Community Blog Redefining Tech with Machine Learning and AI – Part 1

Redefining Tech with Machine Learning and AI – Part 1

Part 1 of this 2-part article discusses how the PAI Platform can provide rich data-centric solutions to help with your digital transformation journey.

By Shantanu Kaushik

How good could your life be if you did not have the ability to learn? Learning is the basis of enlightenment; it is the stimulus for our brains to comprehend daily tasks and worldly affairs. Learning provides us with the abilities to perform tasks and efficiency. Then, deliberations come later, but even that is facilitated with more learning.

I can compare the human brain to a machine. Why? Human brains can process information, store data, and provide output just like a machine. However, there is one key difference machines do not have, impulses. Human impulses define us and help us make decisions. These decisions are based on experiences we have.

Why not extend this ability to machines? After all, machines can work on more optimal settings than we do, right? Before you ask, no, I am certainly not talking about AI-powered machines that lead to human eradication and the apocalypse. I am talking about machines that can produce more productive results for enterprises and organizations to extract higher value from any solution and reach a perfect sync cycle between integration, delivery, and future strategy.

Alibaba Cloud has been the frontrunner with shifting into cloud-native technologies and data analytics powered by artificial intelligence and machine learning.

This article discusses how the Machine Learning Platform for AI can provide rich data-centric solutions to help with your digital transformation journey and business expansion scenarios.

Machine Learning and AI

The Simple Statement

Machine learning is an application of artificial intelligence (AI). Machine learning intends to provide the system with the ability to automatically learn and improve from experiences without any external influence. Machine learning focuses on the development of applications and solutions that can assess data and apply it to learn independently.

The Tech Statement

Machine learning is a subset of artificial intelligence (AI). It is one of the methods that focus on computational science to perform analysis of a presented dataset. Machine learning focuses on the interpretations and recognition of patterns using historical data to assess the presented data to learn, process, and make decisions without any human intervention.

The Process

Machine learning follows the lead of algorithms to process an immense amount of data. This data processing helps the system analyze patterns and task hierarchy and enables the understanding of the solution. Machine learning facilitates a completely data-centric understanding and decision-making process that is so self-sustainable that it can work with configurational changes to the learning behavior of a computer system. Before you ask, no, this is not where machines take over the planet. Instead, this is where we allow machines to make changes to their learning algorithms and become self-sustainable to achieve a proper automation cycle with solutions.

How Does Machine Learning and AI Function?

The first step is always the observation of the provided data. The data is observed based on a pre-set configuration of the system that works as the basic experience index to kickstart the process. The whole machine learning experience is as efficient as this basic configurational algorithm that works like a foundation for solution building.

In order to look for patterns in data and facilitate decision-making in the future, we need to provide a strong use-case pattern to the system, so it can build on it and use them as examples for future task management. The only aim here is to make a system so efficient that it can be plugged into scenarios to make them less complex and more fruitful. This will let us induce better business intelligence and work with a better solutions lifecycle.

Learning Methods

Sometimes, when we read an article or any piece of content, we may be able to read it word-by-word but may not comprehend the intended meaning of the entire piece. Why is that so? This normally happens when we don't fully grasp the underlying meaning or logic of the content, better known as semantics. Similarly, for a device without machine learning and artificial intelligence capabilities, written content is just a string of text without emotion.

Here, artificial intelligence and its ability for semantic analysis mimic the human ability to understand the meaning of the text to induce a better learning curve for the machines. The accuracy of understanding that facilitates learning is equally important between machines and kids at the beginning of their learning journey.

Let us discuss some of the methods that machine learning can work with:

Un-Channeled or Semi-Channeled Machine Learning | (Unsupervised – Semi-Supervised)

Un-channeled machine learning algorithms are typically used when learning is being facilitated for information that is not classified or protected. Un-channeled learning studies how systems gain the ability to detect a hidden structure with unlabeled data. Here, the system is not concerned about the output, but it explores the data and can draw possible outputs from the provided data to describe the unlabeled data and structures.

Semi-Channeled machine learning algorithms use both labeled and unlabeled data for training. This approach is more or less used when the system has tons of unstructured data to sift through, and the system is provided with a starter kit with structured data as an example.

Channeled Machine Learning | (Supervised)

Different algorithms can facilitate a different direction of learning for the machine. Artificial intelligence suggests and allows for behavioral learning to help predict patterns that lead to thousands of combinations that can be utilized by the system to make decisions. Analysis of well-defined training data starts the learning phase, and the learning algorithm gains the capability to predict the output.

After a certain amount of training, the system starts to get self-sufficient. With a channeled learning approach, the defects or mistakes in the learning curve can be easily managed, and proper corrections can be facilitated to work towards a perfect system. As the system encounters any errors, it will try to fix them based on the learning behavior and subset provided by the developer and will correct those errors to modify the model accordingly.

Strict Computational Models of Machine Learning | (Reinforcement Model)

With these models, different sets of behaviors and patterns are identified by machine learning algorithms. The system requires some form of justification to approve these behaviors. Here, agent interaction comes in and provides the approval for the system to justify its decisions.

You can compare this model to an example. You reach out to your teacher or parents to ask for help making a decision. Based on the advice, you make all similar future decisions using the previous advice. We call this reinforcement of actions and foundational justification.

Here, machine learning works with the trial-and-error methodology. This method allows machines to automatically determine the ideal behavior with a pre-set and specific context to maximize performance and learning. These learning models include autonomous driving and auto-pilot scenarios.

Wrapping Up

Machine learning has unlimited growth potential within enterprises. Alibaba Cloud uses machine learning and AI to provide solutions that maintain the observability curve closely to extract value from any solution they provide. Alibaba Cloud’s AI-empowered data analytics, network management, content delivery, and security suite of products implement machine learning and AI for better solution cycles and strategy building.

Part 2 of this 2-part article introduces the Alibaba Cloud Machine Learning Platform for AI.

Upcoming Articles

  1. Redefining Tech with ML and AI – Part 2
  2. Seeding Digital Transformation in Southeast Asia – Part 1: What Is Digital Transformation?
  3. Seeding Digital Transformation in Southeast Asia – Part 2: Challenges and Focus Areas
  4. Seeding Digital Transformation in Southeast Asia – Part 3: An Introduction to Buttercup Spices
  5. Seeding Digital Transformation in Southeast Asia – Part 4: The Strategy
  6. Seeding Digital Transformation in Southeast Asia – Part 5: Application Migration
  7. Seeding Digital Transformation in Southeast Asia – Part 6: Resource Planning and Networking
  8. Seeding Digital Transformation in Southeast Asia – Part 7: Building a Smarter Supply Chain
  9. Seeding Digital Transformation in Southeast Asia – Part 8: Introducing AI for Better Workflow
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