Coming-of-age technology, known as artificial intelligence, is blowing a heavy wind across the world, raising dust in various spheres and generating intellectual discourse among scholars about the fate of humanity. The assertion that "robots are taking over" is a bitter pill to swallow as it creates panic among employees. The 2017 McKinsey Global Institute Report reiterates this assertion, revealing that, by 2030, a good number of people will be forced to acquire new skills because their jobs will be performed by machines. Findings from WorkMarket's 2020 In(Sight) Report: What AI and Automation Really Mean for Work reveal that employers may not be bothered, on the contrary, if they are open to the prospects of artificial intelligence in the workplace. Seventy-eight percent of business leaders believe that 360 hours can be saved annually by the input of automation through artificial intelligence.

The evolution of workforce opportunity in the United States depends on the future of education and our commitment to far-reaching, equitable federal reform. Unfortunately, policy conversations at the federal and state levels about transforming education systems to meet future workforce demands have focused disproportionately on a skills agenda, largely ignoring behavioral competencies that often complement and enhance the value of technical skills. This misguided approach equates 21st-century workforce development with skills acquisition, which only serves to reinforce a two-tiered workforce: those who are best positioned to acquire and monetize their skills will be granted mobility and long-term security while all others continue to be stranded on the bottom rung of the socioeconomic ladder. Developing intelligent policies to combat workforce inequality requires acknowledging that employer demand for "skills" actually refers to a constellation of content knowledge, technical abilities, and applied intelligence. Per the National Association of Colleges and Employers' 2018 Job Outlook survey, eight out of 10 employers reported that applicants' problem-solving and teamwork abilities influenced hiring decisions; only six out of 10 employers reported the same for technical skills.

When Kory Mathewson began his PhD in computing science at the University of Alberta, he didn't think it would lead to doing improv on stage with a robot--but that's exactly what happened. Mathewson has two shows in this year's Edmonton Fringe Fest: TEDxRFT, an improv show that riffs on the popular TED Talks videos, and Human Machine, an AI improv show. "The Human Machine AI improv show is part of my PhD work. I'm studying how humans and machine-learning systems interact, and so this show is a little bit of a play on the work that I've been doing," he said. "I've been building dialogue systems, and this show explores how humans and these dialogue systems--like Siri, Alexa or Google Home--play into our lives, and the funny things they say."

"'Are you replacing me with a robot?' Bryan Fendley, an artificial intelligence expert and the director of instructional technology and web services at the University of Arkansas at Monticello, has heard this line for years. 'Faculty members are worried they're going to be traded in for a computer or the internet,' he says."--Source: Will we see AI replace faculty anytime soon? But artificial intelligence could actually help faculty and staff, saving them time and streamlining their process.

Leverage Machine Learning and TensorFlow in Python to improve your business! The best job to have in 2017 according to Glassdoor? The #1 skill you need to start a career in Data Science? So, if you are interested in a career in data science, algorithmic trading, robotics, or any industry where human labor is getting replaced by machines, you have come to the right place! We have prepared an amazing course not only to get you acquainted with, but help you understand how deep machine learning works!

Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science. This is a valid question and the answer is simple.

For telcos to get the most out of artificial intelligence (AI), they must not only find the necessary technical skills but also ensure that the entire company understands the value of the technology. My last blog focused on how AI augmentation is the future for telcos because it not only allows them to deliver superior customer experience in an increasingly competitive marketplace, but also helps them cut costs, remain competitive and launch new services across complex ecosystems, from autonomous cars and fleet management to healthcare and beyond. I concluded that the biggest challenge doesn't seem to be the implementation of the technology but the upskilling and retraining of human employees. According to a study from Oracle and Future Workplace, 72% of human resources executives surveyed said their organisations do not provide any AI training programme. Now, that study might not be telco-specific, but it certainly illustrates the challenges that AI brings when implementing it in a business.

Originally posted on The Horizons Tracker. I recently took the'Learning How To Learn' course from the University of California, San Diego on Coursera, and they highlight the crucial role analogies play in the way humans learn. It's something that researchers from Northwestern University are trying to rectify. They have developed a model1, called the structure-mapping engine (SME), that allows machines to reason in a way that's reminiscent of how humans do. The model uses analogical problem solving in the same way that we use analogies to solve the various moral dilemmas we may face in our daily lives. "In terms of thinking like humans, analogies are where it's at," the authors say.

Alphabet Inc's Google has launched a spinoff of its Android operating system for home appliances and other machines, following mixed results with Android offshoots for cars, smartwatches and televisions. 'Android Things', which arrives as Google opens its annual conference for developers, could bring its Google Assistant virtual helper to refrigerators and robots and familiar designs to cash registers and vending machines. 'The goal is to enable them to be built faster, cheaper and more secure,' said Venkat Rapaka, a product management director at Google. Google has struggled to extend Android's dominance into other areas over the last four years, technology and financial analysts said

Regardless of where you stand on the matter of Data Science sexiness, it's simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list. So the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers -- and the companies that hire them -- Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress. While having a strong coding ability is important, data science isn't all about software engineering (in fact, have a good familiarity with Python and you're good to go).