Welcome to GradientCrescent's special series on reinforcement learning. This series will serve to introduce some of the fundamental concepts in reinforcement learning using digestible examples, primarily obtained from the" Reinforcement Learning" text by Sutton et. Note that code in this series will be kept to a minimum- readers interested in implementations are directed to the official course, or our Github. The secondary purpose of this series is to reinforce (pun intended) my own learning in the field. Reinforcement Learning has taken the AI world by storm.

For decades machines have been able to understand simple musical features like beats per minute. Now AI is boosting their abilities to the point that they can not only figure out what particular genre of music is playing, but also how to appropriately dance to it. It's obvious that the dancing style in an EDM club is very different from the way people waltz in a hotel ballroom. And even if you're no country music fan, your foot may tap and your head softy sway when you hear the nostalgic "Country Roads" chorus. How our bodies respond to diverse musical stimuli almost seems instinctual -- how to teach that to a machine?

This paper by Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu from University of California, Los Angeles proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. Problem: without any additional human supervision, can we modify a CNN to obtain interpretable knowledge representations in its conv-layers? Bau et al. [1] defined six kinds of semantics in CNNs, i.e. objects, parts, scenes, textures, materials, and colors. In fact, we can roughly consider the first two semantics as object-part patterns with specific shapes, and summarize the last four semantics as texture patterns without clear contours. Filters in low conv-layers usually describe simple textures, whereas filters in high conv-layers are more likely to represent object parts.

From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.

When Amazon debuted the Amazon Echo in 2014, there were decidedly mixed reactions to the black, cylindrical Bluetooth speaker that could pick up voice commands. Few understood why the e-commerce giant had suddenly released a $199 speaker that could talk to you. Today we know that the Echo and other devices Amazon has since released are mere vessels for the real star of the show: Alexa. The voice assistant is available in 15 languages and 80 countries and boasts more than 100,000 "skills," compared to about a dozen five years ago. It can wake up your cat, serve as an interpreter, deter a burglar, help you work out, and streamline your workflow.

Coveo is no stranger to our parent company TMC – we gave them a CRM Excellence Award as well as one for the Product of the Year. The company uses AI and intelligent search technologies to personalize millions of digital experiences for customers, partners, dealers, and employees. Coveo combines unified content, unified interactions and machine learning to deliver relevant information and recommendations across every business interaction. Websites, commerce, contact centers, intranets and digital properties and apps become effortless, content-rich and effective. Coveo is also embedded in many leading business applications from vendors including Salesforce, ServiceNow, Sitecore, Dynamics and more.

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Machine learning is an integral part of Pittsburgh's tech economy, thanks to Carnegie Mellon University's position as one of the nation's foremost research centers on the topic. That's enticed tech giants such as Google and Uber to set up shot in the Steel City. Pittsburghers have varied knowledge on what machine learning is. On a crisp afternoon on Carnegie Mellon University's campus, Adeline Mercier of Squirrel Hill was walking with her young daughter on campus. She said her husband works in machine learning.

What are some of the key processes in which you are using AI? At PNB MetLife, technology enables us to align to two of our core values – 'Make Things Easier' and'Put Customer First'. Our strategy is structured around the concept of 3Ds – Digitise, Data and Disrupt. We digitise our day to day processes, which enables us in seamless customer delivery; we mine data and draw insights to mark predictable behaviour of the customers, thereby paving us the way to be future ready; we believe that the digital landscape is constantly evolving and hence we disrupt to keep our digital journey ongoing. Digital transformation had been a challenge for Indian companies for various reasons.

From May 31 to the 2nd of June I went to my very first international conference: Pain Science in Motion 2019 in Savona, Italy. This conference is organized every two years by the international research group Pain In Motion, of which I am very proud to be a member. This conference gives young PhD researchers the opportunity to do poster presentations as well as oral presentations about their research protocols or ongoing research without final results. This is how I got the possibility to present my PhD research protocol, even though I do not have results to present so far. Did you know ZORA already?