Roundup Hello, here's a list of all this week's AI and machine learning related news beyond what we've already covered.

The unique Tore Knudsen Pour Reception Machine Learning Radio is unlike anything you've seen before.

Machine learning is slowly but surely making inroads in the finance sector with major banks already leveraging its ease-of-use mechanism.

Unified VRM is a cloud-based SaaS platform powered by the world's most advanced attack simulation capabilities and machine learning analytics.

It took our data science team led by Dr Xuxu Wang almost a year to develop the machine learning and deep learning models required," Mr Brown told …

There is a tremendous amount of data generated today -- so much that our normal databases cannot manage. It is estimated that by 2020, every person will be generating 1.7 megabytes of data in just a single second. If you think 1.7MBs are small then you might be thinking about data in terms of storage. But this is in terms of storage; in simple terms, a single character like A, B or 7 accounts for 1 byte, a document containing only 100 characters without any overhead such as symbols would use 100 bytes. One megabyte contains 1,000,000 bytes or one million characters. This means every second one person will be generating 1.7 million characters and subsequently 102 million characters every minute or 6.1 billion characters every hour.

Iyad Rahwan Is the Anthropologist of Artificial Intelligence.

Although numerous instantiations [ He et al., 2017; Liang et al., 2018 ] of CF have been proposed in recent years, matrix factorization (MF) [ Mnih and Salakhut-dinov, 2007; Koren et al., 2009 ] remains the most popular one due to its simplicity and effectiveness, and has been used for large scale recommendations of news [ Das et al., 2007], movies [ Koren et al., 2009 ] and products [ Linden et al., 2003 ] . Recent studies extend the MF framework for item cold-start recommendation by incorporating content information of items. The majority of methods for item cold-start recommendation employ a latent space sharing model. For example, Saveski te al. [ 2014] and Barjasteh et al. [ 2016 ] propose to use MF as the prjection function for both interactions and item contents. LDA [ Wang and Blei, 2011 ], CNN [ Kim et al., 2016 ], DNN [ Ebesu and Fang, 2017 ], SDAE [ Wang et al., 2015; Ying et al., 2016 ] and mDA [ Li et al., 2015 ] are proposed to learn the latent vectors of items from their textual contents. V an den Oord et al. [ 2013] and Wang et al. [ 2014] propose to use CNN to learn the latent vectors of music from their audio signals. The Wasserstein distance, which originates from optimal transport theory [ Rubner et al., 1998; Levina and Bickel, 2001], is a distance metric on probabilistic space and able to leverage the information on feature space. It has been successfully applied to many applications, such as computer vision [ Arjovsky et al., 2017 ] and natural language processing Figure 2: An illustration of problem definition.

Traditional recommendation systems produce static rather than interactive recommendations invariant to a user's specific requests, clarifications, or current mood, and can suffer from the cold-start problem if their tastes are unknown. These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items. In this work, we collect a goal-driven recommendation dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260 conversation turns between pairs of human workers recommending movies to each other. The task is specifically designed as a cooperative game between two players working towards a quantifiable common goal. We leverage the dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. Models are first trained to imitate the behavior of human players without considering the task goal itself (supervised training). We then finetune our models on simulated bot-bot conversations between two paired pre-trained models (bot-play), in order to achieve the dialogue goal. Our experiments show that models finetuned with bot-play learn improved dialogue strategies, reach the dialogue goal more often when paired with a human, and are rated as more consistent by humans compared to models trained without bot-play. The dataset and code are publicly available through the ParlAI framework.