Simulating online recommender system performance is notoriously difficult and the discrepancy between the online and offline behaviors is typically not accounted for in offline evaluations. Recommender systems research tends to evaluate model performance on randomly sampled targets, yet the same systems are later used to predict user behavior sequentially from a fixed point in time. This disparity permits weaknesses to go unnoticed until the model is deployed in a production setting. We first demonstrate how omitting temporal context when evaluating recommender system performance leads to false confidence. To overcome this, we propose an offline evaluation protocol modeling the real-life use-case that simultaneously accounts for temporal context. Next, we propose a training procedure to further embed the temporal context in existing models: we introduce it in a multi-objective approach to traditionally time-unaware recommender systems. We confirm the advantage of adding a temporal objective via the proposed evaluation protocol. Finally, we validate that the Pareto Fronts obtained with the added objective dominate those produced by state-of-the-art models that are only optimized for accuracy on three real-world publicly available datasets. The results show that including our temporal objective can improve recall@20 by up to 20%.

Artificial intelligence is related with human intelligence with similar characteristics such as understanding, reasoning, , learning, problem solving and …

The Artificial Intelligence as a Service market is expected to grow from USD X.X million in 2020 to USD X.X million by 2026, at a CAGR of X.X% during …

An artificial intelligence (AI) algorithm has produced another significant breakthrough using attention mechanisms and a convolutional neural network to …

Using a deep–learning-based image-recognition model to scan through millions of product product listings, MarqVision's platform can detect those …

Their outputs inspired me to start my own journey of intersecting music with machine learning. I've been composing since I was a kid on whatever platform I could find. Classically trained with a music degree while hungry for as much new music as possible makes for a strange hybrid, a musician and performer trying to understand a technologist's world. Amid much struggling and general frustration and many false starts, the stubbornness and late night wrangling paid off. I had my first track and plucked up the courage to share some of my experiments online.

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" by progressively thus improving performance to complete a specific task without actually being programmed to complete said task. One example is when online services/sites such as Spotify, Netflix, and Amazon make recommendations for music to listen to, what to watch, or buy. When you've logged onto a website and been asked to identify street signs, bicycles, or cars by clicking on images (or type in a word or two) in a thing called a CAPTCHA? Then you have participated in machine learning. First coined in 1959 by American pioneer in the field of computer gaming and artificial intelligence Arthur Samuel, machine learning has evolved from the study of pattern recognition and computational to a theory that artificial intelligence can learn from and make predictions on data by using a series of complete algorithms.

Almost every group on earth is working on'deep learning' in some form. In Canada there are the big three research units: MILA at Montreal, Vector at Toronto, AMII at Edmonton. Both MILA and Vector have several research groups/universities affiliated to them in Quebec and Ontario respectively. Weirdly folks at UBC are also affiliated with Vector. AMII is mostly University of Alberta.

… created by Elon Musk’s OpenAI, may be considered or appears to exhibit, something like artificial general intelligence (AGI), the ability to understand …

Techniques to leverage machine learning algorithms, application of the technology across sectors such as agriculture, and data protection techniques in …