Technology

Do Machine Learning and AI Go Hand-in-Hand in Digital Transformation?

0

Artificial Intelligence and Machine Learning have marked their presence in every sphere and industry. Both the technologies play significant role in turning data into assets as part of getting digital transformation. The only thing that matters is that the company must be able to attain deeper knowledge of their place in the process to leverage the technologies for business and process transformations effectively.

Today, AI and ML based custom model  are the terms used as norm but no one really understands these technologies fully. People don’t know that machine learning is a part of AI. There are numerous definitions of machine learning and AI. In simple terms, AI technology focuses on creating intelligent systems without defining rules that determine behavior. Machine learning technology uses data in evolving environmental conditions to predict and optimize results.

There is no denial that both the technologies play different roles, but we cannot ignore the fact that both of these technologies are the key ingredients of strategy development process in several sectors, such as-

  • Life sciences
  • Healthcare
  • IT management
  • Data analysis
  • Cyber security
  • Digital transformation
  • Consumer applications
  • Hybrid smart building technologies and more

Artificial Intelligence – AI means intelligence displayed by machines that copy human intelligence. It is a vast term used for smart machines that can replace manual force with the requirement of cognitive, judgment-based decision making.

 In practice

AI is live and people might not be aware that they are already using AI in several ways, such as –

  • Voice assistants
  • Gmail
  • Weather apps

Types of AI

There are generally four types of AI we shall discuss in this post now-

  • Reactive machines

The widely used basic type of AI system is entirely reactive and has no memory formation feature. For instance, Chess-playing supercomputer by IBM can detect pieces on a chess board to predict next move but has no memory and cannot take help of past experience for future decision making.

  • Limited memory

AI systems with limited memory can take help of past experience for decision making. Many of such functions in autonomous vehicles have been intended this way. Observations are helpful for informing an action happening in the reaching time, for instance, a car has changed lanes. Such observations are temporarily stored.

  • Theory of mind

This is simply means to understand that others have their own desires, beliefs, and intentions that help and impact decision making process. This is a significant differentiating factor between the latest machines and future machine modals.

  • Self awareness

Self awareness type AI systems are intended with a sense of self and consciousness. Such systems are able to understand their present state and can leverage the data to detect the feelings of others.

AI and ML often work together. Let’s learn how these both technologies are related-

Machine Learning is simply an app of AI that empowers systems to automatically learn and grow from experience without being exactly programmed. ML emphasizes on computer program development that can access data and use it for personal growth.

Techniques used by ML

Machine learning uses numerous theories and techniques from data science-

  • Classification
  • Clustering
  • Categorization
  • Anomaly detection
  • Trend analysis
  • Visualization
  • Decision making

Methods of ML

There are three types of methods used in machine learning –

  1. Supervised learning

Supervised learning is a type of ML which is applied for learning models from labeled training data. This method lets experts to predict the future result or unseen data. For instance, voice assistance like Amazon Alexa, Google assistant, Apple Siri are intended and trained to understand what human speak and mean. These chatbots act on the basis of human interactions.

  1. Unsupervised learning

This method of unsupervised learning is a subset of ML and is used to extract inferences from datasets that include input data with no labeled responses. This method is based on clustering. For instance, a news website divides its articles into several categories like technology, business, lifestyle, etc.

  1. Reinforcement learning

This ML method allows the learning system to observe the environment and read the common behavior. For instance, manufacturing units have robots to detect a device from one box and pack it inside a container. The robot learns this using a reward-based learning system that give rewards to it when right action is performed.

By using machine data with existing enterprise data, you can bring a new gen app that is able to analyze and achieve insights from bulk multi-structured machine data.

Conclusion

While AI is specifically focused on learning to provide human-like intelligence, machine learning emphasizes on meeting and enhancing a task through data interpretation with or without intervention of human. What’s more important for a company is to focus on the fact that digital transformation is about embracing a holistic approach to evolve the results and make business grow.

Digital transformation is about rebuilding and re-engineering the business in dynamics 365 operations and cloud environment, mobile, analytics, IoT, and ML and AI technologies are there to enhance processes, decision making, and customer experience. In the end, we can say that machine learning takes over the AI when it is about digital transformation since it can automate heavy tasks and limit the time consumption.

Raj Hirvate
Hi, I'm Raj Hirvate and I am a Tech Blogger from India. I like to post about technology and product reviews to the readers of my blog. Apart from blogging i'm a big Anime fan I Love Watching Naruto, One piece and Death Note.

How to Remove Redundant Files in Mac?

Previous article

Vaping or smoking cannabis why vaporization is better than smoking

Next article

You may also like

More in Technology