Mia's latest modeling work will be seen in the spring with the latest release of Learning Resources' Zoomy 2.0, an all-in-one digital microscope for children ages 4 and up that works with personal computers. When the new version is released, Mia will be featured on the box cover. As part of her photo shoot, Mia learned how to use the device. On March 6, she showed the students in the first-grade class of Sister Sarah Ellen McGuire, IHM, how the microscope worked, showing images of a $5 bill, a quarter, a penny, fingerprints and other items on a whiteboard.

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.

The PantherBots of Pleasant Run Elementary School in Indianapolis -- Elijah Goodwin, 10; Angel Herrera-Sanchez, 9; Jose Verastegui, 10; Manuel Mendez, 9; and Devilyn Bolyard, 9 -- are heading to the robotics Vex IQ World Championship from April 23 to 25, 2017, in Louisville. INDIANAPOLIS -- The day should have been one of glory and celebration for five fourth-graders. The Pleasant Run Elementary students had just won a robotics challenge at Plainfield High School, and the students -- new to bot competition this year -- were one step closer to the Vex IQ State Championship. For the record, their race or ethnicity shouldn't be a part of this story. The team is made up of 9- and 10-year-olds.

We talk about artificial intelligence (AI), robots, and machine learning as if they're coming soon, or are just some tech pipe dream. In fact, a special report from Bank of America, Merrill Lynch predicts the global market for AI and robots will be just under $153 billion by 2020, and some industries will experience up to a 30% productivity increase through the use of those technologies alone. That can either terrify you if you've seen too many sci-fi films, or excite you if you consider the upside and benefits it could yield. The reality probably lies somewhere in the middle. There will be disruption – there will be jobs and perhaps even whole industries that see massive displacement from robots and other "intelligent" machines. And that says nothing of the inherent risk associated with creating something capable of logical thinking without emotion. The robots may not rise up and exterminate humanity any time soon, but the development of true AI is closer than you think.

His high school programming club was arguing about whether artificial intelligence could ever accomplish tasks better than humans. Barrat thought the answer was obvious. A few of his peers, however, weren't so easily convinced and asked for proof by the club's next meeting. "All of the sudden I had a week to make a neural network that could rap," Barrat said. Barrat's story is possible because Silicon Valley has decided AI is becoming indispensable, and big companies need to cultivate more talent to fill the growing demand--Google, Facebook, Microsoft, IBM and other giants like GE are shelling out multi-million dollar salaries for AI programmers. To upend the perceived shortage of talent, tech companies have begun to evangelize for open-source AI code, or software that's free to use, modify, and improve upon.

Scientists at Oxford say they've invented an artificial intelligence system that can lip-read better than humans. The system, which has been trained on thousands of hours of BBC News programmes, has been developed in collaboration with Google's DeepMind AI division. "Watch, Attend and Spell", as the system has been called, can now watch silent speech and get about 50% of the words correct. That may not sound too impressive - but when the researchers supplied the same clips to professional lip-readers, they got only 12% of words right. Joon Son Chung, a doctoral student at Oxford University's Department of Engineering, explained to me just how challenging a task this is.

Matt Coatney is a technology executive, business advisor, entrepreneur, author, and speaker. His focus is on bringing advanced artificial intelligence and analytic technologies to market. He has co-founded three companies, advised several others, and contributed to the early success of two different tech startups. Matt has also launched data analytics products designed for the fields of life sciences, healthcare, government, finance, and law. Currently Matt is the VP of Services for Exaptive; he previously was the IT strategy lead for global law firm WilmerHale, and was in charge of technology and operations for a legal search product at LexisNexis.

Machine learning is typified by algorithms that are capable of deriving patterns and'intelligence' from vast quantities of training data. As company's such as DeepMind are beginning to show us however, the real improvements come when the machines are capable of learning independently of data fed it by us. A recent paper from researchers at the University of Toronto highlights some of the progress being made. The team developed an algorithm that attempts to learn from human instructions. In other words, it doesn't require a database of past examples, as is the norm today.

Artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other. Andrew Ng, chief scientist at Chinese internet giant Baidu Inc. and co-founder of education startup Coursera, and Neil Jacobstein, chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University, sat down with The Wall Street Journal's Scott Austin to discuss AI's opportunities and challenges. What is Baidu focused on? NG: For large enterprises like Baidu, AI creates two big pockets of opportunities. One is our core business.

Advances in applied machine learning fueled our enthusiasm for smarter, more talkative devices. It impacted how we learned about and processed the news--and fake news--of the national elections. Automation grew in sophistication: detecting financial fraud, improving healthcare, deepening business intelligence, and disrupting human work models. While significant flux continues, the fact is that machine learning is now firmly established in business technology. Some obvious big data categories are already dominated, but you need only ingenuity to start putting machine learning insights to new and quantifiable uses.