Automated service agents require well-structured workflows to provide consistent and accurate responses to customer queries. However, these workflows are often undocumented, and their automatic extraction from conversations remains unexplored. In this work, we present a novel framework for extracting and evaluating dialog workflows from historical interactions. Our extraction process consists of two key stages: (1) a retrieval step to select relevant conversations based on key procedural elements, and (2) a structured workflow generation process using a question-answer-based chain-of-thought (QA-CoT) prompting. To comprehensively assess the quality of extracted workflows, we introduce an automated agent and customer bots simulation framework that measures their effectiveness in resolving customer issues. Extensive experiments on the ABCD and SynthABCD datasets demonstrate that our QA-CoT technique improves workflow extraction by 12.16\% in average macro accuracy over the baseline. Moreover, our evaluation method closely aligns with human assessments, providing a reliable and scalable framework for future research.

Multi-armed bandit (MAB) algorithms have achieved significant success in sequential decision-making applications, under the premise that humans perfectly implement the recommended policy. However, existing methods often overlook the crucial factor of human trust in learning algorithms. When trust is lacking, humans may deviate from the recommended policy, leading to undesired learning performance. Motivated by this gap, we study the trust-aware MAB problem by integrating a dynamic trust model into the standard MAB framework. Specifically, it assumes that the recommended and actually implemented policy differs depending on human trust, which in turn evolves with the quality of the recommended policy. We establish the minimax regret in the presence of the trust issue and demonstrate the suboptimality of vanilla MAB algorithms such as the upper confidence bound (UCB) algorithm. To overcome this limitation, we introduce a novel two-stage trust-aware procedure that provably attains near-optimal statistical guarantees. A simulation study is conducted to illustrate the benefits of our proposed algorithm when dealing with the trust issue.

At White Wind Research, we are looking for an AI/ML expert with trading knowledge to work on our AI-assisted cryptocurrency trading project. This includes both fully-automated trading (i.e., a trading bot) and AI-based assistance for further research and analysis to be made by humans. This project is our first at our company, and this position will be our first hire. Please note that this position requires a self-motivated individual. All of the above is a requirement.

The technological capacity exists to use augmented intelligence (AI) algorithms and tools to transform health care, but real challenges remain in ensuring that tools are developed, implemented and maintained responsibly, according to a JAMA Viewpoint column, "Artificial Intelligence in Health Care: A Report From the National Academy of Medicine." "The challenges [to use of AI] are unrealistic expectations, biased and nonrepresentative data, inadequate prioritization of equity and inclusion, the risk of exacerbating health care disparities, low levels of trust, uncertain regulatory and tort environments and inadequate evaluation before scaling narrow AI," the opinion piece concludes. AI is often called artificial intelligence. The Viewpoint column was co-written by two co-authors of the National Academy of Medicine (NAM) report, AI in Healthcare: The Hope, The Hype, The Promise, The Peril. The 2019 NAM publication--a mix of caution and guarded optimism--presents what's known about AI in the clinical setting and serves as a guide on how the field can move forward responsibly and in a way that benefits all patients.

NEWS RELEASE GEORGIAN COLLEGE ************************* As artificial intelligence continues to rapidly transform the way organizations and their employees work, Georgian College's new graduate certificate program, Artificial Intelligence – Architecture, Design and Implementation, will help prepare the next generation of graduates for careers in one of today's fastest-growing transformational technologies. The program starts this January at the Barrie Campus. Students will acquire the necessary background to become AI system designers, programmers, implementers, or machine learning analysts. With a strong focus on applied skills, they'll learn how to design and implement supervised, unsupervised and reinforcement learning solutions for a variety of situations and solve AI challenges for a diverse set of industries. "The AI computing paradigm radically changes the functionality and capabilities of computer systems, and through this program students will solve complex AI challenges and power next generation businesses through the application of machine learning," said Tim Krywulak, Associate Dean, Design and Visual Arts.

Capgemini, a global leader in consulting, technology and outsourcing services, has today announced the findings of "Turning AI into concrete value: the successful implementers' toolkit", a study of nearly 1,000 organizations with revenues of more than $500m that are implementing artificial intelligence (AI), either as a pilot or at scale[1]. The research both counters fears that AI will cause massive job losses in the short term, as 83% of firms surveyed say AI has generated new roles in their organizations, and highlights the growth opportunity presented by AI: three-quarters of firms have seen a 10% uplift in sales, directly tied to AI implementation. The report, which surveyed executives from nine countries and across seven sectors, found that four out of five companies (83%) have created new jobs as a result of AI technology. Specifically, organizations are producing jobs at a senior level, with two in three jobs being created at the grade of a manager or above. Furthermore, among organizations that have implemented AI at scale, more than 3 in 5 (63%) said that AI has not destroyed any jobs in their organization. Alongside the trend towards job creation at management level, the report provides further evidence that organizations see AI as a means of reducing the time employees spend on routine and administrative tasks to enable them to deliver more value.

What concrete benefits are organizations really seeing from AI today? Our comprehensive research provides insights direct from the market on the real-life benefits, the best use cases, and where to invest – a successful AI implementers' toolkit. Artificial Intelligence (AI) is a hot and trending topic and the perception is that many organizations are looking at AI as a technology with immense potential – but in the future. Capgemini wanted to find out more. We analyzed more than 50 AI use cases regarding their adoption, complexity and benefits.

Dijkstra's single-source shortest-path algorithm (DA) is one of the well-known, fundamental algorithms in computer science and related fields. DA is commonly taught in undergraduate courses. Uniform-cost search (UCS) is a simple version of the best-first search scheme which is logically equivalent to DA. In this paper I compare the two algorithms and show their similarities and differences. I claim that UCS is superior to DA in almost all aspects. It is easier to understand and implement. Its time and memory needs are also smaller. The reason that DA is taught in universities and classes around the world is probably only historical. I encourage people to stop using and teaching DA, and focus on UCS only.