Machines don't actually have bias. AI doesn't'want' something to be true or false for reasons that can't be explained through logic. Unfortunately human bias exists in machine learning from the creation of an algorithm to the interpretation of data – and until now hardly anyone has tried to solve this huge problem. A team of scientists from Czech Republic and Germany recently conducted research to determine the effect human cognitive bias has on interpreting the output used to create machine learning rules. The team's white paper explains how 20 different cognitive biases could potentially alter the development of machine learning rules and proposes methods for "debiasing" them. Biases such as "confirmation bias" (when a person accepts a result because it confirms a previous belief) or "availability bias" (placing greater emphasis on information relevant to the individual than equally valuable information of less familiarity) can render the interpretation of machine learning data pointless.

Artificial intelligence will solve Facebook's most vexing problems, chief executive Mark Zuckerberg insists. He just can't say when, or how. Zuckerberg referred to AI technology about 23 times during his five-hour testimony before a joint Senate committee hearing Tuesday, saying that it would one day be smart, sophisticated and eagle-eyed enough to fight against a vast variety of platform-spoiling misbehavior, including fake news, hate speech, discriminatory ads and terrorist propaganda. Over the next five to 10 years, he said, artificial intelligence would prove a champion for the world's largest social network in resolving its most pressing crises on a global scale -- while also helping the company dodge pesky questions about censorship, fairness and human moderation. "We started off in my dorm room with not a lot of resources and not having the AI technology to be able to proactively identify a lot of this stuff," Zuckerberg told the lawmakers, referring to Facebook's famous origin story.

As I reflect on cloud computing and the media industry since last year's NAB, I see two emerging trends. First, content creators and broadcasters such as Rakuten, RTL, and Al Jazeera are increasingly using the global reach, hybrid model, and elastic scale of Azure to create, manage, and distribute their content. Second, AI-powered tools for extracting insights from content are becoming an integral part of the content creation, management and distribution workflows with customers such as Endemol Shine Group, and Zone TV. Therefore, at this year's NAB, we are focused on helping you modernize your media workflows, so you can get the best of cloud computing and AI. We made a number of investments to enable better content production workflows in Azure, including the recent acquisition of Avere Systems.

Ok, I just skimmed through the video but why on earth did you get the names of key places and cities right, but messed up the title? Like those fancy chairs kings sit on. Not the spiky things that grow in the wilderness. You don't want to sit on those.

It's a custom mixtape of 30 songs they've never listened to before but will probably love, and it's pretty much magic. It makes me feel seen. It knows my musical tastes better than any person in my entire life ever has, and I'm consistently delighted by how satisfyingly just right it is every week, with tracks I probably would never have found myself or known I would like. As it turns out, I'm not alone in my obsession with Discover Weekly. The user base goes crazy for it, which has driven Spotify to rethink its focus, and invest more resources into algorithm-based playlists.

I was recently reading a paper on fairness that was all math equations, and even as someone with a math PhD, I was having trouble mapping what this meant into the real world.

I have a model that's trained to detect various concepts on biomedical papers. I'm using the last dense layer before the output to use as vector embeddings for these medical papers. I then use k-means on the embedding vectors to cluster the papers together. I prefer this over a pure unsupervised approach as these embeddings will be forced to focus on biological similarity over biological & semantic. I have about 10M papers and each cluster needs to have 50 elements.

Anomaly Detection has several important applications. In this paper, our focus is on detecting anomalies in seller-reviewer data using tensor decomposition. While tensor-decomposition is mostly unsupervised, we formulate Bayesian semi-supervised tensor decomposition to take advantage of sparse labeled data. In addition, we use Polya-Gamma data augmentation for the semi-supervised Bayesian tensor decomposition. Finally, we show that the Polya-Gamma formulation simplifies calculation of the Fisher information matrix for partial natural gradient learning. Our experimental results show that our semi-supervised approach outperforms state of the art unsupervised baselines. And that the partial natural gradient learning outperforms stochastic gradient learning and Online-EM with sufficient statistics.

The topic modeling discovers the latent topic probability of the given text documents. To generate the more meaningful topic that better represents the given document, we proposed a new feature extraction technique which can be used in the data preprocessing stage. The method consists of three steps. First, it generates the word/word-pair from every single document. Second, it applies a two-way TF-IDF algorithm to word/word-pair for semantic filtering. Third, it uses the K-means algorithm to merge the word pairs that have the similar semantic meaning. Experiments are carried out on the Open Movie Database (OMDb), Reuters Dataset and 20NewsGroup Dataset. The mean Average Precision score is used as the evaluation metric. Comparing our results with other state-of-the-art topic models, such as Latent Dirichlet allocation and traditional Restricted Boltzmann Machines. Our proposed data preprocessing can improve the generated topic accuracy by up to 12.99\%.