Contextual dueling bandits, where a learner compares two options based on context and receives feedback indicating which was preferred, extends classic dueling bandits by incorporating contextual information for decision-making and preference learning. Several algorithms based on the upper confidence bound (UCB) have been proposed for linear contextual dueling bandits. However, no algorithm based on posterior sampling has been developed in this setting, despite the empirical success observed in traditional contextual bandits. In this paper, we propose a Thompson sampling algorithm, named FGTS.CDB, for linear contextual dueling bandits. At the core of our algorithm is a new Feel-Good exploration term specifically tailored for dueling bandits. This term leverages the independence of the two selected arms, thereby avoiding a cross term in the analysis. We show that our algorithm achieves nearly minimax-optimal regret, i.e., $\tilde{\mathcal{O}}(d\sqrt T)$, where $d$ is the model dimension and $T$ is the time horizon. Finally, we evaluate our algorithm on synthetic data and observe that FGTS.CDB outperforms existing algorithms by a large margin.

In recent times, there has been an increasing awareness about imminent environmental challenges, resulting in people showing a stronger dedication to taking care of the environment and nurturing green life. The current $19.6 billion indoor gardening industry, reflective of this growing sentiment, not only signifies a monetary value but also speaks of a profound human desire to reconnect with the natural world. However, several recent surveys cast a revealing light on the fate of plants within our care, with more than half succumbing primarily due to the silent menace of improper care. Thus, the need for accessible expertise capable of assisting and guiding individuals through the intricacies of plant care has become paramount more than ever. In this work, we make the very first attempt at building a plant care assistant, which aims to assist people with plant(-ing) concerns through conversations. We propose a plant care conversational dataset named Plantational, which contains around 1K dialogues between users and plant care experts. Our end-to-end proposed approach is two-fold : (i) We first benchmark the dataset with the help of various large language models (LLMs) and visual language model (VLM) by studying the impact of instruction tuning (zero-shot and few-shot prompting) and fine-tuning techniques on this task; (ii) finally, we build EcoSage, a multi-modal plant care assisting dialogue generation framework, incorporating an adapter-based modality infusion using a gated mechanism. We performed an extensive examination (both automated and manual evaluation) of the performance exhibited by various LLMs and VLM in the generation of the domain-specific dialogue responses to underscore the respective strengths and weaknesses of these diverse models.

Dueling bandits is a prominent framework for decision-making involving preferential feedback, a valuable feature that fits various applications involving human interaction, such as ranking, information retrieval, and recommendation systems. While substantial efforts have been made to minimize the cumulative regret in dueling bandits, a notable gap in the current research is the absence of regret bounds that account for the inherent uncertainty in pairwise comparisons between the dueling arms. Intuitively, greater uncertainty suggests a higher level of difficulty in the problem. To bridge this gap, this paper studies the problem of contextual dueling bandits, where the binary comparison of dueling arms is generated from a generalized linear model (GLM). We propose a new SupLinUCB-type algorithm that enjoys computational efficiency and a variance-aware regret bound $\tilde O\big(d\sqrt{\sum_{t=1}^T\sigma_t^2} + d\big)$, where $\sigma_t$ is the variance of the pairwise comparison in round $t$, $d$ is the dimension of the context vectors, and $T$ is the time horizon. Our regret bound naturally aligns with the intuitive expectation in scenarios where the comparison is deterministic, the algorithm only suffers from an $\tilde O(d)$ regret. We perform empirical experiments on synthetic data to confirm the advantage of our method over previous variance-agnostic algorithms.

Amazon Web Services (AWS), announced today that it is expanding its generative AI services in a bid to make the technology more available to organizations in the cloud. Among the new AWS cloud AI services is Amazon Bedrock, which is launching in preview as a set of foundation model AI services. The initial set of foundation models supported by the service include ones from AI21, Anthropic, and Stability AI as well as a set of new models developed by AWS known collectively as Amazon Titan. In addition, AWS is also announcing the general availability of Amazon EC2 Inf2 cloud instances powered by the company's own AWS Inferentia2 chips, which provide high performance for AI. Rounding out the updates, the Amazon CodeWhisperer generative AI service for code development is now generally available, with AWS making it free for all individual developers.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Machine learning (ML) has undergone rapid transformation and adoption in recent years, driven by a number of factors. There is no shortage of opinions about why artificial intelligence (AI) and ML are growing. A recent report from McKinsey identified industrializing ML and applied AI as among its top trends for the year. In a session at the AWS re:Invent conference this week, Bratin Saha, VP and GM of AI and machine learning at Amazon, outlined the six key trends the cloud giant is seeing that are helping to drive innovation and adoption in 2022 and beyond.

Saha noted that customers approach machine learning in different ways, so AWS seeks to meet them where they are in their implementation. According to Saha, customers fall into one of three layers of development, and AWS offers services for each layer. "At the bottom layer are the machine learning infrastructure services. This is where we provide the machine learning hardware and software that customers can use to build their own machine learning infrastructure," he said. "This is meant for customers with highly custom needs, and that is why they want to build their own machine learning infrastructure."

Be our guest as we celebrate 20 years of AI/ML innovation on October 25, 2022, 9:00 AM – 10:30 AM PT. The first 1,500 people to register will receive $50 of AWS credits. Over the past 20 years, Amazon has delivered many world firsts for artificial intelligence (AI) and machine learning (ML). ML is an integral part of Amazon and is used for everything from applying personalization models at checkout, to forecasting the demand for products globally, to creating autonomous flight for Amazon Prime Air drones, to natural language processing (NLP) on Alexa. And the use of ML isn't slowing down anytime soon, because ML helps Amazon exceed customer expectations for convenience, cost, and delivery speed.

Human resources professionals and job seekers alike may soon be able to better understand a company's unique organizational culture thanks to a new machine-learning approach. Developed by Georgia Tech researchers, the approach is the first of its kind to computationally model organizational culture using publicly available anonymized data sources – including Glassdoor user reviews. These models are illustrated using heat maps that reveal positive and negative sentiment for a company and its business units across 41 dimensions of organizational culture. The heat maps give a "cloud-contributed" sense of what the culture is like in a particular workplace and can provide actionable insights to HR teams, unit managers, and job seekers, according to the researchers. "Right now, to get a measure of organizational culture, companies rely on internal surveys, which are difficult to scale. It's also unlikely that they are getting true responses given factors like organizational bias or employee concerns about anonymity," said Vedant Das Swain, a second-year Ph.D. student studying human-computer interaction at Georgia Tech.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Amazon just unveiled Serverless Inference, a new option for SageMaker, its fully managed machine learning (ML) service. The goal for Amazon SageMaker Serverless Inference is to serve use cases with intermittent or infrequent traffic patterns, lowering total cost of ownership (TCO) and making the service easier to use. VentureBeat connected with Bratin Saha, AWS VP of Machine Learning, to discuss where Amazon SageMaker Serverless fits into the big picture of Amazon's machine learning offering and how it affects ease of use and TCO, as well as Amazon's philosophy and process in developing its machine learning portfolio. Inference is the productive phase of ML-powered applications.

There are further complications with ML IP. SoC verification means that debug must be thought through. "You have to be sure that if something goes wrong at the customer site, you are able to trace that error into your IP," said Saha. "Your physical design will become more complex, and you might get an issue with design closure." This issue persists even after a chip has been deployed into a system. "Let's say you see a problem in the field," he added.