Prescriptive process monitoring methods seek to improve the performance of a process by selectively triggering interventions at runtime (e.g., offering a discount to a customer) to increase the probability of a desired case outcome (e.g., a customer making a purchase). The backbone of a prescriptive process monitoring method is an intervention policy, which determines for which cases and when an intervention should be executed. Existing methods in this field rely on predictive models to define intervention policies; specifically, they consider policies that trigger an intervention when the estimated probability of a negative outcome exceeds a threshold. However, the probabilities computed by a predictive model may come with a high level of uncertainty (low confidence), leading to unnecessary interventions and, thus, wasted effort. This waste is particularly problematic when the resources available to execute interventions are limited. To tackle this shortcoming, this paper proposes an approach to extend existing prescriptive process monitoring methods with so-called conformal predictions, i.e., predictions with confidence guarantees. An empirical evaluation using real-life public datasets shows that conformal predictions enhance the net gain of prescriptive process monitoring methods under limited resources.

CMR Surgical has launched a data-led training programme for its Versius surgical robot system, with the intention of reducing overall training time and improving standardisation in surgical care. The seven-step programme uses data-driven metrics and benchmarking to assess skill levels. This data is captured through the training technology tools and standard observational data, and supports personalised feedback. As a result it is expected that learning curves will be shorter, minimising the cost and time for teams to become proficient. At the same time, the data can help to standardise surgeries with a view to improving outcomes for patients.

The Center introduced three of the robots – two named Milo and another named Jemi – to its curriculum in January for students ages three to 21, Boerio said. While all teachers at the Center are trained in the new curriculum, the Center is primarily using School Psychology Program graduate assistants with the delivery. Both the facilitator and the student utilize an iPad, and one of the robots presents lessons through brief explanations, modeling and general facilitation.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You'll first implement best practices to prepare time series data. You'll also explore how RNNs and 1D ConvNets can be used for prediction.

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses. In this course, you will prepare to take the DP-100 Azure Data Scientist Associate certification exam. You will refresh your knowledge of how to plan and create a suitable working environment for data science workloads on Azure, run data experiments, and train predictive models. In addition, you will recap on how to manage, optimize, and deploy machine learning models into production. You will test your knowledge in a practice exam mapped to all the main topics covered in the DP-100 exam, ensuring you're well prepared for certification success.

Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. A Road map connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them. Machine learning can help with the diagnosis of diseases. Many physicians use chat bot with speech recognition capabilities to discern patterns in symptoms.

Welcome to this course on Probabilistic Deep Learning with TensorFlow! This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic approach to deep learning. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets. This is a crucial aspect when using deep learning models in applications such as autonomous vehicles or medical diagnoses; we need the model to know what it doesn't know. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning.

Artificial intelligence in action is still in its infancy. When AI seems real, human, and like someone you'd trust, it's a perplexing reaction. You can tell that Alexa or Siri is a bot, for instance, but what if you couldn't actually tell an AI-generated podcast from one that was entirely human-created? This podcast--created by podcast producer Rev. Yugen Bond alongside some snarky robots--was written with AI technology. It's a fascinating glimpse into what AI potentially holds for content creation. ABOUT THE PODCAST: This minimalist wants more. Enough-ism is about having enough, already. The world is experiencing an awakening. This podcast about mindfulness, meditation, and minimalism is your modern toolkit to keep your spirit right and your soul bright. One candle can light a fire. ABOUT THE HOST: Reverend Yugen Bond is an author and reiki master with a master’s in metaphysical sciences. She once despised meditation, had both too much and nothing to wear, and didn't know how to slow down her thoughts. What a journey it's been. Time to share it with the world, especially with you. CONTACT INFO:  Can’t get enough of Enough-ism? Follow @IAmEnoughism and visit IAmEnoughism.com | Support the show: Buy the "Enough-ism: This Minimalist Wants More" e-book now on Amazon Kindle! For business inquiries, guest requests, and speaking engagements, email enoughismpodcast@gmail.com. 

Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.

A classical ASR approach treats the process of generating speech as a noisy channel. In this framing, text is drawn from some distribution and statistically transformed into We present a noisy channel generative model speech audio; the speech recognition task is then to invert of two sequences, for example text and speech, this generative model to infer the text most likely to have which enables uncovering the association between given rise to a given speech waveform. This generative the two modalities when limited paired data is model of speech was historically successful (Baker, 1975; available. To address the intractability of the exact Jelinek, 1976; Rabiner, 1989), but has been superseded in model under a realistic data setup, we propose modern discriminative systems by directly modeling the a variational inference approximation. To train conditional distribution of text, given speech (Graves et al., this variational model with categorical data, we 2006; Amodei et al., 2016). The direct approach has the advantage propose a KL encoder loss approach which has of allowing limited modeling power to be solely devoted connections to the wake-sleep algorithm. Identifying to the task of interest, whereas the generative one can the joint or conditional distributions by only be extremely sensitive to faulty assumptions in the speech observing unpaired samples from the marginals is audio model despite the fact that this is not the primary only possible under certain conditions in the data object of interest. However the generative approach allows distribution and we discuss under what type of learning in a principled way from untranscribed speech conditional independence assumptions that might audio, something fundamentally impossible in the direct approach.