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Top Artificial Intelligence Influencers to Follow On LinkedIn

#artificialintelligence

Artificial Intelligence (AI) is evolving at an exponential rate. Today, it has expanded beyond tech and geographical constraints and is slowly bringing massive changes worldwide. In recent times, AI influencers are driving conversations about AI news and trends across social media and beyond while also offering advice to numerous enterprises. Plus, they also help us keep updated with the recent innovations and information about AI. Analytics Insight brings 10 LinkedIn influencers who share the latest trends in the AI domain through insightful articles on their LinkedIn blogs.


Facebook's AI team expands post-grad courses for Black and Latinx students

Engadget

Facebook says that it will expand an online course in deep learning to more students to help improve the diversity of its AI division. After a successful pilot program at Georgia Tech, the company will roll out this graduate-level course in deep learning to more colleges across 2021. The focus will be on offering the system to universities that serve large numbers of Black and Latinx students. It's hoped that, by improving the diversity of the people building these systems, some of the more odious biases will be weeded out. This is part of a broader program to encourage people to enter the computer science field even if their undergraduate training is in another area.


Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities on Social Recommendation

arXiv.org Artificial Intelligence

Many social services including online dating, social media, recruitment and online learning, largely rely on \matching people with the right people". The success of these services and the user experience with them often depends on their ability to match users. Reciprocal Recommender Systems (RRS) arose to facilitate this process by identifying users who are a potential match for each other, based on information provided by them. These systems are inherently more complex than user-item recommendation approaches and unidirectional user recommendation services, since they need to take into account both users' preferences towards each other in the recommendation process. This entails not only predicting accurate preference estimates as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information. The latter is a crucial and distinctive, yet barely investigated aspect in RRS research. This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.


Learning Dynamic Embeddings from Temporal Interactions

arXiv.org Machine Learning

Modeling a sequence of interactions between users and items (e.g., products, posts, or courses) is crucial in domains such as e-commerce, social networking, and education to predict future interactions. Representation learning presents an attractive solution to model the dynamic evolution of user and item properties, where each user/item can be embedded in a euclidean space and its evolution can be modeled by dynamic changes in embedding. However, existing embedding methods either generate static embeddings, treat users and items independently, or are not scalable. Here we present JODIE, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions. JODIE has three components. First, the update component updates the user and item embedding from each interaction using their previous embeddings with the two mutually-recursive Recurrent Neural Networks. Second, a novel projection component is trained to forecast the embedding of users at any future time. Finally, the prediction component directly predicts the embedding of the item in a future interaction. For models that learn from a sequence of interactions, traditional training data batching cannot be done due to complex user-user dependencies. Therefore, we present a novel batching algorithm called t-Batch that generates time-consistent batches of training data that can run in parallel, giving massive speed-up. We conduct six experiments on two prediction tasks---future interaction prediction and state change prediction---using four real-world datasets. We show that JODIE outperforms six state-of-the-art algorithms in these tasks by up to 22.4%. Moreover, we show that JODIE is highly scalable and up to 9.2x faster than comparable models. As an additional experiment, we illustrate that JODIE can predict student drop-out from courses five interactions in advance.


10 Famous Machine Learning Experts

@machinelearnbot

Unlike most other lists of top experts, this one is a hand-picked selection, not based on influence or Klout scores, or the number of Twitter followers and re-tweets, or other similar metrics. Each of these experts has his/her own Wikipedia page. Some might not even have a Twitter account. All of them have had a very strong academic and research career in the most prestigious places. Jeffrey Hawkins is the American founder of Palm Computing (where he invented the Palm Pilot) and Handspring (where he invented the Treo).


10 Famous Machine Learning Experts

@machinelearnbot

Unlike most other lists of top experts, this one is a hand-picked selection, not based on influence or Klout scores, or the number of Twitter followers and re-tweets, or other similar metrics. Each of these experts has his/her own Wikipedia page. Some might not even have a Twitter account. All of them have had a very strong academic and research career in the most prestigious places. Jeffrey Hawkins is the American founder of Palm Computing (where he invented the Palm Pilot) and Handspring (where he invented the Treo).