Difference Between Algorithm and Artificial Intelligence
By 2035 AI could boost average profitability rates by 38 percent and lead to an economic increase of $14 Trillion.
The words Artificial Intelligence (AI), and algorithms are most often misused and misunderstood. There are often used interchangeably when they shouldn’t be. This leads to unnecessary confusion.
In this article, let’s understand what AI and algorithms are, and what the difference between them is.
An algorithm is a form of automated instruction. An algorithm can either be a sequence of simple single if-then statements like if this button is pressed, execute that action, or sometimes it can be more complex mathematical equations.
· Examples where algorithms are used
- YouTube’s algorithm knows what kind of ads should be displayed to a particular user
- The e-commerce giant Amazon’s algorithm knows what kind of products a specific user like and based on it shows similar product details.
· Types of algorithms
The complexity of an algorithm will depend on the complexity of every single step, which is required to execute, as well as on the sheer number of steps the algorithm is required to execute. Mostly the algorithms are quite simpler.
- Basic algorithm
If a defined input leads to a defined output, then the system’s journey can be called an algorithm. This program journey between the start and the end emulates the basic calculative ability behind formulaic decision-making.
- Complex algorithm
If a system is able to come to a defined output based on a set of complex rules, calculations, or problem-solving operations, then that system’s journey can be called a complex algorithm. Same as the basic algorithm, this program journey emulates the calculative ability behind formulaic, but more complex decision-making.
Artificial intelligence is a set of algorithms, which is able to cope with unforeseen circumstances. It differs from Machine Learning (ML) in that it can be fed unstructured data and still function. One of the reasons why AI is often used interchangeably with ML is because it’s not always straightforward to know whether the underlying data is structured or unstructured. This is not so much about supervised and unsupervised learning, but about the way, it’s formatted and presented to the AI algorithm.
The term AI algorithms are usually used to mention the details of the algorithms. But the accurate word to use for this is “Machine Learning Algorithms”. AI is a culmination of technologies that embrace Machine Learning (ML). ML is a set of algorithms that enables computers to learn from previous outcomes and get an update with the information without human intervention. It is simply fed with a huge amount of structured data in order to complete a task.
Based on the data acquired, AI algorithms will develop assumptions and come up with possible new outcomes by considering several factors into account that help them to make better decisions than humans.
In AI algorithms, outputs are not defined but designated depending on the complex mapping of user data that is then multiplied with each output. This program’s journey emulates the human ability to come to a decision, based on collected data. The more an intelligent system can enhance its output based on additional inputs, the more advanced the application of AI becomes.
· Examples where AI algorithms are used
- Self-driving cars are one of the best examples of AI algorithms.
- Recognition-based applications such as facial, speech, and object recognition mapping
· Learning algorithms
Artificial intelligence algorithms are also called learning algorithms. There are three major kinds of algorithms in ML.
- Supervised learning
The supervised learning algorithms are based on outcome and target variable mostly dependent variable. This gets predicted from a specific set of predictors which are independent variables. By making use of this set of variables, one can generate a function that maps inputs to get adequate results. The core algorithms, which are available in supervised learning, are Support Vector Machines (SVM), Decision Tree, and naïve Bayes classifiers, Ordinary Least Squares (OLS), Random Forest, Regression, Logistic Regression, and KNN.
- Unsupervised learning
These are similar to the supervised learning algorithms, but there is no specific target or result, which can be estimated or predicted. As they keep on adjusting their models entirely based on input data. The algorithm operates a self-training process without any type of external intervention. The core algorithms, which are available in unsupervised learning algorithms, are Independent Component Analysis (ICA), apriori algorithm, K-means, Singular Value Decomposition (SVD), and Principal Component Analysis (PCA).
- Reinforcement Learning (RL)
The RL has the constant iteration that depends on trial and error, in which the machines can generate the outputs depending on the specific kind of conditions, the machines are well-trained to take relevant decisions. The machine learns well based on past experiences and then captures the most suitable and relevant information to develop business decisions accurately. The best examples for RL are Q-Learning, Markov Decision Process, SARSA (State – action – reward – state – action), and Deep Mind’s Alpha Zero chess AI.
An algorithm takes automated instructions, which can be simple or complex, takes some input and some logic in the form of code, and offers an output based on the predefined set of guidelines described in the algorithm.
Whereas, an AI algorithm varies based on the data it receives whether structured or unstructured learns from the data and comes up with unique solutions. It also possesses the capability to alter its algorithms and develop new algorithms in response to learned inputs.
Humans and machines must work together to build humanized technology grounded by diverse socio-economic backgrounds, cultures, and various other perspectives. Knowledge of algorithms and AI will help to develop better solutions and to be successful in today’s volatile and complex world.