Farmers have another tool to help them in the field. With smartphones in hand, tech-savvy farmers are able to fly heavy duty drones out to do everything from crop dusting to surveying the health of the plants. And with smarter software, drones can do more autonomously, making the process more efficient and at far lower cost than hiring a specialised pilot to man an aircraft over the fields. While makers have offered agriculture drones to the market for two decades now, adoption is tiny, according to Xaircraft, a Chinese maker that's starting to expand into the U.S. It estimates only 2 percent of farmers in China use drones, in part because of lower awareness, and because the technology wasn't that user-friendly before. But now with autonomous UAVs and smartphone apps, it's all set to change, a spokesperson said.

Andrew Ng, one of the world's leading artificial intelligence researchers, said in a Medium post that he is resigning as the head of AI initiatives at Baidu Corp., one of China's largest Internet companies. Ng said he said he will "continue to shepherd" the growth of AI in society, but provided few clues about what might come next. He portrayed his departure from Baidu as amicable, saying: "the team is stacked up and down with talent; I am confident AI at Baidu will continue to flourish." Ng has held a multitude of high-profile positions in Silicon Valley in the past decade, serving as a computer science professor at Stanford University, as head of the Google Brain project, and as chairman of Coursera, an online-education company that he co-founded with Stanford faculty colleague Daphne Koller.

Responses to a 2016 Tech Pro Research survey indicate that artificial intelligence and machine learning will be a significant area of development for IT in the next few years. In this recent survey, only 28 percent of respondents, most of whom were in IT leadership positions, said they have firsthand experience with artificial intelligence or machine learning. However, if the survey results hold true, the majority of respondents will be using the technologies at work in the next few years. Forty-two percent of respondents, most of whom were in tech leadership positions, said their company lacks the skills necessary to implement and support artificial intelligence or machine learning. Almost as many respondents (41 percent) said all the implementation and support work would be done in house when the time comes, and that their company is working to address artificial intelligence and machine learning in the corporate security plan.

At the start of the year, I was part of a panel that aimed to offer career guidance to graduating students at a local secondary school in Singapore. The panel dished out the usual advice about the need to have passion, work hard, and always strive to do better. A student then posed a question that struck a chord: "How can I ensure the skills I learn in school will not be obsolete by the time I enter the workforce?" These students were no older than 16 or 17 years and, if they took the typical route to university, would not begin life as working adults for at least another five to seven years. With technology changing so rapidly these days, it might very well be possible their course modules would no longer be relevant by the time they graduated.

Lawyers are using artificial intelligence tools for automating tasks, such as contract review and sorting through electronic discovery documents, according to the article. But higher level tasks, especially those that require experience, will take a while, lawyers and other experts told the newspaper. Professor Dana Remus of the University of North Carolina School of Law and labor economist Frank Levy of the Massachusetts Institute of Technology published a paper on the automation of legal work in 2016 and concluded that although the automation of legal tasks reduces the amount of work lawyers must do, it's not enough to put lawyers out of business. Their paper said that if large law firms adopt new legal technology immediately, those lawyers would lose 13 percent of their current work hours. But the authors said it's more realistic to assume that this would happen over five years, which would result in closer to a 2.5 percent reduction in hours per year.

Artificial Intelligence/Machine Learning field is getting a lot of attention right now, and knowing where to start can be a little difficult. I've been dabbling in this field, so I thought of curating the best resources in one place. All of these are curated based on if it's an inspiring read or a valuable resource. I hope this curated list help you get started on what you need to know about AI/Machine Learning on a technical level. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

Kaleigh Lerner demonstrates the use of Siri on her iPhone at ASU's Tempe campus on Friday, March 17, 2017. Present day technology has completely revolutionized and changed the way we live day-to-day. From touch screen phones to self-driving cars, advances in technology have transformed our lives and are rapidly evolving. It seems as though shows like "Black Mirror" are becoming more realistic than we think. Universities like ASU are pushing for the advancement of technological innovation, urging both students and teachers to revolutionize the way education is run.

In his regular courses at Georgia Tech, the computer science professor had at most a few dozen students. But his online class had 400 students -- students based all over the world; students who viewed his class videos at different times; students with questions. Maybe 10,000 questions over the course of a semester, Goel says. It was more than he and his small staff of teaching assistants could handle. "We were going nuts trying to answer all these questions," he says.

Teachers should use teaching methods that utilize technology in the most efficient way. During action research a motivating learning environment was developed, with a digital learning game and the flipped classroom pedagogy. The target group was first year pupils in a primary school. There were seventeen pupils in the class: nine of them were girls and eight were boys. The experiment was held during five weeks and there was one lesson per week.

About Matthew: Matthew Mayo is a Data Scientist and the Deputy Editor of KDnuggets, as well as a machine learning aficionado and an all-around data enthusiast. Matthew holds a Master's degree in Computer Science and a graduate diploma in Data Mining. This post originally appeared on the KDNuggets blog. What is automated machine learning (AutoML)? What are some of the AutoML tools that are available? What does its future hold?