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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."

Google in Computer Science Education

Besides what I wrote recently about Google's Classroom product, educators at all levels should look at the broader "Google in Education" projects. 

One example that is not as well known to educators as their popular tools is their work and research into the teaching of computer science. Their K-12 Year 2 of a Google-Gallup study surveyed over 1,600 students, 1,600 parents, 1,000 teachers, 9,800 principals, and 2,300 superintendents. Some results were that 40% of principals report having CS classes with programming/coding , increasing from 25% in Year 1. Positive perceptions of CS learning and careers persist among all groups, and yet few parents and teachers have specifically expressed support for CS education to school officials, despite their high value of CS learning.

The second report of their research study with Gallup, Inc. dives into data from nearly 16,000 respondents to explore participation in and perceptions of computer science and related careers as well as associated demographic differences.

Google has partnered with the Community College Research Center (CCRC) and ETR on two complementary research reports that explore ways to encourage community college students to pursue bachelor’s degrees in computer science and related fields.

One way to keep up with all their efforts in education is to sign up for their education newsletter at https://lp.google-mkto.com/edu-updates-signup.html. Some of these are also examined in video form on the Education at Google YouTube Channel.

 

Alternative Postsecondary Learning Pathways

arrowsSeveral bills that recently came before the U.S. House of Representatives that would provide funding for people to enroll in alternative postsecondary pathways. As one article on usnews.com points out, this funding comes at the same time as a new study that looks at  the quality of these programs and the evidence of their efficacy.

That report, "The Complex Universe of Alternative Postsecondary Credentials and Pathways" authored by Jessie Brown and Martin Kurzweil and published by American Academy of Arts and Sciences, evaluated alternatives that I have written about here: certificate programs, market-focused training, work-based training, apprenticeships, skills-based short courses, coding bootcamps, MOOCs, online micro-credentials, competency-based education programs and credentials based on skill acquisition rather than traditional course completion.

The report is wide-ranging and worth downloading if these are educational issues that concern you. If they don't concern you and you plan to work in education for another decade, you should really pay attention.

I'm not at all surprised that the earning power for "graduates" of alternative programs varies widely depending on the subject studied. A computer science certificate program graduate, for example, can expect to earn more than twice what a health care or cosmetology certificate recipient will receive.

Who pursues these programs? Certificate programs, work-based training and competency-based programs tend to attract older, lower-income learners who have not completed a college degree. But 80% of bootcamp enrollees and 75% of MOOC participants already have a bachelor's degree.

What do the authors of this study recommend? Policy changes to collect more comprehensive data on educational and employment outcomes and to enforce quality assurance standards. Also to devote resources to investigating efficacy and return on investment. The U.S. News article also points out that 19 organizations have promoted greater federal oversight of career and technical education programs in a June letter to the House of Representatives about the Perkins Act Reauthorization.

Chasing the MUSE

ENIAC

DARPA has a program called MUSE (Mining and Understanding Software Enclaves) that is described as a "paradigm shift in the way we think about software." The first step is no less than for MUSE to suck up all of the world’s open-source software. That would be hundreds of billions of lines of code, which would then need to be organized it in at database.

A reason to attempt this is because the 20 billion lines of code written each year includes lots of duplication. MUSE will assemble a massive collection of chunks of code and tag it so that programmers can automatically be found and assembled. That means that someone who knows little about programming languages would be able to program.  

Might MUSE be a way to launch non-coding programming?

This can also fit in with President Obama’s BRAIN Initiative and it may contribute to the development of brain-inspired computers.

Cognitive technology is still emerging, but Irving Wladawsky-Berger, formerly of IBM and now at New York University, has said “We should definitely teach design. This is not coding, or even programming. It requires the ability to think about the problem, organize the approach, know how to use design tools.”