21st Century Learning and Teaching

Train a custom machine learning model to determine the types of birds that visit a bird feeder. Find this and other hardware projects on Hackster.io.

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Machine learning means learning from data, and just as humans learn from past experiences, machines learn from previous data.

Technically speaking, it is the use of artificial intelligence to enable machines to learn automatically and autonomously.

We humans are, in essence, no longer needed to teach computers. Instead, we create systems that enable them to learn by themselves. Rather than follow rigid, static instructions, computers now make data-driven predictions and decisions.

Let’s say you provide a computer with a series of photographs labeled either “this is an elephant” or “this is not an elephant.” When you present the computer with a fresh batch of photos, it will be able to identify which are of elephants.

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Microsoft says AI and machine learning driven by open source and the cloud

Artificial intelligence and machine learning are rapidly gaining importance, and Mark Russinovich, Microsoft Azure chief technology officer, believes it's because of open-source software and the cloud.

Yes, Microsoft just announced that the next major edition of Windows 10 will support artificial intelligence (AI) and machine learning (ML). But, marketing hype aside, Microsoft knows darn well that the real heavy lifting for AI and ML happens on the cloud with open-source software. That was the message Mark Russinovich, Microsoft's Azure CTO, brought to The Linux Foundation's Open Source Leadership Summit (OSLS) in Sonoma, CA.

Russinovich opened by saying:

AI technologies and techniques are experiencing a renaissance. Open-source technologies and communities have fostered the growth of self-taught machine learning developers with libraries and frameworks. The computing power of the cloud has made the processing of large data sets cost effective and commonplace. As more research continues to be done and shared throughout the communities we will continue to see more intelligent apps driving even greater adoption of open-source technologies across all processing platforms.

Specifically, he mentioned two examples where Microsoft is using the cloud and open source to help provide solutions with customers. The first is with Rolls-Royce aircraft engines which use ML to track their wear and tear. This data is then used with AI to proactively maintain the engines.

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https://www.scoop.it/t/21st-century-learning-and-teaching/?&tag=AI

https://www.scoop.it/t/21st-century-innovative-technologies-and-developments/?&tag=AI

https://www.scoop.it/t/21st-century-learning-and-teaching/?&tag=machine+learning

Those who understand learning and work with young people know that in an uncertain world of rapid change we urgently need to be helping learners to take risks, to work in teams, to develop a greater understanding of the way systems and societies work, and to become more creative. Fundamentally, these things have been washed out of the system in favour of a ‘learn fact, repeat fact’ model.

To get the best out of this technology in the future, educators need to turn the current systems upside down.

In previous technology developments in education – such as multimedia, whiteboards or the internet – initially, most worried that they would upset the world they were used to and feared for their jobs. Only a few imagined how these technologies could change the world and make things better. In reality, new technologies are typically highjacked to inappropriately maintain the status quo and end up powering the exam sausage factory.

Moving forward into 2017, it is not the tech itself that needs to change. In most aspects of our lives, technology has made significant changes (for good and bad), but in education, particularly schools, there is still stubborn resistance.

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https://gustmees.wordpress.com/2015/04/13/dos-and-donts-adapting-to-21st-century-education/

https://gustmees.wordpress.com/2015/07/19/learning-path-for-professional-21st-century-learning-by-ict-practice/

https://gustmees.wordpress.com/2015/12/27/what-are-the-best-ways-of-teaching-and-learning-ideas-and-reflections/

November 17, 2018

The vast majority of the AI advancements and applications you hear about refer to a category of algorithms known as machine learning. (For more background on AI, check out our first flowchart here.)

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Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm.

Machine learning is the process that powers many of the services we use today—recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on.

In all of these instances, each platform is collecting as much data about you as possible—what genres you like watching, what links you are clicking, which statuses you are reacting to—and using machine learning to make a highly educated guess about what you might want next. Or, in the case of a voice assistant, about which words match best with the funny sounds coming out of your mouth.

Frankly, this process is quite basic: find the pattern, apply the pattern. But it pretty much runs the world. That’s in big part thanks to an invention in 1986, courtesy of Geoffrey Hinton, today known as the father of deep learning.

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Machine learning can drive tangible business value for a wide range of industries — but only if it is actually put to use. Despite the many machine learning discoveries being made by academics, new research papers showing what is possible, and an increasing amount of data available, companies are struggling to deploy machine learning to solve real business problems. In short, the gap for most companies isn’t that machine learning doesn’t work, but that they struggle to actually use it.

How can companies close this execution gap? In a recent project we illustrated the principles of how to do it. We used machine learning to augment the power of seasoned professionals — in this case, project managers — by allowing them to make data-driven business decisions well in advance. And in doing so, we demonstrated that getting value from machine learning is less about cutting-edge models, and more about making deployment easier.

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Big Data is powerful on its own. So is artificial intelligence. What happens when the two are merged?

Big data is moving to a new stage of maturity — one that promises even greater business impact and industry disruption over the course of the coming decade. As big data initiatives mature, organizations are now combining the agility of big data processes with the scale of artificial intelligence (AI) capabilities to accelerate the delivery of business value.

Big Data and AI at MetLife

Pete Johnson is one of the most experienced executives working in the field of big data and AI within industry today. Having worked in the field of artificial intelligence for a generation dating back to his academic career at Yale University, Johnson now leads big data and AI initiatives as a fellow at MetLife. Johnson previously held positions as senior vice president for Strategic Technology with Mellon Bank and served as the executive vice president and chief technology officer of Cognitive Systems Inc. (CSI), an early artificial intelligence company specializing in natural language processing, expert systems, case-based reasoning, and data mining. CSI was founded by several members of the Yale University faculty in 1981, when Johnson completed his MS in computer science.

Johnson, whom I’ve known for over a decade, is a regular participant in a series of executive thought-leadership breakfasts that I host for senior industry executives to share perspectives on topics in big data, AI, and machine learning among their peers. Participants in the most recent executive breakfasts have included chief data officers, chief analytics officers, chief digital officers, chief technology officers, and heads of big data for firms including AIG, American Express, Blackrock, Charles Schwab, CitiGroup, General Electric (GE), MetLife, TD Ameritrade, VISA, and Wells Fargo, among others.

As a long-suffering expert in the field of artificial intelligence, Johnson observes three critical ways in which big data is now empowering AI:

1. Big data technology — We have the ability now to process huge quantities of data that previously required extremely expensive hardware and software, or “commodity parallelism.”
2. Availability of large data sets — ICR, transcription, voice and image files, weather data, and logistics data are now available in ways that were never possible in the past; even old “paper sourced” data is coming online.
3. Machine learning at scale — “Scaled up” algorithms such as recurrent neural networks and deep learning are powering the breakthrough of AI.

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https://www.scoop.it/t/21st-century-learning-and-teaching/?&tag=AI

https://www.scoop.it/t/21st-century-innovative-technologies-and-developments/?&tag=AI

https://www.scoop.it/t/21st-century-learning-and-teaching/?&tag=machine+learning

https://www.scoop.it/t/21st-century-learning-and-teaching/?&tag=Deep+Learning

Video: How to tell the difference between AI, machine learning, and deep learning.

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