Each week on a Wednesday Nikolas Badminton, Futurist highlights the top stories from the past week relating to the incredible rise of artificial intelligence and its application in society, the workplace, in cities, and in our lives.
In Artificial Intelligence Bulletin – Human Resources Revolution we see examples of how AI is being applied to HR, real-time AI translation at scale and Microsoft’s vision.

Can Artificial Intelligence Make Employee Feedback More Human?

The enterprise software company builds employee work profiles, known as “Work Graphs,” based on data from integrations with Google Apps, email, and Office 365, as well as Salesforce, JIRA, and Slack. The machine-learning algorithm specifically tracks each employee’s goal progress, goal alignment, comments, cheers, nudges, cross-functional collaboration, recognition hashtags, and more, according to Duggan.
“BetterWorks then uses the Work Graph to prompt feedback and recognition from the relevant people, whether it’s managers or peers,” he says. “We want to bring feedback and recognition into the weekly workflow of managers, making it more natural and ingrained in their relationships with their reports,” adds Duggan.
Read more at Fast Company

Now Artificial Intelligence Will Find the Right Job For You

Artificial Intelligence (AI) is transforming the way we do business today and HR is no exception to this. AI is a force that will drive the new employment economy. From fiction in the movie Transformers, to an impressive reality (Watson and Deep Blue), AI algorithms are challenging human intelligence. With their inherent pattern recognition, self-learning and cognitive capabilities, AI powered algorithms have the ability to perform complex jobs with speed and accuracy.
AI coupled with analytics is gaining ground within the HR domain. From talent acquisition and workforce optimization to workforce transformation, AI can act as a strategic enabler for HR. In the talent acquisition space, AI is bringing about a sea change in the speed, accuracy and timeliness of delivery. Auto-sourcing, just-in-time hiring, and self-serve hiring are expected to improve utilization and drive revenue growth. AI driven applications can act as HR’s weather man helping them analyze the engagement level of employees, determine flight risk, uncover great talent in the frontline and more.
Read more at Entrepreneur

A Neural Network for Machine Translation, at Production Scale

Today we announce the Google Neural Machine Translation system (GNMT), which utilizes state-of-the-art training techniques to achieve the largest improvements to date for machine translation quality. Our full research results are described in a new technical report we are releasing today: “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation” [1].
A few years ago we started using Recurrent Neural Networks (RNNs) to directly learn the mapping between an input sequence (e.g. a sentence in one language) to an output sequence (that same sentence in another language) [2]. Whereas Phrase-Based Machine Translation (PBMT) breaks an input sentence into words and phrases to be translated largely independently, Neural Machine Translation (NMT) considers the entire input sentence as a unit for translation.The advantage of this approach is that it requires fewer engineering design choices than previous Phrase-Based translation systems. When it first came out, NMT showed equivalent accuracy with existing Phrase-Based translation systems on modest-sized public benchmark data sets.
Since then, researchers have proposed many techniques to improve NMT, including work on handling rare words by mimicking an external alignment model, using attention to align input words and output words and breaking words into smaller units to cope with rare words. Despite these improvements, NMT wasn’t fast or accurate enough to be used in a production system, such as Google Translate. Our new paper describes how we overcame the many challenges to make NMT work on very large data sets and built a system that is sufficiently fast and accurate enough to provide better translations for Google’s users and services.
Read more at Google Research Blog

Intelligent agents, augmented reality & the future of productivity – Satya Nadella, CEO, Microsoft

Nikolas Badminton, Futurist: Artificial Intelligence Keynote

Nikolas Badminton, Futurist talks about Artificial Intelligence, it’s history, it’s evolution, and how it can be practically applied in today’s world. Nikolas’ keynote was supported by COMMUNITECH and held in a packed house at the Tech Leadership Summit in 2016.

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Nikolas Badminton is a world-respected futurist speaker that researches, speaks, and writes about the future of work, how technology is affecting the workplace, how workers are adapting, the sharing economy, and how the world is evolving. He appears at conferences in Canada, USA, UK, and Europe. Email him to book him for your radio, TV show, or conference.