We often think about artificial intelligence (AI) in terms of the benefits it can provide by helping us complete tasks more efficiently. It's important to remember, though, that this technology can be used just as easily for malicious ends. Could it pose more of a cybersecurity threat than we think? Because AI can learn on its own and use that knowledge to complete tasks autonomously, it can help us complete work more efficiently, more cost-effectively, more accurately and with less hands-on effort. Those benefits apply to virtually every sector.

Ever since the world has become one enormous marketplace, we have seen a constant change in how businesses take place. This has been further fueled by new technologies and rapidly evolving customer expectations. Even the highly regulated banking and finance sector in recent times has witnessed constant metamorphosis of its business models to stay ahead in disruptive times. Hence, when it comes to the financial services ecosystem, the FinTech industry plays a significant role in determining how the sector moves forward. Today, FinTech disruptors are changing how everything works –lending, payments, insurance, credit settlements, and more.

Amazon's Alexa continues to learn new party tricks, with the latest being a "newscaster style" speaking voice that will be launching on enabled devices in a few weeks' time. You can listen to samples of the speaking style below, and the results, well, they speak for themselves. The voice can't be mistaken for a human, but it does incorporate stresses into sentences in the same way you'd expect from a TV or radio newscaster. According to Amazon's own surveys, users prefer it to Alexa's regular speaking style when listening to articles (though getting news from smart speakers still has lots of other problems). Amazon says the new speaking style is enabled by the company's development of "neural text-to-speech" technology or NTTS.

Every day we hear and read about how machine learning is changing the face of technology. From social media to virtual assistants like Siri IoT, Alexa, and even automobiles, algorithms analyze terabytes of data and make faster decisions. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Why you should learn Machine learning?

This week in Las Vegas, Amazon rolled out dozens of new features, upgrades, and new products at AWS re:Invent. Here's a quick roundup of news out of the annual conference that may matter to members of the AI community. A disproportionate amount of money is spent on inference versus training when it comes to AI models, AWS CEO Andy Jassy said, and GPUs can be terribly inefficient. To address these issues, Amazon custom-designed a chip named Inferentia due out next year and created Elastic Inference, a service that identifies parts of a neural network that can benefit from acceleration. To speed up training of AI models, Amazon introduced AWS-Optimized TensorFlow, which can train a model with the ResNet-50 benchmark in 14 minutes.

The increasing role of recommender systems in many aspects of society makes it essential to consider how such systems may impact social good. Various modifications to recommendation algorithms have been proposed to improve their performance for specific socially relevant measures. However, previous proposals are often not easily adapted to different measures, and they generally require the ability to modify either existing system inputs, the system's algorithm, or the system's outputs. As an alternative, in this paper we introduce the idea of improving the social desirability of recommender system outputs by adding more data to the input, an approach we view as as providing `antidote' data to the system. We formalize the antidote data problem, and develop optimization-based solutions. We take as our model system the matrix factorization approach to recommendation, and we propose a set of measures to capture the polarization or fairness of recommendations. We then show how to generate antidote data for each measure, pointing out a number of computational efficiencies, and discuss the impact on overall system accuracy. Our experiments show that a modest budget for antidote data can lead to significant improvements in the polarization or fairness of recommendations.

The amount of dialogue history to include in a conversational agent is often underestimated and/or set in an empirical and thus possibly naive way. This suggests that principled investigations into optimal context windows are urgently needed given that the amount of dialogue history and corresponding representations can play an important role in the overall performance of a conversational system. This paper studies the amount of history required by conversational agents for reliably predicting dialogue rewards. The task of dialogue reward prediction is chosen for investigating the effects of varying amounts of dialogue history and their impact on system performance. Experimental results using a dataset of 18K human-human dialogues report that lengthy dialogue histories of at least 10 sentences are preferred (25 sentences being the best in our experiments) over short ones, and that lengthy histories are useful for training dialogue reward predictors with strong positive correlations between target dialogue rewards and predicted ones.

This post adresses the general problem of constructing a deep learning based recommender system. The particular architecture discribed in the paper is the one powering the new smart feed of the iki service, pushing your skills on daily basis -- to check its performance, please try product beta. If you feel familiar with the general idea of recommender systems, mainstream approaches and would like to go straight to the details of our solution, please skip first two sections of the paper. Recommender systems have changed the way we interact with lots of services. Instead of providing static data they bring interactive experience, an option to leave your feedback and to personalise the information you are given.

Amazon is pretty platform-agnostic when it comes to music. Its Echo speakers obviously work with Amazon Music as well as Spotify, Pandora, IHeartRadio and a number of other options. Starting on December 17th, Echo owners will have another option: Apple Music. The second-biggest streaming service in the US will work with Alexa, with users able to request songs, albums, artists, radio stations and playlists from Apple Music. While Amazon says that Apple Music will work with Echo speakers starting the week of December 17th, it doesn't say how fast the rollout will go, so Echo owners may have to be a little patient.

In news that might help you make some sense of your fragmented, frustrating device set up, Amazon announced today that its Echo devices will support Apple Music starting December 17. It's a small breakthrough in the streaming wars, one that should help bring some sense to your streaming strategy. And you've got Apple's increasing need to branch out beyond hardware to thank. When the Apple Music Alexa skill goes into effect next month, all you'll need to do to tap into Beats 1 is enable it and link your account. The simplicity of switching it on belies the tangled threads that will have gotten it there in the first place.