Jesus Diaz wrote the following for Fast Company: "I don't care how efficient the company says the algorithm is -- from my personal experience, it doesn't work. A machine can never fully replace personal taste and exploration based on human interaction." He continued in the op-ed: "We're sick of algorithms telling us where to go, who to listen to, and what to watch. Your machine predicts a 98% chance that I would like to watch Frozen. In fact, I have yet to find an instance of any algorithm surprising me with a smart suggestion."

Sign up here to get the day's top stories delivered straight to your inbox. The routine is familiar now. Off from the day, lounging on a couch, ice cream at the ready, the remote or mouse clicks onto the preferred streaming site, perhaps to watch the show everyone is talking about or to be reacquainted with an old favorite. Streaming television and movie services are so deeply ingrained in the quotidian now, binge-watching is a common weekend activity and there are colloquial dating terms that invoke them -- "Netflix and chill." People log onto Netflix, Hulu, and other streaming services at all times of the day: during their commute; in the morning while getting ready; and when curling up at night.

Artificial intelligence has been the topic of countless Sci-Fi movies. There have been nearly ten Terminator movies or television shows so far, all using the same theory: AI will mean the demise of our race. While some have been saying that is a possibility, others have been quick to dismiss such claims. What is pretty obvious though, is that the advances made in the field cannot be ignored anymore, there are real concerns and we need to be prepared for whatever the future holds. Thankfully, some companies working with AI are taking the concerns surrounding AI seriously.

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RoadRunner will use the capital to expand into new markets, increase hiring and further develop its artificial intelligence/machine learning technology.

Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news, which can lead to harmful consequences. We observe that the images used in satirical news articles often contain absurd or ridiculous content and that image manipulation is used to create fictional scenarios. While previous work have studied text-based methods, in this work we propose a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT. To this end, we create a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection. We fine-tune ViLBERT on the dataset and train a convolutional neural network that uses an image forensics technique. Evaluation on the dataset shows that our proposed multi-modal approach outperforms image-only, text-only, and simple fusion baselines.

Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around items. State-of-the-art TCF methods employ recurrent neural networks (RNNs) to model such aspects. These methods deploy matrix-factorization-based (MF-based) approaches to learn the user and item representations. Recently, graph-neural-network-based (GNN-based) approaches have shown improved performance in providing accurate recommendations over traditional MF-based approaches in non-temporal CF settings. Motivated by this, we propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics. A challenge with this method lies in the increased data sparsity, which negatively impacts obtaining meaningful quality representations with GNNs. To overcome this challenge, we train a GNN model at each time step using a set of observed interactions accumulated time-wise. Comprehensive experiments on real-world data show the improved performance obtained by our method over several state-of-the-art temporal and non-temporal CF models.

My idea for this project started when I found out about the existence since 2010 of the Million Song Dataset, a freely-available collection of audio features and metadata for a million contemporary popular music tracks. Since music is one of my passions, it seemed appropriate to base one of my first Data Science projects on this subject. The core of the dataset was provided by the company The Echo Nest. Its creators intended it to perform music identification, recommendation, playlist creation, audio fingerprinting, and analysis for consumers and developers. In 2014 The Echo Nest was acquired by Spotify, which incorporated that song information into their systems.

The Key to E-Commerce Success: Blending Artificial Intelligence with Intelligent (Human) Agents. Article Featured Image. COVID-19 will forever change …

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