After logging into Watson Studio, select New Modeler Flow. Enter a name, keep the default settings, and then click Create. Next expand the Import menu, drag the Data Asset node onto the stream canvas and select Titanic training data file (train.csv) in the node settings to load data into the project. Right-click the node and select Preview to see your detailed dataset. To build a modeler stream look under Record Operations.

Within the search marketing space, there has been a lot of talk about voice search. Many are projecting voice search as the next big thing – in fact, as the next marketplace disruptor. But the truth is, voice search probably isn't going to be the next big thing. Yes, voice search is disrupting text-based searches, and this is causing a few raised eyebrows. However, voice is only a small part of the disruption that's happening today.

IBM's Watson supercomputer has beat Jeopardy champions, reconstituted recipes, and even helped create highlight reels for the World Cup. Now it's taking on a new tech challenge; changing how the construction industry operates. A new partnership between IBM and Fluor, a global engineering and construction company, will put the supercomputer's computational skills to work on making building more efficient. The new Watson-based system, in development since 2015 and now in use on select projects, will be able to analyze a job site "like a doctor diagnoses a patient," according to Leslie Lindgren, Fluor's vice president of Information Management. That degree of risk analysis, predictive logistics, and comprehension is no small challenge given the complexity of today's construction megaprojects.

Within the search marketing space, there has been a lot of talk about voice search. Many are projecting voice search as the next big thing – in fact, as the next marketplace disruptor. But the truth is, voice search probably isn't going to be the next big thing. Yes, voice search is disrupting text-based searches, and this is causing a few raised eyebrows. However, voice is only a small part of the disruption that's happening today.

Abstract--With the rapid development of knowledge base, question answering based on knowledge base has been a hot research issue. In this paper, we focus on answering singlerelation factoid questions based on knowledge base. We build a question answering system and study the effect of context information on fact selection, such as entity's notable type, outdegree. Experimental results show that context information can improve the result of simple question answering. Question answering (QA) is a classic natural language processing task, which aims at building systems that automatically answer questions formulated in natural language [1]. In recent years, several large-scale general purpose knowledge bases (KBs) have been constructed, including Freebase [2], YAGO [3], DBpedia [4] and Wikidata [5] .

Many services that perform information retrieval for Points of Interest (POI) utilize a Lucene-based setup with spatial filtering. While this type of system is easy to implement it does not make use of semantics but relies on direct word matches between a query and reviews leading to a loss in both precision and recall. To study the challenging task of semantically enriching POIs from unstructured data in order to support open-domain search and question answering (QA), we introduce a new dataset POIReviewQA. It consists of 20k questions (e.g."is this restaurant dog friendly?") for 1022 Yelp business types. For each question we sampled 10 reviews, and annotated each sentence in the reviews whether it answers the question and what the corresponding answer is. To test a system's ability to understand the text we adopt an information retrieval evaluation by ranking all the review sentences for a question based on the likelihood that they answer this question. We build a Lucene-based baseline model, which achieves 77.0% AUC and 48.8% MAP. A sentence embedding-based model achieves 79.2% AUC and 41.8% MAP, indicating that the dataset presents a challenging problem for future research by the GIR community. The result technology can help exploit the thematic content of web documents and social media for characterisation of locations.

Many developers, including myself, want to use AI in their applications. Building Machine Learning models, however, often requires a lot of expertise and time. This article describes a technique called AutoML, which can be used by developers to build models without having to be data scientists. While developers only have to provide the data and define the goals, AutoML figures out the best model automatically. Cognitive services are provided by most cloud providers these days.

Marketers get excited about data, artificial intelligence and the internet of things because of their combined power to potentially impact consumers' everyday lives. Across the commerce landscape, the potential applications may be limitless: Farmers are now using satellite data to help increase crop yields and improve the quality of the food we eat. Shippers are deploying blockchain technology to modernize the supply chain and get products into stores more safely and quickly. Banks are relying on encrypted mainframe computers to help protect consumers' personal data and prevent cybercrime. One of the areas in which marketers have only just begun to tap the exponentially increasing unstructured data of the internet is the weather.

One of the most difficult challenges faced by businesses in asset-intensive industries is how to control and scale the half billion and growing "smart" devices that make up the Internet of Things (IoT)? As much as 80 percent of IoT data in any organization is unstructured. And, let's be honest, "smart" devices really aren't that smart yet. As part of its giant rollout of AI solutions pre-trained for specific industries and professions, IBM Services is launching a new Connected Manufacturing offering that includes a method and approach to help clients accelerate their IoT transformation–from strategy, implementation, and security to managed services and ongoing operations. This combined capability, IBM said, will help its clients connect all of their manufacturing equipment, sensors, and systems to enable business improvement across OEE, quality, lead times and productivity.

DIRECT OPTIMIZA TION OF F-MEASURE FOR RETRIEV AL-BASED PERSONAL QUESTION ANSWERING Rasool Fakoor†, Amanjit Kainth, Siamak Shakeri, Christopher Winestock, Abdel-rahman Mohamed, Ruhi Sarikaya Amazon ABSTRACT Recent advances in spoken language technologies and the introduction of many customer facing products, have given rise to a wide customer reliance on smart personal assistants for many of their daily tasks. In this paper, we present a system to reduce users' cognitive load by extending personal assistants with long-term personal memory where users can store and retrieve by voice, arbitrary pieces of information. The problem is framed as a neural retrieval based question answering system where answers are selected from previously stored user memories. We propose to directly optimize the end-to-end retrieval performance, measured by the F1-score, using reinforcement learning, leading to better performance on our experimental test set(s). Index Terms-- Question Answering, Spoken information retrieval, Reinforcement Learning, Personal Assistants 1. INTRODUCTION Recent advances in speech recognition [1, 2], speech enhancement [3, 4], natural language understanding [5, 6], question answering [7, 8, 9], and dialogue systems [10, 11] have fueled the current surge in research and development for smart personal assistants [12] like Alexa, Siri, Google assistant, and Cortana, with many use cases around shopping, music, etc. In this paper we present a system for providing personal assistants a long term personal memory that enable users to store anything they want to remember by voice, and then later ask questions about it. An example use case is shown in Table 1. This system extends long-term memories of users and enables them to store and retrieve arbitrary pieces of information they are juggling in their minds.