January 28, 2021

Leena Sanap


Machine learning and Artificial Intelligence both are making a buzz in the market and can sometimes be used interchangeably, but they do not entirely refer to the same things. There is a lot of confusion between machine learning and artificial learning, and most companies look at this difference as an opportunity for advertising and sales.

Before we move ahead to explain the differentiation and to know how they fit together, let's have a look at the most commonly used acronyms -

  1. Artificial intelligence - The broad discipline to create intelligent machines
  2. Machine learning - refers to systems that can learn from experience

What is Machine Learning?

AI and machine learning are both about constructing intelligent computer programs, where Machine learning is a branch of intelligence that relies on big data sets to remind the data to find common patterns. Its algorithms allow computer programs to improve through experience automatically. An algorithm can be a complete set of instructions, specifying any particular computer programmer, which a computer can process. For example,- If you provide machine learning program images and conditions with what those conditions mean, the algorithms can mine those data and images to help to analyze skin conditions in the future. It examines images and identifies patterns that exist between these images that have similar requirements. It is a method of training algorithms and makes them able to learn how to make decisions. Additionally, a computer-employing machine learning algorithm can recognize that object in new, previously unseen scenarios. ML intends to enable machines to discover themselves by using the provided data and making accurate predictions. A machine learning company uses AI to distinguish daily network activity from critical risks

What is (Artificial) AI Intelligence?

Well, when we are aware of what exactly machine learning is, now let's discuss Artificial intelligence. So in a simple language, Artificial Intelligence learns by acquiring knowledge and learning how to apply it, while aiming to increase the chances of success and to find the optimal solution and interpreting to mean incorporating human intelligence to machines.

The last few years have seen several unique techniques that have previously been in the realm of science fiction and now have transformed into reality. AI is now being seen as the potential to introduce new growth sources and change the way work was earlier done across industries.

Basically, it is a method of making a computer or software think intelligently like the human mind by analyzing the cognitive process. As an example- Pinterest classifying images technology uses narrow intelligence AI to perform specific tasks.

Both Artificial intelligence and Machine learning are being increasingly used in various industries.

Artificial intelligence (AI) was developed with the core focus on solving tasks that humans can do but cannot be done by computers. As a subfield of computer science, AI can be approached in many ways, like writing several computer programs ruled by devised domain experts. We can also say that AI enables machines to obey commands given by programmers and to process data accordingly. And the process data will mimic the cognitive functions of a human that resembles learning and problem-solving abilities.

On the other hand, Machine Learning can be considered as the subfield of AI. Along with the algorithms, the statistical models in machine learning are used to perform a specific task without human interference in giving instructions so that computers can automatically learn (predictive) models from data.

Machine learning definitely helps to develop "AI," however, it is not necessary that AI needs to be developed using machine learning only – although, machine learning can make "AI" much more convenient.

Is Machine learning the future of AI?

AI was existing on a spectrum; many scientists concurred that human-level AI was just around the corner. However, undelivered affirmations caused a general disenchantment with the industry along with the public. That led to the AI time, a period where funding and interest in the field subsided considerably.

After that, organizations tried to separate themselves from AI with uncorroborated hype and were looking to different utilized terms to refer to their work. During this time, other terms, such as big data, and machine learning, started gaining adhesion and popularity and made great strides. Several organizations had suddenly begun to use machine learning for advertising their products. It has begun to perform tasks such as speech and face recognition, natural language processing, image classification, and that were impossible to do with classic rule-based programming.

For those who worked with AI, the effects of machine learning almost seemed as "magic", since a fraction of the fields of neural networks and machine learning have begun and are considered off-limits for computers.

The machine learning algorithms and processes include- neural networks, associations, and sequence discovery, gradient boosting and bagging, self-organizing maps, support vector machines, Bayesian networks, Gaussian mixture models, and more.

Processes that leverage various machine learning algorithms include -

  1. Comprehensive data management and quality
  2. Interactive data exploration and visualization of the results model
  3. Automated ensemble model evaluation to determine the best performers
  4. GUIs for building models and process flows
  5. An integrated end-to-end platform for the automation of the data-to-decision process

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