Machine Learning (ML) Vs. Artificial Intelligence (AI)

Automation evolves from customers' demands and expectations. As a result, companies worldwide continually strive to innovate their ideas to maintain demand standards. 

The failure to accommodate or be indifferent to your client's requirements will make your competitors the boss. And one fine day, customers' attitude towards you becomes questionable - a high-risk gamble you have stepped into.

In today's world, where almost all manual tasks are automated, the term 'manual' is transforming. Artificial Intelligence (AI) and Machine Learning (ML) are among the most sensational buzzwords, as companies are leveraging these innovative approaches to create intelligent applications.

Though these terms influence business conversations worldwide, many have difficulty differentiating them, mainly when ML and AI are interchangeably used.

In this article, we will see the difference between an ML and AI technology.

What is AI & ML?

a. Machine Learning (ML)

The concept of Machine Learning has been around for ages; however, the automation idea of complex mathematical calculations to big data has only been around for a few years - with more popularity these days.

ML is a branch of AI that brings out the power of data in different ways. This technology aids computer systems to learn and enhance from experience by creating computer programs that automatically access data and perform activities through predictions.

When Machine Learning models are exposed to novel data, these applications learn, transform, grow, and develop by themselves.

To make it simple, Machine Learning involves computers' finding meaningful information without being told where to look. Instead, they use algorithms that learn from experience in an iterative approach.

Image recognition is the most significant and widespread example of ML in the real world - identifying an object as a digital image based on the pixel intensity in black and white or color pictures.

b. Artificial Intelligence (AI)

In 1950s, Minsky and McCarthy described the AI as any task performed by a machine that was previously considered to need human intelligence.

However, the modern definition for tech trend is the ability of a digital computer to execute tasks that are associated with skillsets. It is often applied to the developing system projects invested with the intellectual method characteristics of humans like the potential to reason, identify insights, or learn from prior experience.

According to a PWC article, AI is predicted to contribute $15.7Tr to the global economy by 2035, and the countries that benefit the most from the AI boom are China and the US - accounting for nearly 70 percent of the worldwide impact.

Some of the proven dominances of AI are:

• Personal assistants - Alexa, Siri and Google Assistant
• Image and speech recognition
• Ride-sharing apps - Uber, Ola, Lyft and more
• Navigation apps - Google Maps, Apple Maps.

Different Types of ML and AI

a. ML

• Supervised Learning: Here, labelled data is used for training the data. The input goes through the ML algorithm and is leveraged to train the model. Once it’s done, we can feed unknown data into the trained ML model and obtain a new desired response.
• Unsupervised Learning: In this type of ML, the training data is unlabelled and unknown. This data is used in the algorithm for training the model. The trained model searches for a pattern and generates the desired outcome. In this case, it is similar to the Enigma machine trying to break code without human intervention.
• Reinforcement Learning: The ML algorithm identifies data through a trial-and-error process in reinforcement learning and then decides what action yields higher benefits. 3 significant components of this ML type are - the agent, the environment, and the actions. This type of ML occurs when the decision-maker chooses activities that increase the expected profit over a given period

b. AI

• Reactive Machines: It’s solely reactive, without developing memories or creating judgments based on prior experiences. These devices are designed to execute specific duties. Programmable coffeemakers and washing machines, for example, are built to fulfil certain tasks but lack memory, i.e., they can’t perform according to previous experience.
• Mind-Body Theory: These AI computers can socialize and understand human emotions and a cognitive understanding of people based on their surroundings, facial traits, and other factors. Such powers have yet to be developed in machines. This sort of AI is the subject of a lot of research.
• Limited Memory: This type of AI makes decisions based on previous experiences and present data. These machines have limited memory and integrated a memory-running application; they cannot generate new concepts. Modifications in these machines demand re-programming.
• Self-Awareness: This is a type of AI where machines will be equipped with technologies to be self-aware of their surroundings. This phase is also a continuation of the Mind-body Theory phase, in which devices will be aware of themselves for a reason. This will elevate the machine's intellect to an entirely different level.

Why ML and AI Popular?

a. ML

The main objective of Machine Learning technology is to help companies improve their overall productivity, decision-making process and workflow.

Let’s look at why ML is popular:

1. Business Transformation

Machine Learning has been transforming businesses with its potential to offer valuable insights. The insurance and finance sectors use the technology to determine meaningful patterns within big data sets, prevent fraud, and provide personalized plans to various customers.

When considering the healthcare industry, fitness and wearable sensors powered by Machine Learning technology allow users to take charge of their health, accordingly reducing the pressure of healthcare experts.

This technology is also leveraged in the oil and gas industry to identify new energy sources, analysis of ground minerals, system failure predictions, etc.

This futuristic trend highlights how it plays a vital role in business transformation, and excelling in the adequate skills will keep you on the path where opportunities are boundless.

2. Prompt Analysis and Assessment

Since businesses revolve around a surplus count of data moving in and out of an organization, employees find it tedious to deal with it daily. Thanks to the evolution of ML, the algorithms can aid the workforce in conducting prompt analysis and strategical assessments.

When an employee creates a business model by browsing through many data sources, they get to see essential variables. Similarly, ML can assist you in understanding customer feedback, interaction, and behaviour, thus resulting in seamless customer acquisition and digital marketing strategies.

3. Instantaneous Predictions

A feature that fascinates the ML practitioners is the rapid processing of insightful data from myriad sources - making instantaneous predictions that can be valuable for organizations.

ML offer meaningful data on various customers' buying and spending patterns, which allows businesses to devise procedures that can reduce loss and maximize profits.

It also helps determine the backlogs of marketing campaigns and customer acquisition policies. With these data, employees can adjust their business procedures and enhance overall customer satisfaction.

An additional benefit of the ML is the churn analysis - identifying those customer segments that are likely to leave the business brand.

b. AI

AI-related devices are gaining much attention not just from youngsters but middle-to-old-age people.

Let’s see why AI gains lots of importance these days:

1. Top-notch Accuracy

Through deep neural networks, AI obtains top-notch accuracy. For instance, your interactions with Google and Alexa are based on Deep Learning (DL). These products are getting more precise when leveraged regularly.

In the medical industry, AI methods from DL and object recognition can be leveraged to determine cancer on medical images with enhanced precision.

2. Improving Existing Products

AI adds intelligence to the existing products/services. Several products that we use in our routine life are being enhanced with AI potentials, much like Alexa and Siri, which were added as virtual voice assistant features.

To enhance technologies, automation, transforming platforms, intelligent machines, and bots can be incorporated with massive data. If you look at your workplace and home, AI has upgraded the range from security intelligence and intelligent cameras to investment analysis.

3. Progressive Learning Algorithms

To enable the data to do all the programming work, AI modifies through advanced learning algorithms. AI finds data regularities and structures so that algorithms can obtain skills. Like an algorithm undergoing self-study to play chess, it can teach itself what product to recommend following online.

Application of AI & ML

a. ML

• Product suggestions/recommendations
• Sales forecasting for various products
• Prediction of stock price
• Fraud analysis in banking and finance sector.

b. AI

• AI bots like Aibo and Sophia
• Machine Translation like Google Translate
• Speech recognition apps like OK Google and Apple's Siri
• Autonomous cars such as Google Waymo