The past decade has witnessed dramatic gains in natural language processing and an unprecedented scaling of large language models. These developments have been accelerated by the advent of few-shot techniques such as chain of thought (CoT) prompting. Specifically, CoT pushes the performance of large language models in a few-shot setup by augmenting the prompts with intermediate steps. Despite impressive results across various tasks, the reasons behind their success have not been explored. This work uses counterfactual prompting to develop a deeper understanding of CoT-based few-shot prompting mechanisms in large language models. We first systematically identify and define the key components of a prompt: symbols, patterns, and text. Then, we devise and conduct an exhaustive set of experiments across four different tasks, by querying the model with counterfactual prompts where only one of these components is altered. Our experiments across three models (PaLM, GPT-3, and CODEX) reveal several surprising findings and brings into question the conventional wisdom around few-shot prompting. First, the presence of factual patterns in a prompt is practically immaterial to the success of CoT. Second, our results conclude that the primary role of intermediate steps may not be to facilitate learning how to solve a task. The intermediate steps are rather a beacon for the model to realize what symbols to replicate in the output to form a factual answer. Further, text imbues patterns with commonsense knowledge and meaning. Our empirical and qualitative analysis reveals that a symbiotic relationship between text and patterns explains the success of few-shot prompting: text helps extract commonsense from the question to help patterns, and patterns enforce task understanding and direct text generation.

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Insite AI, a platform for consumer packaged goods companies that uses AI to provide recommendations on how to price, distribute and promote their products in physical stores, today launched out of stealth with $19 million in Series A capital from NewRoad Capital and M12, Microsoft's corporate venture arm. Co-founder Shaveer Mirpuri says that the funding will be put toward customer onboarding, building a team of industry experts to help shape product initiatives and an expanded feature set. Mirpuri and Jonathan Reid co-launched Insite with the belief there was a large addressable market for brick-and-mortar sales revenue growth management software. It's true retailers -- and by extension, brands -- face considerable challenges in this area, particularly as the economy takes a precipitous turn. According to NPD, more than 80% of U.S. consumers said in May that they'd rein in product spending within the next three to six months.

The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering, feature extraction, and applications of unsupervised learning.

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Alexander Felfernig is a full professor at the Graz University of Technology (Austria) since March 2009 and received his PhD in Computer Science from the University of Klagenfurt. He directs the Applied Software Engineering (ASE) research group. His research interests include configuration systems, recommender systems, model-based diagnosis, software requirements engineering, different aspects of human decision making, and knowledge acquisition methods. In these areas, he is engaged in national research projects as well as in a couple of European Union projects. Alexander Felfernig has published numerous papers in renowned international conferences and journals (e.g., AI Magazine, Artificial Intelligence, IEEE Transactions on Engineering Management, IEEE Intelligent Systems, Journal of Electronic Commerce) and is a co-author of the book on "Recommender Systems" published by Cambridge University Press.

In year three of a global pandemic, consumers want businesses to be more empathetic. In response, brands are aligning more closely to changing consumer preferences. To deliver a superior customer experience, we see several B2C brands investing in technologies that enable personalization. But, doing this at scale, with millions of consumers, is quite a challenge. Conversational AI helps brands navigate this.