The mantra for technology evolution has been to replace or minimize human assistance with machines. Traditionally human support systems are rapidly being replaced by machines & by automation. The dependence on human decision-making is shrinking fast. If we take a domestic cooking gas stove as an example, we now have the automated safety gas stove where the gas supply can be cut off completely by itself in case of gas leakage or mishandling, thus avoiding fatal accidents and domestic fires. All this has been possible with the advent of automation in technology that has completely transformed our way of daily life.
Now coming to the technical field of working IT professionals and L&D leaders, new technologies are evolving at a rapid speed and it is imperative for them to keep up with it. In this article, I am going to focus on Machine Learning and Artificial Intelligence in particular which are two of the most important technologies in the technology market.
What are Machine Learning & AI?
First, let’s look at what these fancy technology terms actually mean. According to Arthur Samuel, a machine learning pioneer, Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed. AI, on the other hand, is not just one technology, it is a broad field constituting many disciplines from robotics to machine learning. The ultimate goal of AI, most of us affirm, is to build machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence. In order to get there, machines must be able to learn these capabilities automatically instead of having each of them explicitly programmed end-to-end.
5 Trends in AI and Machine Learning to Watch Closely
Building Smaller Models by Utilizing Less Data- Machine Learning traditionally involves studying and analyzing enormous amounts of data to perform a suitable task. Without large amounts of data, these models wouldn’t perform particularly well for complex models and tasks. But with the help of multiple collaborative neural networks it is possible to transfer information and thus the learning models needn’t contain extensive amounts of information in the individual networks. This would help in more efficient distributed training because data needs to be communicated between servers, less bandwidth to export a new model from the cloud to an edge device, and improved feasibility in deploying to hardware with limited memory.
© 2021 TechTarget, Inc.
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central