The workplace is going to look drastically different ten years from now. The coming of the Second Machine Age is quickly bringing massive changes along with it. Manual jobs, such as lorry driving or house building are being replaced by robotic automation, and accountants, lawyers, doctors and financial advisers are being supplemented and replaced by high level artificial intelligence (AI) systems. So what do we need to learn today about the jobs of tomorrow? The robots and computers of the future will be based on a degree of complexity that will be impossible to teach to the general population in a few short years of compulsory education.

Advancements in AI, Big Data, the Internet of Things and automation have many industries worried that these systems will push them out, absorb their work, make humans redundant, or accelerate the speed of business too fast for them to adapt. From formal models of legal reasoning to automated information extraction from legal databases and texts, the interaction of artificial intelligence and law will disrupt the contemporary status of legal practice. With the latest'wonder automation' in the form of JP Morgan's COIN, building upon the adoption of AI systems by firms such as Clifford Chance, and development projects like Denton's NextLaw Labs leading the way, are lawyers set to be replaced? AI will spell the end of lawyers. However, the age of automation and digitisation gives birth to an even more beautiful legal specialist: the cyber-lawyer – an augmented specialist, combining the processing power of AI with powerful searches of legal indexes in mere seconds through Big Data, produced through a human interface.

MIYAZAKI – Labor authorities referred to prosecutors on Thursday the operator of a Japanese language school in Miyazaki Prefecture, suspecting it forced its Indonesian students to work at care facilities owned by the same corporation. Additionally, documents related to Yutaka Shimizu, 70, the head of the group that runs Houei International Japanese Language Academy, and four others were sent to prosecutors for their roles in allegedly forcing six Indonesians into effectively unpaid work between December 2015 and June 2016. The students were allegedly forced to use their wages to pay for tuition fees, according to a local labor standards inspection office. Authorities deemed the Miyakonojo, Miyazaki Prefecture-based school's educational program and labor requirements as inseparable. The school teaches Japanese to students from across Asia who are interested in the medical and welfare fields.

Artificial Intelligence (AI), Big Data and machine learning have all come under fire in some corners for potentially exposing humanity to unknown dangers. It makes sense because we don't understand it as much as we have invested in current known and familiar systems. In recent times, AI and Big Data have been utilised in education for more effective learning and teaching. There are also some controlled tests in the farming industry where we may soon see more innovations. In a white paper by Pearson called Intelligence Unleashed An argument for AI in Education, the team delves into learning systems that are driven by Artificial Intelligence in Education (AIEd) to shed light into the topic.

As a data scientist in Silicon Valley, I am humbled by the amount of attention our field has received in the past few years. Harvard Business Review has called data science the sexiest job of the 21st century and Forbes released a report explaining why data scientist is the best job to pursue in 2016. These headlines pique the interest of many industry professionals interested in crunching data and looking for the next exciting phase in their career. With so much interest in the data science field, I often get questions to the tune of: How did you fall into data science as a career? What inspired you to become a data scientist?

Authors: Lili Jiang, Shuo Chang, and Nikhil Dandekar In order to build a high-quality knowledge base, it's important that we ensure each unique question exists on Quora only once. Writers shouldn't have to write the same answer to multiple versions of the same question, and readers should be able to find a single canonical page with the question they're looking for. For example, we'd consider questions like "What are the best ways to lose weight?", "How can a person reduce weight?", and "What are effective weight loss plans?" to be duplicate questions because they all have the same intent. To prevent duplicate questions from existing on Quora, we've developed machine learning and natural language processing systems to automatically identify when questions with the same intent have been asked multiple times.

When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Coursera co-founder ANDREW NG, adjunct professor of computer science, says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is." Ng, who is chief scientist at Baidu Research, spoke to the Graduate School of Business community as part of a series presented by the Stanford MSx Program, which offers experienced leaders a one-year, full-time learning experience.

We can't stop progress or robots, but we can adopt wise policies that maximize both supply and demand for human capital, encouraging employers to pay the best price for high-quality labor. Right now, we're trying to force them to pay more than the labor is really worth because our government has not been able to establish a better set of labor, immigration and education policies. BurgerFlipBot is a symbol of that government failure -- and a stark warning of what lies at the end of that road.

Many data and analytics leaders face a chicken and egg situation. Without experienced data scientists, venturing into machine learning and data science is difficult. Without any successful pilots, getting serious business commitment to fund data science projects and hire data scientists is equally challenging. Most organisations are still in the early phases of their data science journey and struggle to understand what machine learning and data science can do for them. They don't exactly know which skills are needed and hiring data scientists seems really difficult.

Powell High School teacher Jimmy Waters stands in front of the San Jose Semaphore in California. Waters cracked a code broadcast by the building that had been unsolved for more than four years. Jimmy Waters, 31, teaches advanced algebra, geometry and trigonometry at Powell High School. Last summer, Waters devoted a month of his vacation time to solving the puzzle presented by the San Jose Semaphore, a project created by New York-based artist Ben Rubin in 2006. Adobe planned on rewarding Waters with a one-year subscription to its Creative Cloud software, but at his request, the company is donating 40 one-year subscriptions to Powell High School's computer lab, on top of a 3-D printer "to help the students push the boundaries of creativity even further," according to a company news release.