Gone are the days of classroom teaching and learning processes for high-grade learning. With the advent of superior technology, we now have machine learning and Artificial Intelligence techniques coming up which has no doubt boosted the e-learning industry. With the state-of-the-art infrastructure and cutting edge technology, it is without a doubt that machine learning would soon take over every platform for education. These processes not only allow any software to behave in a more intelligent manner but also automatically upgrades by itself.
How can e-learning be beneficial from Machine Learning and Artificial Intelligence?
Enabling a better delivery of the content
Designing any course online via a Learning Management Application is not just a one-time thing. The content of the course needs to be revised time and again according to the feedback given by the students who take up the courses. This feedback can be in a sort of any comment, or by a questionnaire or simply by ratings, quizzes and results. Artificial Intelligence enables the utilization of an artificial network of neural connections or even deep-seated algorithms for processing the information to optimize the content which requires a very minimal amount of human intervention.
Machines and artificial intelligence are known to take over our world. Such trends have led us, humans, to adapt ourselves to the ever-changing trends in the environment. But with the rise of AI and loss of departments are you worried whether AI will replace human intelligence? If so, this article will help you understand why it will not do so.
What is Artificial Intelligence?
Artificial intelligence is referred to as any form of a scientific operator which mimics human intelligence and behavior pattern. It involves specialization in tasks like speech recognition, use of algorithms for pattern identification and application of machine learning.
Top 5 Reasons AI will Not Replace Human Intelligence
Here we have listed the top 5 reasons why artificial intelligence will not replace human intelligence in the future:
The Internet might be filled at the brim with a lot of information about training as well as evaluating the recommenders, but too much info could overwhelm you. Are you looking to get a clear understanding of the ways to beat different challenges revolving around a full-scale system? If yes, then start reading:
- Dynamic Prediction
When you are dealing with a large user-base dimensionality, it could be a big deal to pre-compute every recommendation.
Even though cats and dogs are different animals, what is the point of distinction between them? You might term the ability to distinguish between a cat and a dog to be common sense, but it is described as deep learning. People are not programmed to recognize different attributes in an object by inputting external information. These capabilities are inherent and cannot be induced through external stimuli, and thus are unnoticeable for us humans.
Computers, on the other hand, need gradual feeding- in the form of deterministic algorithms- to detect even the most simple judgments. Despite the surge in machine learning and connectivity, a computer cannot do what a toddler does unintentionally. The following are the developments in deep learning:
- Over the past six years, deep learning, which is a branch of artificial intelligence, has made tremendous progress, taking inspiration from the neural networks of the human brain. Facebook’s AI lab has built a system that can answer basic questions, to which it has never been exposed to. Amazon’s smart speaker, Echo uses deep learning as well. And three years back, Microsoft’s chief research officer took everyone by surprise at a lecture in China where the machine used deep learning to translate his English into Mandarin with the output in his own voice and an error rate of only 7%. Continue reading