As a precocious teen looking to improve my college application, I sat in on a business studies class. I figured taking two extra A-Levels at night school alongside those I took during the day would make me irresistible to admissions tutors. The class I watched examined if it was worth a large factory keeping its own trucks and drivers in-house rather than outsourcing them. The data showed selling the trucks and firing the workers was more expensive in the long run, and yoked the company to the whims of any third-party logistics company in the local area. Not to mention, if you don't own a mission-critical component of your business, you're a lot less powerful when negotiating with your suppliers.

When Spike Jonze's Her came out in 2013, the film about a lonely man falling for an artificially intelligent operating system won widespread praise. Watching today, the qualities critics celebrated at the time are still there--it's a gentle, enjoyably melancholy story, twee but not damnably so--but something else stands out. Though set in the near-future, Her captures Obama-era techno-optimism better than any other movie. It's a time capsule, preserving dreams about the future that appear more naive the further we get from the 2010s. Her takes place in a highly-stylized version of Los Angeles from a future near enough that its protagonist is a former LA Weekly journalist but distant enough that the skyline rivals Shanghai.

One of the biggest areas to watch, of course, will be generative AI, particularly how it changes social media, political campaigning, and the fight over election misinformation. This confluence of new tech and big elections is also happening while the social media industry is going through major changes, including shifts in moderation approaches, legal battles, cuts to trust and safety teams, and platform shake-ups. This is all poised to make the future of the fight against misinformation murky, to say the least. It's a topic my colleagues and I take very seriously and have covered extensively in the past. And recently in MIT Technology Review, former Google boss Eric Schmidt penned an op-ed that lays out what he calls "a paradigm shift for social media platforms": The role of Facebook and others has conditioned our understanding of social media as centralized, global "public town squares" with a never-ending stream of content and frictionless feedback.

In the fall of 2016--the year in which the proportion of online adults using social media reached eighty per cent--I published an Op-Ed in the Times that questioned the popular conception that you need to cultivate a strong social-media brand to succeed in the job market. "I think this behavior is misguided," I wrote. "In a capitalist economy, the market rewards things that are rare and valuable. Social media use is decidedly not rare or valuable." I suggested that knowledge workers instead spend time developing useful skills, with the goal of distinguishing themselves in their chosen fields.

This paper studies ML systems that interactively learn from users across multiple subpopulations with heterogeneous data distributions. The primary objective is to provide specialized services for different user groups while also predicting user preferences. Once the users select a service based on how well the service anticipated their preference, the services subsequently adapt and refine themselves based on the user data they accumulate, resulting in an iterative, alternating minimization process between users and services (learning dynamics). Employing such tailored approaches has two main challenges: (i) Unknown user preferences: Typically, data on user preferences are unavailable without interaction, and uniform data collection across a large and diverse user base can be prohibitively expensive. (ii) Suboptimal Local Solutions: The total loss (sum of loss functions across all users and all services) landscape is not convex even if the individual losses on a single service are convex, making it likely for the learning dynamics to get stuck in local minima. The final outcome of the aforementioned learning dynamics is thus strongly influenced by the initial set of services offered to users, and is not guaranteed to be close to the globally optimal outcome. In this work, we propose a randomized algorithm to adaptively select very few users to collect preference data from, while simultaneously initializing a set of services. We prove that under mild assumptions on the loss functions, the expected total loss achieved by the algorithm right after initialization is within a factor of the globally optimal total loss with complete user preference data, and this factor scales only logarithmically in the number of services. Our theory is complemented by experiments on real as well as semi-synthetic datasets.

Despite progress in automated fact-checking, most systems require a significant amount of labeled training data, which is expensive. In this paper, we propose a novel zero-shot method, which instead of operating directly on the claim and evidence sentences, decomposes them into semantic triples augmented using external knowledge graphs, and uses large language models trained for natural language inference. This allows it to generalize to adversarial datasets and domains that supervised models require specific training data for. Our empirical results show that our approach outperforms previous zero-shot approaches on FEVER, FEVER-Symmetric, FEVER 2.0, and Climate-FEVER, while being comparable or better than supervised models on the adversarial and the out-of-domain datasets.

We present an open-source Python library for building and using datasets where inputs are clusters of textual data, and outputs are sequences of real values representing one or more time series signals. The news-signals library supports diverse data science and NLP problem settings related to the prediction of time series behaviour using textual data feeds. For example, in the news domain, inputs are document clusters corresponding to daily news articles about a particular entity, and targets are explicitly associated real-valued time series: the volume of news about a particular person or company, or the number of pageviews of specific Wikimedia pages. Despite many industry and research use cases for this class of problem settings, to the best of our knowledge, News Signals is the only open-source library designed specifically to facilitate data science and research settings with natural language inputs and time series targets. In addition to the core codebase for building and interacting with datasets, we also conduct a suite of experiments using several popular Machine Learning libraries, which are used to establish baselines for time series anomaly prediction using textual inputs.

omputer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.omputer Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.C Index Terms building lighting, lamp detection, pose estimation, building information modelling Lighting is one of the most important aspects in the design, cost and maintenance of a building. Approximately, one-third of the electricity consumed in buildings corresponds to artificial lighting [1, 2, 3], with a global demand that represents 19% of all the electricity used in the world [4].

The rise of Large Language Models (LLMs) has sparked interest in their application to sequential recommendation tasks as they can provide supportive item information. However, due to the inherent complexities of sequential recommendation, such as sequential patterns across datasets, noise within sequences, and the temporal evolution of user preferences, existing LLM reasoning strategies, such as in-context learning and chain-of-thought are not fully effective. To address these challenges, we introduce a novel reasoning principle: Dynamic Reflection with Divergent Thinking within a retriever-reranker framework. Our approach starts with a collaborative in-context demonstration retriever, which collects sequences exhibiting collaborative behaviors as in-context examples. Following this, we abstract high-level user preferences across multiple aspects, providing a more nuanced understanding of user interests and circumventing the noise within the raw sequences. The cornerstone of our methodology is dynamic reflection, a process that emulates human learning through probing, critiquing, and reflecting, using user feedback to tailor the analysis more effectively to the target user in a temporal manner. We evaluate our approach on three datasets using six pre-trained LLMs. The superior performance observed across these models demonstrates the efficacy of our reasoning strategy, notably achieved without the need to fine-tune the LLMs. With our principle, we managed to outperform GPT-Turbo-3.5 on three datasets using 7b models e.g., Vicuna-7b and Openchat-7b on NDCG@10. This research not only highlights the potential of LLMs in enhancing sequential recommendation systems but also underscores the importance of developing tailored reasoning strategies to fully harness their capabilities.

Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions. This paper aims to address this gap by incorporating psychological theories to gain a holistic understanding of emotions in generative AI models. Specifically, we propose three approaches: 1) EmotionPrompt to enhance AI model performance, 2) EmotionAttack to impair AI model performance, and 3) EmotionDecode to explain the effects of emotional stimuli, both benign and malignant. Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. Additionally, EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain. Our work heralds a novel avenue for exploring psychology to enhance our understanding of generative AI models. This paper is an extended version of our previous work EmotionPrompt (arXiv:2307.11760).