Collaborative Robots at Work

collaborative robotRobots, yes - but cobots? The term 'cobot' is a portmanteau of  "collaborative robot", a robot designed for human interaction. Traditional industrial robots would typically be isolated from humans for safety reasons. Cobots operate alongside people within the same space.

Collaborative robots are promoted as being cost-effective, safe, and flexible to deploy. Cobots designed to share a workspace with humans make automation easier in a variety of applications, according to Universal Robots.

Robots that will be able to exist next to people in our homes, factories, and offices and navigate safely around us is seen as possible in the next 5-10 years.

Similar to industrial robots, cobots can automate manual processes but can also do jobs that humans don't want to do. What kind of jobs does that include? Tasks that are repetitive, tedious, dirty, or dangerous. So, injury reduction is one of the benefits of working with cobots. Strenuous lifting and repetitive movement are common workplace injuries.

Not to insult the humans reading this, but robots and cobots offer far higher levels of consistency than humans. That is a key benefit in tasks that require a high degree of precision.

The cobots we are using emerge tend to be more compact and lightweight than conventional robots. They are also more user-friendly and require fewer or no engineers or programmers to set up ad monitor operations.

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Schools Using AI

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                                             Image:Gerd Altmann

I wrote earlier here about teaching AI in classrooms and a former colleague who read it emailed and said that I need to also consider not just how students are learning about AI but also how schools use AI.

In that earlier article, I said that many people are unaware of AI used already in their everyday lives. It's not that the AI is deliberately hidden from view (though in some cases it actually is deliberately hidden, such as with chatbots). If you use apps on a smartphone, you are using AI. If you use Google search or Gmail, you are using AI. If your car has navigation or safety features that keep you on the road, you are using AI.

In education, AI is making it possible to provide more personalized learning experiences for students. By automating tasks that take teachers more time, AI facilitates these tasks so that time can be spent with students providing one-to-one feedback. The AI can evaluate progress, analyze and make recommendations for further study. Digital tools with AI integrations can create a personalized learning path based on each student’s responses and based on their needs. There are platforms that have AI which helps to automate tasks and so can provide adaptive learning and more personalized experiences for students. Students would also have access to intelligent tutoring systems through AI.

Schools using AI administrative;y and pedagogically are more likely to see the value in having students learn in the classroom about how AI works and how it operates in their lives in and out of classrooms.

Machine Learning MOOC Updated

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Photo by Christina Morillo

Andrew Ng's Machine Learning course on Coursera has been revamped and updated and it is getting good student ratings.

There are fewer online courses that I consider to be true MOOCs now. Massive is small. Open is more closed. But the "OC" portion remains for many. The three courses that make up the Machine Learning Specialization offered by DeepLearning.AI and Stanford on the Coursera platform still fit the MOOC definition more closely.

You can earn a certificate at the end, and enjoy the full experiences including quizzes and assignments if you enroll and pay a monthly subscription but the courses are free (Open) to audit and view the course materials. The Massive in this course is massive with over 20,000 students enrolled.

Andrew Ng is the co-founder of Coursera and was the founding lead of Google’s Brain Project, and served as Chief Scientist at Baidu. He then did two artificial intelligence startups - Deeplearning.ai (a training company founded in 2017) and Landing.ai (for transforming enterprises with AI). He remains an adjunct professor at Stanford University. His course on Machine Learning was one of the very first courses from Coursera when it first launched in 2012. I audited the course that year though I knew the content was way above my abilitiees but I was curious as to the structure of the course from a design perspective.

At that time, Machine Learning was a new concept and was close to applied statistics. Ng goes way back because his Stanford lectures were on YouTube in 2008 and got 200,000 views. Then, he converted them to an online format in Fall 2011 and they were offered for free. He had 104,000 students and 13,000 of them gained certificates.

On the tech side, this updated version:
uses Python rather than Octave
expanded list of topics including modern deep learning algorithms, decision trees, and tools such as TensorFlow
new ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is doing
programming exercises
practical advice section on applying machine learning based on best practices from the last decade