Progress in Artificial Intelligence manuscript No. (will be inserted by the editor) Abstract Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, data sets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features, thus they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working towards this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced towards this goal, relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state of the art performance. Finally the evaluation shows that our fully data-driven similarity measure design outperforms state of the art methods while keeping training time low. Keywords Similarity Measure, Data Science, Neural Networks, Data Analytics, Case-Based Reasoning, Similarity Function, Siamese Networks, Similarity metrics, Distance Metrics This work was supported by the Research Council of Norway through the EXPOSED project(grant number 302002390) and the Norwegian Open AI Lab 1 Introduction Many artificial intelligence and machine learning (ML) methods, such as k-nearest neighbors (k-NN) rely on a similarity (or distance) measure [21] between data points. In Case-based reasoning (CBR) a simple k-NN or a more complex similarity function is used to retrieve the stored cases that are most similar to the current query case.

It's impossible to talk about artificial intelligence without mentioning IBM's Watson. A pioneer in cognitive computing, the American computer giant has found multiple health applications for Watson. Pascal Sempé, senior sales consultant for Watson Health Solutions in France, explained how Watson functions and what's at stake. ME e-mag: Could Watson ever replace doctors? Pascal Sempé: Watson is a tool that helps the doctor, certainly not one that tells the doctor what to do.

In my previous blog post, I outlined the challenges faced by local authorities across the country to consistently deliver resident services amidst increasingly stringent budgetary cuts. In this post, I will propose some suggestions. Whilst it certainly will not undo the financial tight spot authorities find themselves in, I believe it will help alleviate funding pressures and, over time, bring costs down to a manageable level. Firstly, it is imperative to understand not just current resident needs, but future needs. Secondly, authorities collect data in abundance, and it is time to start using this data smartly. The introduction of analytics, machine learning and AI will equip authorities to find previously undetected insight from their data to improve decision making, in particular on which services to focus their attention.

In my previous blog post, I outlined the challenges faced by local authorities across the country to consistently deliver resident services amidst increasingly stringent budgetary cuts. In this post, I will propose some suggestions. Whilst it certainly will not undo the financial tight spot authorities find themselves in, I believe it will help alleviate funding pressures and, over time, bring costs down to a manageable level. Firstly, it is imperative to understand not just current resident needs, but future needs. Secondly, authorities collect data in abundance, and it is time to start using this data smartly. The introduction of analytics, machine learning and AI will equip authorities to find previously undetected insight from their data to improve decision making, in particular on which services to focus their attention.

Wong's Mandarin class meets four times a week. On Mondays and Fridays, he attends a class in a traditional classroom with Helen Zhou, Associate Professor at the RPI. There he learns new vocabulary and gets an introduction to phrases and grammatical structures. On Tuesdays and Thursdays, the class meets in the CIR, where students conduct conversations with virtual agents. In a restaurant environment, Wong said students can go through the entire process of sitting down in the restaurant, looking at a menu, ordering food, speaking with a waiter on how the food is prepared, and paying the bill.

Published as a conference paper at ICLR 2020M ETA-L EARNING WITHOUT M EMORIZATION Mingzhang Yin 12, George T ucker 2, Mingyuan Zhou 1, Sergey Levine 23, Chelsea Finn 24 mzyin@utexas.edu, Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks. However, most meta-learning algorithms implicitly require that the meta-training tasks be mutually-exclusive, such that no single model can solve all of the tasks at once. For example, when creating tasks for few-shot image classification, prior work uses a per-task random assignment of image classes to N-way classification labels. If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes. This requirement means that the user must take great care in designing the tasks, for example by shuffling labels or removing task identifying information from the inputs. In some domains, this makes meta-learning entirely inapplicable. In this paper, we address this challenge by designing a meta-regularization objective using information theory that places precedence on data-driven adaptation. This causes the meta-learner to decide what must be learned from the task training data and what should be inferred from the task testing input. By doing so, our algorithm can successfully use data from non-mutually-exclusive tasks to efficiently adapt to novel tasks. We demonstrate its applicability to both contextual and gradient-based meta-learning algorithms, and apply it in practical settings where applying standard meta-learning has been difficult. Our approach substantially outperforms standard meta-learning algorithms in these settings. Meta-learning (Schmidhuber, 1987) has emerged as a promising approach for enabling systems to quickly learn new tasks by building upon experience from previous related tasks (Thrun & Pratt, 2012; Koch et al., 2015; Santoro et al., 2016; Ravi & Larochelle, 2016; Finn et al., 2017). Meta-learning accomplishes this by explicitly optimizing for few-shot generalization across a set of meta-training tasks. The meta-learner is trained such that, after being presented with a small task training set, it can accurately make predictions on test datapoints for that meta-training task.

We do not want to write another technical AI book as the bottleneck of this technology is actually implementing it into our business and life rather than keep researching it in the labs. More and more people need to aware of how this technology work like how people realised how internet could have changed our lives and adopt it. I really hope the book can not only be a tool for me to explain my idea in a classroom but also to express my view of two points, that this technology is going to change our live like the internet did to us, and we must embrace it while considering all the social aspects of how it would change us.

Systems powered by artificial intelligence are being developed to be more user-friendly by communicating with users in a progressively human-like conversational way. Chatbots, also known as dialogue systems, interactive conversational agents, or virtual agents are an example of such systems used in a wide variety of applications ranging from customer support in the business domain to companionship in the healthcare sector. It is becoming increasingly important to develop chatbots that can best respond to the personalized needs of their users so that they can be as helpful to the user as possible in a real human way. This paper investigates and compares three popular existing chatbots API offerings and then propose and develop a voice interactive and multilingual chatbot that can effectively respond to users mood, tone, and language using IBM Watson Assistant, Tone Analyzer, and Language Translator. The chatbot was evaluated using a use case that was targeted at responding to users needs regarding exam stress based on university students survey data generated using Google Forms. The results of measuring the chatbot effectiveness at analyzing responses regarding exam stress indicate that the chatbot responding appropriately to the user queries regarding how they are feeling about exams 76.5%. The chatbot could also be adapted for use in other application areas such as student info-centers, government kiosks, and mental health support systems.

The IBM Developer NYC team will be hosting a Holiday Tech Showcase & Party! Please join us & the community for a fun-filled night of food & drinks, networking, IBM's BIG IDEAS for 2020, exclusive project demos using IBM technologies, & a special holiday gift from IBM Developer as a token of appreciation. Sign up for IBM Cloud (here: http://ibm.biz/IBMHolidayParty) to receive a special IBM holiday gift. Pooja, Roger, Grant, Nigel, Jenna, & Mofi Special Message: The IBM Developer NYC team would like to thank you ALL for being the best part of our events this year! This year, IBM Developer New York has grown over 70%! Thank you for showing up, participating & being excited to learn with us!

Assist AI doesn't need data scientists to train or refine models for maintenance work.