Learning Platform

Learning Platform

Deep Learning Platform

One of the most disruptive technologies that is emerging today is called Deep Learning. It is an Artificial Intelligence technology. Unfortunately, most companies are blind to the existence of Deep Learning. Even the companies that do have an awareness, there is very little understanding as to how to take advantage of this technology.

Businesses need a guide, a playbook that gives details about the methodology and strategy to move forward.

The most effective way to think about Deep Learning adoption is to see how and where it can enhance a platform strategy. This is because we want to leverage the networking effects as best described by this picture:

That is, more users lead to more data. This leads to smarter Deep Learning algorithms and therefore better products. The cycle then feeds into itself. In a world of constant disruption, networking effects are essential for any defensible business.

However, it takes more than understanding why this is important. It requires an understanding of platforms that enable it as well as the kinds of Deep Learning algorithms that enhance these platforms.

A most intriguing platform is the Learning platform.

What if each computer would acquire more features and functions when it is connected with more computers? What if its features multiplied at a faster rate as more computers joined the network? This is the second level of network effect .

People become more effective and capable as a participant in the Industry 4.0 learning network, as they further improve.

Deep Learning vs Learning Platform

So how does Deep Learning enhance a Learning platform? Deep Learning technology can be employed to augment many tasks. One task is to speed up digesting of information by a worker. In today’s information-rich environments, we are constantly inundated by more and more information.

Deep Learning technology can help parse, digest, curate and present that information such that we can focus on the most useful, value-added activity. This can be further improved by tightening the feedback loop through the augmentation of agile processes.

One concrete example of this is in the context of the mining industry. One of the big problems with mining is that the sequence of equipment are daisy chained like Christmas lights. If in the event of failure of one piece, the entire production grinds to a very expensive halt.

We can certainly place Deep Learning monitoring devices on the equipment to be able to predict future failure, however, to do so, requires data of different kinds of failures across different kinds of devices.

This problem of lack of data can be addressed by having a learning platform where multiple mining companies come together to share their data from the field.

That is why SME's that are NOT sharing their data and are NOT sharing their learning experiences will probably perish.

Intelligence is expressed in language. This encompasses languages that humans use today, and via complex mathematical languages all the way to machine designed languages. Deep Learning is all about using machine assisted language creation.

Machine learning will accelerate learning in the education space, as online bots will pick up on a student’s strengths and weaknesses and use a series of algorithms to tailor the lessons accordingly.

Research suggests that this personalized method is one of the most effective ways to raise a kids’ overall achievement.

MBA courses are very linear, rational studies, and create as a consequence good product managers.

Transformation practices are not a rational, but a political process, that has 3 goals:

  1. To identify and strengthen the champions of change, wherever possible,
  2. To identify and neutralize the enemies of change, difficult because they do not self-identify, and often agree to change with a smile on their face, then go to their office and plot how to undermine the process,
  3. The art of war: if you have to engage the enemy in battle, you already have lost the war. Key is to postpone the confrontation between 1) and 2) as long as possible, until you have neutralized as many of the 2’s, and strengthened as many of the 1’s as possible. Not all platforms are created equal. Some have, some doo not have that potential to do so.

There are ways to drive change through scaling edges, which have a better change to avoid the resistance and achieve the change that is required.

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