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"Computational thinking has been trending, but what is it, really? Simply put, computational thinking is a method of reasoning that teaches students how to solve real-world, complex problems with strategies that computers use. Computational thinking and the design thinking process are frameworks for problem-solving to help address the need for 21st-century skills across our nation's K-12 school system. While computation governs the world around us, computational thinking as a teaching and learning framework is a new concept for many. These skills are becoming progressively important due to the constant evolution of technology and its place in our economy. An increasingly automated workforce means students who have had exposure to tech-thinking will be more likely to succeed. To help get students future-ready, I've identified seven effective thinking strategies to equip young innovators with valuable problem-solving abilities. Using these tips, students will not only be learning important skills, but will be preparing for what lies ahead post-graduation."

"In the last few years, machine learning applications have quietly entered every aspect of life: social media to speech recognition, radiology to retail, warfare to writing articles, coding to customer service, robotics to route optimization. During the 40 year information age, we told computers what to do. With advances in artificial intelligence, particularly machine learning, and faster processing chips we can feed computers giant data sets and they can (in narrow slivers) draw some inferences on their own. As we reported in Ask About AI, the rise of code that learns marks the beginning of a new era of augmented intelligence. It's a great opportunity for us to expand access to a great education and for young people to make a big contribution. Given the importance of relationships in human development, AI will augment rather than replace the work of educators in many ways. We'll all have to get better at collaborating with teams that include smart machines. In other professions, augmentation will lead to automation with the potential for significant dislocation. Amazon's workforce, for example, is about 20% robots."

"Essential Skills Today's Students Need for Jobs in Tomorrow's Age of Automation"

"Computational thinking is generally defined as the mental skills that facilitate the design of automated processes. Although this term traces back to the beginnings of computer science in the 1950s, it became popular after 2006 when educators undertook the task of helping all children become productive users of computation as part of STEM education. If we can learn what constitutes computational thinking as a mental skill, we may be able to draw more young people to science and accelerate our own abilities to advance science. The interest from educators is forcing us to be precise in determining just what computational thinking is."

" I heard a great talk a few months ago. It was Conrad Wolfram (probably one of the world's leading mathematicians) who suggested, that we should stop teaching our kids maths. Whoah! He said, and I'm paraphrasing, we needed to teach them computational thinking instead. What is that? He said every problem needs breaking apart, exploding it into its parts - if we are to begin to properly solve them. And better still that we explain to kids how to put that idea into practice. Explain to them, for example, that to make their bike go faster they might figure out how much bigger the peddle wheel needs to be than than the one on the back wheel. According to Wikipedia computational thinking is an iterative process based on three stages: 1. Problem formulation (abstraction) 2. Solution expression (automation) 3. Solution execution and evaluation (analyses) That REALLY works for me."

"As society anticipates a future filled with artificial intelligence, experts are theorizing ways that we humans can outperform the computers that are being programmed to perfection. Some believe educators should focus on building soft skills like empathy and interpersonal communication so humans and robots can complement one another. However, other education thought leaders are ready to beat computers at their own game by teaching people to think like intelligent machines. Why do so many of our kids struggle with math problem-solving? Because they don't know where to start; they don't know how to decompose the problem. Heidi Williams The term for getting humans to think like computers has been coined Computational Thinking, and the idea is taking off. Author Heidi Williams can attest to its popularity after her book on the subject, No Fear Coding Computational Thinking Across the K-5 Curriculum, sold out at the International Society for Technology in Education conference. Inside the book, Williams breaks down computational thinking standards into four parts: 1. Formulating problems through data analysis, abstract models and algorithmic thinking; 2. Collecting, analyzing and presenting data; 3. Breaking down problems into parts and extracting information to understand the system in place; and 4. Using algorithmic thinking to develop sequences and testing automated solutions."

"When it comes to productivity, humans don't come close to robots. Machines don't need sleep, won't slack off or ask for a raise and generally don't need vacation days so they can sunbathe in Bali. According to a study from Oxford University and the Oxford Martin School, 47% of jobs in the United States are "at risk" of becoming "automated in the next 20 years." PwC has similar findings, estimating that 38% of U.S. jobs are at risk of being replaced by robots and artificial intelligence in the next 15 years. And while two-thirds of Americans believe robots will take over most of the workforce in the next 50 years, they're also in denial: 80% say their job will "probably" or "definitely" be around in five decades. Here are five robots that are coming to take some jobs from unsuspecting humans: "

"Dating from 1991, the Python programming language was considered a gap-filler, a way to write scripts that "automate the boring stuff" (as one popular book on learning Python put it) or to rapidly prototype applications that will be implemented in other languages. However, over the past few years, Python has emerged as a first-class citizen in modern software development, infrastructure management, and data analysis. It is no longer a back-room utility language, but a major force in web application creation and systems management, and a key driver of the explosion in big data analytics and machine intelligence."

Creativity is often said to be the key to the future. The essentially human attribute that will ensure our utility in a world dominated by automation. It is said to be an essential ingredient in education but it will not be truly learned unless we provide students with opportunities to dive fully into its waters. 

"For many attending the Future of Work symposium on Wednesday, there wasn't any question whether automation is going to take over jobs-but rather when, and how education should respond. Hosted at Stanford University, the day-long event brought together dozens of minds who are thinking about what careers and skills students need to prepare for, and how an increasingly digital higher-education system will need to adapt to help get them there. Speakers including edX CEO Anant Agarwal, associate dean and director of Stanford's Diversity and First-Gen office Dereca Blackmon, and Deborah Quazzo, a co-founder of investment firm GSV, shared their ideas on what that might look like. Here are a few major themes we heard throughout the day:"

"It's time we considered the increasing impact of AI in education. Educators have already previewed examples of the changes coming their way. Automation technology has been introduced for a variety of basic teaching tasks."

"From autonomous vehicles, predictive analytics applications, facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI are many. However, regardless of the application of AI, there is commonality to all these applications. Those who have implemented hundreds or even thousands of AI projects realize that despite all this diversity in application, AI use cases fall into one or more of seven common patterns.  The seven patterns are: hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. Any customized approach to AI is going to require its own programming and pattern, but no matter what combination these trends are used in, they all follow their own pretty standard set of rules. These seven patterns are then applied individually or in various combinations depending on the specific solution to which AI Is being applied."

"The adoption of generative artificial intelligence (genAI) has shuffled the list of top skills businesses want from professionals in 2024, according to a new job site study and education industry data. Far from replacing workers, genAI appears poised to transform the way technologists and others work, allowing them to focus more on creative tasks such as product development, and less on mundane tasks that can be automated."

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Haven't you yet built your first ROBOT, when it's become so easy a task. Come on, lets make one in just three days.