Learning to lead change

Non-associative learning[ edit ] Non-associative learning refers to "a relatively permanent change in the strength of response to a single stimulus due to repeated exposure to that stimulus.

Learning to lead change

September 28,5: Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies. Most obviously, the speech-recognition functions on our smartphones work much better than they used to.

When we use a voice command to call our spouses, we reach them now. Chinese search giant Baidu says customers have tripled their use of its speech interfaces in the past 18 months. Machine translation and other forms of language processing have also become far more convincing, with Google googlMicrosoft msftFacebook fband Baidu bidu unveiling new tricks every month.

Google Translate now renders spoken sentences in one language into spoken sentences in another for 32 pairs of languages, while offering text translations for tongues, including Cebuano, Igbo, and Zulu.

Then there are the advances in image recognition. The same four companies all have features that let you search or automatically organize collections of photos with no identifying tags. You can ask to be shown, say, all the ones that have dogs in them, or snow, or even something fairly abstract like hugs.

The companies all have prototypes in the works that generate sentence-long descriptions for the photos in seconds. To gather up dog pictures, the app must identify anything from a Chihuahua to a German shepherd and not be tripped up if the pup is upside down or partially obscured, at the right of the frame or the left, in fog or snow, sun or shade.

At the same time it needs to exclude wolves and cats.

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How is that possible? Click to enlarge the graphic.

The advances in image recognition extend far beyond cool social apps. Better image recognition is crucial to unleashing improvements in robotics, autonomous drones, and, of course, self-driving cars—a development so momentous that we made it a cover story in June.

Ford fTesla tslaUber, Baidu, and Google parent Alphabet are all testing prototypes of self-piloting vehicles on public roads today. The most remarkable thing about neural nets is that no human being has programmed a computer to perform any of the stunts described above. In fact, no human could.

In short, such computers can now teach themselves. The concept dates back to the s, and many of the key algorithmic breakthroughs occurred in the s and s. That dramatic progress has sparked a burst of activity. There were funding rounds for such startups in the second quarter ofcompared with 21 in the equivalent quarter ofthat group says.

Google had two deep-learning projects underway in Today it is pursuing more than 1, according to a spokesperson, in all its major product sectors, including search, Android, Gmail, translation, maps, YouTube, and self-driving cars. Some companies are already integrating deep learning into their own day-to-day processes.

Says Peter Lee, cohead of Microsoft Research: For its part, Google revealed in May that for over a year it had been secretly using its own tailor-made chips, called tensor processing units, or TPUs, to implement applications trained by deep learning.

Tensors are arrays of numbers, like matrices, which are often multiplied against one another in deep-learning computations. Indeed, corporations just may have reached another inflection point.

Within that realm is a smaller category called machine learning, which is the name for a whole toolbox of arcane but important mathematical techniques that enable computers to improve at performing tasks with experience.

Learning to lead change

Finally, within machine learning is the smaller subcategory called deep learning. Is this spam or not? Input usage patterns on a fleet of cars, and the output could advise where to send a car next.

Learning to lead change

The category includes deep learning. DEEP LEARNING The subset of machine learning composed of algorithms that permit software to train itself to perform tasks, like speech and image recognition, by exposing multilayered neural networks to vast amounts of data.

Deep learning, in that vision, could transform almost any industry. Neural nets are good at recognizing patterns—sometimes as good as or better than we are at it. The first sparks of the impending revolution began flickering in InMicrosoft introduced deep-learning technology into its commercial speech-recognition products, according to Lee.

Google followed suit in August But the real turning point came in October English Computerized Learning’s online suite of products now includes speech recognition to instantly tell you which lessons to practice to improve your English pronunciation.

George Couros is a leading educator in the area of innovative leadership, teaching, and learning. He has worked with all levels of school, from K as a teacher and technology facilitator and as a school and district administrator. Pathways to Equity ASCD Conference on Teaching Excellence.

Join your colleagues and experts at this "learn-and-do" conference to experience transformative learning through deep . Learning to Lead Change From all sectors of governmental logistics and education, leaders have recognized that change in societal institutions, both public and private hold the answer to the growth and development of their people and processes.

My Future learning goals are to research and find a doctorate program that will enhance my leadership studies and provide me with the certification to teach and develop my leadership theories involving multi-generational work teams.

Learning to Lead Change In his new book from Wharton Digital Press, Leading Successful Change, Greg Shea notes that “constant change is the norm rather than the exception.

Globalization, increased competition, and constant technological turnover mean that no organization can run in place: change is .

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