Conversational AI is the use of chatbots, messaging apps, and voice-based assistants to automate customer communications with your brand. From providing customer support to guiding them through the various products that you offer, it can be used for a variety of purposes. The adoption of this technology is being fuelled by the rise in the usage of messaging apps and voice-based assistants. WhatsApp, the most popular messaging app, has over 1.6 billion users, followed by Facebook Messenger with 1.3 billion users. So, what does that mean for your business?

A common definition for machine learning is "The field of study that gives computers the ability to learn without being explicitly programmed." We give the computer large amounts of data, and it can learn how to make decisions about the data. For instance, in rules-based warranty claims validation, there is a pre-determined set of warranty rules, and the violation of those rules may result in rejection or adjustment of the claim. In machine learning, the warranty rules are not pre-set in the system, but learned through example cases. Although machine learning may be new to us, most of us may encounter machine learning on a daily basis.

Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.

Might AI help improve customer service for the millions of people who shop on Amazon.com? Amazon intends to find out. In a blog post, the Seattle tech giant revealed that it's testing two AI-based systems to handle incoming shopper inquiries. One fields requests from customers automatically and without human intervention, while the other helps human service agents respond more quickly and easily. "It is difficult to determine what types of conversational models other customer service systems are running, but we are unaware of any announced deployments of end-to-end, neural-network-based dialogue models like ours," wrote Kramer.

Until a decade ago, the only option people had to reach out to a company was to call or email their customer service team. Now, companies offer a chat team to provide better round-the-clock customer service. According to a Facebook-commissioned study by Nielsen, 56% of people would prefer to message rather than call customer service, and that's where bots come into play. Bots are revolutionizing the way companies interact with their customers. A decade ago, bots were considered a passing tech fad.

AI and deep learning have been shining in dealing with unstructured data, from natural language understanding and automatic knowledge base construction to classifying and generating images and videos. Structured data, however, which is trapped in business applications such as product repositories, transaction logs, ERP and CRM systems are being left behind! Tabular data is still being processed by an older generation of data science techniques, like rule-based systems or decision trees. These methods use handcrafted features, are tedious to maintain, and require lots of manually labelled data. While the recent advancement of AI advances allowed mining huge value out of unstructured data, it would be remiss to not pay the same attention to the value of structured data in driving business, revenues, health, security and even governance.

We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learns an approximation of a quality function from examples (consisting of situations, choices and associated qualities) and is then able to make an optimal choice as well as predict the quality of a choice in a given situation. One area of application for SupRB is parametrization of industrial machinery. In this field, acceptance of the recommendations of machine learning systems is highly reliant on operators' trust. While an essential and much-researched ingredient for that trust is prediction quality, it seems that this alone is not enough. At least as important is a human-understandable explanation of the reasoning behind a recommendation. While many state-of-the-art methods such as artificial neural networks fall short of this, LCSs such as SupRB provide human-readable rules that can be understood very easily. The prevalent LCSs are not directly applicable to this problem as they lack support for continuous choices. This paper lays the foundations for SupRB and shows its general applicability on a simplified model of an additive manufacturing problem.

Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However, when the studied language is low-resourced and the amount of training data is insufficient, these models can benefit from the integration of natural language grammar-based information. We propose two approaches to dependency parsing especially for languages with restricted amount of training data. Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser. In the rule-based approach, the parsing decisions made by the rules are encoded and concatenated with the vector representations of the input words as additional information to the deep network. The morphology-based approach proposes different methods to include the morphological structure of words into the parser network. Experiments are conducted on the IMST-UD Treebank and the results suggest that integration of explicit knowledge about the target language to a neural parser through a rule-based parsing system and morphological analysis leads to more accurate annotations and hence, increases the parsing performance in terms of attachment scores. The proposed methods are developed for Turkish, but can be adapted to other languages as well.

Automated game design is the problem of automatically producing games through computational processes. Traditionally these methods have relied on the authoring of search spaces by a designer, defining the space of all possible games for the system to author. In this paper we instead learn representations of existing games and use these to approximate a search space of novel games. In a human subject study we demonstrate that these novel games are indistinguishable from human games for certain measures.

Machine learning is increasingly used across fields to derive insights from data, which further our understanding of the world and help us anticipate the future. The performance of predictive modeling is dependent on the amount and quality of available data. In practice, we rely on human experts to perform certain tasks and on machine learning for others. However, the optimal learning strategy may involve combining the complementary strengths of humans and machines. We present expert-augmented machine learning, an automated way to automatically extract problem-specific human expert knowledge and integrate it with machine learning to build robust, dependable, and data-efficient predictive models. Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert.