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.

IBM is introducing a number of "AI-powered solutions" intended help brands better manage the balance between human intelligence and machine learning. For marketers, IBM's WEATHERfx Footfall with Watson represents a new AI ad solution that's specifically designed to help drive in-store traffic by optimizing marketing campaigns. By improving conversion rates with contextually relevant advertising to reach the right audience at the right time, Subway successfully increased sales and attracted new clients. Subway's WEATHERfx Footfall with Watson work involved designing ads based on shifting weather patterns. "Why advertise hot sandwiches when it's 100 degrees out?" is the thinking behind the creative ad units.

IBM has ushered its customers into the era of enterprise data science for more than a decade, starting with the investment of the ILOG CPLEX and SPSS acquisitions. As the data science market evolved, new macro trends developed, and IBM invested in advanced technologies and platforms to respond to this shift. In 2016, IBM introduced Data Science Experience and several Watson offerings, which blurred the lines between our new and old technologies. We have now made the decision to simplify our portfolio for our customers under one single brand -- IBM Watson Studio. IBM Watson Studio was first announced in the IBM Public Cloud at our Think Conference in March 2018, which included the integration of the capabilities of Data Science Experience Cloud and a new interface for SPSS Modeler.

We propose a video story question-answering (QA) architecture, Multimodal Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism with late fusion. MDAM uses self-attention to learn the latent concepts in scene frames and captions. Given a question, MDAM uses the second attention over these latent concepts. Multimodal fusion is performed after the dual attention processes (late fusion). Using this processing pipeline, MDAM learns to infer a high-level vision-language joint representation from an abstraction of the full video content. We evaluate MDAM on PororoQA and MovieQA datasets which have large-scale QA annotations on cartoon videos and movies, respectively. For both datasets, MDAM achieves new state-of-the-art results with significant margins compared to the runner-up models. We confirm the best performance of the dual attention mechanism combined with late fusion by ablation studies. We also perform qualitative analysis by visualizing the inference mechanisms of MDAM.

State Farm is moving forward with several digital initiatives as the largest personal lines P&C insurer in the U.S. by market share rides the digitalization wave shaking up the industry. The company has launched a $100 million fund, State Farm Ventures, with the goal of increasing its involvement in and adoption of insurtech. Led by innovation executive Michael Remmes, the unit will focus on "acquiring startups or strategic alliances that support our core products," says spokesperson Angie Harrier. With a major thrust of insurtech being use cases for artificial intelligence, State Farm is beginning to explore that technology as well. The insurer is running an ad campaign along with the Weather Company and IBM Watson through Halloween that uses Watson's cognitive computing technology to deliver relevant storm-preparation content to affected customers.

Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectively-gated attention mechanism. This boosts the model's performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show that our background knowledge enhancements are generalizable and improve performance on QAngaroo-WikiHop, another multi-hop reasoning dataset.

Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that rewrites a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query rewriting, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.