Artificial intelligence has become a technological buzzword, often solely referred to AI rather than depicting the possibly infinite amount of practical applications that artificial intelligence can actually provide, or the intricacies involved from industry to industry, and region to region. To discuss some of the many applications for artificial intelligence, as well as some of the considerations to be taken into account to create more accurate and less biased machine learning systems, I had the pleasure of speaking with Nitendra Rajput, VP and Head of Mastercard's AI Garage. Nitendra Rajput is the Vice President and Head of Mastercard's AI Garage, setting up the centre to enable it to solve problems across various business verticals globally with machine learning processes, increasing efficiencies across the business as well as mitigating instances of fraud. Nitendra has over 20 years experience working in the fields artificial intelligence, machine learning, and mobile interactions, after realising a gap in the market for developing speech recognition systems for vocally-led countries, such as India. Prior to Mastercard's AI Garage, he spent 18 years at IBM Research, working on different aspects of machine learning, human-computer interaction, software engineering and mobile sensing.

From recommending movies or restaurants to coordinating fashion accessories and highlighting blog posts and news articles, recommender systems are an important application of machine learning, surfacing new discoveries and helping users find what they love. At Google, we have spent the last several years exploring new deep learning techniques to provide better recommendations through multi-task learning, reinforcement learning, better user representations and fairness objectives. These and other advancements have allowed us to greatly improve our recommendations. Today, we're excited to introduce TensorFlow Recommenders (TFRS), an open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy. Built with TensorFlow 2.x, TFRS makes it possible to: TFRS is based on TensorFlow 2.x and Keras, making it instantly familiar and user-friendly.

Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP-CF) model as a unified solution for both problems. AMP-CF breaks down the user to several latent 'personas' (profiles) that identify and discern the different tastes and inclinations of the user. Then, the revealed personas are used to generate and explain the final recommendation list for the user. AMP-CF models users as an attentive mixture of personas, enabling a dynamic user representation that changes based on the item under consideration. We demonstrate AMP-CF on five collaborative filtering datasets from the domains of movies, music, video games and social networks. As an additional contribution, we propose a novel evaluation scheme for comparing the different items in a recommendation list based on the distance from the underlying distribution of "tastes" in the user's historical items. Experimental results show that AMP-CF is competitive with other state-of-the-art models. Finally, we provide qualitative results to showcase the ability of AMP-CF to explain its recommendations.

Easter, Passover, Holi, and Ramadan were just a few of the religious milestones that used virtual tools during the pandemic to replace traditional observation. The intersection of technology and spirituality is coming much faster than many expected. In the 1983 Star Wars film Return of the Jedi, artificially intelligent android C3P0 finds out what it's like to become the subject of worship. "They think I'm some sort of God," he said, as fuzzy creatures hover around him chanting in prayer. But the intersection of machines and religion is happening in real life.

Doron Adler and Justin Pinkney, two software engineers, recently released a "Toonification translation" AI model that turns real faces into flawless cartoon representations. And while the toonification tool, "Toonify," was originally available to the public, it became too popular to sustain cheaply. But some people managed to Toonify a ton of celebrities before the tool was pulled, and all the animations are stellar. After much training of neural networks @Norod78 and I have put together a website where anyone can #toonify themselves using deep learning!https://t.co/OQ23p30isC In a series of blog posts, which come via Gizmodo, Pinkney outlines how he and Adler created Toonify.

I grew up in the Star Wars era. I remember sitting rapt in the theater, watching Luke Skywalker and Han Solo battle the dark forces in one intergalactic battle after another. So it's not lost on me when I read an article like this one in Popular Mechanics detailing how Artificial Intelligence (AI) is putting us closer to a Star Wars world every day. Beyond the clouds, AI is helping citizens prepare for the COVID-19 crisis. Not to mention recognizing and interpreting human emotions.

We were used to hearing that we'll be out of a job in twenty years, because of robots. Then the virus came, and now many are out of a job a bit faster, and not because of anything more intelligent or capable than themselves. Here are five currently existing robots that score pretty high on the creepiness scale, even without threatening to take away one's job. Sophia has somehow become the flagship of humanoid robotics. Constructed in Hong Kong, it has taken part in major TV talk shows and has been granted Saudi Arabian citizenship, although it is, essentially, not more than a "chatbot with a face" [1]. What the citizenship thing really means is unclear: Can Sophia vote?

The Fourth Industrial Revolution just took a huge step forward, thanks to a breakthrough artificial intelligence (AI) model that can learn virtually anything about the world -- and produce the content to tell us about it. The AI program is GPT-3 by OpenAI, which started out as a language model to predict the next word in a sentence and has vastly exceeded that capability. Now, drawing from voluminous data -- essentially all of Wikipedia, links from Reddit, and other Internet content -- GPT-3 has shown it can also compose text that is virtually indistinguishable from human-generated content. Asger Alstrup Palm, Area9's chief technology officer, explained that GPT-3 was tasked with testing the "scaling hypothesis" -- to see if a bigger model with ever-increasing amounts of information would lead to better performance. Although it's too early to call the scaling hypothesis proven, there are some strong indications that this is, indeed, the case. Further validating the potential of GPT-3, Microsoft recently announced it will exclusively license the model from OpenAI, with the intention of developing and delivering AI solutions for customers and creating new solutions using natural language generation.

In this post, I will show you how to build a movie recommender program using Python. This will be a simple project where we will be able to see how machine learning can be used in our daily life. If…

We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items. These slates can be hierarchical in nature. The central idea of our approach is to learn low dimensional embeddings of these slates. We present a novel way to learn these embeddings by making use of the (unknown) statistics of the underlying distribution generating the hierarchical data. Our representation learning algorithm can be viewed as a simple composition rule that can be applied recursively in a bottom-up fashion to represent arbitrarily complex hierarchical structures in terms of the representations of its constituent components. We demonstrate our ideas on two real world recommendation systems datasets including the one used for the RecSys 2019 challenge. For that dataset, we improve upon the performance achieved by the winning team's model by incorporating embeddings as features generated by our approach in their solution.