UNSW and Swinburne launch initiatives exploring the future of work


With technology's role in the workplace evolving at pace, the University of New South Wales (UNSW) and Swinburne University of Technology (Swinburne) have each launched their own respective initiatives to educate about the future of work. UNSW announced on Thursday in collaboration with AMP the launch of Designing the Future of Work, a massive open online course (MOOC) that explores how employers and employees can adapt to a rapidly evolving environment in which artificial intelligence, robotics, and big data are changing the way we live and work. The course will answer questions such as: What new, disruptive technologies are on the horizon; how will jobs change; what challenges will employers and employees face; and how the design process can help create innovative solutions for employers and employees. Associate dean of education at UNSW Sydney Art & Design professor Simon McIntyre said the MOOC will investigate design strategies that businesses can adopt to make the transition towards new technologies a more efficient process. "By working with leading futurists and business innovators from AMP Amplify, we [are] able to bring both academic and practical perspectives to give learners real-world examples and strategies to help them become predictive, adaptive, and secure in their own work futures," McIntyre said.



Recitations from Tel-Aviv University introductory course to computer science, assembled as IPython notebooks by Yoav Ram. Exploratory Computing with Python, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. Each Notebook includes a number of exercises (with answers) that should take less than 4 hours to complete. Developed by Mark Bakker for undergraduate engineering students at the Delft University of Technology.

Data Science Academy: Master Data Science In R Udemy


THIS IS GONNA BE A OVER 40 HOUR OF CONTENT COURSE! This is Your Complete Guide to mastering statistical modelling, data visualization, machine learning and basic deep learning in R. BOOST YOUR CAREER TO THE NEXT LEVEL: This course covers ALL the aspects of practical data science, which makes this course The Only Data Science Training You Need. By the end of the course, you'll be able to store, filter, manage, and manipulate data in R to give yourself & your company a competitive edge. My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).



Hoy traemos a este espacio al FOURTH GLOBAL MACHINE INTELLIGENCE SUMMIT, que tedrá lugar el 28 - 29 JUNE 2017 en Amsterdam Informar de un error de Maps Postillion Convention Centre Amsterdam Paul van Vlissingenstraat 8 The Postillion Convention Centre Amsterdam is very conveniently located between the city and the arterial roads and 20 minutes from Amsterdam Airport Schiphol. TOPICS WE COVER NATURAL LANGUAGE PROCESSING INDUSTRIAL AUTOMATION Where machine learning meets artificial intelligence. The rise of intelligent machines to make sense of data. The Machine Intelligence Summit: where machine learning meets artificial intelligence. The rise of intelligent machines to make sense of data in the real world. Explore how AI will impact transport, manufacturing, healthcare, retail and more. The full speaker line up will be announced shortly.

They Should Know How We Feel! Using AI to Measure Our Psychology (with Daniel McDuff)


During my last interview I had a great talk with Daniel McDuff. Daniel's research is at the intersection of psychology and computer science. He is interested in designing hardware and algorithms for sensing human behavior at scale, and in building technologies that make life better. Applications of behavior sensing that he is most excited about are in: understanding mental health, improving online learning and designing new connected devices (IoT). Listen to more about why it is important to collect data from much larger scales and help computers read our emotional state. Key Learning Points: 1. Understanding the impact, intersection, and meaning of Psychology and Computer Science 2. Facial Expression Recognition 3. How to define Artificial Intelligence, Deep Learning, and Machine Learning 4. Applications of behavior sensing with Online Learning, Health, and Connected Devices 5. Visual Wearable sensors and heart health 6. The impact of education and learning 7. How to build computers to measure phycology, our reactions, emotions, etc 8. Daniel is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. He is currently Director of Research at Affectiva, a post-doctoral research affiliate at the MIT Media Lab and a visiting scientist at Brigham and Womens Hospital. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotion responses. Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR, the Center for Integrated Medicine and Innovative Technology (CIMIT) and several IEEE conferences. His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel has been named a 2015 WIRED Innovation Fellow.