The advance of Large Language Models (LLMs) has greatly stimulated research interest in developing multi-modal LLM (MLLM)-based visual anomaly detection (VAD) algorithms that can be deployed in complex environments. The challenge is that in these complex environments, the anomalies are sometimes highly contextual and also ambiguous, and thereby, uncertainty quantification (UQ) is a crucial capacity for an MLLM-based VAD system to succeed. In this paper, we introduce our UQ-supported MLLM-based VAD framework called ALARM. ALARM integrates UQ with quality-assurance techniques like reasoning chain, self-reflection, and MLLM ensemble for robust and accurate performance and is designed based on a rigorous probabilistic inference pipeline and computational process. Extensive empirical evaluations are conducted using the real-world smart-home benchmark data and wound image classification data, which shows ALARM's superior performance and its generic applicability across different domains for reliable decision-making.

This study explores linguistic differences between human and LLM-generated dialogues, using 19.5K dialogues generated by ChatGPT-3.5 as a companion to the EmpathicDialogues dataset. The research employs Linguistic Inquiry and Word Count (LIWC) analysis, comparing ChatGPT-generated conversations with human conversations across 118 linguistic categories. Results show greater variability and authenticity in human dialogues, but ChatGPT excels in categories such as social processes, analytical style, cognition, attentional focus, and positive emotional tone, reinforcing recent findings of LLMs being "more human than human." However, no significant difference was found in positive or negative affect between ChatGPT and human dialogues. Classifier analysis of dialogue embeddings indicates implicit coding of the valence of affect despite no explicit mention of affect in the conversations. The research also contributes a novel, companion ChatGPT-generated dataset of conversations between two independent chatbots, which were designed to replicate a corpus of human conversations available for open access and used widely in AI research on language modeling. Our findings increase understanding of ChatGPT's linguistic capabilities and inform ongoing efforts to distinguish between human and LLM-generated text, which is critical in detecting AI-generated fakes, misinformation, and disinformation.

With the deluge of data coming from every corner of modern enterprises, where does instinct fit into the business strategies? We have come to learn that intuitive or impulsive decisions often lead us astray, hence the exploitation of data in virtually any part of a business. In this article, we will assess both perspectives: Whether gut-based decisions or data-driven insights are the ultimate answers to success. In most cases, decisions are based on the predictability of the market condition in accordance with an organization's rational decision-making model, a bias-free and completely data-driven thought process. It's obvious that data is the key to unlock tremendous insights that could change the course of an industry, but is that all there is?

Many times, complex models are not enough (or too heavy), or not necessary, to get great, robust, sustainable insights out of data. Deep analytical thinking may prove more useful, and can be done by people not necessarily trained in data science, even by people with limited coding experience. Here we explore what we mean by deep analytical thinking, using a case study, and how it works: combining craftsmanship, business acumen, the use and creation of tricks and rules of thumb, to provide sound answers to business problems. These skills are usually acquired by experience more than by training, and data science generalists (see here how to become one) usually possess them. This article is targeted to data science managers and decision makers, as well as to junior professionals who want to become one at some point in their career.

The rapid pace of innovation and the constantly exploding collection of possibilities is a major contributor to the fun we all have in digital jobs. There is never a boring moment, there is never time when you can't do something faster or smarter. The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. They never had to worry that they have to be in a persistent forward motion… sometimes just to stay current. This reality powers my impostor syndrome, and (yet?) it is the reason that I love working in every dimension of digital. We are at an inflection point in humanity's evolution where in small and big ways, we can actually change the world. With that context, this post is all about career management in the digital space. Like this blog, it will be particularly relevant for those who are in digital analytics and digital marketing. I would offer that the higher-order-bits in each of the three sections will provide valuable food-for-thought for anyone in a digital role.