Most of us have used Siri, Google Assistant, Cortana, or even Bixby at some point in our lives. What are they? They are our digital personal assistants. They 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 the assistant will respond with the relevant information by either going through your phone or searching 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:
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What is Artificial Intelligence?
Artificial Intelligence is the ability of a computer program to learn and think. John McCarthy coined the term ‘Artificial Intelligence’ in the 1950s. He said, ‘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.’
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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 Artificial Intelligence:
- 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’ that comprised of 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.
- 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.
Fascinating, isn’t it? But, how to make AI think or learn by itself? Let’s find that out in the next section.
How does Artificial Intelligence work?
Computers are good at following processes, 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 an 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 labeled data, and the rest 20 percent will be our test 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.
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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.
What are the major subfields of Artificial Intelligence?
Artificial Intelligence works with large amounts of data that are first combined with the fast, iterative processing and smart algorithms that allow the system to learn from the patterns within the data. This way, the system would be able to deliver accurate or close to accurate outputs. As it sounds, AI is a vast subject, which involves much-advanced and complex processes, and it is a field of study that includes many theories, methods, and technologies. The major subfields under AI are explained below:
Machine Learning: Machine Learning is the learning in which a machine can learn on its own from examples and previous experiences. The program developed for it need not be specific and will not be static. The machine tends to change or correct its algorithm as and when required.
Artificial Intelligence (AI) and Machine Learning (ML) are the two most commonly misinterpreted terms. Generally, people tend to understand that they are the same, which leads to confusion. ML is a subfield of AI. However, both terms are recalled simultaneously and repeatedly whenever the topics of Big Data or Data Analytics, or some other related topics, are talked about.
Neural Networks: Artificial Neural Networks (ANNs) were developed getting inspired by the biological neural network, i.e., the brain. ANNs are one of the most important tools in Machine Learning to find patterns within the data, which are far too complex for a human to figure out and teach the machine to recognize.
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Deep Learning: In Deep Learning, a large amount of data is analyzed, and here, the algorithm would perform the task repeatedly, each time twisting/editing a little to improve the outcome.
Cognitive Computing: The ultimate goal of cognitive computing is to imitate the human thought process in a computer model. How can this be achieved? Using self-learning algorithms, pattern recognition by neural networks, and natural language processing, a computer can mimic the human way of thinking. Here, computerized models are deployed to simulate the human cognition process.
Computer Vision: Computer vision works on allowing computers to see, recognize, and process images, the same way as the human vision does, and then it provides an appropriate output. Computer vision is closely related to Artificial Intelligence. Here, the computer must understand what it sees, and then analyze it, accordingly.
Natural Language Processing: Natural language processing means developing methods that help us communicate with machines using natural human languages like English.
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Now that we understand what Artificial Intelligence is and we are familiar with its subfields, we would consider why it is really in demand in the current world. To begin with, here is a quote from Forbes:
‘Machines and algorithms in the workplace are expected to create 133 million new roles, but cause 75 million jobs to be displaced by 2022 according to a new report from the World Economic Forum (WEF) … This means that the growth of Artificial Intelligence could create 58 million net new jobs in the next few years.’
Interesting, isn’t it? If you are looking out for a change in your job, then Artificial Intelligence can be your best bet for your sustainable career growth. There is a huge demand for AI professionals, right now.
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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||Artificial Intelligence||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.|
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.
Top-paying AI Jobs
Once we identify which all jobs most frequently require AI skills, we want to know how much corporates 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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Advantages of Artificial Intelligence
- Reduced human error: With humans involved in the tasks where precision is required, there will always be a chance of error. However, if programmed properly, machines do not make mistakes and easily perform repetitive tasks without making many errors, if not at all.
- Risk avoidance: Replacing humans with intelligent robots 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.
Limitations of Artificial Intelligence
- 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.
- No emotions: There is no doubt that machines are much 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.
- Box thinking: 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 decision as we humans do. But, at present, it can only do the tasks it is programmed for. AI 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 a living organism, it will not stop even if it hits a deer and knock it off.
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. The AI systems learn from this data and then predict which transactions are fraudulent and which are not based on these huge training datasets.
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
for 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.
AI in Retail
The market size of AI software is expected to reach up to US$36 million by 2025. This hype in the market has caused retailers to pay attention to Artificial Intelligence. Thus, the majority of big and small-scale industries are adopting AI tools in novel ways across the entire product life cycle—right from the assembling stage to the post-sale customer-service interactions.
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.
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, AI 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.
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 take decisions accordingly.
An autopilot in a commercial plane can take over the control after the 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.
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 researches 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 myopic 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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