Artificial Intelligence Vs Machine Learning: What's the difference?

AI and Machine Learning are predominant terms that are creating a lot of buzz in the technology world. The terms can often be used interchangeably but that’s not the case, AI and ML are way more different from each other in their approach, algorithms and logical thinking.

Let’s go by the stats to see how AI and ML will fare in the global market or is there a scope for AI and ML in the near future. As per stats by The Motley Fool , The AI market will grow to a $5.05 billion dollar industry by 2020. Such predominant stats have reassured the assurance in the cascading power of these intimidating technologies. 

Going by the stats, it has opened up horizons not only from a business perspective but from a machine learning developer perspective, who have evangelized job opportunities in a deep learning environment.

Often people, business owners, and developers got confused between Artificial Intelligence and Machine Learning. They often don’t have a basic idea of the potential of AI and ML.  To resolve all the doubts, I bring in for you the differentiation between AI and ML.

Before getting into the crux of the blog, I will give you a quick overview of AI and Machine Learning.

The term Artificial Intelligence has been co joined by terms like Artificial and Intelligence which somewhat means the artificial ability to think. The biggest misconception about Artificial Intelligence is that it is a system but AI exactly is not a system, it can be implemented within the system for machines to have the logical ability for performing operations.  

Combining all the statements through, Artificial Intelligence can be defined as an area of computer science that has an emphasis on the creation of intelligent machines that can work and react like humans.

Machine Learning can be defined as a subset of AI or can be termed as an application of Artificial Intelligence. In Machine Learning, machines have the ability to learn on their own without being explicitly programmed. 

It allows applications to modify themselves based on data in real-time scenarios.

After digging into the basic overview of Artificial Intelligence and Machine Learning, I bring in the crux of the blog. 

Tensorflow is an open-source software library that can be used for numerical computation with the help of the data flow graph. It has been brought into notice by engineers and researchers working on the Google Brain Team. The flexible architecture of Tensor Flow allows you to deploy computation to multiple GPUs and CPUs in a server/mobile/desktop/ using a single API.

So, if you are looking to integrate Tensorflow, hire dedicated Tensorflow developers right away!

IBM has been a profound name in the field of Artificial Intelligence as it has been working and researching on the technology for a long time. They have their in-house AI platform that includes AI tools for both the business users and developers.  IBM Watson is available as a set of open APIs, by which users can access a lot of smarter kits and sample codes. It can be used to make virtual agents and cognitive search engines. It is also a chatbot building platform for beginners that requires lesser lines of code for machine learning syntax.

An open-source machine learning library, which has been used by major Tech IT giants like Yandex, IBM and Facebook AI Research Group. It can also be termed as a scientific computational framework and a scripting language based on Lua programming language.  After a successful run on web platforms, it has also been extended for Android and Ios.

I have come to the end of the blog and there is so much that I can say to elaborate more about AI and Machine Learning. The differences between AI Vs Machine Learning has been illustrated well in the above table. There has been a huge debate on AI Vs ML.  The choice is ultimately yours when you are looking forward to choosing between AI and ML. 

But I can conclude one thing about AI that it has been a never-ending journey of building modern machinery with human intellect. It is a far-fetched approach to be able to colonize the human mindset for systematic operations. The programmatic implementation for AI might take some time.  As far as Machine Learning is a concern, you can start working on small sets of data for initial tasks screening and adoption. As Machine Learning is a subset of AI, still it will take time to develop and deliver.If you wish to implement artificial intelligence technology in your business then you must hire an expert artificial intelligence developer from the best artificial intelligence development company for effective results.