Today, consumers are buying mobile phones and laptops powered by Artificial Intelligence (A.I.) that recognize faces and iris of a person in the frame. But, they don’t trust the AI technology of facial recognition because it may not work 100% of the time. The expectations from Machine Learning Big Data analytics are growing with every passing day! Why?
Simply putting, consumers are expecting their machines to learn fast and very well from their commands.
Most machine learning algorithms can be categorized into three categories. These are based on the level of human-level supervision. The categories are:
- Unsupervised learning (fully machine driven)
- Semi-supervised learning
- Supervised machine learning (the most common type you see today in the modern gadgets)
What machines really learn?
Machines driving with AI use Supervised learning that recognizes objects, images, faces, fingerprints, iris, voice and text by signs and labels. Machines compare through millions of data already fed into the system via machine learning algorithms. The data analytics processed by machines occurs at a pace of lightning – millions of photos and texts are clipped and supervised for direct information based on the query put in by the consumer.
In short, machines learn what they are fed.
But, can machines learn without labeled data?
That’s where human skills come into the picture. Machine learning big data analytics teams are working on something called ‘Cognitive Learning’.
The real difference between machine learning algorithms of 2019 and the ones that are going to come in the future would be about ‘how AI turns machines into Cognitive performance gadgets?”
According to a research by the National University of Singapore, AI actually has the potential to enhance machine learning. Soon, it could open up new avenues for promising Machine learning big data analytics applications in personalized cognitive learning in Marketing, Sales, Customer Services, FinTech, Blockchain, and Robotics.
Making Complex Decisions?
What are complex decisions that human supervisors don’t yet trust machines for?
These are mostly related to medicine, radiology, Fintech, Stock trading, etc. Machine learning engineers are partnering with Neuroscientists to build strong AI projects. Big data applications are organizing computational neuroscience with machine learning to model brain circuit on cognitive learning templates.
Innovative projects bringing Neuroscience into Deep Learning has taken a flight upward to success already at Google.
Why we need Unsupervised Learning?
First, Big data teams currently focus on building structured architectures for controlling systems for attention, recursion and various forms of short- and long-term memory storage. This is a costly, error-prone operation.
Second, to manage supervised machine learning functions, companies have to build training procedures. With unsupervised machine learning, this cost would be removed.
Soon, unsupervised learning algorithms would help to build a better Big Data analytics team and powerful data-driven organizations.