In April 2024, NASA selected three finalists to design, build, and pitch their own Lunar Terrain Vehicle (LTV) for the Artemis program within 12 months. Ever since, Intuitive Machines, Venturi Astrolab, and Lunar Outpost have raced to meet the impending deadline to deliver the best moon car plan possible. Lunar Outpost's Lunar Dawn team revealed its latest high-fidelity prototype, the Lunar Outpost Eagle, on April 8. The vehicle will officially debut at Space Symposium 2025 in Colorado Springs and provide attendees with the closest look yet at the Artemis program hopeful. Eagle is the fourth prototype iteration so far, and was built in collaboration from General Motors, Goodyear, MDA Space, and Leidos, the Eagle is envisioned as the "quintessential Space Truck," according to AJ Gemer, Lunar Outpost CTO.

Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning (ICL), where models adapt to new tasks through example-based prompts without requiring parameter updates. However, understanding how tasks are internally encoded and generalized remains a challenge. To address some of the empirical and technical gaps in the literature, we introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads within the transformer architecture. This approach computes a single task vector as a weighted sum of attention heads, with the weights optimized causally via gradient descent. Our findings show that existing methods fail to generalize effectively to modalities beyond text. In response, we also design a benchmark to evaluate whether a task vector can preserve task fidelity in functional regression tasks. The proposed method successfully extracts task-specific information from in-context demonstrations and excels in both text and regression tasks, demonstrating its generalizability across modalities. Moreover, ablation studies show that our method's effectiveness stems from aligning the distribution of the last hidden state with that of an optimally performing in-context-learned model.

This paper introduces a recurrent neural network approach for predicting user lifetime value in Software as a Service (SaaS) applications. The approach accounts for three connected time dimensions. These dimensions are the user cohort (the date the user joined), user age-in-system (the time since the user joined the service) and the calendar date the user is an age-in-system (i.e., contemporaneous information).The recurrent neural networks use a multi-cell architecture, where each cell resembles a long short-term memory neural network. The approach is applied to predicting both acquisition (new users) and rolling (existing user) lifetime values for a variety of time horizons. It is found to significantly improve median absolute percent error versus light gradient boost models and Buy Until You Die models.

Customer Life Time Value (LTV) is the expected total revenue that a single user can bring to a business. It is widely used in a variety of business scenarios to make operational decisions when acquiring new customers. Modeling LTV is a challenging problem, due to its complex and mutable data distribution. Existing approaches either directly learn from posterior feature distributions or leverage statistical models that make strong assumption on prior distributions, both of which fail to capture those mutable distributions. In this paper, we propose a complete set of industrial-level LTV modeling solutions. Specifically, we introduce an Order Dependency Monotonic Network (ODMN) that models the ordered dependencies between LTVs of different time spans, which greatly improves model performance. We further introduce a Multi Distribution Multi Experts (MDME) module based on the Divide-and-Conquer idea, which transforms the severely imbalanced distribution modeling problem into a series of relatively balanced sub-distribution modeling problems hence greatly reduces the modeling complexity. In addition, a novel evaluation metric Mutual Gini is introduced to better measure the distribution difference between the estimated value and the ground-truth label based on the Lorenz Curve. The ODMN framework has been successfully deployed in many business scenarios of Kuaishou, and achieved great performance. Extensive experiments on real-world industrial data demonstrate the superiority of the proposed methods compared to state-of-the-art baselines including ZILN and Two-Stage XGBoost models.

As a measure of the long-term contribution produced by customers in a service or product relationship, life-time value, or LTV, can more comprehensively find the optimal strategy for service delivery. However, it is challenging to accurately abstract the LTV scene, model it reasonably, and find the optimal solution. The current theories either cannot precisely express LTV because of the single modeling structure, or there is no efficient solution. We propose a general LTV modeling method, which solves the problem that customers' long-term contribution is difficult to quantify while existing methods, such as modeling the click-through rate, only pursue the short-term contribution. At the same time, we also propose a fast dynamic programming solution based on a mutated bisection method and the memoryless repeated experiments assumption. The model and method can be applied to different service scenarios, such as the recommendation system. Experiments on real-world datasets confirm the effectiveness of the proposed model and optimization method. In addition, this whole LTV structure was deployed at a large E-commerce mobile phone application, where it managed to select optimal push message sending time and achieved a 10\% LTV improvement.

Non-stop growth in artificial intelligence is undoubtedly increasing the efficiency of business operations. Machine Learning (ML) analytics is simplifying various internal activities in an organization which were once considered impossible without human assistance. Machine Learning capabilities are evolving with every passing day and are bringing a new edge to business operations. With highly result-oriented Machine learning models, businesses are now able to reach output targets not imagined before. Automating multiple organizational operations and creating sentient machines with an accurate response system Businesses are seeing an unprecedented level of growth in every sector.

Driven by customer demand, marketers are looking beyond acquisition and vanity metrics to full-funnel marketing that tracks Lifetime Customer Value (LTV). With the help of AI, they're also freeing up time to think more strategically. Let's start with what the customer wants In the Fourth Industrial Revolution, customers are now in control. They're always connected, they have options and they can vote with their feet if they're unhappy. Salesforce's recent State of the Connected Customer research shows that 57% of customers have stopped buying from a company because a competitor provided a better experience.

For instance, players with a very rapid in-game Nowadays most video games are played online and every progression (who reach a high level after a relatively short action by every player is recorded. This generates extremely playtime, regardless of their lifetime) and low spend might rich datasets that--with the aid of machine learning be overlooked by traditional segmentation methods due to techniques--can provide deep insights on user behavior, their lack of direct economic value; however, these are the including accurate predictions of the future actions of each most skillful players, and a careful study of their traits and player. Increasingly diverse demographics are now playing behavior--allowed by our approach--could provide developers games in a highly competitive market. Furthermore, we are with a lot of useful insights.

Nowadays, video game developers record every virtual action performed by their players. As each player can remain in the game for years, this results in an exceptionally rich dataset that can be used to understand and predict player behavior. In particular, this information may serve to identify the most valuable players and foresee the amount of money they will spend in in-app purchases during their lifetime. This is crucial in free-to-play games, where up to 50% of the revenue is generated by just around 2% of the players, the so-called whales. To address this challenge, we explore how deep neural networks can be used to predict customer lifetime value in video games, and compare their performance to parametric models such as Pareto/NBD. Our results suggest that convolutional neural network structures are the most efficient in predicting the economic value of individual players. They not only perform better in terms of accuracy, but also scale to big data and significantly reduce computational time, as they can work directly with raw sequential data and thus do not require any feature engineering process. This becomes important when datasets are very large, as is often the case with video game logs. Moreover, convolutional neural networks are particularly well suited to identify potential whales. Such an early identification is of paramount importance for business purposes, as it would allow developers to implement in-game actions aimed at retaining big spenders and maximizing their lifetime, which would ultimately translate into increased revenue.

Artificial intelligence steps in to save the day, letting you harness that remaining 95 percent in order to enhance conversions and drive retention.