They were also discussed in 2015 in the Computing at School (CAS) framework and guide for teachers to enable teachers in the U.K. to incorporate computational thinking into their teaching work.10 CSTA/ISTE and CAS also provide pedagogical approaches to embed these capabilities across the curriculum in elementary and secondary classes. For example, CSTA/ISTE describes how the nine core computational thinking concepts and capabilities could be practiced in science classrooms by collecting and analyzing data from experiments (data collection and data analysis) and summarizing that data (data representation). Computational thinking is often mistakenly equated with using computer technology. Algorithms are central to both computer science and computational thinking.

Images produced with innovation engines were not only accepted to a selective art competition and displayed at the University of Wyoming Art Museum, but they also were among the 21% of submissions that won an award. It is not unusual to hear a student is taking an advanced placement computer science (AP CS) course these days, but eyebrows raise when Jackeline Mendez tells people about it, because Mendez is a senior at Boston Arts Academy where, as the name implies, the emphasis is on the arts. "I had a free block, and I was surprised how [computer science is] more than just systems and machines and the Internet," explains Mendez, who plans to major in physics in college. "People have a mind set that it's machines, but it's really not. It's what the world is right now. We use computer science for everything."

The Penn State EdTech Network is excited to announce that the top 10 proposals have received $5,000 each in seed money for development of their ideas for the Nittany Watson Challenge. The challenge encourages teams of Penn State students, faculty, and staff to leverage IBM Watson to improve the student experience and the selected proposals did not disappoint. "We received 39 incredibly strong proposals in the initial phase of the Nittany Watson Challenge, and what emerged were 10 proposals that exemplify the best of the creative and innovative energy across our University," said Brad Zdenek, Innovation Strategist for the Penn State EdTech Network. "The roughly 50 team members in these projects are spread throughout our colleges and campuses, representing undergraduate students, graduate students, faculty, and staff. What they have in common is that each is focused on leveraging the capabilities of Artificial Intelligence to create a better experience for both current and prospective students at Penn State."

Even with universities now offering master's degree programs in data science (as opposed to only PhDs), that still won't produce enough pros. "You need teams of data scientists who can actually understand neural networks and tweak them," says Matthew Zeiler, who founded the visual recognition startup Clarifai in 2013 after earning his PhD in computer science. Machine learning, which digests huge amounts of data to identify patterns, is the hottest branch of AI today, with applications as diverse as organizing cell-phone photos, teaching computers to drive autonomous cars, and studying cancer. As Gilbert explains, "No matter how many [people] we train--and other companies are doing the same thing--it's just not enough to make machine-learning AI mainstream." Which is why the industry is moving toward a point-and-click AI revolution.

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.