Humans Learning About Machines Learning

GoDeep learning might sound like that time when we get really serious about what we are thinking about, and go deeper into the subject and learning. But it is not about the human brain. It is about machine learning. Also known as deep structured learning or hierarchical learning, it is part of the study of machine learning methods. It is about machines getting smarter on their own as they complete tasks.

The theories do look at biological nervous systems as models. Neural coding attempts to define a relationship between various stimuli and associated neuronal responses in the brain The terms used are many. Deep learning architecture, deep neural networks, deep belief networks and recurrent neural networks are all labels used in computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics and drug design. That means a machine is producing results instead of human experts.

Google's artificial intelligence software, DeepMind, has gotten a fair amount of press coverage. It has the ability to teach itself many things, including how to walk, jump, and run. In the press, it will defeat the world's best player of the Chinese strategy game, Go, but deep learning is more serious than that.

You can take a free, 3-month course on Deep Learning offered through Udacity, taught by Vincent Vanhoucke, the technical lead in Google's Brain team.

Machine learning is a fast-growing and exciting field of study and deep learning is at its "bleeding edge. This course is considered to be an "intermediate to advanced level course offered as part of the Machine Learning Engineer Nanodegree program. It assumes you have taken a first course in machine learning, and that you are at least familiar with supervised learning methods."

Should You Be Teaching Systems Thinking?

An article I read suggests that systems thinking could become a new liberal art and prepare students for a world where they will need to compete with AI, robots and machine thinking. What is it that humans can do that the machines can't do?

Systems thinking grew out of system dynamics which was a new thing in the 1960s. Invented by an MIT management professor, Jay Wright Forrester,  it took in the parallels between engineering, information systems and social systems.

Relationships in dynamic systems can both amplify or balance other effects. I always found examples of this too technical and complex for my purposes in the humanities, but the basic ideas seemed to make sense.

One example from environmentalists seems like a clearer one. Most of us can see that there are connections between human systems and ecological systems. Certainly, discussions about climate change have used versions of this kind of thinking to make the point that human systems are having a negative effect on ecological systems. And you can look at how those changed ecological systems are then having effect on economic and industrial systems.

Some people view systems thinking as something we can do better, at lest currently, than machines. That means it is a skill that makes a person more marketable. Philip D. Gardner believes that systems thinking is a key attribute of the "T-shaped professional." This person is deep as well as broad, with not only a depth of knowledge in an area of expertise, but also able to work and communicate across disciplines.  

coverJoseph E. Aoun believes that systems thinking will be a "higher-order mental skill" that gives humans an edge over machines. 

But isn't it likely that machines that learn will also be programmed one day to think across systems? Probably, but Aoun says that currently "the big creative leaps that occur when humans engage in it are as yet unreachable by machines." 

When my oldest son was exploring colleges more than a decade ago, systems engineering was a major that I thought looked interesting. It is an interdisciplinary field of engineering and engineering management. It focuses on how to design and manage complex systems over their life cycles.

If systems thinking grows in popularity, it may well be adopted into existing disciplines as a way to connect fields that are usually in silos and don't interact. Would behavioral economics qualify as systems thinking? Is this a way to make STEAM or STEM actually a single thing?

 


David Peter Stroh, Systems Thinking for Social Change

Joseph E. Aoun, Robot-Proof: Higher Education in the Age of Artificial Intelligence

The UX of Course Design

UXI stumbled upon a post on Medium by John Spencer called "8 Ways UX Design Theory Transformed My Approach to Course Design - How a Small Side Project Changed the Way I Teach."  As someone who has taught for a quartet of decades and done UX design and even taught UX, I was intrigued by what he might have learned about "how to build community, communicate clearly, and set up effective systems as we design our courses."

A few basics to start: User experience design theory is confusingly abbreviated as XD, UX, UXD or UED, But it is about focusing on the user experience of a device, tool, platform or web application. In doing this, a designer considers accessibility, usability and the easy to overlook pleasure someone might get from the interaction. Do you think Facebook would be as popular if people didn't get pleasure from using it?

Spencer says he first embraced UX design when he worked on creating a blogging platform for students called Write About.

As with any design, you make the best that you can, add features you think users will want - but then you have to deal with how users react and use it.

Is there a connection to teaching?

Every lesson has a design and teachers learn to design based on what works with a course or even with a specific group of students. Even larger in the design scheme is our current use of classroom systems and course architecture.

Building tools and systems that can be used intuitively understand with a minimum of additional instruction or training is key to UX. If you as a teacher spend a lot of time teaching procedures and methods rather than teaching your content and concepts.

Some of Spencer's takeaways make a lot of sense to me. For example, embrace onboarding. Onboarding is the mechanism through which new employees acquire the necessary knowledge, skills, and behaviors to become effective organizational members. When you sign into a website or register for a service, you might get virtual tour and buttons have pop-ups or rollover text. The designers want you to feel comfortable as you navigate that first experience. Do we offer that to students when they enter a course?

Read Spencer's post, but maybe think about course design as a system that should seem invisible. I don't know that you need to be a UX designer to teach, or that we can all create a course that when you enter it you immediately know where to go and what to do, but we can certainly put the learner at the center of the design.

Learning and Working in the Age of Distraction

screensThere is a lot of talk about distraction these days. The news is full of stories about the Trump administration and the President himself creating distractions to keep the public unfocused on issues they wish would go away (such as the Russias connections) and some people believe the President is too easily distracted by TV news and Twitter.

There are also news stories about the "distraction economy."  So many people are vying for your attention. The average person today is exposed to 1,700 marketing messages during a 24-hour period. Most of these distractions are on screens - TV, computers and phones.  Attention is the new currency of the digital economy.

Ironically, a few years ago I was reading about "second screens," behavioral targeting and social media marketing and that was being called the "attention economy." There is a battle for attention, and the enemy is distraction.

Google estimates that we spend 4.4 hours of our daily leisure time in front of screens. We are still using computers mostly for work/productivity and search. We use smartphones for connectivity and social interactions. Tablets are used more for entertainment. My wife and I are both guilty of "multi-screening." That means we are part of the 77% of consumers watching TV while on other devices. I am on my laptop writing and researching and she is on her tablet playing games and checking mail and messages. It is annoying. We know that.

Of course, the original land of distraction is the classroom. Students have always been distracted. Before the shiny object was a screen full of apps, passing notes was texting, and doodling in your notebook and the cute classmates sitting nearby were the social media. But I have seen four articles on The Chronicle website about "The Distracted Classroom" lately. Is distraction on the rise?

If you are a teacher or student, does your school or your own classroom have a policy on using laptops and phones? If yes, is it enforced?  Anyone who has been in a classroom lately of grade 6 or higher knows that if students have phones or laptops out in class for any reason they are texting, surfing the web, or posting on social media.

Good teachers try to make classes as interactive as possible. We engage students in discussions, group work and active learning, but distractions are there.

Banning devices isn't a good solution. Things forbidden gain extra appeal.

distractionsA few books I have read discuss the ways in which distraction can interfere with learning. In The Distracted Mind: Ancient Brains in a High-Tech World , the authors say that distraction occurs when we are pursuing a goal that really matters and something blocks our efforts to achieve it. Written by a neuroscientist, Adam Gazzaley, and a psychologist, Larry D. Rosen, they join other researchers who report that our brains aren't built for multitasking. This compares to a time a few decades ago when being able to multitask was consider a positive skill.

It seems that the current belief is that we don't really multitask. We switch rapidly between tasks. Any distractions and interruptions, including the technology-related ones - act as "interference" to our goal-setting abilities. 

But is this a new problem or has our brain always worked this way? Is the problem really more about the number of possible distractions and not our "rewired" brains?

Nicholas Carr sounded an alarm in 2011 with The Shallows: What the internet is doing to our brains, arguing that our growing exposure to online media means our brains need to make cognitive changes. The deeper intellectual processing of focused and critical thinking, gets pushed aside in favor of the faster processes like skimming and scanning.

Carr contends that the changes to the brain's "wiring" is real. Neural activity shifts from the hippocampus' deep thinking, to the prefrontal cortex where we are engaged in rapid, subconscious transactions. Substitute speed for accuracy. Prioritize impulsive decision-making over deliberate judgment. 

In the book Why Don't Students Like School?: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom  the author asks questions such as Why Do Students Remember Everything That's on Television and Forget Everything I Say? and Why Is It So Hard for Students to Understand Abstract Ideas? and gives some science and suggestions as answers. But these are difficult questions and simple answers are incomplete answers in many cases.

Some teachers decide to use the tech that is being a distraction to gain attention. I had tried using a free polling service (Poll Everywhere) which allows students to respond/vote using their laptops or phones. You insert questions into your presentation software, and that allows you to track, analyze, and discuss the responses in real time. The problem for me is that all that needs to be pre-planned and is awkward to do on-the-fly, and I am very spontaneous in class with my questioning. Still, the idea of using the tech in class rather than banning it is something I generally accept. But that can't be done 100% of the time, so distracted use of the tech is still going to occur.

bubbleAnd the final book on my distraction shelf is The Filter Bubble. The book looks at how personalization - being in our own bubble - hurts the Internet as an open platform for the spread of ideas. The filter bubble puts us in an isolated, echoing world. The author, Eli Pariser, subtitles the book "How the New Personalized Web Is Changing What We Read and How We Think." Pariser coined the term “filter bubble.” The term is another one that has come up o the news in talking about the rise of Donald Trump and the news bubble that we tend to live in, paying attention to a personalized feed of the news we agree with and filtering out the rest.

Perhaps creating a filter bubble is our way of coping with the attention economy and a way to try to curate what information we have to deal with every day.

Then again, there were a number of things I could have done the past hour instead of writing this piece. I could have done work that I actually get paid to do. I could have done some work around my house. But I wrote this. Why? 

Information overload and non-stop media is hurting my/our discipline for focus and self-control.

Michael Goldhaber defined the attention economy in this more economic way: "a system that revolves primarily around paying, receiving and seeking what is most intrinsically limited and not replaceable by anything else, namely the attention of other human beings.” In order for that economy to be profitable, we must be distracted. Our attention needs to be drawn away from the competition.

As a secondary school teacher for several decades, I saw the rise of ADHD. That was occurring before the Internet and lack of attention, impulsivity and boredom were all symptoms. It worsened after the Internet was widespread, but it was there before it and all the personal digital devices.

Back in 1971,  Herbert A. Simon observed that “what information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”

We are collectively wiser than ever before. We have the wisdom of the world in a handheld computer connected to almost everything. But it is so difficult to filter out the distractions and garbage that we don't have a lot of success translating information into knowledge. People used to say that finding out something on the Internet was like taking a sip from a fire hose. Search filtering has helped that, but so far the only filters for our individual brains are self-created and often inadequate.

 

Machine Learning :: Human Learning

AI - “artificial intelligence” - was introduced at a science conference at Dartmouth University in 1956. Back then it was a theory, but in the past few decade it has become something beyond theoretical. been less theory and more in practice than decades before.

The role of AI in education is still more theory than practice.

A goal in AI is to get machines to learn. I hesitate to say "think" but that is certainly a goal too. I am reading The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution currently and in that history there is a lot of discussion of the people trying to get machines to do more than just compute (calculate) but to learn from its experiences without requiring a human to program those changes. The classic example is the chess playing computer that gets better every time it wins or loses. Is that "learning?"

But has it had an impact on how you teach or how your students learn?

It may have been a mistake in the early days of AI and computers that we viewed the machine as being like the human brain. It is - and it isn't.

But neuroscientists are now finding that they can also discover more about human learning as a result of machine learning. An article on opencolleges.edu.au points to several interesting insights from the machine and human learning research that may play a role in AI in education.

One thing that became clear is that physical environment is something humans learn easier than machines. After a child has started walking or opened a few doors or drawers or climbed a few stairs, she learns how to do it. Show her a different door, drawer, or a spiral staircase and it doesn't make much of a difference. A robot equipped with some AI will have a much steeper learning curve to learn these simple things. It also has a poor sense of its "body." Just watch any videos online of humanoid robots trying to do those things and you'll see how difficult it is for a machine.


Then again, it takes a lot longer for humans to learn how to drive a car on a highway safely. And even when it is learned, our attention, or lack thereof, is a huge problem. AI in vehicles is learning how to drive fairly rapidly, and its attention is superior to human attention. Currently, it is still a fall back human error in mist cases, but that will certainly change in a decade or two. I learned to parallel park a car many years ago and I am still lousy at doing it. A car can do it better than me.

Although computers can do tasks they are programmed to do without any learning curve, for AI to work they need to learn by doing - much like humans. The article points out that AI systems that traced letters with robotic arms had an easier time recognizing diverse styles of handwriting and letters than visual-only systems. 

AI means a machine gets better at a task the more it does it, and it can also apply that learning to similar but not identical situations. You can program a computer to play notes and play a series of notes as a song, but getting it to compose real music requires AI.

Humans also learn from shared experiences. A lot of the learning in a classroom comes from interactions between the teacher and students and student to student. This makes me feel pretty confident in the continued need for teachers in the learning process.

One day, I am sure that machines will communicate with each other and learn from each other. This may be part of the reason that some tech and learning luminaries like Elon Musk have fears about AI

I would prefer my smart or autonomous vehicle to "talk" to other vehicles on the roads nearby and share information on traffic, obstructions and vehicles nearby with those quirky human drivers only.

AI built into learning systems, such as an online course, could guide the learning path and even anticipate problems and offer corrections to avoid them. Is that an AI "teacher" or the often-promoted "guide on the side?"

This year on the TV show Humans, one of the human couples goes for marriage counseling with a "synth" (robot). She may be a forerunner of a synth teacher.

Humans TV
The counselor (back to us) can read the husband's body language and knows he does not like talking to a synth marriage counselor.

 

What Is a Modern Learning Experience?

social on mobile

Jane Hart, who I have been following online for many years, is the Director of the Centre for Modern Workplace Learning, which she set up to help organizations and learning professionals modernize their approaches to workplace learning. Reading her online Modern Workplace Learning Magazine has alerted me to trends outside academia and outside the United States.  

She recently posted an article titled "Designing, delivering and managing modern learning experiences" and that made me consider how I would define "modern learning." It would include school experiences for some of us, but for most people today it is more likely an experience that occurs in the workplace and on our own. That itself seems like a big shift from the past. Or is it?

If in 1917, someone had wanted to become a journalist, he could go to college, but he could also get a job without a degree - if he could show he was a good writer. He could do some freelance writing with or without pay to get some experience and samples. Move 50 years to 1967, and the path was more likely to be a school of journalism. What about today?

As Jane points out, the modern learning experience path for the workplace probably includes using: 

  • Google and YouTube to solve their own learning and performance problems
  • social networks like Twitter and LinkedIn to build their own professional network (aka personal learning network)
  • messaging apps on their smartphones to connect with colleagues and groups
  • Twitter to participate in conference backchannels and live chats
  • participating in online courses (or MOOCs) on platforms like Coursera, edX and FutureLearn

The modern learning experience is on demand and continuous, not intermittent, and takes place in minutes rather than hours. It occurs on mobile devices more than on desktop computers.

Jane Hart believes it is also more social with more interacting with people, and that it is more of a personally-designed experience. I don't know if that is true for educational learning. Is it true for the workplace on this side of the pond? Does the individual design the learning rather than an experience designed by someone "in charge."

Modernizing classroom learning has often been about making learning more autonomous (self-directed, self-organized and self-managed) but that model does not easily fit into the model used for the past few hundred years in classrooms.