each suboptimal arm must be pulled in UCB. In contrast, we show that the free-rider cannot beat the standard single-player regret bounds in certain partial information settings.

Six systems were entered, exploiting a variety of technologies. None of the systems were able to advance from the first round to the second and final round. The Winograd Schema Challenge is concerned with finding the referents of pronouns, or solving the pronoun disambiguation problem. Doing this correctly appears to rely on having a solid base of commonsense knowledge and the ability to reason intelligently with that knowledge. This can be seen from considering an example of a Winograd schema. The referent of it in sentence 1 is the backpack; the referent of it in sentence 2 is the water bottle.