Low-rank approximations of data matrices have become an important tool in Machine Learning in the field of bio-informatics, computer vision, text processing, recommender systems, and others. They allow for embedding high dimensional data in lower dimensional spaces which mitigate effects due to noise, uncover latent relations, or facilitate further processing. In general, MF is a process to find two factor matrices, P R, k m and Q R, k n, to describe a given m-by-n training matrix R in which some entries may be missing. MF can be found in many applications, but we only use collaborative filtering in recommender systems as examples. It is based on the assumption that the entries of R are the historical users' preferences for merchandises, and the task on hand is to predict unobserved user behavior (i.e., missing entries in R) to make a suitable recommendation. In this blog, I discuss about different types of matrix factorization techniques for real-time recommendation engines and their corresponding Python libraries.

FOX Business' Maria Bartiromo reports on the growth of artificial intelligence and the impact it could have on jobs. It sounds like a plot of a science-fiction novel: A future that doesn't need human beings. But is it more reality than fantasy? Most people use artificial intelligence and probably don't even realize they're using it -- whether it's asking Siri a question or trying to avoid a traffic jam using GPS or even using our faces as a password to access our smartphones. Artificial intelligence is defined as the capability of a machine to imitate intelligent human behavior.

Trash talk has been part of sport and human competition for as long as people have been competitive, but now robots are getting in on the game. Researchers from Carnegie Mellon University, in Pittsburgh, Pennsylvania, programmed a robot called Pepper to use mild insults such as'you are a terrible player' and'your playing has become confused'. It would then use these insults while challenging a human to a game called'Guards and Treasures' that is designed to test rationality. Even though the robot used very mild language, the human player's performance got worse while they were being insulted, according to lead author Aaron M. Roth. The team say tests like this could help work out how humans will respond in future if a robot assistant disagrees with a command, such as over whether to buy healthy or unhealthy food.

Can you imagine a world without the kind of voice assistant technology provided by Amazon Alexa, Google Assistant, Siri on the iPhone or Cortana for Windows? Probably not, as we tend to take such technological leaps forward pretty much for granted. But behind the scenes there's a whole new world of machine-learning that drives their collective ability to seemingly answer any question put to them. It's not so much knowing the answer that's the technological miracle – because, well, the internet – but rather that these virtual assistants are able to understand the question in the first place. Machine-learning is, in the broadest possible terms, what you might expect in that computer algorithms can be trained to understand how to correctly respond to an input by way of a human telling it what that response should be.

Any examination of bias in AI needs to recognize the fact that these biases mainly stem from humans' inherent biases. The models and systems we create and train are a reflection of ourselves. So it's no surprise to find that AI is learning gender bias from humans. For instance, natural language processing (NLP), a critical ingredient of common AI systems like Amazon's Alexa and Apple's Siri, among others, has been found to show gender biases – and this is not a standalone incident. There have been several high profile cases of gender bias, including computer vision systems for gender recognition that reported higher error rates for recognizing women, specifically those with darker skin tones.

Finn AI, the world's leading AI-powered conversational banking technology provider, today announced that one of the world's largest financial services corporations has chosen Finn AI to help improve customer service and to enhance customer acquisition workflows. Under the terms of the engagement, Finn AI has developed a virtual assistant to pre-qualify prospects for the company's personal and small business banking products, ensuring human sales agents are engaged only when an inquiry is sales-based, thus reducing the cost to acquire. The virtual assistant also interacts with customers outside of regular call center hours, allowing the financial institution to extend their support hours to a 24/7 model. Additionally, the virtual assistant is used to expedite the product application process for new and existing customers, providing information about banking products and in-the-moment guidance, through to applying for a product. "This customer engagement is an excellent example of how Finn AI can help financial institutions achieve very specific, and often complex, business objectives," said Jake Tyler, CEO at Finn AI. "This particular use case leverages a number of our pre-built Customer Acquisition features including Smart Routing, Product Comparison, and Product Recommender. Used in combination, the customer is able to reduce distractions to sales agents while improving the consumer experience."

Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expected value of information (EVOI) . Unfortunately, it is computationally prohibitive to construct queries with maximum EVOI in RSs with large item spaces. We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning (ML) computational frameworks (e.g., TensorFlow, PyTorch). We exploit this to develop a novel, scalable Monte Carlo method for EVOI optimization, which is more scalable for large item spaces than methods requiring explicit enumeration of items. While we emphasize the use of this approach for pairwise (or k -wise) comparisons of items, we also demonstrate how our method can be adapted to queries involving subsets of item attributes or "partial items," which are often more cognitively manageable for users. Experiments show that our gradient-based EVOI technique achieves state-of-the-art performance across several domains while scaling to large item spaces.

We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning. For such problems, we introduce natural notions of universality and we show a local equivalence among them. Our analysis is naturally expressed via information geometry, and represents a conceptually and computationally useful analysis. The development reveals the complementary roles of the singular value decomposition, Hirschfeld-Gebelein-R\'enyi maximal correlation, the canonical correlation and principle component analyses of Hotelling and Pearson, Tishby's information bottleneck, Wyner's common information, Ky Fan $k$-norms, and Brieman and Friedman's alternating conditional expectations algorithm. We further illustrate how this framework facilitates understanding and optimizing aspects of learning systems, including multinomial logistic (softmax) regression and the associated neural network architecture, matrix factorization methods for collaborative filtering and other applications, rank-constrained multivariate linear regression, and forms of semi-supervised learning.

How often, or little, they're used by employees to increase their efficiency and improve customer satisfaction. For IBM, the only way to make AI a core part of the workflow across entire companies is to take a cue from a decades-old office staple: email. It's dangerous to think of AI as a magic tool, said Daniel Hernandez, VP of data and AI at IBM, speaking at the Gartner IT Symposium/Xpo in Orlando, Florida last week. Instead, it should be seen as a strategy to empower staffers, helping them make more effective decisions through data while boosting employee experience. "Email gets no respect because it's boring," said Hernandez.

This research summary is just one of many that are distributed weekly on the AI scholar newsletter. To start receiving the weekly newsletter, sign up here. Conversational assistants are one of the most interesting AI advances that we have witnessed recently. So far, we have seen them increasingly become a meaningful part of our personal lives as well as businesses to improve customer service. No doubt the future of these assistants is exciting and will keep expanding -- the smart virtual assistant market is estimated to grow at a CAGR of more than 26 % to reach over $12 billion U.S. dollars by 2024.