Jordan French is a multi-media journalist on the editorial staff at TheStreet.com He is also the Founder and Executive Editor at Grit Daily News. Formerly an engineer and attorney he represented the "People of the United States" in energy market manipulation cases as an enforcement attorney at the Federal Energy Regulatory Commission. As an engineer he worked on the Mars Gravity Biosatellite Program and later co-founded BeeHex, Inc., the personalized nutrition and robotics company that popularized 3D-printed pizza. The author of forthcoming book, The Gritty Entrepreneur, he is a frequent public speaker, technology evangelist and media moderator.

Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing multi-modality or skewness. Besides, the previous models lack correlation modeling between the gates, which would be a new method to adopt domain knowledge. This paper proposes a new gate structure with the bivariate Beta distribution. The proposed gate structure enables hierarchical probabilistic modeling on the gates within the LSTM cell, so the modelers can customize the cell state flow. Also, we observed that our structured flexible gate modeling is enabled by the probability density estimation. Moreover, we theoretically show and empirically experiment that the bivariate Beta distribution gate structure alleviates the gradient vanishing problem. We demonstrate the effectiveness of bivariate Beta gate structure on the sentence classification, image classification, polyphonic music modeling, and image caption generation.

We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing. Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer. We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline. For Subtask B, all systems were below a majority class baseline, but several systems were very close to it. The leaderboard and the data from the competition can be found at http://competitions.codalab.org/competitions/20022

In deep neural nets, lower level embedding layers account for a large portion of the total number of parameters. Tikhonov regularization, graph-based regularization, and hard parameter sharing are approaches that introduce explicit biases into training in a hope to reduce statistical complexity. Alternatively, we propose stochastically shared embeddings (SSE), a data-driven approach to regularizing embedding layers, which stochastically transitions between embeddings during stochastic gradient descent (SGD). Because SSE integrates seamlessly with existing SGD algorithms, it can be used with only minor modifications when training large scale neural networks. We develop two versions of SSE: SSE-Graph using knowledge graphs of embeddings; SSE-SE using no prior information. We provide theoretical guarantees for our method and show its empirical effectiveness on 6 distinct tasks, from simple neural networks with one hidden layer in recommender systems, to the transformer and BERT in natural languages. We find that when used along with widely-used regularization methods such as weight decay and dropout, our proposed SSE can further reduce overfitting, which often leads to more favorable generalization results.

Whether you're studying for an exam or revising for a presentation, a quiz on identifying different learning methods promises to help you maximise the amount of information you can retain. Formed of ten questions, the quick quiz by Tutor House asks participants to consider how they would respond in a series of scenarios. This technique reveals if they would benefit most from visual, auditory, read and write or kinesthetic (interactive) learning methods. Created by Tutor House in partnership with educational experts, the quiz considers the widely used VARK (Visual, Aural, Read/write, and Kinesthetic) learning styles developed by Fleming's in 1987. Visual learners are likely to respond to visual stimuli like photos and videos to remember things.

Sounds a lot like the road that led to an AI winter historically... I think we're well past the point where that's a genuine risk of AI interest globally cooling down at all (it's already very practical and profitable in many arenas just with what we have) but openAI themselves? If historical trends are any indication, that kind of talk will buy them at most 5 years of normal investor questions, 5 years of severe questions, then bankruptcy. They've very generously got a decade to figure something actually practical out, and realistically the clock might only have five years on it or less. Wonder if they'll invent a thing that'll teach them to make money before then, haha.

I really think the fundamental idea of the paper is really cool for everyone doing deep learning.

The Personalization team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music and podcasts better than anyone else so that we can make great recommendations to every individual person and keep the world listening. Everyday, hundreds of millions of people all over the world use the products we build which include destinations like

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".

As if the world of deep-faked pictures and video wasn't scary enough, researchers from Samsung's AI center in Moscow have demonstrated an algorithm that can fabricate videos using only one image. In a video demonstration and a paper published in the pre-print journal ArXiv, the researchers show the capabilities of what is described as'one-shot' and'few-shot' machine learning. The results of their system bring to life popular faces like those of surrealist painter Salvador Dali and actress Marilyn Monroe using a single still image. The more images that are fed into the program, the more realistic the resulting video becomes. Though a single image translated into a moving face may look noticeably altered, a sample of 32 images produces a moving picture with near lifelike accuracy.