Most of us have used Siri, Google Assistant, Cortana, or even Bixby at some point in our lives. What are they? They are our artificially intelligent digital personal assistants. The goal of our artificially intelligent assistants is to help us find useful information when we ask for it using our voice. We can say, ‘Hey Siri, show me the closest fast-food restaurant’ or ‘Who is the 21st President of the United States?’, and my AI assistant will respond with the relevant information by either going through your phone or searching it on the web. This is a simple example of Artificial Intelligence! Let’s read more about it!
In this blog on Artificial Intelligence, we will be covering the following topics:
Table of Contents:
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What is Artificial Intelligence?
Artificial Intelligence (AI) is like giving computers or machines the ability to think and make decisions similar to how humans do, but using algorithms and data. It’s all about teaching computers to learn from experiences, solve problems, and even understand and respond to human language. AI is used in many things, like self-driving cars, virtual assistants, and helping doctors make medical diagnoses. It’s like making machines smart and capable of doing tasks that usually require human intelligence.
For example, when you talk to Siri, Alexa, or Google Assistant, they use AI to grasp your voice commands and offer answers or do tasks. Services like Netflix and YouTube rely on AI to recommend movies or videos you might enjoy, taking into account your previous choices.
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History of Artificial Intelligence
As mentioned above, the term ‘Artificial intelligence’ was coined by John McCarthy in the year 1956 at Dartmouth College at the first-ever AI conference. Later that year, JC Shaw, Herbert Simon, and Allen Newell created the first AI software program named ‘Logic Theorist.’
Although, the idea of a ‘machine that thinks’ dates back to the Mayan civilization. In the modern era, there have been some important events since the advent of electronic computers that played a crucial role in the evolution of AI:
Maturation of Artificial Intelligence (1943–1952)
Walter Pitts and Warren S McCulloch, two mathematicians, published ‘A Logical Calculus of the Ideas Immanent in Nervous Activity’ in the Journal of Mathematical Biophysics. They described the behavior of human neurons with the help of simple logical functions that inspired an English mathematician Alan Turing to publish ‘Computing Machinery and Intelligence’ which comprised a test. This Turing Test is used to check a machine’s ability to exhibit intelligent behavior.
The birth of Artificial Intelligence (1952–1956)
Logic Theorist, the first AI program was created in the year 1955 by Allen Newell and Herbert A Simon. It proved around 52 mathematical theorems and improved the proofs for other theorems. Professor John McCarthy coined the term ’Artificial Intelligence at the Dartmouth conference, and it was accepted as an academic field.
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Golden years – early enthusiasm (1956–1974)
After the invention of high-level languages such as LISP, COBOL, and FORTRAN, researchers got more excited about AI and developed algorithms to solve complex mathematical problems. Joseph Weizenbaum, a computer scientist, created the first chatbot named ‘ELIZA’ in the year 1966. A year later, Frank Rosenblatt built a computer named ‘Mark 1 Perceptron.’ This computer was based on the biological neural network (BNN) and learned through the method of trial and error that was later coined as reinforced learning. In 1972, Japan built the first intelligent humanoid robot named ‘WABOT-1.’ Since then, robots are constantly being developed and trained to perform complex tasks in various industries.
A boom in AI (1980–1987)
The first AI winter (1974–1980) was over, and governments started seeing the potential of how useful AI systems could be for the economy and defense forces. Expert systems and software were programmed to simulate the decision-making ability of the human brain in machines. Al algorithms like backpropagation, which uses neural networks to understand a problem and find the best possible solution, were used.
The AI Winter (1987–1993)
By the end of the year 1988, IBM successfully translated a set of bilingual sentences from English to French. More advancements were going on in the field of AI and Machine Learning, and by 1989, Yann LeCun successfully applied the backpropagation algorithm to recognize handwritten ZIP codes. It took three days for the system to produce the results but was still fast enough given the hardware limitations at that time.
The emergence of intelligent agents (1993–2011)
In the year 1997, IBM developed a chess-playing computer named ‘Deep Blue’ that outperformed the world chess champion, Garry Kasparov, in a chess match, twice. In 2002, Artificial intelligence for the first time stepped into the domestics and built a vacuum cleaner named ’Roomba.’ By the year 2006, MNCs such as Facebook, Google, and Microsoft started using AI algorithms and Data Analytics to understand customer behavior and improve their recommendation systems.
Deep Learning, Big Data, and Artificial General Intelligence (2011–Present)
With computing systems becoming more and more powerful, it is now possible to process large amounts of data and train our machines to make better decisions. Supercomputers take the advantage of AI algorithms and neural networks to solve some of the most complex problems of the modern world. Recently, Neuralink, a company owned by Elon Musk, successfully demonstrated a brain–machine interface where a monkey played the ping pong ball video game from his mind.
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Why is Artificial Intelligence important?
Artificial Intelligence (AI) serves a wide range of purposes and has become increasingly important in various aspects of modern society. Here are some key reasons why we need artificial intelligence:
Automation and Efficiency
Artificial Intelligence (AI) is capable of handling tasks that are monotonous, time-intensive, or pose risks to humans. This results in heightened efficiency and productivity across diverse sectors, including manufacturing, healthcare, finance, banking, and transportation.
AI systems can process and analyze vast amounts of data quickly and accurately. This enables better decision-making in fields like healthcare diagnosis, financial trading, and logistics planning.
Personalization and Recommendations
AI-driven systems possess the ability to comprehend individual preferences and behaviors, enabling them to offer tailored experiences. This is used in platforms such as Netflix, Spotify, and Amazon, which employ AI to recommend content or products based on a user’s past interactions.
Predictive Analytics and Forecasting
Artificial Intelligence has the capability to check past data in order to make projections and anticipations. This holds significant value in areas like weather forecasting, demand projection, and making predictions in the stock market.
Enhanced Customer Service
AI-powered chatbots and virtual assistants are capable of providing round-the-clock customer support, answering queries, and solving problems in real-time, improving customer satisfaction.
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How does Artificial Intelligence work?
Computers are good at following instructions, i.e., sequences of steps to execute a task. If we give a computer steps to execute a task, it should easily be able to complete it. The steps are nothing but algorithms. An algorithm can be as simple as printing two numbers or as difficult as predicting who will win elections in the coming year!
So, how can we accomplish this?
Let’s take the example of predicting the weather forecast for 2020.
First of all, what we need is a lot of data! Let’s take the data from 2006 to 2019.
Now, we will divide this data into an 80:20 ratio. 80 percent of the data is going to be our training data, and the rest 20 percent will be our testing data. Thus, we have the output for the entire 100 percent of the data that has been acquired from 2006 to 2019.
What happens once we collect the data? We will feed the labeled data (train data), i.e., 80 percent of the data, into the machine. Here, the algorithm is learning from the data which has been fed into it.
Next, we need to test the algorithm. Here, we feed the test data, i.e., the remaining 20 percent of the data, to the machine. The machine gives us the output. Now, we cross-verify the output given by the machine with the actual output of the data and check for its accuracy.
While checking for accuracy if we are not satisfied with the model, we tweak the algorithm to give the precise output or at least somewhere close to the actual output. Once we are satisfied with the model, we then feed new data to the model so that it can predict the weather forecast for the year 2020.
With more and more sets of data being fed into the system, the output becomes more and more precise. Well, we have to note a point that none of the algorithms can be 100 percent correct. None of the machines have been able to attain 100 percent efficiency as well.
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Types of Artificial Intelligence
There are four types of AI:
|Theory of Mind
|Simple classification and pattern recognition tasks
|Complex classification tasks
|Understands human reasoning and motives
|Human-level intelligence that can by-pass human intelligence too
|Great when all parameters are known
|Uses historical data to make predictions
|Needs fewer examples to learn because it understands motives
|Sense of self-consciousness
|Can’t deal with imperfect information
|Current state of AI
|Next milestone for the evolution of AI
|Does not exist yet
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Advantages and Disadvantages of Artificial Intelligence
The following are the advantages of Artificial Intelligence:
- Reduced human error: When humans handle tasks demanding precision, errors can occur. Yet, well-programmed machines excel in executing repetitive tasks without many mistakes. They learn from past experiences, ensuring accuracy and efficiency.
- Risk avoidance: Replacing humans with intelligent robots to do dangerous tasks is one of the biggest advantages of Artificial Intelligence. AI robots are now doing risky things replacing humans in places such as coal mines, exploring the deepest parts of the ocean, sewage treatment, and nuclear power plants to avoid any disaster.
- Replacing repetitive jobs: Our day-to-day work includes many repetitive tasks that we have to do every day without any change. For example, washing your clothes or mopping the floor doesn’t require you to be creative and find new easy to do it every day. Even big industries have production lines where the same number of tasks has to be done in an exact sequence. Now, machines have replaced these tasks so that humans can spend this time doing creative things.
- Digital assistance: With digital assistants to interact with users 24/7, organizations can save the need for human resources and deliver faster service to customers. It is a win-win situation for both the organization and the customers. In most cases, it is really hard to determine whether a customer is chatting with the chatbot or a human being.
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There are some disadvantages of AI as well which are listed below:
High cost of creation
It may sound a little spooky, but the rate at which computational devices are upgraded is phenomenal. Machines need to be repaired and maintained with time to keep the latest requirements in check, which needs a lot of resources.
There is no doubt that machines are much more powerful and faster than human beings. They can perform multiple tasks simultaneously and produce results in a split second. AI-powered robots can also lift more weight, thereby increasing the production cycle. However, machines cannot build an emotional connection with other human beings, which is a crucial aspect of team management.
Machines can perfectly execute the preassigned tasks or operations with a definite range of constraints. However, they start producing ambiguous results if they get anything out of the trend.
Can’t think for Itself
Artificial Intelligence aims to process data and make a conscious decisions as we humans do. But, at present, it can only do the tasks it is programmed for. These systems cannot make decisions based on emotions, compassion, and empathy. For example, if a self-driving car is not programmed to consider animals like deer as living organism, it will not stop even if it hits a deer and knock it off.
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Examples of Artificial Intelligence
The goal of an AI is to make tasks easier for humans. Due to this AI is used in different types of technologies today. For example,
ChatGPT, a product of OpenAI, exemplifies the power of Artificial Intelligence (AI) in today’s world. It operates through a straightforward two-step process. First, it takes in the text input provided by the user. Then, using its profound understanding of language and context, it seamlessly generates relevant and coherent responses.
This exceptional AI tool can engage in conversations just like a human, making it a valuable virtual chat partner. Moreover, it goes beyond mere conversation. ChatGPT can perform a wide range of tasks, including coding, data analysis, and content creation. With added features and tools, it’s a prime example of AI’s ability to enhance productivity and offer diverse solutions in our increasingly digital landscape.
It helps computers act without the need for programming. There are three types of machine learning:
- Supervised learning – Patterns can be recognized using labeled data sets and then used to label new data sets.
- Unsupervised learning – Data sets can be sorted according to how similar or different they are.
- Reinforcement learning – The AI system is given feedback after actions are performed.
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Tasks can be enhanced when automation tools are coupled together with AI. Big enterprise jobs can be automated while the intelligence from AI is passed on to process changes.
- Machine Vision – Machine Vision uses a camera, digital signal processing, and analog-to-analog conversion, to capture and then analyze visual information. It is used in signature analysis to medical analysis.
- Self-driving Cars – Automatic vehicles use deep learning, image recognition, and machine vision to make sure the vehicle stays in the proper lane as well as dodges pedestrians.
- Robotics – Robotics is an engineering field that focuses on the designing and manufacturing of robots. Nowadays, Machine Learning is being used to build robots so that they can interact with society.
What are the applications of Artificial Intelligence?
Now, it is time for us to know various real-life applications of AI.
Every time you make a transaction online/offline, using your credit or debit card, you receive a message from your bank asking if you have made that transaction. The bank also asks you to report if you haven’t made the transaction.
Banks feed their Artificial Intelligence systems with data regarding both fraudulent and non-fraudulent transactions. These systems learn from this data and then predict which transactions are fraudulent and which are not based on these huge training datasets.
Also Read – What is Information Retrieval?
Music and Movie Recommendations
Did you know that Mark Zuckerberg created Synapse, a music player which suggested songs that users would likely listen to?
Netflix, Spotify, and Pandora also recommend music and movies to users based on their past interests and purchases. These sites accomplish this by garnering the choices users had made earlier and providing these choices as inputs into the learning algorithm.
Users often look for more than just recommendations; they want a music app that understands their emotions. For instance, if someone feels sad, they might want music that can uplift their spirits. Similarly, if they are angry, they might prefer tunes that help them calm down. These emotional connections are essential, and the learning algorithms used by apps like Spotify and Pandora aim to cater to these specific needs, making the user experience more personalized and meaningful.
Also, check out the blog on Logical Thinking in AI.
AI in Retail
The impact of Artificial Intelligence (AI) on the retail industry is remarkable. The AI software market is predicted to soar to an impressive US$36 million by 2025, drawing substantial attention from retailers. This heightened interest has driven both big and small-scale businesses to embrace AI in innovative ways throughout the entire product life cycle.
From the initial stages of product assembly to post-sale customer-service interactions, AI is revolutionizing the retail landscape. Retailers are harnessing AI to enhance customer experiences, streamline inventory management, and optimize marketing strategies.
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With AI technology, a pilot only needs to put the system on autopilot mode, and then the majority of operations on the flight will be taken care of by AI itself. It is reported by the New York Times that only 7 minutes of human intervention (which mostly relates to takeoff and landing) is required for the average flight of a Boeing plane.
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AI in Healthcare
With the help of radiological tools like MRI machines, X-rays, and CT scanners, AI can identify diseases such as tumors and ulcers in the early stages. For diseases like cancer, there is no solid treatment, but the risk of premature death can be greatly reduced if the tumor is detected in its early stage. Similarly, It can suggest medication and tests by analyzing their R-Health records.
AI is also used to study the effects of certain drugs on the human body and alternates for pre-existing ones.
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AI in Transportation
Autonomous vehicles are truly breaking the barrier between fiction and reality. With advanced AI algorithms, cameras, LIDAR, and other sensors, vehicles can collect the data of their surroundings, analyze it, and make decisions accordingly.
An autopilot in a commercial plane can take over the control after takeoff and make sure that all the parameters are matched. Moreover, advanced navigation systems are used for swift adaptations to save precious time and adapt to the changing conditions in the ocean, which might be dangerous for cargo ships.
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Data Science vs Artificial Intelligence vs Machine Learning
Data Science, Machine Learning, and Artificial Intelligence are interconnected, but each one of them uniquely serves a different purpose.
Below are the key differences between Data Science, Artificial Intelligence, and Machine Learning:
|Data Science is used for data sourcing, cleaning, processing, and visualizing for analytical purposes.
|AI combines iterative processing and intelligent algorithms to imitate the human brain’s functions.
|Machine Learning is a part of AI where mathematical models are used to empower a machine to learn with or without being programmed regularly.
|Data Science deals with both structured and unstructured data for analytics.
|AI uses decision trees and logic theories to find the best possible solution to the given problem.
|Machine Learning utilizes statistical models and neural networks to train a machine.
|Some of the popular tools in Data Science are Tableau, SAS2, Apache, MATLAB, Spark, and more.
|Some of the popular libraries to run AI algorithms include Keras, Scikit-Learn, and TensorFlow.
|As a subset of AI, Machine Learning also use the same libraries, along with tools such as Amazon Lex2, IBM Watson, and Azure ML Studio.
|Data Science includes data operations based on user requirements.
|AI includes predictive modeling to predict events based on the previous and current data.
|ML is a subset of Artificial Intelligence.
|It is mainly used in fraud detection, healthcare, BI analysis, and more.
|Applications of AI include chatbots, voice assistants, and weather prediction.
|Online recommendations, facial recognition, and NLP are a few examples of ML.
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Future Goals of Artificial Intelligence
Artificial intelligence has the ability to greatly affect humanity. Some of the goals for the future of Artificial Intelligence include:
- Enhanced Automation: Develop AI systems to automate repetitive tasks and processes, improving efficiency and productivity.
- Advanced Personalization: Create AI that tailors recommendations and services to individual preferences for a more personalized user experience.
- Improved Healthcare: Utilize AI for early disease detection, personalized treatment plans, and efficient healthcare management.
- Autonomous Vehicles: Achieve fully autonomous cars and transportation systems to enhance safety and reduce traffic congestion.
- Natural Language Understanding: Enhance AI’s ability to understand and interact with human language for more natural and effective communication.
- Environmental Solutions: Utilize AI to address environmental challenges, such as climate modeling and resource management.
- Ethical AI: Develop AI systems with strong ethical frameworks and transparency to ensure responsible AI use.
- Advanced Robotics: Create robots with increased dexterity and problem-solving abilities for applications in industries like manufacturing and healthcare.
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Top 10 Jobs That Require AI Skills
Given below are the top job roles with job descriptions that have AI, and related technologies, frequently mentioned in them. The table also shows the percentage of jobs available even after 60 days of their opening.
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Top-paying AI Jobs
Once we identify which jobs most frequently require Artificial Intelligence skills, we want to know how much corporations pay for each of these profiles. In this way, we would get a sense of how competitive the market is for this vast cutting-edge technology.
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There is a growing fear that the widespread implementation of AI will erode human jobs. Not just commoners but entrepreneurs like Elon Musk are voicing alerts at the growing pace of research undertaken in the AI domain. They are also in a view that AI systems may pave a way for large-scale violence in the world. But that is a very narrow way of looking at things!
In recent decades, technology has grown rapidly and massively. During the entire course, for every job lost to technology, there were always fresh and new job roles emerging. If it had been the case where a new technology replaced all human jobs, then, by now, the majority of the world would have gone jobless. Even the Internet during its inception had garnered many negative reviews. But, it is now obvious that the Internet can never be replaced. You wouldn’t be reading this blog if that was the case. Similarly, though it automates much of the human capabilities, it will rise in its potential and goodwill and benefit mankind in general.
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