Film


No Time Like Now to Leverage AI - TEK2day

#artificialintelligence

In deploying artificial intelligence ("AI") or one of its sibling technologies – machine learning and deep learning – the first order of business is defining the business problem. Next, understand your enterprise data and third party data in terms of scope and quality. Once those elements are in place, you are ready to embark on your AI journey upon which your imagination will be the primary limiting factor. These are problems that can be answered by deploying some combination of AI, machine learning, deep learning/neural networks and/or natural language processing ("NLP"). Data quality is important – "garbage in, garbage out".


How AI could stop users from sharing Netflix login with other users

#artificialintelligence

I've been building out my new funnel inside of ClickFunnels, and after doing it, the idea of using anything else is daunting to me. I would have had to have membership software, landing pages, order forms and then still figure out how to tie them all together. I'll never have to go through that again because of ClickFunnels!


Retail Has Big Hopes For A.I. But Shoppers May Have Other Ideas

#artificialintelligence

Walmart has opened a store in Levittown, N.Y. that is intended to showcase the power of artificial intelligence. The store, announced last week, is packed with video cameras, digital screens, and over 100 servers, making it appear more like a corporate data center than a discount retailer. All that machinery helps Walmart automatically track inventory so that it knows when toilet paper is running low or that milk needs restocking. The company's goal is to create "a glimpse into the future of retail," when computers rather than humans are expected to do a lot of retail's grunt work. Walmart's push into artificial intelligence highlights how retailers are increasingly adding data crunching to their brick and mortar stores.


Machine Learning: Building Recommender Systems

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The scikit-learnthe library has functions that enable us to build these pipelines by concatenating various modules together. We just need to specify the modules along with the corresponding parameters. It will then build a pipeline using these modules that processes the data and trains the system. The pipeline can include modules that perform various functions like feature selection, preprocessing, random forests, clustering, and so on. In this section, we will see how to build a pipeline to select the top K features from an input data point and then classify them using an Extremely Random Forest classifier.


r/artificial - DeOldify: Fun Silent Movie Colorization Demo Reel [Based on Deep Learning]

#artificialintelligence

Yes it was because of MSE loss! I'm still new to deep learning and not familiar with GANs so I tried to do it with a traditional auto-encoder CNN and ended up with that result. I will check your work later when I'm free for sure, it sounds like an interesting read


Are there any good movies based on video games?

The Guardian

What makes a good video game movie? Is there even such a thing? The curse of the video game movie has long been documented, and the stigma that it's impossible to make a good one regardless of how much money you throw at it or who plays the lead has dogged the genre for years. Video games are more lucrative than Hollywood films overall, yet video game adaptations still struggle to be taken seriously by studio executives, who often misunderstand what makes the source material so popular to begin with. The anatomy of what makes a game-to-film adaptation tick is particularly relevant now with the release of Detective Pikachu, an adaptation of one of the franchise's lesser-known properties, a spinoff crime-solving game by the same name.


Not quite film, or games … is interactive mixed reality the future of storytelling?

The Guardian

Will it printed on paper or projected in 3D? Prophesying the future is hard. But, like fortune telling with tea leaves, sometimes the future can be glimpsed in what's here right now. Last year, Charlie Brooker's Black Mirror: Bandersnatch – a nihilistic choose-your-own-adventure style film with five main endings – introduced Netflix viewers to a term that has only recently entered the TV lexicon: interactive storytelling. Following up-and-coming developer Stefan as he works tirelessly to create the most complex video game of 1984, Bandersnatch calls on the viewer to make his choices. Do you angrily douse your computer in tea or yell at your dad to blow off steam?


Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

arXiv.org Artificial Intelligence

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations.


Artificial Intelligence In Humanoid Robots

#artificialintelligence

When people think of Artificial Intelligence (AI), the major image that pops up in their heads is that of a robot gliding around and giving mechanical replies. There are many forms of AI but humanoid robots are one of the most popular forms. They have been depicted in several Hollywood movies and if you are a fan of science fiction, you might have come across a few humanoids. One of the earliest forms of humanoids was created in 1495 by Leonardo Da Vinci. It was an armor suit and it could perform a lot of human functions such as sitting, standing and walking.


Hierarchical Context enabled Recurrent Neural Network for Recommendation

arXiv.org Machine Learning

A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our \textit{interest drift assumption}. As we suggest a new RNN structure, we support HCRNN with a complementary \textit{bi-channel attention} structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.