The pace of technological change is rendering many job activities -- and the skills they require -- obsolete. Research by McKinsey suggests that globally more than 50% of the workforce is at risk of losing their jobs to automation, and a survey by the World Economic Forum suggests that 42% of the core job skills required today will change substantially by 2022. In this landscape of constant disruption, individuals, companies, and governments are fighting to ensure they have the skills to remain competitive. To shed light on the global skills landscape, Coursera recently released the first edition of our Global Skills Index (GSI) report. As the world's largest platform for higher education, Coursera brings together 40 million learners around the world with over 3,000 courses from leading universities and companies.
It's often said that data is the oil of the twenty-first century, and artificial intelligence is the driving force. Companies in all sectors are combining the reasoning abilities of the human mind with the processing power of computers, developing algorithms that can trawl through colossal data sets to help businesses make more informed decisions. That means that tomorrow's future business leaders need more than a passing familiarity with AI. For this reason, several of the world's best business schools have launched specialist master's programs in AI. Canada's Smith School of Business, the University of Bologna in Italy, and Imperial College London are among the top-tier institutions running AI MSc courses that give students the technical, managerial and interpersonal skills they need to master machines.
Music is the most popular art form that is performed and listened to by billions of people every day. There are many genres of music such as pop, classical, jazz, folk etc. Each genre has different music instruments, tone, rhythm, beats, flow etc. Digital music and online streaming have become very popular these days due to the increase in the number of users. To create a machine learning model, which classifies music samples into different genres.
You already know what is Keras and to build a deep learning model using it. Instead of using TensorFlow directly you use Keras to build the model. But wait do you know you can also use the tools that are included in TensorFlow using Keras. There is a tool in the TensorFlow that is Tensorboard that lets you visualize your model's structure and monitor its training. In this entire intuition, you will learn how to view Tensorboard callbacks through Keras and do some analytics to improve your deep learning model.
Back in 2015, chatbots were big. And one of the most hyped ones was Facebook's M, which the company meant to be a flexible, general-purpose bot that could do lots of different things such as purchase items, arrange gift deliveries, reserve restaurant tables, and plan travel. But the buzz was far bigger than the bot. When Facebook tested M with a group of 2,500 people in the Bay Area, the software failed to carry out most of the tasks it was asked to do. After the initial burst of enthusiasm for M and other chatbots ("bots are the new apps," Microsoft CEO Satya Nadella proclaimed), a wave of disappointment followed.
Do you have expertise in Machine Learning? Could you use this experience to help us create game-changing solutions for healthcare problems? The George Institute for Global Health, part of the Nuffield Department of Women's & Reproductive Health at the University of Oxford, is looking for a Machine Learning Scientist to join the team and contribute to the development and implementation of the algorithmic core of a series of exciting new projects in Oxford Martin School's prestigious programme on Deep Medicine. The programme is focused on tackling major healthcare problems – in both policy and practice - with the application of modern machine learning algorithms (including, but not limited to deep learning) to large multi-modal medical data (e.g., medical records, genetics, medical imaging, and wearable). Your responsibilities will include: employing existing (and develop new) Machine Learning algorithms that can learn personalised and population-level patterns in multi-modal data; mapping the results of the Machine Learning works to innovative solutions for the delivery of care (e.g.
Repeated practice is necessary to achieve mastery, which is no exception for surgical residents who often train directly on patients for four to six years. However, in this hands-on learning environment, even a minor mistake can be serious. To protect against such fatalities, a McGill research team constructed a solution. "The implementation of competency-based surgical education, along with advances in virtual reality, has resulted in the development and utilization of virtual reality-based surgical simulators," Rolando Del Maestro, professor emeritus in neuro-oncology at McGill, said in an interview with The McGill Tribune. The Neurosurgical Stimulation and Artificial Intelligence Learning Centre recently published a study in JAMA Network Open.
They found that models trained on a small randomised sample of reactions outperformed those trained on larger human-selected datasets. The results show the importance of including experimental results that people might think are unimportant when it comes to developing computer programs for chemists. Machine learning models are a valuable tool in chemical synthesis, but they're trained on data from the literature where positive results are favoured, whereas the dark reactions – the experiments that were tried but didn't work – are usually left out. 'Including these failures is essential for generating predictive machine learning models,' says Joshua Schrier of Fordham University, US, who was part of a team that studied hydrothermal syntheses of amine-templated metal oxides and found that biases were introduced into the literature by people's choices of the reaction parameters. 'We considered extra dark reactions – a class of reactions that humans don't even attempt, not because of scientific or practical reasons, but simply because it's humans who make the decisions,' Schrier says.
ReadyAI Lab believes that all students should have access to artificial intelligence, not only students with computer science backgrounds or those who attend schools with highly developed technology programs. At ReadyAI Lab, we want to make AI learning a reality and help students to be empowered to use AI to change the world. ReadyAI's curriculum sparks curiosity, builds confidence, and fosters teamwork. We emphasize both STEM education and the non-technical components of learning such as collaboration, teamwork, problem-solving, performing arts, and multimedia presentations. Create projects that use AI to help address society's greatest needs in healthcare, transportation, public safety, and many more areas.
Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children. But today, the 29-year-old does data labelling and makes up to Rs 15,000 a month. The money isn't much but it's more than she made as a teacher, and enough to pay her kids' school fees and her own expenses. Today, she scans videos and marks and labels objects encountered by self-driving cars. Her output is used to train artificial intelligence algorithms powering such cars.