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Artificial Intelligence vs. Machine Learning. What’s the difference?

A glance at two most-often confused terms and their career scope

Over the years, we have only become more reliant on technologies. Thanks to the improved systems, smart advancements and path-breaking innovations, we’ve all heard ‘to err is human’, but what about machines? What if it’s possible to do more such humane activities with less chances of error? Let us introduce you to the buzzwords of this century ‘Artificial Intelligence’ and ‘Machine Learning’.

Rewardingly, Artificial Intelligence (AI) and Machine Learning (ML) have become the most in-demand career fields right now that offer plenty of opportunities globally with hefty paychecks. As per Gartner, AI is expected to generate more than 2 million jobs by the end of 2020 itself.

Not just employability, AI is the project of the future in which the broad concept is to build computers that can function like humans. And it’d not be surprising to say that most of our innovations, especially today, revolve around comforting the life of a human. And this is just another future project where even complex activities that require human intervention are also expected to be taken care by bots. We can all imagine the growth an individual can expect while working in this field.


Do you know these advancements (though at an early stage) are already a part of our lives? Al the social media platforms including video streaming apps like YouTube, Netflix, etc. rely heavily on machine learning, which is on a broader note a subset of artificial intelligence. 

Artificial Intelligence and Machine Learning in real life 

AI vs ML

Artificial Intelligence

A term that was first coined at a computer science conference in 1956 by John McCarthy, has now become the hottest topic of discussion and a field that may shape the near future. The agenda of this conference was to understand the functionality of the human brain and digitalising to develop something similar artificially. Well, the result of the two-month project didn’t produce any significant result in that era, but it definitely started a revolution. 


Broadly, Artificial Intelligence is classified into three categories:

1. ANI – Artificial Narrow Intelligence (Weak AI)
Weak AI is designed to ace a particular task, however, it will not be able to perform any other task apart from the one it is designed for. Some of the famous examples of Weak AI are Deep Blue (designed to win at chess game), Mr. Roboto, etc. At the moment, this technology is widely used in business functions also for better performance and forecasting.
 

2. AGI – Artificial General Intelligence (General/Strong AI)
AGI is the capability of machines to perform exactly like humans, they can do all kind of activities that a human is capable of including emotional intelligence. Scientists are yet to achieve this level of AI and right now it is only a concept.
 

3. ASI – Artificial Super Intelligence (Strong AI)
It is the stage of an AI in which machines will surpass the human abilities and become super-intelligent machines to outdo humans in any task known to mankind. At the moment, this stage has been perceived as a threat to humans, which is reflected in various fictional movies and TV series that are set in the era of ASI.

Machine Learning

Let’s understand machine learning this way – it is a subset of artificial intelligence the result of which highly relies on the data it is fed. Machine learning interprets and learns from past experiences or data sets provided to the system. This functionality is built to learn on its own without specifically programmed to function in a specific manner. The more you engage with such programs, the better functionality it provides. Some of the day-to-day examples of machine learning is google search or social media algorithms (Facebook, Instagram), music apps (Spotify, Wynk, YouTube), etc. 

There are majorly three different types of machine learning. And since it heavily relies on data, the data is also further structured into two parts – labelled data and unlabelled data.

A labelled data has clearly defined input and output parameters in the machine-readable format, but this sort of structuring requires major human intervention to label the data. On the other hand, unlabeled data one or none of the parameters and thus requires lesser human efforts but more complex solutions.


Here are the three types of machine learning 

1. Supervised Learning
The most basic type that entirely functions on labelled data. The aim of this is to gather accurate results and thus it relies heavily on the accuracy of the data it is fed.

In the initial phase of developing this process, the algorithm is provided with a sample data set to train and perform. Accordingly, the results are analysed on required parameters. Once the algorithm is trained, the final dataset is deployed into its system. And with time, the algorithm keeps improving on the trained parameters and deliver better results.
 

2. Unsupervised Learning
As the name suggests, it doesn’t require much human intervention and thus works with unlabeled data. In supervised learning, a clear connection is defined by establishing cause and effect (by labelling), which is not the case with unsupervised learning. Since, unsupervised learning is designed to work on its own, it produces various hidden structures making the entire system versatile.

Thus, unsupervised learning has the capability to mould the hidden structures as per the data and produce results that may require post-deployment supervision.
 

3. Reinforcement Learning
In simple language, it works on a trial and error system, much like how human functions whenever we are in a totally alien situation. This algorithm learns from itself by establishing a relation between the results and a reward system. 

An interpreter is deployed to analyse each outcome and decide whether the result is favourable or not. The better the result, the higher the reward will be for the algorithm, and in cases of an unfavourable result, its system is reinforced to produce correct results. The aim of this learning is to produce the best possible result by reinforcing the system multiple times if required.

Key differences between AI and ML 

 

Artificial Intelligence

Machine Learning

Functionality

This technology aims at building machines that can imitate human behaviour or work like humans

This technology is a subset of AI and relies on the past data to produce results without explicitly designed to do that

Scope

Has a wider scope and requires decoding complex natural systems

ML has a narrow scope

Functionality

AI is designed to perform multiple tasks

ML is designed to ace at a particular task

Types

AI is divided into Weak AI, General AI and Strong AI

ML is divided into Structured Learning, Unstructured Learning and Reinforcement Learning

Aim

AI is about learning and making its own decisions

ML is about providing knowledge

Example

Sophia, a humanoid built in Hong Kong

Google algorithms, social media apps, etc.


If you are serious about choosing this stream as your expertise, there are a few institutions that offer degree courses in these fields. To be eligible, you’d require to have a bachelor’s degree with subjects like Computer Science, Information Technology, Mathematics and Statistics, Finance and Economics. Additionally, if you have a knack for data crunching with a creative mindset, and are proficient with analytical skills and problem-solving skills, you’ll be thriving in this field from day 1. 

Speaking of the top institutions to study AI and ML, most of them are in the US. The five most sought-after institutions you must explore to pursue these fields include Carnegie Mellon University (US), Massachusetts Institute of Technology (US), Stanford University (US), Harvard University (US), and University of Edinburgh (UK). 

Now your next question would be what’s the scope of specialisation in this field. How to decide which is the right stream for you?

Well, currently, top positions in this field revolve around Data Analytics, Computer Science and Artificial Intelligence Research, Software Engineering, Big Data Engineer, Research Scientist. Some of the organizations that are quite renowned for hiring professionals in this field are Amazon, NVIDIA, Microsoft, IBM, Accenture, Facebook, Intel, Samsung, Lenovo, Adobe, Uber, etc.

Conclusion

Undeniably, artificial intelligence and machine learning are undergoing mass development on a day-on-day basis, thus there are more advancements yet to be witnessed. One thing is for sure, a career in this field will be very bright. As much as it may sound worrying that this stream will take away one-third of jobs by 2030 in the US (as per McKinsey), while at the same time it will also add at least an equal number of jobs too. Thus, if you are planning to go for sciences, it will be a good idea to take one of your subjects as AI or ML to be future-ready. Because ‘now’ is the best time to prepare for tomorrow. 

Updated on September 23, 2020

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