The role of a market maker is to simultaneously offer to buy and sell quantities of goods, often a financial asset such as a share, at specified prices. An automated market maker (AMM) is a mechanism that offers to trade according to some predetermined schedule; the best choice of this schedule depends on the market maker's goals. The literature on the design of AMMs has mainly focused on prediction markets with the goal of information elicitation. More recent work motivated by DeFi has focused instead on the goal of profit maximization, but considering only a single type of good (traded with a numeraire), including under adverse selection (Milionis et al. 2022). Optimal market making in the presence of multiple goods, including the possibility of complex bundling behavior, is not well understood. In this paper, we show that finding an optimal market maker is dual to an optimal transport problem, with specific geometric constraints on the transport plan in the dual. We show that optimal mechanisms for multiple goods and under adverse selection can take advantage of bundling, both improved prices for bundled purchases and sales as well as sometimes accepting payment "in kind." We present conjectures of optimal mechanisms in additional settings which show further complex behavior. From a methodological perspective, we make essential use of the tools of differentiable economics to generate conjectures of optimal mechanisms, and give a proof-of-concept for the use of such tools in guiding theoretical investigations.

The last major Windows 11 23H2 feature update was released in Fall 2023. Contrary to previous fears, Windows 10 users are not completely excluded from further development. Microsoft is also planning new functions for the older operating system. Given the popularity of Windows 10, there is even speculation as to whether Microsoft could extend support beyond the planned end in October 2025. There's also speculation about Windows 11 24H2, which could be called Windows 12 . There have been no official announcements to date. The only thing that seems certain is that the high hardware requirements will remain.

In my wandering around the various data science tools and frameworks, I discovered Deepnote, an online framework that allows you to create and run notebooks in Python. Compared to the more famous Jupyterlab and Colab frameworks, Deepnote allows you to write Python notebooks collaboratively and in real time. Your collaborator may even comment your code! Deepnote can be easily integrated with the most popular cloud services, such as Google Drive and Amazon S3, as well as the most popular databases, such as PostgresSQL and MongoDB. In addition, projects can be integrated with Github and published over the Web, since Deepnote provides each user with a dedicated Web page, which can be used as a portfolio.

We introduce ObSynth, an interactive system leveraging the domain knowledge embedded in large language models (LLMs) to help users design object models from high level natural language prompts. This is an example of specification reification, the process of taking a high-level, potentially vague specification and reifying it into a more concrete form. We evaluate ObSynth via a user study, leading to three key findings: first, object models designed using ObSynth are more detailed, showing that it often synthesizes fields users might have otherwise omitted. Second, a majority of objects, methods, and fields generated by ObSynth are kept by the user in the final object model, highlighting the quality of generated components. Third, ObSynth altered the workflow of participants: they focus on checking that synthesized components were correct rather than generating them from scratch, though ObSynth did not reduce the time participants took to generate object models.

Adaptive and intelligent user interfaces have been proposed as a critical component of a successful extended reality (XR) system. In particular, a predictive system can make inferences about a user and provide them with task-relevant recommendations or adaptations. However, we believe such adaptive interfaces should carefully consider the overall \emph{cost} of interactions to better address uncertainty of predictions. In this position paper, we discuss a computational approach to adapt XR interfaces, with the goal of improving user experience and performance. Our novel model, applied to menu selection tasks, simulates user interactions by considering both cognitive and motor costs. In contrast to greedy algorithms that adapt based on predictions alone, our model holistically accounts for costs and benefits of adaptations towards adapting the interface and providing optimal recommendations to the user.

Taco Bell has added AI to its mobile app, offering personalized menu suggestions for heavy users ... [ ] (Photo by Justin Sullivan/Getty Images) Artificial intelligence was identified by a number of restaurant tech providers as a top trend for 2020 and, so far, those predictions seem pretty spot on. Up until now, we've seen just a surface scratch of AI's potential in the restaurant space, with chains like Chick-fil-A quietly leveraging the technology to identify food safety issues, and TGI Fridays using it to sell more alcohol, for example. But thanks in large part to McDonald's $300 million investment in Dynamic Yield last March, AI is no longer an under-the-radar experimentation. And now, Taco Bell is making its case for AI, announcing a broader partnership with Certona last week to create a more personalized experience for its mobile app users, of which there are 5 million. Through the technology, machine learning derives the most relevant menu items for app users and also pushes promotions and content based on an individual customer's behavior.

Taco Bell has added AI to its mobile app, offering personalized menu suggestions for heavy users ... [ ] (Photo by Justin Sullivan/Getty Images) Artificial Intelligence was identified by a number of restaurant tech providers as a top trend for 2020 and, so far, those predictions seem pretty spot on. Up until now, we've seen just a surface scratch of AI's potential in the restaurant space, with chains like Chick-fil-A quietly leveraging the technology to identify food safety issues, and TGI Fridays using it to sell more alcohol, for example. But thanks in large part to McDonald's $300 million investment in Dynamic Yield in March, AI is no longer an under-the-radar experimentation. And now, Taco Bell is making its case for AI, announcing a broader partnership with Certona last week to create a more personalized experience for its mobile app users, of which there are 5 million. Through the technology, machine learning derives the most relevant menu items for app users and also pushes promotions and content based on an individual customer's behavior.

Yelp is home to nearly 200 million user-submitted reviews and even more photos. This data is rich with information about businesses and user opinions. Through the application of cutting-edge machine learning techniques, we're able to extract and share insights from this data. In particular, the Popular Dishes feature leverages Yelp's deep data to take the guesswork out of what to order. The Popular Dishes feature highlights the most talked about and photographed dishes at a restaurant, gathering user opinions and images in one convenient place.

The rapid rise of meal delivery services is happening in plain sight, as companies like Deliveroo and Uber Eats send riders and drivers zipping around town with takeout of every variety. But as so often happens with new platforms, a secondary and less visible revolution is rippling across the restaurant industry thanks to the rise of virtual kitchens. London-based Taster is an example of how the intersection of meal delivery services, artificial intelligence, and data is creating opportunities that threaten the restaurant industry with even greater disruption. While meal delivery services initially seemed like a boom for local restaurants, it is virtual kitchens -- with their ability to optimize and automate -- that may ultimately win the food wars. Taster was founded two years ago by Anton Soulier, an early employee of Deliveroo who wanted to take this food transformation further. "I thought there was a big opportunity to build a food company on top of these platforms," he said.

Food waste could become a thing of the past thanks to an AI powered smart bin that let's you know the type of items you throw away most regularly. The system uses a camera, a set of smart scales and the same type of machine learning technology found in self-driving cars. It comes pre-programmed with common items and learns to recognise different foods being thrown away regularly. It uses this information to calculate the financial and environmental cost of this wasted food, so that you can tailor your next food order accordingly. The smart bin is currently aimed at commercial kitchens but could one day be a common feature in people's homes, the firm hopes. Food waste could become a thing of the past thanks to an AI powered smart bin that let's you know the type of items you throw away most regularly.