Clients across all industry sectors will be served by a new specialty practice in Artificial Intelligence (AI) at Heidrick & Struggles (Nasdaq: HSII), a premier provider of executive search, leadership assessment and development, organization and team effectiveness, and culture shaping services globally. Machine learning and other advanced forms of AI can help companies in every sector move beyond process automation that has helped drive efficiency and growth. Integrating and optimizing adaptive changes made possible by AI can provide massive competitive advantage. But there is a critical shortage of leaders with the ability to apply a deep understanding of AI to completely rethink and transform an organization's business model. Led by Ryan Bulkoski, a San Francisco-based partner, Heidrick & Struggles' AI Specialty Practice will help clients identify and develop senior talent needed to bring the power of emerging technologies to their business.

In today's blog post you are going to learn how to perform face recognition in both images and video streams using: As we'll see, the deep learning-based facial embeddings we'll be using here today are both (1) highly accurate and (2) capable of being executed in real-time. To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading! Inside this tutorial, you will learn how to perform facial recognition using OpenCV, Python, and deep learning. We'll start with a brief discussion of how deep learning-based facial recognition works, including the concept of "deep metric learning". From there, I will help you install the libraries you need to actually perform face recognition. Finally, we'll implement face recognition for both still images and video streams. As we'll discover, our face recognition implementation will be capable of running in real-time.

Thanks to the digitized instruments found on Pro Tools and GarageBand, any wannabe music producer can command a virtual orchestra in 2018 using no more hardware than a single laptop. If they're really serious about their craft, they might plug in an MIDI keyboard, a guitar, or a stand-alone sampler to go one step further. That's nothing compared to Italian electronic music producer Leonardo Barbadoro. For his latest album, he's still using a computer to program his instruments -- but, thanks to some impressive robot technology, the instruments are all real. In all, Barbadoro's music is composed using a robot orchestra that's capable of playing more than 50 acoustic instruments on command.

When you want to purchase a new car, will you walk up to the first car shop and purchase one based on the advice of the dealer? You would likely browser a few web portals where people have posted their reviews and compare different car models, checking for their features and prices. You will also probably ask your friends and colleagues for their opinion. In short, you wouldn't directly reach a conclusion, but will instead make a decision considering the opinions of other people as well. Ensemble models in machine learning operate on a similar idea. They combine the decisions from multiple models to improve the overall performance.

A bug in World of Warcraft's beta test for the new Battle for Azeroth expansion coming out in August left the damage unchanged for a Christmas Story-themed novelty item in the game, while health and all other statistics were drastically slashed as part of the expansion's stat squish. The Red Rider Air Rifle is an item purchased from toy vendors in game that, like innumerable items in Warcraft, refers to a pop culture icon: in this case, the Red Ryder BB gun that Ralphie wanted with all his heart in the classic movie A Christmas Story, which every adult told him would shoot his eye out. "I want an official Red Ryder, carbine action, two-hundred shot range model air rifle!" Ralphie says in the movie, and Warcraft developers obliged, tweaking the spelling just a bit. "An official Red Rider Carbine-Action 200-Shot Range Model Air Rifle!" the description says.

Artificial intelligence is now smart enough to paint in the missing regions of a face to fix a blink or turn a frown into a smile. Those A.I. programs use a stranger's photograph, so while the programs may fix a bad blink, they also create a Frankenstein of you with someone else's eyes. Facebook researchers, however, may have come up with a solution by training an A.I. that uses your actual eyes from a previous photograph to fix that blink. Facebook published the research on June 18. Repairing a photograph with A.I. is nothing new -- earlier this year, Nvidia researchers created an A.I. healing tool that could replace missing pixels in a portrait, including missing eyes.

Understanding and predicting the popularity of online itemsis an important open problem in social media analysis. Considerable progress has been made recently in data-driven predictions, and in linking popularity to external promotions. However, the existing methods typically focus on a single source of external influence, whereas for many types of online content such as YouTube videos or news articles, attention is driven by multiple heterogeneous sources simultaneously – e.g. microblogs or traditional media coverage. Here, we propose RNN-MAS, a recurrent neural network for modeling asynchronous streams. It is a sequence generator that connects multiple streams of different granularity via joint inference. We show RNN-MAS not only outperforms the current state-of-the-art Youtube popularity prediction system by 17%, but also captures complex dynamics, such as seasonal trends of unseen influence. We define two new metrics: the promotion score quantifies the gain in popularity from one unit of promotion for a Youtube video; the loudness level captures the effects of a particular user tweeting about the video. We use the loudness level to compare the effects of a video being promoted by a single highly-followed user (in the top 1% most followed users) against being promoted by a group of mid-followed users. We find that results depend on the type of content being promoted: superusers are more successful in promoting Howto and Gaming videos, whereas the cohort of regular users are more influential for Activism videos. This work provides more accurate and explainable popularity predictions, as well as computational tools for content producers and marketers to allocate resources for promotion campaigns.

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both types and targets of hateful comments, and 2) experimenting with machine learning, including Logistic Regression, Decision Tree, Random Forest, Adaboost, and Linear SVM, to generate a multiclass, multilabel classification model that automatically detects and categorizes hateful comments in the context of online news media. We find that the best performing model is Linear SVM, with an average F1 score of 0.79 using TF-IDF features. We validate the model by testing its predictive ability, and, relatedly, provide insights on distinct types of hate speech taking place on social media.

Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context. Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN's multi-pointer-generator decoder is key to this success and performance further improves with an anti-curriculum training strategy. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.

Not much technical detail in the article (sorry), but I thought it was sufficiently interesting, nonetheless.