Artificial Intelligence

Artificial Intelligence

As industries scale, automation has become a big chunk of efficient manufacturing and service. Artificial Intelligence (AI) can be looked at as an efficient automation simulated as human intelligence. It enables machines to sense, comprehend, learn and act in a situation and teach themselves according to the outcome.

AI Technology

Broadly speaking, all AI technologies fall under the two umbrella definitions:

Weak AI

Carries out simple and result oriented tasks. Examples include personal assistants like Siri, IBM’s Deep Blue, industrial robots etc.

Strong AI

Is able to simulate human intelligence more closely by engaging in fuzzy logic to apply cross-domain expertise and finding a solution without human intervention.

Machine Learning in AI

Applying various domain principles from Deep Learning to parallel data processing, an AI is able to fine-tune its algorithm based on case outcomes to better suit human feedback. Machine Learning and big data algorithms allow AI software to sift through terabytes of data to appropriate a proper response. Automation and adaptive learning are core AI principles powered by their roots in machine learning.

Applications of AI Technology

The ability of an AI system to sift through massive loads of data provides it with wide applications in nearly all fields of industry:

Ranging from chatbots that take in basic medical feedback to complex algorithms that ascertain cancer in patients, or even identifying fake drugs, AI has brought upon an innovative revolution to traditional healthcare.
Personal AI tutors that adapt to the students’ pace, automated attendance, and grading systems are all novel ideas that can help boost efficiency in a modern classroom.
Sorting through big data generated by a business, repetitive tasks like data entry, basic customer care (through chatbots) are all tasks automated with advancements in AI Technology saving time and resources for driving growth forward.
Image and voice recognition is another field in AI infrastructure. AI also motivates in scanning and classifying terabytes of voice/facial recognition data for easier categorizing.

However, as the data set for Machine Learning is chosen by a human, some bias inadvertently slips in, especially when training an AI for deep learning or a Generative Adversarial Networks (GAN).

Supra in AI

A strong base in the parent technologies of Deep Tech coupled with experienced professionals coalesce seamlessly in SupraES to provide efficient models on AI platforms. Development and deployment of AI incorporating deep learning, cloud AI, and machine learning enables us to craft perfect solution to suit business needs.