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

Now let us give you an example which’d make you realise that such advancements (though on an early stage) is already a part of your life. For example, all 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.

As fascinating as these facts sound, it is alright to get confused with Artificial Intelligence and Machine Learning. So, let’s understand that in detail and evaluate one topic at a time.

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 digitalizing to develop something similar artificially. Well, the result of the two-month project didn’t produce 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 the 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 the 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 the 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.

Types of machine learning

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 – labeled data and unlabeled data.

A labeled 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 less 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 labeled 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 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 relation between the results and a reward system.An interpreter is deployed to analyse each outcome and decide whether the result is favorable or not. The better the result, the higher the reward will be for the algorithm, and in cases of an unfavorable result, it 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 behavior 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 offering 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.