According to a research, “The AI development market size will become $100 billion.”
It’s true, the technology is poised to transform businesses, and shifting towards a more customer centric approach providing them a more streamlined services with better experiences.
The term coined by cognitive scientist John McCarthy frequently appears with two other terms that are machine learning and deep learning and more often swapped with AI in the realm of big data. The unclear elucidation of the differences between the three distinct technologies, which are interrelated, muddies the water.
Taking a step ahead, let’s clear up the air between artificial intelligence (AI), machine learning (ML), and Deep learning (DL):
The biggest Reality: Deep learning is the subset of machine learning and machine learning is further the subset of artificial intelligence.
To comprehend it better, let’s reverse the tape. Artificial intelligence is the intelligence of machines that are powerful enough to perform the tasks like a real human being. Yes, just the way you might have seen in Ex Machina where the programmed hero possesses the superior capabilities which human displays. Or a more realistic and real-life example of Jarvis connecting with IoT.
The wild guess- the intelligent machines will be working based on the set of pre-determined rules. Right? If it’s so, then you are absolutely incorrect. Creating a software to Mimic the human behavior on the grounds of if-then-else function for various events is implausible.
Here, comes the Role of Machine Learning.
The machine can gain the human intelligence when it learns instead of programmed. Using complex algorithms, the machine analyzes the huge data, recognize the pattern and then make the most probable predictions. The computer keeps on learning with new data, and suggest changes in the algorithm to better process the information and carry out decisions equivalent to human intelligence. A perfect example of this will be how the technology is boosting Ecommerce industry by providing a more personalized service.
How the Computers are trained to share a Resemblance to a Human Brain?
It’s with the aid of neural networks. The neural networks that are fashioned after the human brain are actually a series of the algorithms which is capable of categorizing and classifying the information with information pattern recognition. Afterwards, they process the information to make the sense out of it. The neural networks keep on repeating the process when it encounters new information and thereby are able to work like a human brain.
In this manner, machines can extract the meaning from complicated data, learn with examples, easily identify the patterns and detect the trends, and perform at quite high speed with a greater accuracy as opposed to the human brain. That’s why applications of AI are gaining momentum industry-wide to tap the potential to the fullest.
Well, the answer to human queries and human intelligence will never be limited to Yes or No. The questions may involve hierarchy where the nested data is complicated to understand and carry out the decision upon them, even for the humans.
This is where Deep Learning with Deep Neural Networks comes in.
The deep neural networks have multiple hidden layers which iron out learning the feature hierarchy. The data passed through multiple layers and then a multitude of mathematical operations is performed over it, which is a Herculean task, but with deep learning, it’s a piece of cake. The computational intensivity distinguishes the deep learning technology.
The computers are trained for deep learning where they are exposed to massive data to exactly imitate the human brain’s connectivity, dataset classification and then finding the correlation between them. The data-driven insights enable the most precise predictions.
The deep neural network has set new records when it comes to accuracy in many applications like- sound recognition, image recognition, or recommended system.
For instance: The facial recognition based on artificial intelligence brilliantly work and recognize the face of an individual to a high degree of accuracy with deep learning. The DL algorithms are trained with the data set of faces, the lighting variations, the angle and distance from which photo is taken, the face of the person, the specific points on the face to measure, and how to process the information to provide the results for the best match.
In a nutshell, the differences between the AI, ML, and DL is not a comparison between apple and oranges, while it lies in its concept itself, which is highly nuanced.
What’s next? The Data Stays at the Core.
It’s all a game of data. Simulating human brain decision by machine largely depends upon the data it’s receiving. A single flaw in the data or a single miss leads to a great chaos. Consider, the Google’s first facial recognition system when rolled out, it has tagged may Black people as gorillas just because the algorithms was trained for some specific faces.
Now, the conceptual difference which is creating hell a lot of confusion is over. Do you find anything missing on the chart that can explicitly differentiate the three? With such intelligent technology accessible through a mobile, apps are all set to use the AI potential to a greater extent.