We are living in interesting times, where digital assistants schedule meetings, chatbots work alongside humans as teaching assistants, and your suitcase can now become self driving luggage as showcased at CES, 2018. The implications are just starting to be felt in the workplace. In 2017, I wrote about how The Employee Experience is the Future of Work. Now, as we enter 2018, the next journey for HR leaders will be to leverage artificial intelligence combined with human intelligence and create a more personalized employee experience. As we increase our personal usage of chatbots (defined as software which provides an automated, yet personalized, conversation between itself and human users), employees will soon interact with them in the workplace as well. Forward looking HR leaders are piloting chatbots now to transform HR, and, in the process, re-imagine, re-invent, and re-tool the employee experience.

Artificial Intelligence may seem like something from the future, with its inclusion in sci-fi thrillers and movies. While AI has yet to take over the world with destructive prone robots, it is becoming more and more prevalent in our everyday lives. Whether we know it or not, most of us probably use AI every day for simple tasks like taking a picture, parking our car, asking our phones what the weather looks like, or using our personal home assistants to turn on the lights. Our technology has yet to reach the level of self-awareness, but that doesn't mean it isn't …

This tutorial is an introduction to Artificial Intelligence which explains the need to study AI, AI growth, concept of AI, its use cases and various intelligence types in detail. If you've enjoyed this video, Like us and Subscribe to our channel for more similar informative videos and free tutorials. Got any questions about AI? Ask us in the comment section below. Are you looking for something more? Enroll in our Artificial Intelligence & Deep Learning training course and become a certified AI Expert (https://goo.gl/RdA17B).

About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).

The release goes on, "The new solution, which embeds the most advanced machine learning and big data architecture into optical modelling, revolutionizes the way customers utilize metrology measurement data and expands the metrology performance envelope to tighten process windows, avoid process excursions and improve yield. The platform was designed to interface with other process control tools for accuracy improvement and shorter training and control cycles. With this new significant supplement, NOVAFit, in conjunction with Nova's Fleet Management, is now connecting all of Nova's metrology fleet to one big data cluster, which applies smart algorithms to improve the measurements' accuracy and process feedback across multiple fabrication steps. This new approach enhances Nova's capability in measuring multiple parameters in a much more robust method and improves its competitive position."

If you'd go by the marketing newsletters of leading IT solutions vendors of the world, it would appear that artificial intelligence and machine learning are ideas that have come into being, almost magically, in the past two to three years. Artificial intelligence, in fact, is a term that was coined way back in the 1950s by computer programmers and researchers to describe machines that could respond with appropriate behaviors to abstract problems without human input. Machine learning is one of the more prominent approaches to making artificial intelligence a reality. It is centered on the idea of creating algorithms that are inherently capable of identifying patterns in data and improving their outcomes based on the large datasets. This guide is dedicated to helping you understand and identify the fundamental skills you need to master machine learning technologies and find fulfilling employment in this hot and growing field.

As artificial intelligence continues to fill factory positions typically held by high school graduates, some community colleges are innovating to meet the new need for worker "retraining" programs. According to an analysis by Ball State University, automation in American manufacturing has cut nine of every ten jobs in the private sector since 2000. Though many jobs in manufacturing still exist that cannot be performed by machines, they now require workers with more training than a traditional high school diploma. In other words, a mismatch exists between current workforce skills and the demands of 21st-century manufacturing jobs that did not exist before the turn of the century. Without a clear educational path to reclaiming their displaced jobs, middle-age to older workers often choose not to re-enter the workforce or re-enroll in school, preferring instead to collect unemployment checks.

About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).

LAS VEGAS – While finished products get most of the attention at the annual CES consumer electronics show, "ingredient technologies" are the unsung heroes, setting the stage for exciting new devices and services in the years ahead. Two of those "ingredient technologies" being touted at CES 2018 are 5G wireless and artificial intelligence, officials say. "5G and AI are heralds for the coming data age," said Steve Koenig, senior director of research for the Consumer Technology Association, owner and sponsor of CES, which opened Tuesday and runs through Friday. "CES is going to set the pace for that." It will fuel new services and technologies such as the Internet of Things, augmented reality, autonomous vehicles and smart cities.

There is probably no other industry that could benefit more from Artificial Intelligence (AI) than higher education. The U.S. educational system is struggling with funding challenges, teacher shortages, overcrowded classrooms and poor student retention. All of these issues and others can be alleviated by selectively using AI to take over mundane tasks, allowing professors and other educational professionals to do more of what they do that is irreplaceable. For example, AI can automate a myriad of mundane administrative tasks, requiring fewer resources and freeing administrators to do a better job of running the institution. The more AI augments administrative and academic processes, the more time teachers have to teach.