Artificial Intelligence and Machine Learning: What Do We Know So Far?

Introduction

Artificial Intelligence and Machine Learning are the buzzwords of the tech world. Since both the terms are based on statistics and maths, people often get confused between them.

Every piece of tech content remains unfinished without the mention of artificial intelligence and machine learning. Today, the terms are equally hyped and are interchangeably used to explain an intelligent system or software. In fact, when we dive deeper into the broader branches of technology (like Big Data or Analytics), both terms frequently appear on the front face. As a result, most people use the terms synonymously—which leads to confusion.

But, don’t worry! In this blog, we will cover the major differences between artificial intelligence and machine learning to eliminate this very confusion. However, before we proceed with learning the differences, let me help you grasp a broader understanding of what artificial intelligence and machine learning are.

Artificial Intelligence

To begin with, artificial intelligence is a computer’s ability to imitate or mimic human intelligent behavior and perform tasks the way humans do. Basically, it performs tasks that require human intelligence such as thinking, reasoning, applying logic, and essentially, making own decisions.

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” —Larry Page

In layman’s, the words ‘artificial’ and ‘intelligent’ combine to imply “a human-made thinking power.” Currently, AI is being incorporated into our day-to-day chores and in every sector. From finance to lifestyle, every sector has integrated artificial intelligence to streamline various processes. But, how did the useful branch of technology come into play?

Timeline of Artificial Intelligence

AI Then

Although AI has been around for several years, numerous people had begun exploring it in the 90s itself. Rockwell Anyoha’s 2017 paper on “The History of Artificial Intelligence,” which begins with the subhead ‘Can Machines Think?’, cites the Tin man from The Wizard of Oz as well as the young British polymath Alan Turing to enunciate the existence of AI. The paper further cites how it was Turing who explored the mathematical possibility of artificial intelligence.

Turing’s paper published in the 1950s (Computing Machinery and Intelligence) discusses how to build intelligent machines and test their intelligence. Under this, he argues if humans use available information and reason to solve problems and make decisions, why can machines not do the same? 5 years later, Herbert Simon along with Allen Newell and John Shaw altogether created the first program written to emulate humans’ problem-solving skills— ‘Logic Theorist’.

Furthermore, the term ‘artificial intelligence’ did not come into existence until McCarthy coined it in a proposal for a summer research conference. He turned the tides for AI through his proposal which read:

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

AI Now

Fast forward to the 2000s and AI has already started to integrate into our daily lives. We visualized self-driving cars, personalized virtual assistants, robotic management, and many more when we envisioned the future. However, these aspects have been embraced in the present itself—making the future more enthralling! Although AI has been around for more than a few years, it has exponentially grown and has increased our dependency on it.

As we have transcended to the evolution of AI, I would like to mark the words of Stephen Hawking (someone who requires no introduction),

“The development of full artificial intelligence could spell the end of the human race. It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.”

AI Likely in Future

While Hawking had subtly warned humans against the evolution of AI in the past, the present-day leaders are also advocating their arguments over the same. Speaking of AI’s evolution, we can not neglect to mention the popular tech billionaire, Elon Musk who, despite being paramount of his affinity for technology, especially AI, has said, “Mark my words—A.I. is far more dangerous than nukes.”

The concerns over the increasing dependency on AI does not limit to tech enthusiasts and billionaires. Previously, several people have expressed their concerns against AI robots taking over humans in various fields of work as well as life.

To summarize, the incorporation of AI has its own set of advantages as well as drawbacks. To better understand the technology, let us have a look at some of its examples.

3 Common Examples of AI Incorporation

Artificial Intelligence is commonly used in our everyday lives. Following are some of the notable instances of AI incorporation:

1. Personalized AI Assistants

Alexa by Amazon, Siri by Apple, S Voice by Samsung, Cortana by Microsoft, and Google Assistant. All of these are perfect and most popular examples of personalized AI assistants. These tools have enabled human interactions with gadgets and have enabled us to do a plethora of things from hotel bookings to window shopping.

2. Robotics

AI robots are another example of AI integration. Think of the world’s first humanoid robot, Sofia, who is incorporated with artificial intelligence. Her creators claim that Sofia personifies their dreams for the future of AI. She imitates human gestures and facial expressions and is able to answer certain questions. Sofia can also initiate conversations on a variety of predefined topics. In fact, AI robots have a keen role to play in the future.

3. Marketing

AI has a great role to play in facilitating the future of marketing. With tools like Slack and Grammarly, today marketers are allocating huge amounts of financing towards incorporating AI in their marketing tactics.

Now that we have learned about AI and its examples in a brief manner, let us move forward to understanding Machine Learning in depth.

Machine Learning

According to IBM, Machine Learning is

“A branch of artificial intelligence and computer science that focuses on the use of data and algorithms to imitate the way humans learn, and gradually improves its accuracy.”

According to Wikipedia, Machine learning is

“A field of inquiry devoted to understanding and building methods that learn, that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.”

In layman’s, Machine Learning or ML is the subset of AI with an ability to automatically learn from the data without explicitly being programmed or assisted by domain expertise. The ‘learning’ in ML refers to a machine’s ability to learn based on data as well as on an ML algorithm’s ability to train a model, evaluate its performance or accuracy and then make predictions.
“A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.” —Dave Waters

To simplify it further, Machine Learning is a current application of AI, that is based on the idea that we should be able to give machines access to data and let them learn from it for themselves.

How Did Machine Learning Come Into Being?

There are two important breakthroughs that led to the evolution of ML as the facilitating vehicle that is driving AI development forward with lightning speed.

• Firstly in 1959, Arthur Samuel realized that instead of teaching computers everything they need to know about the world and how to carry out tasks, it is better for them to learn for themselves.
• Secondly, the emergence of the internet and the boom of digital information that is generated, stored and made available for analysis.

When these innovations were in place, engineers realized that it would be efficient for computers. Machines to learn for themselves instead of being taught to. It would be wise to code them to think like humans. Then plug them into the internet for giving them access to all available information. Thus, began the era of MACHINE LEARNING.

Let us now explore some classic examples of Machine Learning.

3 Common Examples of Machine Learning

Today, ML is relevant in many fields as well as industries and has the potential to further grow over time. For instance, you might be aware of image and speech recognition. These two are common real-world examples of ML.

1. Image and Speech Recognition

Image recognition is a widespread example of ML. It helps identify an object as a digital image, based on the intensity of the pixels in black and white images or color images. For example, labeling an x-ray, assigning a name to a photographed face, recognizing handwriting, and many more. ML is also used for facial recognition within an image in which using a database of people, the system identifies commonalities and matches them to faces.

Moreover, ML can also be used to translate speech into text. Certain software apps are capable of converting live voice and recorded speech into a text file. Here, the speech can be segmented by intensities on time-frequency bands too.

2. Medical Diagnosis

In the past few years, Machine Learning has played a significant role in the diagnosis of diseases. Various physicians use chatbots with speech recognition capabilities to discern patterns in symptoms. Assisting in formulating a diagnosis or recommending treatment options requires the incorporation of ML. In fact, oncology and pathology also use machine learning to recognize cancerous tissues and analyze body fluids.

3. Data Extraction

ML helps extract structured information from unstructured data. Several organizations collect huge chunks of data from customers and using ML algorithm, they automate the process of annotating datasets for predictive analytics tools. Examples: Generating models to predict vocal cord disorders, developing methods for prevention, diagnosis and treatment of disorders, and many more.

Since the data extraction process is tedious, ML simplifies it by tracking and extracting information to obtain huge volumes of data samples.

How Do AI and ML Work to Solve Problems?

Machine Learning and Deep Learning are Subfields of AI. Artificial Intelligence, as a whole, consists of various subfields, including neural networks, deep learning, computer vision and natural language. To understand how AI incorporates the various subsets of ML to solve problems and complexities. We have to first understand the meaning and processes involved in the below-listed terminologies.

1. Neural Network

Machine learning automates analytical model building by using methods from neural networks, statistics, operations research and physics to find hidden insights in data. It does so without being explicitly programmed where to look or what to conclude.

So, a neural network is a kind of machine learning that is inspired by the functioning of the human brain. It is made of interconnected units (which look similar to neurons in a human body) and processes information by responding to external inputs, and relaying information between each unit. The entire process requires multiple passes at the data to find connections and derive meaning from undefined data.

2. Deep Learning

Deep Learning is one of the frequently used terms in the world of machine learning. So, what exactly is deep learning?

The process uses huge neural networks with several layers of processing units. Deep Learning leverages advances in computing power and improved training techniques to learn complex patterns in large volumes of data. Being one of the most important parts of AI, Deep Learning has significantly contributed to the field. However, it requires huge amounts of data to extract useful inputs. Some of the common applications of deep learning are image and speech recognition.

3. Computer Vision

In the case of computer vision, they rely on pattern recognition. Deep learning to recognize all the elements in a picture or video. When machines can process, analyze and understand the images. They can better capture images or videos in real-time while interpreting their surroundings.

4. Natural Language or NLP

It is, basically, the ability of computers to analyze, understand and generate human language, including speech. Its next stage is natural language interaction—a process that allows humans to communicate with computers using normal and regular language to perform tasks. Although machine learning is all about the idea that machines should be able to learn. And adapt through experience, AI, however, concerns a broader idea where machines can smartly execute tasks.

In the end, AI applies machine learning, deep learning and other techniques to solve actual problems.

Why Do People Often Confuse Artificial Intelligence and Machine Learning?

(This section requires your complete attention). Although machine learning is a subset of artificial intelligence, there are a few basic differences between both aspects of technology. We explored (in brief) the definitions and common examples of AI and ML. Till now, you would have understood how these terms are co-related and what their actual work involves.

Considerably, ML is a subset of AI. As both terms are interchangeably used, and hence, people confuse them to be synonymous. However, both terms are different from each other in various ways. While AI implies the general ability of computers to imitate human thoughts and perform tasks in real-world environments, ML refers to the technologies and algorithms that enable systems to identify patterns, make decisions and improve themselves through experience and data. Moreover, machine learning and deep learning are subfields of AI.

To further clarify the differences, I have put together a list of factors/features that differentiates AI from ML in the below table.

Difference Between Artificial Intelligence and Machine Learning

To put into context, “All machine learning is AI, but not all AI is machine learning.” Below is a table enlisted with the major differences between artificial intelligence and machine learning.

“Everything that moves will be autonomous someday, whether partially or fully. Breakthroughs in AI have made all kinds of robots possible, and we are working with companies around the world to build these amazing machines.” —Jensen Huang, Nvidia CEO

 

Artificial intelligence or AI	Machine Learning or ML
Artificial intelligence enables a machine to simulate human behavior.	Machine Learning is a subset of AI that allows a machine to automatically learn from past data without programming explicitly.
The main work of AI is decision-making.	The main work of ML is to allow systems to learn new things from data.
AI is wisdom/intelligence-oriented.	ML is focused on learning.
It mimics humans to solve problems.	It is inclined towards creating self-learning algorithms.
AI is focused on creating an intelligent system that can perform various complex tasks.	Machine learning’s main purpose includes creating machines that can only perform those specific tasks for which they are trained.
AI focuses on maximizing the chances of success.	Machine learning is mainly concerned with accuracy and patterns.
The main applications/examples of AI are customer support chatbots, personal virtual assistants like Siri, Cortana and others, Expert systems, Online game playing, and intelligent humanoid robots, among others.	Common examples or applications of machine learning include Online recommender systems, search algorithms of SERPs like Google and Bing, auto friend tagging suggestions for social media platforms, and many more.
AI is of three types (based on capabilities): Weak AI, General AI, and Strong AI.	Machine learning can be divided into mainly three types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
AI is more specific about learning, reasoning, and self-correction.	Machine Learning is specific to learning and self-correction (when introduced with new data).

 
The listed aspects are some of the common differences between artificial intelligence and machine learning. Essentially, artificial intelligence is a broader family consisting of machine learning and deep learning as its components, whereas machine learning is a subset of artificial intelligence.

Capabilities of AI and Machine Learning in Business

Till now, we have comprehensively learned about artificial intelligence and machine learning in detail. You might have gained a thorough idea of what these technologies are, how exactly they work and how they’re different from each other. You also might have concluded that AI and ML are some of the necessary factors to be successful in any industry. Speaking of success, organizations must be able to transform their data into actionable insight. And this advantage of automating a plethora of manual processes (that involve data and decision making) is provided by AI and ML.

In a nutshell, incorporating AI and ML into systems and strategic plans allows leaders and the management to better understand and act on data-driven insights with greater speed and efficiency.

For Machine Learning:

ML is already pivoting various applications that you use every day:

• For example, Meta (formerly Facebook) uses ML to personalize the news feed of users. This is why you keep receiving similar posts or posts by those creators whose content you have previously liked. (In simple words, if you have liked various posts of Kim Kardashian, your feed will be populated by more posts by Kim K.)
• Did you know that your GPS navigation service also uses machine learning to analyze traffic data and predict high-congestion areas on your commute?
• Even your email spam filter is using machine learning when it routes unwanted messages away from your inbox!

Apart from its integration in our daily lives, ML has a great role to play in the enterprises as well.

• It can help pull insights from large amounts of customer data. So that companies can deliver personalized services and targeted products based on individual needs.
• In the case of regulated industries like healthcare and financial services, ML helps strengthen security. Compliance by analysing activity records to identify suspicious behaviour, uncover fraud and improve risk management.
• Generally, ML and other AI techniques can provide an organization with greater real-time transparency so the company can make better decisions.

For Artificial Intelligence:

Companies integrate AI into various areas of their operations. From customer services to sales and marketing, AI plays a vivid role in helping companies succeed. Let us have a look at how AI is helping companies and enterprises:

• For customer services, AI is used for answering customer questions via AI-powered chatbots, improving credit card fraud detection, analyzing customer feedback and surveys via survey apps, and many more.
• For sales and marketing, AI helps create accurate forecasts by studying historical and market data, updating customer contact information, generating new leads and optimizing lead scoring, and many more. In fact, companies use AI to create personalized messages as well as curated content streams. And digital ad programs that deliver offers customers want, and optimize pricing in real-time based on competitive and market factors.

Opinion: What Can We Expect From Artificial Intelligence and Machine Learning?

(You’ve finally reached the end of the blog. So, congratulations!) Artificial Intelligence and Machine Learning are already blooming now. In fact, numerous companies are investing billions of dollars in AI and ML. While there are several things that AI and ML can do to accentuate humans. But there are many things that they cannot do. There are certain limitations to these technologies.

50 years down the lane, when historians decide to go through the book of (crazy) advances in the 2020s. They will analyze how impactful AI and ML have been for the future of the world in general. Today, we are building machines that can mimic humans and their language, creativity as well as their thoughts. And what would that mean for the future? Consequently, AI and ML will only propel the future of all industries and sectors. By now, the hype of these technologies has exceeded the likes of reality. The advances in various important areas have become equal and even surpassed the capabilities of humans.

So, if you have not paid attention to artificial intelligence and machine learning yet, it is high time that you should.

‘Also Asked’ for Artificial Intelligence and Machine Learning

1. What Is the Main Difference Between Artificial Intelligence and Machine Learning?

While AI is a technology that enables machines to imitate human behavior. ML is a subset of AI that allows machines to automatically learn from past data without programming explicitly. In short, the goal of AI is to build a smart computer system, comprising human intelligence, to solve complex problems.

2. Who Is the Father of AI?

John McCarthy is known as the father of artificial intelligence.

3. Which Language is Frequently Used for AI Programming?

Python is widely used for artificial intelligence. It comes with packages for several applications including General AI, Machine Learning, Natural Language Processing, and Neural Networks.

4. Who Invented Machine Learning?

Arthur Samuel (1901-1990), an American pioneer in the field of computer gaming and artificial intelligence, coined the term “machine learning” in 1959. He defined it as a “field of study that gives computers the ability to learn without being explicitly programmed”.

5. What Is the Main Difference Between Machine Learning and Deep Learning?

Machine learning is about computers being able to think and act with less human intervention. Deep learning is about computers learning to think using structures modeled on the human brain.