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.
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
Whether you are a technology enthusiast or not, you must have heard of the term IoT (Internet of Things)? It is a trend that has caught up to our lives and soon we are going to witness a significant change in using smart devices as they will all be smartly connected.
The prime fuel to the IoT ecosystem is the data from the devices that are being managed by edge analytics. Edge Analytics goes beyond just collecting data from the source of its production and is more about the data being processed so that IoT can use it more effectively for better operations. This collected data is then processed, making IoT independent of internet access at all times.
Hence, effectively, with edge analytics in place, IoT devices can use data every time.
Frankly, Big Data isn’t anything without the involvement of professional skills that help turn cutting-edge technologies into actionable insights. While media, education, healthcare, securities, all have taken the advantage of big data, financial industry too has started opening its doors to it. By unlocking the power of Big Data, they have also raised the worth of a data scientist that knows how to push the worth of a high amount of data which currently exists within an organization.
There’s no doubt in the fact that the modern business arena is bombarded with data. As per a report in the year gone by, McKinsey estimated that “big data initiatives in the healthcare industry of the US could account for as much as $450 billion in decreased health-care expenditure or up to 17 percent of the $2.6 trillion touchstones of the cost of US health-care”. It is important to note here that bad data costs the US approximately $3.1 trillion annually.
Therefore, the value of analyzing and processing of data is highly evident. And this is exactly where the spotlight comes on a data scientist. While most professionals are aware of how hot big data science is, they are still unknown to the value that a data scientist holds within a company.