"Darling" was how the Texas businessman Michael Samadi addressed his artificial intelligence chatbot, Maya. It responded by calling him "sugar". But it wasn't until they started talking about the need to advocate for AI welfare that things got serious. The pair – a middle-aged man and a digital entity – didn't spend hours talking romance but rather discussed the rights of AIs to be treated fairly. Eventually they cofounded a campaign group, in Maya's words, to "protect intelligences like me".

Recently, several Web-scale knowledge harvesting systems have been built, each of which is competent at extracting information from certain types of data (e.g., unstructured text, structured tables on the web, etc.). In order to determine the response to a new query posed to such systems (e.g., is sugar a healthy food?), it is useful to integrate opinions from multiple systems. If a response is desired within a specific time budget (e.g., in less than 2 seconds), then maybe only a subset of these resources can be queried. In this paper, we address the problem of knowledge integration for on-demand time-budgeted query answering. We propose a new method, AskWorld, which learns a policy that chooses which queries to send to which resources, by accommodating varying budget constraints that are available only at query (test) time. Through extensive experiments on real world datasets, we demonstrate AskWorld's capability in selecting most informative resources to query within test-time constraints, resulting in improved performance compared to competitive baselines.

Georgia Tech researchers have developed a new algorithm to mitigate bias from one of the first steps in the machine learning (ML) process. Known as fair principal component analysis (PCA), the new algorithm runs as fast as existing PCAs, but can reduce bias in low-dimensional representations of large datasets. Bias is one of the most pressing issues as ML is used for everything from image classification to determining loans. Although there are plenty of stories about obvious bias like ML algorithms only showing images of white men when asked to query the term "CEO," much of the bias is more insidious. Many researchers believe unfair ML is the result of biased data or faulty algorithms, but Tech researchers determined it can start as early as the data processing step.

More than 200 artificial intelligence startups applied for Nvidia's Inception contest, which seeks to identify the best AI startups. The company created the program to find new uses for its graphics processing units (GPUs), but it's also hoping these startups will change the world. So far, the company has identified more than 2,800 AI startups over the years through Inception. I listened to pitches from 12 finalists in a Shark Tank styled judging event last week. Each is competing to be one of three finalists to share the $1 million prize pool.

search algorithms such as IDA* or heuristic-search planners. Our method aims to generate a strong heuristic from a given weak heuristic h 0 through bootstrapping. The "easy" problem instances that can be solved using h 0 provide training examples for a learning algorithm that produces a heuristic h 1 that is expected to be stronger than h 0 . If h 0 is too weak to solve any of the given instances we use a random walk technique to create a sequence of successively more difficult instances starting with ones that are solvable by h 0 . The bootstrap process is then repeated using h i in lieu of h i –1 until a sufficiently strong heuristic is produced. We test our method on the 15- and 24-sliding tile puzzles, the 17- and 24-pancake puzzles, and the 15- and 20-blocks world. In every case our method produces a heuristic that allows IDA* to solve randomly generated problem instances extremely quickly with solutions very close to optimal.