Introduction to Artificial Intelligence and Machine Learning
What is Artificial Intelligence?
AI is the creation of software that imitates human behaviors and capabilities. John McCarthy was the first person to coin the term Artificial Intelligence. In simple words, artificial intelligence is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. So machines are programmed to think like humans and mimic human actions.
Machine learning — This is often the foundation for an AI system, and is the way we “teach” a computer model to make predictions and draw conclusions from data.
Types of Artificial Intelligence-
Type I: Based on Capabilities
1. Weak AI or Narrow AI: Narrow AI is a type of AI which is able to perform a dedicated task with intelligence. The most common and currently available AI is Narrow AI in the world of Artificial Intelligence.
2. General AI: General AI is a type of intelligence which could perform any intellectual task with efficiency like a human.
3. Super AI: Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence, and can perform any task better than humans with cognitive properties. It is an outcome of general AI.
Type II: Based on Functionality
1. Reactive Machines: Purely reactive machines are the most basic types of Artificial Intelligence. Such AI systems do not store memories or past experiences for future actions
2. Limited Memory: Limited memory machines can store past experiences or some data for a short period of time. These machines can use stored data for a limited time period only.
3. Theory of Mind: Theory of Mind AI should understand the human emotions, people, beliefs, and be able to interact socially like humans
4. Self-Awareness: Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent and will have their own consciousness, sentiments, and self-awareness.
How do Machines Learn?
The answer is, from data. In today’s world, we create huge volumes of data as we go about our everyday lives. Data scientists use all of that data to train machine learning models. This data can be from the text messages, emails, and social media posts we send to the photographs and videos we take on our phones, we generate massive amounts of information.
Let’s understand this by an example:
An environmental conservation organization wants volunteers to identify and catalog different species of wildflower using a phone app.
So we will go through the steps one by one —
- A team of botanists and data scientists collects samples of wildflowers.
- The team labels the samples with the correct species.
- The labeled data is processed using an algorithm that finds relationships between the features of the samples and the labeled species.
- The results of the algorithm are encapsulated in a model.
- When new samples are found by volunteers, the model can identify the correct species label.
Types of Machine Learning —
Supervised Learning — Uses labeled datasets to train algorithms to classify data or predict outcomes accurately. In supervised learning, we teach or train the machine using data that is well labeled…After that, the machine is provided with a new set of examples(data) so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data. The example we saw earlier is an example of supervised learning.
Unsupervised Learning — In unsupervised learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training… In essence, unsupervised learning can be thought of as learning without a teacher. Uses algorithms to identify patterns in data sets containing data points that are neither classified nor labeled.
Reinforcement Learning — It is based on Markov decision processes, in which a combination of action and a particular state of the environment entirely determines the probability of getting a particular amount of reward as well as how the state will change. The reinforcement learning theory is probably the closest to how we as humans learn. The algorithm or the agent learns continually from its environment by interacting with it. It gets a positive or a negative reward based on its action.
Let’s consider this by example: Analysing customers with bank loans. A Reinforcement Learning algorithm looks at the information of a customer and classifies him/her as a high-risk customer. When the customer defaults, the algorithm gets a positive reward. If the customer doesn’t default, the agent receives a negative reward. The reward in both the cases helps the agent understand the problem and the environment better, and thus helps to make better decisions on our behalf.
