Artificial Intelligence (AI) is on the rise. Life with smart machines is rapidly affecting the way we live and work. A visual signal is the number of companies mentioning it. Kevin Jones, a cancer researcher, describes his work as "taking a bath in uncertainty and unknowns and exceptions and outlie...
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Artificial Intelligence (AI) has stopped being just a thing of Sci-Fi novels and movies. From self-driving cars and grocery shopping without cash registers (Amazon Go), to algorithms that detect diseases and speech recognition that allows us to have conversations with robots (Apple's Siri, for example) artificial intelligence is everywhere. It's slowly but surely creeping into all aspects of our daily lives. And the near future will have more and more of it. Perhaps AI is not spread into education as much as it is in other fields, but this doesn't mean the future's not bright.
Column n The Educational Advances in Artificial Intelligence column discusses and shares innovative educational approaches that teach or leverage AI and its many subfields at all levels of education (K-12, undergraduate, and graduate levels). By restructuring the course into a format that was roughly half lecture and half small-group problem solving, I was able to significantly increase student engagement, their understanding and retention of difficult concepts, and my own enjoyment in teaching the class. The ACTIVE Center's design was based on research on the power of collaborative learning to promote student success and retention, particularly for women, underrepresented minorities, and transfer students, who benefit greatly from building stronger connections with their peers through shared active learning experiences (Zhao, Carini, and Kuh 2006; Rypisi, Malcolm, and Kim 2009; Kahveci, Southerland, and Gilmer 2006). The ACTIVE Center, a 40-student classroom, includes movable furniture (20 trapezoidal tables and 40 lightweight rolling chairs) that is typically grouped into 10 hexagonal table clusters but that can also be arranged into lecture-style rows, a boardroom or seminar-style rectangular layout, or individual pair-activity tables. The room also has an Epson Brightlink "smart projector" at the front of the room, four flat-panel displays (which can be driven centrally by the instructor's laptop or individually through HDMI ports), and 10 rolling 4 x 6 foot whiteboards for use during group problem-solving activities, as well as smaller, portable tabletop whiteboards.
The average respondent used 7.7 tools/methods, similar to 2016 poll. Next, we compared the top 16 methods in this year's poll with their share last year - see Figure 1. We note a significant increase in Random Forests, Visualization, and Deep Learning share of usage, and decline in K-nn, PCA, and Boosting. Gradient Boosting Machines was a new entry in 2017. Deep Learning, despite its amazing successes, is reported used by only about 20% of KDnuggets readers.
We've spent so long wringing our hands and worrying about artificial and virtual intelligence that we forgot to roll out the welcome mat when they finally arrived. Now, when major tech companies give their annual keynotes, they can't help but pepper the narrative with phrases like "machine learning." What does it all mean, though? Should we crank up the worry now that it looks like every tent-pole feature of self-learning software could also be a critical flaw? The future is here -- and it's equal parts exciting and terrifying.
Many tasks in which humans excel are extremely difficult for robots and computers to perform. Especially challenging are decision-making tasks that are non-deterministic and, to use human terms, are based on experience and intuition rather than on predetermined algorithmic response. A good example of a task that is difficult to formalize and encode using procedural programming is image recognition and classification. For instance, teaching a computer to recognize that the animal in a picture is a cat is difficult to accomplish using traditional programming. Artificial intelligence (AI) and, in particular, machine learning technologies, which date back to the 1950s, use a different approach.
Project-based learning opportunities come in all forms at MIT, as Melanie Chen discovered during her internship at Lincoln Laboratory this year. A computer science major, she served as a teaching assistant, curriculum developer, and mentor to high school students participating in Cog*Works, part of the Beaver Works Summer Institute. Now, finishing up her fall sophomore semester, Chen is finding plenty of opportunities to apply the lessons she has learned from her hands-on experience teaching others. "One of the greatest skills I've learned is effective communication," she notes. "Whether it's students, peers, colleagues, or mentors, I've learned what it takes to be able to create a trusting relationship, so that we can effectively work on a project of this scale together."