The simplest way to discuss about AI is by considering the perspective of humans. We know that humans are the most intellectual creatures in this world. So, it is better to compare Artificial Intelligence with Human Intelligence to get a clear vision of AI.
Human vs. AI (in Communication)
AI, a wide branch of Computer Science, is used to create intelligent machines that can recognize human speech, detect objects, solve problems and learn like humans. Humans can write and read text-data in any language. In AI, this is done by the field called Natural Language Processing (NLP), which deals with the application of computational techniques to analyze and synthesize natural language’s text and speech. ‘Google Assistant’ is the very good example for NLP domain. Humans can speak and listen to communicate with others using the natural language. In NLP, this is taken care by the Speech Recognition domain. Most of the NLP techniques follow statistical approaches to learn and understand the natural language and hence it is called Statistical Learning.
Human vision vs. Computer vision
Humans can see through their eyes and process the information with the help of their brain. In AI, this is aided by the field called Computer Vision. Computer Vision deals with how computers can gain a high-level understanding of digital images or videos. Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a set of images. Humans can recognize the scenario around them through their eyes which are captured as images. In AI, this is taken care of by the field called Image processing.
Human vs. Machine
Humans can recognize their surroundings and move around the environment easily. In AI, this field is called Robotics that deals with the study of creating intelligent and efficient robots. Robots are the artificial agents which perform various tasks in the same way as humans do.
Humans have the capability to observe patterns such as the combination of similar objects. In AI, this is done by the field called Pattern Recognition. Pattern recognition can be defined as the classification of data based on the previously gained knowledge. Machines are better than humans in Pattern Recognition because they use more dimensions of data. This is the field of Machine Learning that provides the ability to the computers/machines to automatically learn and improve from their previous experiences.
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AI Research areas
Human brain vs. Neural networks
Now, let’s discuss about the human brain, which is the command center for the human nervous system. It is the network of neurons which are used to learn things. When we can copy the structure and function of the human brain, we might be able to get the cognitive capabilities in machines. This is the field of Neural Networks. These networks are more complex and deeper and we use these networks to learn complex things that are in the field of Deep Learning. Deep learning is a division of the state-of-the-art systems in various disciplines, particularly computer vision and automatic speech recognition. When we get the networks to scan the images from left to right and top to bottom then it is called Convolutional Neural Networks (CNN). The CNN is used to recognize an object by Object Recognition through computer vision in AI. Humans can remember the past. We can get a neural network to remember some limited number of past events. This is called Recurrent Neural Network (RNN).
Basically, the AI works in two ways: One is symbolic based and the other one is data based. The data based side, called as machine learning, needs us to feed a lot of data to the machines for learning. Normally, for humans, only two/three dimensions are easy to understand the concept for learning. However, machines can learn many dimensions of data and determine patterns. Once machines learn these patterns, it can make predictions which may not be even possible by humans. Machine learning algorithms are basically used to do Classification or Prediction on a set of data.
Types of Machine Learning
Another way to discuss about machine learning algorithms in AI is based on input data, training data, and results. When we train an algorithm with (labeled) data, which also includes the answers, then it is called Supervised Learning. If we train an algorithm with data (not labeled) and we want the machine to discover the patterns from the input data, then it is called Unsupervised Learning. If we give any goal to an algorithm and expect the machine to achieve the goal based on trial and error, then it is called Reinforcement Learning. If we have a small amount of labeled data with a large amount of unlabeled data, then it is called Semi-supervised learning. Semi-supervised learning falls between Unsupervised learning and Supervised learning.
The query, Humans vs. Artificial Intelligence, is not the matter of who will succeed? but about their interlinkability and togetherness in work? AI technologies are used to increase the computing power to present effective and more precise models. Moreover, Machine Learning techniques provide enhanced representations and use the combinations of large data sets. However, even if these advanced technologies can perform various tasks with greater efficiency and accuracy, human expertise still plays a vital role in implementation by exploiting the AI technology. The question of whether AI can mimic humans will always remain unanswered.
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