In the field of machine learning, online learning refers to the collection of machine learning methods that learn from a sequence of data provided over time. In online learning, models update continuously as each data point arrives. You often hear online learning described as analyzing "data in motion," because it treats data as a running stream and it learns as the stream flows. Classical offline learning (batch learning) treats data as a static pool, assuming that all data is available at the time of training. Given a dataset, offline learning produces only one final model, with all the data considered simultaneously.
For Peter Cao, who has dedicated 16 years of his career to teaching chemistry in a high school in central China's Anhui province, in every teacher there lives a "doctor". He spends two to three hours a day grading assignments, a process the 38-year-old describes as "diagnosing". "By reviewing the homework of my pupils, I can have an overall picture about their understanding of the lessons I give," Cao said, adding that this "diagnosis" helps him draw up a teaching plan for the following day. But if the Chinese online education start-up Master Learner has its way, Cao and his 14 million fellow teachers in China will be able to hand this time-consuming review process to a "super teacher", a powerful "brain" capable of answering nearly 500 million of the most tested questions in China's middle schools as well as scoring high points in each Gaokao test, China's life-changing college entrance exam, for the past 30 years. If the super teacher sounds too smart to be human, that is because it is not.
The history of Artificial Intelligence isn't a long one, around 60-70 years, but the advances in recent years has been huge. The Modern Artificial Intelligence Infographic shows how technology coupled with studies of the human brain have aided in making AI a reality, and a reality we can use everyday. Machines are already intelligent, but we fail to recognise it. When a machine demonstrates intelligence we counter it by saying'it's not real intelligence'. Therefore Al becomes whatever has not been accomplished so far by a machine.
If you're concerned (or super excited) about machine learning (ML) becoming mainstream, a recent survey by Oxford Economics on behalf of human resources (HR) and IT asset management company ServiceNow should pique your interest. The report, which surveyed 500 Chief Information Officers (CIOs) in 11 countries and across 25 industries, found that 49 percent of the companies are already using ML to improve traditional business processes. Of the 500 CIOs surveyed, 200 said they're already beyond the pilot stage and have begun deploying ML in some capacity. CIOs are hoping to limit user error and errors in judgement by introducing automation. Almost 70 percent of CIOs said decisions made by machines will be more accurate than those made by humans.
ServiceNow is rolling out a machine-learning engine that it says automatically categorizes, routes and assigns customer service processes. Dubbed Agent Intelligence for Customer Service Management, the platform is designed to accelerate case assignment, shorten response times, and reduce manual work for a variety of customer support functions. In a pilot program, ServiceNow said customers saved 8 percent of their service desk's time through improved categorization, prioritization and assignment of incidents. ServiceNow said Agent Intelligence is its first machine learning product and that it will be tied to the next release of its Now platform, code-named Kingston. However, earlier this year the company announced a more broad-based machine learning engine called Intelligent Automation Engine designed to predict outages, automate routing and workflow, predict outcomes, and benchmark performance.
If you have access to AI experts like professors, PhD students, or good researchers, talk to them too. Sometimes I've learned a ton from a 5 minute conversation with people like Geoff Hinton, Yoshua Bengio, Yann LeCun; but also from my PhD students at Stanford, team members at deeplearning.ai, Despite the importance of having friends to work with, if your friends disagree with your ideas, sometimes you should still implement it and try it out to see for yourself. If you have access to AI experts like professors, PhD students, or good researchers, talk to them too. Sometimes I've learned a ton from a 5 minute conversation with people like Geoff Hinton, Yoshua Bengio, Yann LeCun; but also from my PhD students at Stanford, team members at deeplearning.ai, Despite the importance of having friends to work with, if your friends disagree with your ideas, sometimes you should still implement it and try it out to see for yourself.
Though traditional personality-assessment techniques, such as the Myers–Briggs test, are designed for objectivity, somewhere along the way "managers still inject personal bias," says Mark Newman, founder and CEO of HireVue, a recruiting-technology company. Koru, another human resources software developer, also gauges personal attributes, using a written test to evaluate "impact skills," such as grit, curiosity, and polish. The year-old company Interviewed, which has worked with clients such as Instacart and IBM, administers "blind auditions" in which applicants for customer-service jobs field chats or calls from bots that represent consumers. An algorithm's ability to understand something like empathy, Bakke says, points to a new hiring technique--one in which machines assess, but humans make the final call.
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. You can apply to the degree program either before or after you begin the Specialization.
Serengil received his MSc in Computer Science from Galatasaray University in 2011. Currently, he is a member of AI and Machine Learning team as a Data Scientist. His current research interests are Machine Learning and Cryptography. Nowadays, he enjoys speaking to communities about these disciplines, also blogging and creating online courses related to his research interests.