In the current world, the most trending words in tech we listen frequently are Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. Many people get confused with these words. Let's have a look at these terms and what they actually imply in the computer world of ours.
In our day to day life, we are using AI directly or indirectly to meet our needs like a Google search to get the information about a topic. But in-depth, Google uses a trained Neural Network to get the result with good accuracy levels. Here the Deep Learning comes into play. We get videos recommendation on YouTube based on our watched videos and liked videos. Here the Machine Learning comes into play. Google uses Big Data tools and techniques to understand our requirements based on several parameters like search history, location, trends etc.., Here comes the Data Science into the role again.
Artificial Intelligence:
Coined in 1955 by John McCarthy as "the science and engineering of making intelligent machines," artificial intelligence (or AI) is software that is able to use and analyze data, algorithms, and programming to perform actions, anticipate problems and learn to adapt to a variety of circumstances with and without supervision. AI is generally broken down into specialized or general and strong or weak AI, depending on its applications.
Artificial Intelligence is broadly divided into three types namely:
1. Artificial Narrow Intelligence or Weak AI
Artificial Narrow Intelligence is the one that we use in our day to day life. The intelligence that learns about only one task like checking the weather, changing the song, setting up the alarm, etc.., These are considered to be weak intelligence as this is nowhere related to our human brain and the need for human interference is a must.
Ex: Voice Assistant like Alexa, Siri, Google Voice Assistant etc..,
Face recognition tools that tag our pictures on Facebook,
Bots that are present on websites.
2. Artificial General Intelligence (AGI) or Strong AI
AGI is the one that can exhibit human intelligence merely. The intelligence that learns anything. One characteristic of AGI is able to learn and evolve. AGI is expected to be able to reason, solve problems, make judgments under uncertainty, plan, learn, integrate prior knowledge in decision-making, and be innovative, imaginative, and creative.
But for machines to achieve true human-like intelligence, they will need to be capable of experiencing consciousness.
3. Artificial Super Intelligence
The point at which the Artificial Intelligence exceeds human intelligence. This is the type of AI that many people are worried about, and the type of AI that people like Elon Musk think will lead to the extinction of the human race.
Machine Learning
Arthur Samuel, a pioneer in the field of artificial intelligence and computer gaming, coined the term “Machine Learning”. He defined machine learning as – “Field of study that gives computers the capability to learn without being explicitly programmed”.
ML is exactly opposite to Traditional programming.
Traditional Programming: We feed in DATA (Input) + PROGRAM (logic), run it on a machine, and get output.
Machine Learning: We feed in DATA(Input) + Output, run it on a machine during training and the machine creates its own program(logic), which can be evaluated while testing.
Ex: Targeted Audience, Detecting Cancer, IMDB ratings, etc.,
ML is also divided into 3 types:
1. Supervised Learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data with labeled responses.
[or]
Supervised Learning is a type of machine learning task of learning a function that maps an input to an output based on example input-output pairs.
2. Unsupervised Learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.
3. Reinforcement Learning is defined as a Machine Learning method that is concerned with how software agents should take actions in an environment. Reinforcement Learning is a part of the deep learning method that helps you to maximize some portion of the cumulative reward.
Data Science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining and big data.
Why we need Data Science?
During the last two decades, data had been grown exponentially. So to maintain the data in a well-structured format and to get a hypothesis from existing data this field helps a lot even to grow our business etc.,
Ex: Internet Search, Airline Route Planning, Price Comparison Websites, Delivery Logistics etc.,
ML vs DS
Frequently we get confused between these two terms and they look almost the same but they differ from one another.
Key steps of a Machine Learning:
Collect data
Train Model: Iterate many times until good enough
Deploy Model
- Get data back
- Maintain / update model
Key steps of a Data Science:
Collect Data
Analyze Data: Iterate many times to get good enough
Suggest Hypothesis/actions
- Deploy Changes
- Re-analyze new data periodically
In the field of agriculture if we implement DS and ML we may get the following conclusions.
DS :
We can get the analysis of crop from previous data like weather conditions, water availability in seasons, fertility rates, etc .., By knowing the above attributes we may choose wisely to grow crops in a particular season.
ML :
By using ML techniques we can find the weeds easily. Even we can make a robot to remove weeds. So the time that need to spend on weed killing may reduce so that farmer can focus on other areas to yield more crop.
Deep Learning
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabelled. Also known as deep neural learning or deep neural network.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
It plays a major role. Here we can see the concept of neural network. The main reason for becoming AI is one of the trending Technology is a neural network.
A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes.
Ex: Automated Driving, Aerospace and Defence, Medical Research, Industrial Automation, etc.,
From the above conclusions if we try to make a correlation we may get this:
Google Assistant.
The Assistant is a voice-enabled virtual assistant which responds to humans and provides necessary information to the user and puts questions frequently. This is powered by AI.
Even the Google assistant has certain limitations and it is also pre-programmed and it asks and answers to the user for some set of questions and beyond that, it can’t handle. Answering to some set of questions is powered by ML techniques.
If a user searches in Google through Image then the image is broken down into pixels and forms into a matrix. By using this matrix Google image search works. All this is achieved by using Neural Networks and Deep Learning techniques.
If you are interested, in reading a certain type of article frequently in a certain category then by using your data Google gives you a personalized news feed by using Big data tools and techniques.
Conclusion: Machine learning is a part of AI that focuses on a narrow range of activities and data science uses Machine learning to solve real-world problems and we can say that data science is an application of machine learning which also uses big data analysis etc.
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