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Erick Prospero is an expert on video-based learning and the CEO of Ninja Tropic eLearning - the largest eLearning animation studio focused on video based learning and animation with over 60 inhouse learning designers. He has led production and development for online training material for some of the largest brands in corporate training - Producing 1000’s of videos that have been seen by millions.

Erick Prospero

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Training Video Script Examples for Corporate E-Learning

 _“Some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower.”—_Alan Kay Raymond Kurzweil, the director of engineering at Google and a renowned futurist predicted in The Singularity Is Near: When Humans Transcend Biology that by 2029 computers will be cleverer than humans. While such a mindboggling prediction can be a matter of debate, there is little doubt that in the future machines will be much more sophisticated than what they are today. Indeed, these smart machines will be able to learn on their own—without human intervention—using technology!   Erik Brynjolfsson and Andrew McAfee, co-authors of The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies say, “Technologies that used to seem like science fiction are becoming everyday reality.” Machines that can use such technologies to learn and autonomously adapt to the changes will cause serious disruption to well-established business models. Likewise, Gartner predicts that “by 2017, a significant and disruptive digital business will be launched that was conceived by a computer algorithm.” All of these predictions are based on one common observation: Machines can learn exceptionally fast, and it is possible to build machines that can learn without any active human supervision. In fact, several of the top 10 emerging technology trends of 2015, as predicted in a recent report by World Economic Forum (WEF), are technologies for learnable machines. Neuromorphic Technology With advances in machine learning and rapid processing of high volumes of data, the next generation of powerful computing will be built on chips that will process information by mirroring the brain's architecture. As explained in the WEF report, such computers will be energy efficient and will have much more computing power than a conventional CPU (central processing unit). Moreover, these computers will be able to “anticipate and learn”—instead of just responding in a pre-programmed fashion. IBM has already demonstrated this technology in a prototype form through their million-neuron TrueNorth chip in August 2014. Gartner has predicted that by 2017, 10 percent of computers will be learning rather than processing. The report also mentions, that, “The Defense Advanced Research Project Agency (DARPA) and Ecole Polytechnique Federal de Lausanne are funding the SyNAPSE and The Human Brain projects, respectively, fostering neuromorphic computing techniques intended for pattern recognition applications, including facial recognition, object recognition, drug discovery and medical diagnostics.” Emergent Artificial Intelligence We are already seeing significant advancements in artificial intelligence through self-driving cars and automated flying drones. Emergent artificial intelligence takes this step further with machines that can learn automatically by using large volumes of data. Carnegie Mellon University's NELL (the Never-Ending Language Learning) computer system "not only reads facts by crawling through hundreds of millions of web pages, but attempts to improve its reading and understanding competence in the process in order to perform better in the future." Machines with emergent artificial intelligence are not only capable of processing huge amount of information in a short time frame and arrive at an optimal solution, they also can avoid human errors and emotional biases. However, many experts are raising early warnings for potential pitfalls associated with such super-intelligent machines, which will not only replace the jobs of many human workers, but also may eventually “overcome and enslave humans.”    Programmable Material In case of 3D printing, computer readable designs are fed to a machine that can process the design and produce a finished object using additive manufacturing technology. Medical applications of 3D printing have reached such an advanced stage that even a living tissue or a cell can be printed in this process. Other fields of application of 3D printing include automotive, aerospace, electronics as well as housing and construction. Researchers at MIT have pioneered a technology in which the finished objects will be made from “smart materials” that can be pre-programmed. This new technology is called 4D printing, because it incorporates the additional dimension of “time.”   “While 3D printing is purposed around building static items, 4D printing employs dynamic materials that evolve or adapt to their external environment in real-time and in direct response to changing conditions. This ability to transform is embedded within the material itself. Objects created through 4D printing are assemble and alter their structure when the material of which they are composed comes in contact with different conditions, such as moisture or humidity. The ultimate goal is to create stimuli-responsive components, materials that modify their own form or self-assemble new patterns automatically and in predictive ways,” explains a September 2014 Georgetown Journal of International Affairs article. Advanced Robotics Next-generation robots will learn to collaborate with humans to become their co-workers. They will no longer be used only in factory assembly lines and separated from humans by safety cages. The WEF report talks about an example: “In Japan, robots are being trialed in nursing roles: they help patients out of bed and support stroke victims in regaining control of their limbs.” Boston Dynamics, a wholly owned subsidiary of Google, builds “advanced robots with remarkable behavior: mobility, agility, dexterity and speed.” These robots can walk, run, and climb in a variety of terrains and surfaces by learning to deal with environmental variations. An interesting Tech  Times article discusses how researchers at University of Maryland are looking at whether robots can learn a skill, like cooking, by watching YouTube videos.  Apart from the five technologies mentioned above, technologies that are going to impact our lives in a profound manner are Big Data, Analytics and Internet of Things (IoT). Among these, analytics will be a de-facto technology for a learning machines. Embedded Analytics According to Gartner’s “Top 10 Strategic Technology Trends for 2015,” analytics will be embedded in every app so that it will continue to learn and develop insights on a continuous basis. Such technology-based learning will lead to “advanced, pervasive, and invisible analytics,” through which the apps will be able to intelligently monitor events, gather data, and learn about the context to take better decisions. By continuously analyzing data and by developing insights, these apps will create deep understanding of the business domains. Chris Argyris, in his seminal HBR article on organizational learning, “Teaching Smart People How to Learn,” coined the terms single loop and double loop learning. Argyris explained single loop learning with an analogy of a thermostat that automatically turns on the moment the temperature of the room drops below 68 degrees. “A thermostat that could ask, ‘Why am I set at 68 degrees?’ and then explore whether or not some other temperature might more economically achieve the goal of heating the room would be engaging in double-loop learning.” The article raised the issue of how highly skilled professionals can be very good at single loop learning but miserably fail at double loop learning. With the advancement of technology, we can now have thermostats that are intelligent enough to use double loop learning. Nest Labs, a Google company, specializes in making sensor driven self-learning thermostats and smoke detectors.   Bottom line: Machines will no longer remain dependent on their masters for learning. More importantly, like humans, learning machines will be able to use technology to enhance their skills. B. F. Skinner once said, “The real problem is not whether machines think but whether men do.” The time has come to seriously ponder over the implications of this remark.

Learning Technologies for Machines

All speakers should work to narrow the gap between themselves and the audience.

Code Switching: It's Not Always Proper to Speak Properly

With modern technology, accurately measuring the impact of virtual training programs is a very real possibility. When used properly, this kind of measurement catalyzes change in industries like healthcare, financial services, consulting, manufacturing, and others that rely heavily on effective training programs. But the ability to measure doesn’t guarantee success. You also need to know what metrics to track, how to avoid bias, and how to get buy-in from stakeholders and instructors.Let’s examine how best to use data and automation to save instructors and administrators time and improve the learner experience.3 Top Challenges to Effective AnalysisWhile there are several things to consider when implementing the most effective measurement program for virtual training, let’s take a closer look at three top challenges that we recommend tackling first, as well as our tips for clearing these hurdles with ease.1. Difficulty quantifying engagement. Look for a virtual training measurement platform that collects and analyzes the data for you, assessing factors like tardiness, the number of minutes instructors speak versus learners, and the number of learners raising their hands and using customized reactions, to name a few. Once you start measuring, set benchmarks based on your historical data, setting ambitious yet realistic goals for improvement. 2. Unintentional bias using qualitative measurement.Those responsible for measuring the effectiveness of training programs shouldn’t completely ignore qualitative methods of judging engagement, but must also introduce data to reinforce the findings of qualitative measurement to ensure accuracy.3. Lack of instructor focus on measuring engagement. Put plainly, the onus should not be solely on instructors here. The introduction of technology and data can be a welcome solution. Teachers can focus on teaching while reliable tech measures the results in the background, helping tell a complete story.Use Results to Improve InstructionTo improve instruction, it’s critical to analyze various metrics like total class time, instructor speaking time, participant speaking time—including the total number of times participants spoke as well as the average speaking time from all participants—and the total number of times participants raised their hands or gave feedback through the platform. Technology solutions can help compile all these metrics into one easy-to-digest dashboard and allow you to view results for an entire session, as well as drill down into individual participant results. Use the insights from your results to inform the changes you make to your training programs, and continue to iterate as more results come in.Technology can also automate time-consuming tasks like attendance recording, time in, time out, and tardiness, which both increases the accuracy of this data and frees instructors up to focus on quality education, improving the learning experience overall.Demonstrating Program EffectivenessIf you’re trying to effect change in your training programs by determining what’s working and not working, having quantifiable results and trends can go a long way in showing stakeholders the impact. Remember to utilize automation in your reporting technology, which can help expand reporting efforts and demonstrate effectiveness at a larger scale. When you’re communicating the value of your reporting to stakeholders, it also helps to have a framework for your metrics that’s backed by research and proven success. The Kirkpatrick Model for evaluating learner engagement lends itself perfectly here. This model distills training evaluation down to four distinct levels: The reaction of learners, including their thoughts on the training experienceThe learners’ increase in knowledge after the training experienceThe learners’ behavioral change and performance after applying the skills learnedThe results the learners’ performance has on the business or organizationMake sure whatever metrics you’re tracking to measure effectiveness follow a similar model to the Kirkpatrick Model. The key is persistent tracking to evaluate success at several points in time, allowing you to present a complete picture with your results.Access to accurate data and smart automation is the backbone of a successful training program. A powerful virtual classroom platform that’s purpose-built for learning and development, like Class, ensures that you can always keep your finger on the pulse of how your programs are performing, allowing you to focus on innovating and providing the best possible education for your learners.

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