Smartphone owners are being given new rights to have their device repaired under laws introduced by the EU that could put an end to'throwaway culture'. Manufacturers will made to fix broken electronic devices under the EU's new Circular Economy Action Plan (CEAP), which will also cover the UK despite Brexit. The plan, unveiled on Wednesday by the European Commission, will give Europeans'the right to repair' by making devices easier to fix. The laws, which will also apply to tablets, laptops and printers, focus on a more circular economy – where electronic resources are kept in use as long as possible. Major tech companies making devices hard to fix, including Apple, Samsung and Huawei, is creating an electronic and electrical rubbish mountain – wasting resources and blighting the environment, say green campaigners.

Many aspects of human reasoning, including language, require learning rules from very little data. Humans can do this, often learning systematic rules from very few examples, and combining these rules to form compositional rule-based systems. Current neural architectures, on the other hand, often fail to generalize in a compositional manner, especially when evaluated in ways that vary systematically from training. In this work, we present a neuro-symbolic model which learns entire rule systems from a small set of examples. Instead of directly predicting outputs from inputs, we train our model to induce the explicit system of rules governing a set of previously seen examples, drawing upon techniques from the neural program synthesis literature. Our rule-synthesis approach outperforms neural meta-learning techniques in three domains: an artificial instruction-learning domain used to evaluate human learning, the SCAN challenge datasets, and learning rule-based translations of number words into integers for a wide range of human languages.

Machine learning using behavioral and text data can result in highly accurate prediction models, but these are often very difficult to interpret. Linear models require investigating thousands of coefficients, while the opaqueness of nonlinear models makes things even worse. Rule-extraction techniques have been proposed to combine the desired predictive behaviour of complex "black-box" models with explainability. However, rule-extraction in the context of ultra-high-dimensional and sparse data can be challenging, and has thus far received scant attention. Because of the sparsity and massive dimensionality, rule-extraction might fail in their primary explainability goal as the black-box model may need to be replaced by many rules, leaving the user again with an incomprehensible model. To address this problem, we develop and test a rule-extraction methodology based on higher-level, less-sparse "metafeatures". We empirically validate the quality of the rules in terms of fidelity, explanation stability and accuracy over a collection of data sets, and benchmark their performance against rules extracted using the original features. Our analysis points to key trade-offs between explainability, fidelity, accuracy, and stability that Machine Learning researchers and practitioners need to consider. Results indicate that the proposed metafeatures approach leads to better trade-offs between these, and is better able to mimic the black-box model. There is an average decrease of the loss in fidelity, accuracy, and stability from using metafeatures instead of the original fine-grained features by respectively 18.08%, 20.15% and 17.73%, all statistically significant at a 5% significance level. Metafeatures thus improve a key "cost of explainability", which we define as the loss in fidelity when replacing a black-box with an explainable model.

Artificial intelligence has mastered even extremely complex games like chess and Go. However, these games have pre-defined rules and very specific methods of interaction that don't lend themselves to creative choices. A role-playing game like Dungeons and Dragons (DnD) has infinitely more ways to play than a game of chess does, but this hasn't stopped researchers from trying to develop AI systems capable of improvising storylines for DnD or similar tabletop role-playing games. AI researchers are constantly working on new ways to improve the generative language abilities of AI. One of the biggest advances in the past couple of years is the development GPT-2, which was able to generate coherent stories on the fly.

Artificial intelligence has mastered even extremely complex games like chess and Go. However, these games have pre-defined rules and very specific methods of interaction that don't lend themselves to creative choices. A role-playing game like Dungeons and Dragons (DnD) has infinitely more ways to play than a game of chess does, but this hasn't stopped researchers from trying to develop AI systems capable of improvising storylines for DnD or similar tabletop role-playing games. AI researchers are constantly working on new ways to improve the generative language abilities of AI. One of the biggest advances in the past couple of years is the development GPT-2, which was able to generate coherent stories on the fly.

The insurance industry has been creating innovative products around the emerging online shopping activities. Such e-commerce insurance is designed to protect buyers from potential risks such as impulse purchases and counterfeits. Fraudulent claims towards online insurance typically involve multiple parties such as buyers, sellers, and express companies, and they could lead to heavy financial losses. In order to uncover the relations behind organized fraudsters and detect fraudulent claims, we developed a large-scale insurance fraud detection system, i.e., InfDetect, which provides interfaces for commonly used graphs, standard data processing procedures, and a uniform graph learning platform. InfDetect is able to process big graphs containing up to 100 millions of nodes and billions of edges. In this paper, we investigate different graphs to facilitate fraudster mining, such as a device-sharing graph, a transaction graph, a friendship graph, and a buyer-seller graph. These graphs are fed to a uniform graph learning platform containing supervised and unsupervised graph learning algorithms. Cases on widely applied e-commerce insurance are described to demonstrate the usage and capability of our system. InfDetect has successfully detected thousands of fraudulent claims and saved over tens of thousands of dollars daily.

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.