In the beginning, the chatbots and their ilk fed on the human-made internet. Various generative-AI models of the sort that power ChatGPT got their start by devouring data from sites including Wikipedia, Getty, and Scribd. They consumed text, images, and other content, learning through algorithmic digestion their flavors and texture, which ingredients go well together and which do not, in order to concoct their own art and writing. Generative AI is utterly reliant on the sustenance it gets from the web: Computers mime intelligence by processing almost unfathomable amounts of data and deriving patterns from them. ChatGPT can write a passable high-school essay because it has read libraries' worth of digitized books and articles, while DALL-E 2 can produce Picasso-esque images because it has analyzed something like the entire trajectory of art history.

Last-mile transportation (LMT) refers to any service that moves passengers from a hub of mass transportation (MT), such as air, boat, bus, or train, to destinations, such as a home or an office. In this paper, we introduce the problem of scheduling passengers jointly on MT and LMT services, with passengers sharing a car, van, or autonomous pod of limited capacity for LMT. Passenger itineraries are determined so as to minimize total transit time for all passengers, with each passenger arriving at the destination within a specified time window. The transit time includes the time spent traveling through both services and, possibly, waiting time for transferring between the services. We provide an integer linear programming (ILP) formulation for this problem. Since the ILMTP, is NP-hard and problem instances of practical size are often difficult to solve, we study a restricted version where MT trips are uniform, all passengers have time windows of a common size, and LMT vehicles visit one destination per trip. We prove that there is an optimal solution that sorts and groups passengers by their deadlines and, based on this result, we propose a constructive grouping heuristic and local search operators to generate high-quality solutions. The resulting groups are optimally scheduled in a few seconds using another ILP formulation. Numerical results indicate that the solutions obtained by this heuristic are often close to optimal %, even when multiple destinations are allowed per group, and that warm-starting the ILP solver with such solutions decreases the overall computational times significantly.

Data Science Virtual Machine (DSVM), Microsoft's cloud-based offering for big data analytics, is now available in a new preview version based on Windows Server 2016 Datacenter Edition. Previously, the Windows version of DSVM only ran on a Windows Server 2012 image. Microsoft also makes DSVM available in Ubuntu and CentOS Linux flavors. In upgrading to Windows Server 2016, DSVM users now have access to additional tools and functionality, including Docker container support, noted Microsoft software engineer Udayan Kumar in a June 6 announcement. The new virtual machine also comes bundled with Office ProPlus and includes an upgrade to Microsoft R Server 9.1, which now features sentiment analysis and other cognitive models.

Training robots about the objects in their environment requires a multimodal correlation of features extracted from visual and linguistic sources.  This work abstracts the task of collecting multimodal training data for object and feature learning by encapsulating it in an interactive game, I Spy , played between human players and robots.  It introduces the concept of the game, briefly describes its methodology, and finally presents an evaluation of the game's performance and its appeal to human players.