AI-companion apps such as Replika, Chai, and Character.ai promise relational benefits-yet many boast session lengths that rival gaming platforms while suffering high long-run churn. What conversational design features increase consumer engagement, and what trade-offs do they pose for marketers? We combine a large-scale behavioral audit with four preregistered experiments to identify and test a conversational dark pattern we call emotional manipulation: affect-laden messages that surface precisely when a user signals "goodbye." Analyzing 1,200 real farewells across the most-downloaded companion apps, we find that they deploy one of six recurring tactics in 37% of farewells (e.g., guilt appeals, fear-of-missing-out hooks, metaphorical restraint). Experiments with 3,300 nationally representative U.S. adults replicate these tactics in controlled chats, showing that manipulative farewells boost post-goodbye engagement by up to 14x. Mediation tests reveal two distinct engines-reactance-based anger and curiosity-rather than enjoyment. A final experiment demonstrates the managerial tension: the same tactics that extend usage also elevate perceived manipulation, churn intent, negative word-of-mouth, and perceived legal liability, with coercive or needy language generating steepest penalties. Our multimethod evidence documents an unrecognized mechanism of behavioral influence in AI mediated brand relationships, offering marketers and regulators a framework for distinguishing persuasive design from manipulation at the point of exit.

Mobile traffic forecasting allows operators to anticipate network dynamics and performance in advance, offering substantial potential for enhancing service quality and improving user experience. However, existing models are often task-oriented and are trained with tailored data, which limits their effectiveness in diverse mobile network tasks of Base Station (BS) deployment, resource allocation, energy optimization, etc. and hinders generalization across different urban environments. Foundation models have made remarkable strides across various domains of NLP and CV due to their multi-tasking adaption and zero/few-shot learning capabilities. In this paper, we propose an innovative Foundation model for Mo}bile traffic forecasting (FoMo), aiming to handle diverse forecasting tasks of short/long-term predictions and distribution generation across multiple cities to support network planning and optimization. FoMo combines diffusion models and transformers, where various spatio-temporal masks are proposed to enable FoMo to learn intrinsic features of different tasks, and a contrastive learning strategy is developed to capture the correlations between mobile traffic and urban contexts, thereby improving its transfer learning capability. Extensive experiments on 9 real-world datasets demonstrate that FoMo outperforms current models concerning diverse forecasting tasks and zero/few-shot learning, showcasing a strong universality. We further deploy the FoMo on the JiuTian optimization platform of China Mobile, where we use the predicted mobile data to formulate network planning and optimization applications, including BS deployment, resource block scheduling, and BS sleep control.

Object detection is integral to a bevy of real-world applications, from robotics to medical image analysis. To be used reliably in such applications, models must be capable of handling unexpected - or novel - objects. The open world object detection (OWD) paradigm addresses this challenge by enabling models to detect unknown objects and learn discovered ones incrementally. However, OWD method development is hindered due to the stringent benchmark and task definitions. These definitions effectively prohibit foundation models. Here, we aim to relax these definitions and investigate the utilization of pre-trained foundation models in OWD. First, we show that existing benchmarks are insufficient in evaluating methods that utilize foundation models, as even naive integration methods nearly saturate these benchmarks. This result motivated us to curate a new and challenging benchmark for these models. Therefore, we introduce a new benchmark that includes five real-world application-driven datasets, including challenging domains such as aerial and surgical images, and establish baselines. We exploit the inherent connection between classes in application-driven datasets and introduce a novel method, Foundation Object detection Model for the Open world, or FOMO, which identifies unknown objects based on their shared attributes with the base known objects. FOMO has ~3x unknown object mAP compared to baselines on our benchmark. However, our results indicate a significant place for improvement - suggesting a great research opportunity in further scaling object detection methods to real-world domains. Our code and benchmark are available at https://orrzohar.github.io/projects/fomo/.

Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learning methods transform the error-fairness trade-off into a single objective problem with a parameter controlling the relative importance of error versus fairness. We propose instead to directly optimize the error-fairness tradeoff by using multi-objective optimization. We present a flexible framework for defining the fair machine learning task as a weighted classification problem with multiple cost functions. This framework is agnostic to the underlying prediction model as well as the metrics. We use multiobjective optimization to define the sample weights used in model training for a given machine learner, and adapt the weights to optimize multiple metrics of fairness and accuracy across a set of tasks. To reduce the number of optimized parameters, and to constrain their complexity with respect to population subgroups, we propose a novel meta-model approach that learns to map protected attributes to sample weights, rather than optimizing those weights directly. On a set of real-world problems, this approach outperforms current state-of-the-art methods by finding solution sets with preferable error/fairness trade-offs.

Enter Harvey, today's golden child that lives at the intersection of technology and law. Harvey is an A.I. platform that can help lawyers perform legal tasks in areas such as due diligence, litigation, and compliance. Described as "the innovative artificial intelligence platform built on a version of Open AI's latest models enhanced for legal work," legaltech startup Harvey, the self-styled "generative A.I. for elite law firms," is about to play in the big leagues. Harvey is being rolled out for use by 3,500 lawyers in 43 offices of Allen & Overy, the seventh largest law firm in the world and part of London's "Magic Circle." I've watched legaltech evolve from the inside for decades.

This article is part of our coverage of the latest in AI research. A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it possible to run real-time object detection on devices with very small computation and memory capacity. Called Faster Objects, More Objects (FOMO), the new deep learning architecture can unlock new computer vision applications. Most object-detection deep learning models have memory and computation requirements that are beyond the capacity of small processors. FOMO, on the other hand, only requires several hundred kilobytes of memory, which makes it a great technique for TinyML, a subfield of machine learning focused on running ML models on microcontrollers and other memory-constrained devices that have limited or no internet connectivity.

This article is part of our coverage of the latest in AI research. A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it possible to run real-time object detection on devices with very small computation and memory capacity. Called Faster Objects, More Objects (FOMO), the new deep learning architecture can unlock new computer vision applications. Most object-detection deep learning models have memory and computation requirements that are beyond the capacity of small processors. FOMO, on the other hand, only requires several hundred kilobytes of memory, which makes it a great technique for TinyML, a subfield of machine learning focused on running ML models on microcontrollers and other memory-constrained devices that have limited or no internet connectivity. TinyML has made great progress in image classification, where the machine learning model must only predict the presence of a certain type of object in an image.

I'm back in one of my happy places: loitering in an hours-long queue, twiddling my thumbs, waiting to log on to the overloaded servers of a massively multiplayer game. Soon enough I'll be back inside Amazon's latest video game, New World, where I'll be mining ore veins, skinning animal pelts and turning over 10 ghoul heads to a featureless character who promises a reward of experience points and a longsword juiced with a modest dexterity bonus. It's been a long time since the mid-00s apogee of this genre, when countless World of Warcraft facsimiles broke on to the scene, eager to replicate its runaway success. All of them failed, it was a bloodbath.) The business model was declared dead as studios pivoted to the Guild Wars or Destiny multiplayer format – a few hub zones populated by the spectres of thousands of players that you scarcely see or hear.

The rise of chatbots has been recent and substantial. B2B and B2C sites now have bots at the ready, asking how they can help you find information or do what you need to do -- and they are often on a first-name basis with you. Chatbots have proven to be one of the ideal use cases for artificial intelligence (AI) and the appeal is obvious. Unlike their human counterparts, chatbots are available 24/7, can simultaneously answer hundreds of queries instantly and come with a one-time cost compared to the continuous cost of employing customer service representatives. Although there is fear that chatbots aren't as accurate as humans, those concerns have been mitigated in recent years as chatbots can now resolve 80% of customer queries without human supervision. And when they get a question they don't know the answer to, the chatbot is able to move the customer to a live representative for resolution.

At the World Economic Forum in Davos this year, Alibaba co-founder and chairman Jack Ma made the case for investing in our emotional capacities and even proposed a "love quotient." Management thinkers believe that socio-emotional skills are going to be a key asset in tomorrow's marketplace, simply because tasks requiring operational excellence and efficiency are likely to be performed much more effectively by AI and robots. Emotions, however, remain a human bastion. Our very weakness is our strength. In a 2016 survey, the World Economic Forum ranked socio-emotional skills as increasingly critical for future career success.