Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight, and is a consequence of the selective attention that enables you to remain focused on important parts of the world without distraction from irrelevant details. It is believed that this selective attention mechanism enables people to condense broad sensory information into a form that is compact enough to be used for future decision making. While this may seem to be a limitation, such "bottlenecks" observed in nature can also inspire the design of machine learning systems that hope to mimic the success and efficiency of biological organisms. For example, while most methods presented in the deep reinforcement learning (RL) literature allow an agent to access the entire visual input, and even incorporating modules for predicting future sequences of visual inputs, perhaps reducing an agent's access to its visual inputs via an attention constraint could be beneficial to an agent's performance?

For the Hemingway one, please note that the first 3 sentences are me, not GPT-3. In my various tweets, ' ' delimits the prompt (because there's nothing else in the playground UI right now which keeps visual track). Anyway, the interesting thing here is that I spent a good hour trying to get GPT-3 to do this. I knew it could do it based on how it could do Tom Swifties and the Turing dialogue and the other things, but I just couldn't quite figure out how to get it to do so. At one point, I was trying the idea of writing a plot summary and asking it to then "Please write a plot summary of Harry Potter in the style of Ernest Hemingway", and it copied my plot summary verbatim and then added on the sarcastic comment: It still sounds like it came straight from Harry Potter and the Philosopher's Stone.

In July 2017, China's State Council released the country's strategy for developing artificial intelligence (AI), entitled'New Generation.

The growing use of artificial intelligence has the potential to transform the care and outcomes of complex diseases."

Artificial intelligence (AI) advancements are showing no signs of slowing down.

The feature, rolled out in partnership with AI music startup Mubert, will generate an instrumental track in any of more than two dozen musical styles designed to match the mood of a given clip. Trained on a library of over 1 million beats, samples and patterns, the system also has the benefit of being completely royalty-free, since each snippet it generates is a wholly original composition. "AI, of course, cannot replace human imagination, but it will become the new virtual creative assistant that will expand and fine-tune one's overall skills," PicsArt CEO Hovhannes Avoyan said. "In time, we'll begin to see more novice users being able to produce vastly superior content than they ever thought possible because of AI." Among the genres of music offered by the feature are "techno," "lofi" and "dub" as well as mood-based styles including"happy" or "romantic" and activity-based ones such as "study" or "yoga."

Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box recommender systems. In particular, we propose to interpret feature interactions from a source recommender model and explicitly encode these interactions in a target recommender model, where both source and target models are black-boxes. By not assuming the structure of the recommender system, our approach can be used in general settings. In our experiments, we focus on a prominent use of machine learning recommendation: ad-click prediction. We found that our interaction interpretations are both informative and predictive, e.g., significantly outperforming existing recommender models. What's more, the same approach to interpret interactions can provide new insights into domains even beyond recommendation, such as text and image classification.

The upcoming iOS 14 software release will include some notable changes to the Apple's Podcasts app, such as a feature that suggests podcasts you may like based on what you currently listen to, according to 9to5Mac. The leak comes ahead of the virtual WWDC next Monday, where Apple is expected to reveal more about iOS 14. The new podcast feature will reportedly work similar to Apple Music's "For You" feature, which uses an algorithm to suggest new songs, albums and artists, a source told 9to5Mac. Apple Podcasts will learn from your listening preferences and suggest new podcasts. Another reported update to Apple Podcasts is the option for podcast creators to offer bonus content -- similar to what is currently offered when you buy a movie Apple TV.

In this session, we will be going through some of the fundamental methods that are used to tackle Natural Language Processing (NLP) problems. We will first be going through some NLP theory and then will walk through some code showing how a sentiment analysis model can be trained from scratch on a real-world IMDB movie review dataset.