Artificial Intelligence is like another hurricane. When it came, it changed every life……… Created a revolution……Brought in greater hopes ……. Molded a daunting future……. And as a disaster, it took away many jobs replacing it with elite and advanced ones. Thus it’s making a massive impact on every walks of life. But, AI is not a kind of simulation as people think it is. It is not about intelligence at all. It is all about how mathematics influences our lives. Nowadays we are hearing news about Data Science(DS), Artificial Intelligence(AI), Machine Learning(ML), Deep Learning(DL), and so on. So how are they different from each other?
Generating business from the available or existing data is called Data Science. It includes analyzing the data, finding out the problem or issue, generating a feasible solution, and then applying that. Implementing the algorithm based on what the data scientist will think for finding a solution and then finally executing them using a set of programming codes, like Julia, Python, Java, etc. is called Machine Learning. So machine learning is the automation of data science. And the application of machine learning is called Artificial Intelligence.
The AI doesn’t replace manpower. Rather, its aimed at extending the capabilities of every man and woman. This technology enables one to accomplish the tasks that neither humans nor machines could do on their own. The intelligence that exists in these machines is what is fed to it by its creator. And for sure, it will only exist within the domains up to how much a human brain can think. We help these machines to examine things and end up into conclusions by performing the required calculations. When the machines are provided with a set of input data and the desired output, the learning is performed in different ways. It can be either Supervised learning, Unsupervised learning, or Reinforcement Learning. To learn more about types of learning refer to the article Types of Learning in Machine Learning.
However, basic tasks include:
So artificial intelligence is a set of mathematical algorithms that enable us to have computers find those very deep patterns that we may not have known existed without us having to hard code them manually.
In Julia, you don't code the math, code itself is the math. - Alan Edelman
Programming languages are a medium for communication between two parties -human to machine, machine to human, machine to machine, and human to human as preferred. The language Julia has established a sudden growth in the programming industry because it was able to create a community that thinks together. Unlike other languages, Julia has got that superpower of uniting the people at different realms helping them to co-operate. the quality of discourse and competencies among people are taken into account while developing Julia.
Limits of my language mean the limits of my world. -Ludwig Wittgenstein
The developers of Julia -Viral B. Shah, Alan Edelman, Jeff Bezanson, Stefan Karpinski, and Keno Fischer want to make a language that was common to the common man. Hence, it became a general-purpose.
1. People wanted a language that was easy to understand. The languages like Python and Julia are developed in a fashion in which they look more human-understandable. And hence they are pretty easier to learn.
2. It delivers high performance and high productivity. Sometimes, the languages that are simpler to code would lack in performance and if we demand high performance, then we have to be a brilliant coder. Julia was developed to tackle both the problems and find a common solution. People working on different things using Julia can contribute to user productivity as a single goal.
3. Speed was another major concern. Python was a language working on an interpreter. Whereas Julia is a compiled language. Compilers are used to translate the entire code at once. Interpreters perform line by line execution. (Refer: Difference Between Compiler and Interpreter) While using Python to code the machine learning algorithms, sometimes we do have to face an issue with their execution-style. Just take an example. If we have got a default value set at the end of code and it has something to do with the other statements in the code, the interpreter will sometimes generate an error, even though there exists no error from the programmer’s logic. So there are instances where a compiler wins over an interpreter in code analysis, speed, and execution. You can grab more information on the disadvantages of Python language from the article Disadvantages of Python.
4. Software: A complex interconnected planet needs more from its software. The rise of machine learning has brought a fundamental change in the way software was developed. And when it comes to machine learning Julia is highly preferred for so many reasons. Julia can create magic with its code in the mathematical world we exist, deal with linear algebra, various weird data types, and new programming paradigms. Machine Learning is mathematics under computation to find patterns in data whether it is structured or unstructured. It helps one explore the patterns using mathematics without coding them manually.
5. Dealing with Assumptions: How can we generate program snippets for the logical reasoning questions? Is it easier to generate an algorithm that can compute the number of strands in a hair? Is it easier to generate a program that can calculate the weight of the total creatures in the water? All these data values taken may vary, and hence the output obtained too. Julia is now spreading its wings in the area of climatic science where it can generate solutions to save our mother Earth.
All in all, the language was born as a princess and is now being trained to be the ruler. Still there exist cases in the world of Artificial Intelligence, the situation where Python overhauls Julia because of the rich Python libraries available. But technology is growing fast and Julia will one day become the leader of general computing. Machine learning developers have got a mathematical language and support their level of thinking about the data and numerical abstractions. Julia is capable to provide total optimization from start to finish.
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