Today, when we talk about technology, we use the terms Artificial Intelligence and Machine Learning synonymously. But, this is a misconception that is observed among most people. Although they are correlated, they couldn’t be more different from each other.
Big industries rely on AI and Machine Learning to drive more value for their business. In fact, 82% of businesses rely on Machine Learning for risk management. They help in data analysis and predict possible outcomes of certain actions. Based on these insights, the industries formulate the steps to achieve their goals. In this blog, we will take a look at how Artificial intelligence and Machine Learning operate and their practical applications.
An analogy of Artificial Intelligence and Machine Learning
In this section, we will demystify the AI and Machine Learning. However, let us first have a look at how it came into existence.
How it All Started?
Machine Learning started off as codification of the data analysis process. The rules of the domain expertise were codified to solve simple problems. However, there were certain limitations to it. Thus, modern GPU architecture was introduced, followed by the Graphic processor Units.
When this evolved, along with a transition to cloud computing, it led to the inception of AI. Later, Big Data tools like Spark, Hadoop, Map Reduce, etc. ensured that the businesses were able to handle exabytes of data each minute.
Here we will get a detailed insight into the working of the two technologies.
What is Artificial Intelligence(AI)?
AI does not require any preprogramming. It relies on algorithms that can work on their own intelligence. Furthermore, it is dependent on the Machine Learning algorithms and modules such as artificial neural networks or Reinforcement learning algorithms.
It is estimated that by 2025, the AI market will grow to a $190 billion industry. In fact, 54% of businesses have stated that the incorporation of AI in businesses has led to the enhancement of productivity.
Components of AI
AI finds application in Siri, Google’s Alphogo, etc. If you are wondering, how it operates, we need to delve deep into the various components. The main components involve:
- Machine Learning – It enables the computer systems to learn automatically, without being programmed explicitly.
- Deep Learning – It is a subset of Machine Learning. It processes data by implementing artificial neural networks.
- Neural Network – Neural networks mimic the human biological neural network, and it learns through iteration (by processing training examples).
- Cognitive Computing – It is aimed at improving the interaction between humans and machines. And, it seeks to replicate the human thought process in a computer model through simulations.
- Neural Language Processing (NLP) – It allows computers to recognize and recreate human speech and language. It is aimed at creating a seamless interaction with the machines. For instance, Cortana, Siri, etc.
- Computer Vision – It is a technique that identifies patterns and implements deep learning to scrutinize the content of an image. It searches for histograms, resolutions, etc.
- GPUs – It provides computer systems with massive computing power to process and retrieves millions of data and calculations quickly.
- Internet of Things – It refers to the cumulative network of devices (laptops, smartphones, supercomputers) that are connected via the Internet.
- Intelligent Data Processing – It is employed for swift multi-level analysis of data. Researchers are currently working on it to predict rare events and comprehend unique situations.
As you can see, AI is a broad spectrum. When all the above mentioned components work in conjunction, it is known as AI. Thus, it is a set of algorithms that will evolve with time and become better with age. At present, we are only in the developmental phase (grass root level). It is expected that 75% of the commercial enterprises will use AI by 2021.
What is Machine Learning(ML)?
From the above discussion, we got to see that Machine Learning is a small aspect of AI. It is an algorithm that learns on its own, taking the help of the historical data at its disposal. The main objective is to acquire the most precise output based on the data sets that are presented to it.
If you are a student of mathematics, you will be able to understand it clearly. Machine Learning can be denoted as learning a target function (f) that maps input variables A to output variables B
And if you consider the error associated with it (which you must, if aim for higher accuracy), the expression would look like:
B= f(A) + e
Machine Learning (ML) has often been described as “AI applied to Big Data Analytics”. And, it couldn’t be truer than that. The ML algorithms spot the patterns in the data sets to predict the valuable insights.
Subdomains of Machine Learning
Machine Learning is, in turn, comprised of the Neural Networks and Deep Learning. Here, multiple layers of nodes are used with different weights, which results in a Deep Neural Network (DNN). And, the input and output nodes are separated by multiple layers. Machine Learning makes use of the Perceptrons and Deep Learning Tensorflow to:
- Implement the logic gates
- Explain artificial neurons
- Discuss the Sigmoid activation function and units
Basically, Perceptron is a single layer neural network, which supervises the learning of the binary classifiers. And, here comes the other aspects that are associated with ML, which are
- Supervised Learning – It makes use of the data labels to structure the data. Here, the ML model involved is the Bayes algorithm, SVM, linear and logistic regression training and random decision forests.
- Unsupervised Learning – It makes use of the unlabelled data, and the ML algorithms train itself as per its own pace, maximizing the rewards depending on the feedback.
Hopefully, now you have an insight into the AI and Machine Learning, and how it works. As you can observe, AI is a huge topic, where Machine Learning plays a small yet important part. Now, you might wonder, “Is Machine Learning the direction AI wants to focus on?” Well, that is hard to say, as interesting prospects crop up each day in Computer Vision, NLP, etc. But, we do know for certain is that AI, as a whole, has the potential to revolutionize the business industry.