Artificial Intelligence and Machine Learning are gaining lots of traction for a good reason. There has been significant development in both. Today, AI and ML are two critical technologies responsible for digital transformation worldwide.

This is the guide if you are new to AI and ML. I have compiled a comprehensive guide for beginners to discuss the two technologies and their phenomenal applications.

So, let’s dive in.

What is Artificial Intelligence?

Artificial Intelligence is a technology mimicking human intelligence. It enables computer applications to learn from experience through algorithm training and iterative processing. The interaction makes the system competent enough to predict and complete the given task.

AI is more efficient in completing a given task than humans. They become more competent and faster with an interactive process, making them the best choice for comprehensive and intelligent decision-making.

AI is an extensive branch of computer science associated with building intelligent machines that can perform tasks that require human intelligence. Machine Learning, on the other hand, is a type of AI that enables software applications to be more accurate at analyzing and predicting outcomes.

AI has influenced consumer products and has been responsible for many breakthroughs in physics and healthcare.

AI’s importance and components have been known for a long time now. They are taken as techniques and tools that make the world better. You do not always have to go to fancy tech gadgets to use them. AI simplifies or even reduces human efforts. With AI, humans don’t have to deal with mundane tasks anymore. 

AI is more efficient, less error-prone, and can more precisely complete its tasks with minimal human intervention. 

What is Machine Learning?

Machine Learning is a branch of AI where a computer learns from data. Like us, human beings learn by various means such as books, training, podcasts, etc. A computer learns from data using statistical methods and algorithms.  

Enabling machines to think like humans isn’t as easy as it sounds. A Strong Artificial Intelligence is possible only through machine learning.

Machine learning is a procedure where a system analyses a large amount of historical data and identifies trends and correlations between data points using statistical methods. The system uses these learnings and builds algorithms to make accurate predictions.

The key to a successful Machine learning solution is the availability of reliable and relevant data. These days, almost all organizations build sophisticated data pipelines to extract, load, and transform data that could be used to create Machine Learning algorithms. 

What is the Significance of AI and ML in Present Times?

AI and ML are used widely by large and small business organizations to increase their efficiency and advance innovation. Studies have shown that 41% of the companies rushed their AI rollout in 2021 due to the pandemic. 31% of the companies already had AI in production or are piloting AI technologies before a pandemic.

Here’s why AI and ML are so significant in present times,

• Healthcare: In Health care, AI is used for diagnosing and predicting treatments for patients. AI is extensively used during drug development, which helps speed up drug development and reduce cost. 
• Finance: Trading brokerages, fintech companies, and banks use MI algorithms for automating trading and providing financial advisories to investors.
• Retail: Retail chains use ML algorithms to develop AI engines that provide relevant product suggestions based on the customers’ past buying habits and geographic, demographic, and historical data.
• Scam and Fraud Detection: Artificial Intelligence has emerged as an effective tool for avoiding financial crimes due to its increased efficiency. Banking institutions use ML to analyze vast numbers of transactions to uncover fraud trends, subsequently used to detect fraud in real-time.
• Data Security:  ML models can quickly identify data security risks before they become breaches. It goes through previous experiences to predict high-risk activities.

What is the Difference Between Artificial Intelligence and Machine Learning?

ML and AI are often used interchangeably, but they are not the same. Let’s take a look at the difference between the two.

Artificial Intelligence	Machine Learning
Enables Machines to simulate human behavior.	A subset of Al allows machines to automatically learn from past data without explicit programming.
An innovative intelligent system behaves like humans to solve complex problems.	The AI system constantly learns from external data and the predictions it made before. (Self-Correction aka Re-enforcement learning)
The objective of AI is to build a system that will be able to perform complex tasks without any manual intervention.	The objective of ML is to create a system that can predict or perform a specific task for which the system was trained using data from the past.
The AI system constantly learns from external data and their previous predictions. (Self-Correction aka Re-enforcement learning)	ML learnings are limited to the data which the system already knows. Re-enforcement learnings must be manually performed
Examples: Voice Assistants such as Siri and Alexa Self-Driving cars, Modern Games	Examples: Recommendation Systems, Image recognition, Speech Recognition, etc.
Subsets Machine Learning. Deep Learning. Natural Language processing. Robotics. Machine Vision.	Subsets Deep Learning.

How are AI and ML Changing the World?

In this modern age, AI and ML dramatically improve our efficiency and make the world better. The chances are very high that you may have unlocked your device using facial recognition even for reading this blog. The usage of AI in our daily life has gone up exponentially. Let’s take a quick peek at a few areas where AI brings dramatic changes to our day-to-day lives. 

Medicine and Healthcare

The Modern medical industry heavily relies on Artificial intelligence. Here are a few examples that will help you understand how AI and ML radically change modern medicine and healthcare.

Diagnosis – Based on the Agency of Healthcare Research and quality study, misdiagnosis was the primary cause of 10% of the patient’s death. Currently, Machine learning is used to determine the precise location of the tumor and determine whether it’s malignant or benign with around 88% accuracy. AI also played a vital role in speeding up the development of the COVID 19 Vaccine. 

Robotic Surgeries – Though completely autonomous robotic surgeries are not a reality. AI and ML help in surgical pre-operative planning, decreasing surgical trauma, etc. Modern AI-Driven surgical robots take over mundane tasks, which allow surgeons to focus on complex aspects of the surgery. (More can be read here.)

Precision Medicines – Traditionally, medicines are prescribed to patients based on their symptoms (symptom-driven). They are often generic. While in Precision medicines are medicines that use information about a person’s own genes or proteins to prevent, diagnose or treat disease (cancer.gov). AI  and ML play a vital role in determining Precision medicines. 

The FDA approved artificial technology in 2018 to detect diabetic retinopathy by scanning the patient’s eye. The system can operate independently, and even a low-skilled worker can easily take the scans. These types of systems can lead to faster and more accurate diagnoses.

AI can also be used to make drug discovery less expensive and faster. With the help of research papers and clinical trials, the technology can easily detect candidate compounds that react with the pathology of a specific disease. AI drug discovery system compares samples from a patient with and without the disease. It can help discover new details about a specific disease.

Agriculture

According to a study conducted by Michigan State University, the global population is expected to reach 9.2 Billion by 2050. We need to bring radical innovation to ensure enough produce to feed all with the limited farmland. Artificial Intelligence and Machine learning play a vital role in ensuring Profitability, sustainability, efficiency, and productivity in the agricultural sector with decreased manpower. 

Below are a few examples of a few use-cases of AI along with IoT devices such as soil sensors, cameras, and Drones in the agricultural industry. 

Crop Selection – AI is used to select the high potential seeds and predict the best time for planting and harvesting, thus providing the maximum return on crops and improving efficiency. 

Monitoring –  AI (Computer Vision) is used for in-field monitoring of corps to detect and deter pests, weeds, and plant diseases. Drones can monitor vast areas of agricultural lands quickly and efficiently and generate reports. These reports can also be automatically fed into Autonomous robots to take immediate corrective actions. 

Precision Application of water and pesticides –  With Drones, GPS, and computer vision, farmers can apply pesticides, weed control, and irrigation more precisely. This reduces the wastage up to 90%, which drastically reduces the cost and improves efficiency. Another advantage of the precision application is a reduction in environmental pollution. 

Autonomous Robots –  Robots are used by farmers to assist them in analyzing and carrying out multiple activities such as harvesting. With the help of computer vision, these robots can detect the plants ready for harvesting and carry out the harvesting quickly and efficiently. (Read more)

Soil Monitoring and Analysis – Soil fertility is heavily dependent on parameters such as pH level, temperature, moisture content, humidity, carbon level, etc. IoT devices such as soil sensors can be used to collect these data, and AI can prescribe timely corrective action. 

Predictive Weather Modeling. 90 % of crop losses are due to weather events. Crop yields are affected by temperature, humidity, and rain. Detailed real-time and predictive weather information helps farmers make informed decisions to increase the maximum corps return. 

Banking and Financial Institutions.

Around 40% to 50% of banks and financial institutions worldwide are using AI to optimize at least some part of their offerings. Let’s take a quick peek at a few of the usages of AI in Fintech.  

Credit decisions – Traditionally, the lenders use just the borrower’s credit score to make credit decisions. However, with AI and ML, lenders can detect potential risk factors that may not be too obvious. AI can also speed up decision-making with less human intervention, thus reducing the fee and better interest rates. (Read More)

Mortgage and Loan Processing – Natural language processing (NLP) can improve the efficiency and accuracy of gathering, reviewing, and verifying supporting documents. This can drastically improve the time taken for underwriting. 

AI-Driven Trading – AI can analyze historical data and other relevant factors such as sentiment analysis to make accurate predictions about stock prices and perform automated trading based on investors’ long- and short-term investment strategies. 

Real-time Risk Management – AI and ML are used by financial institutions to quickly churn through vast data points to identify, assess, and mitigate risks. (Read More)

Fraud Detection and Prevention – AI Systems can monitor users’ location and transaction history and derive their spending habits. Any transactions that don’t fit their spending habits can be blocked or flagged to prevent fraudulent transactions.

Personalized Banking – AI can identify the customers’ needs based on their banking history, types of accounts, balance, social profiles, etc., and provide them with the most relevant financial product. This can be done through their websites, IVR systems, Mobile Apps, or Chatbots. 

Security – AI-enabled Banking systems can use Bio-metrics or voice to authenticate and authorize users while performing banking operations.

Energy Sector

Like Agriculture, the energy sector also faces challenges due to the rise in demand. As more and more nations develop, their energy consumption is also rising. More and more energy companies rely on AI to achieve sustainability and efficiency. 

Let us look at a few use cases here. 

Renewable Energy –  Most common renewable energy is derived from solar panels and windmills. Accurate weather prediction and historical weather data can improve the accuracy of energy generated from solar and wind. This helps in the efficient usage of conventional energy sources such as generators. 

Fault Prediction – Energy companies are using the data from various sensors on the electrical grid to generate AI Models that can accurately predict issues in the grid. This helps to prevent power outages. 

Nuclear Energy –  The nuclear energy sector is also tapping into the benefits of Artificial Intelligence and Machine Learning. It uses the technologies to streamline and optimize nuclear power plant maintenance and operation. The technologies are helping with advanced nuclear power technologies.

There has been news about DeepMind training an AI to control nuclear fusion. DeepMind is an artificial intelligence company backed by Google. Scientists previously used magnetic coils to configure nuclear fusion reactions by nudging them into the perfect position and shaping it like a potter shapes a lump of clay on the wheel. The coils have to be controlled to prevent plasma from touching the corners of the vessels and damaging the wall. Thus, the fusion reaction is reduced. However, whenever the researchers look to change the plasma configuration and try some new shapes, it will yield cleaner plasma or more power. It requires a large amount of design and engineering work. DeepMind has come with an AI that can control the plasma autonomously. It is more suitable for controlling plasma.

Types of Artificial Intelligence

Artificial Intelligence systems can be classified in two ways. They are the following. 

• Classification based on functionalities 
• Classifications based on capabilities. 

Classification based on functionalities

1. Reactive Machine

These are the basic form of AI,  Reactive Machines, as the name suggests, reacts to a certain condition. They function as they are programmed and will always respond to an identical situation in an identical way. These systems work with the data that was provided to them and will not have past memory (what it did in the past). These systems are good at the task for which it is programmed for, and will not be able to perform anything else.  

Some great examples of Reactive Machines are Netflix recommendation engine or spam filters. They don’t interact with the world, but they respond to identical situations, in the same manner, every time the AI encounters the same scenarios.

2. Limited Memory

AI systems with limited memory are able to use past experiences to inform their current decisions, but they can only retain a certain amount of information. These systems are able to remember certain events or actions that have occurred within a specific timeframe and use them to shape their current behavior. For example, a self-driving car with limited memory may remember a particular route it has taken in the past and use that information to navigate a similar route in the future. However, it will not be able to remember every route it has ever taken and may not be able to adapt to significantly different routes.

Limited memory AI systems are often used in practical applications, such as virtual assistants, customer service chatbots, and language translation systems. They can remember specific interactions and use that information to provide more personalized responses or improve their performance over time. However, their ability to retain and use past experiences is limited. They cannot adapt to significant environmental changes or learn new tasks as quickly as more advanced AI systems.

3. Theory of Mind

The theory of mind refers to the ability to understand and infer other agents’ thoughts, beliefs, and intentions (human or machine). It is a crucial component of human social cognition and is essential for understanding and predicting the behavior of others. AI systems with a theory of mind can understand that other agents have their own perspectives and can anticipate their behavior based on that.
For example, an AI system with a theory of mind may understand that a person wants to go to a particular destination and can infer their intentions based on their past behavior and the current context. It could then use this information to suggest a route or provide directions.
Theory of mind is a relatively new area of research in AI and has not yet been widely implemented in practical applications. However, it has the potential to improve the performance of AI systems in a variety of tasks, such as natural language processing, decision-making, and social interaction.

4. Self Aware

Self-aware AI refers to artificial intelligence systems with a sense of self and can reflect upon their own thoughts and actions. These systems can understand their own limitations and can learn and adapt over time. They may be able to introspect on their own mental states and understand the relationships between their thoughts, emotions, and behaviors.
Self-aware AI is still in the research stage and has not yet been widely implemented. But it has the potential to significantly improve the performance of AI systems in a variety of tasks, such as decision-making, problem-solving, and social interaction.
For example, a self-aware AI system may be able to understand its own limitations and seek out additional information or resources to help it solve a problem.

Self-aware AI is a highly complex and controversial topic, and there is an ongoing debate about the feasibility and ethical implications of creating truly self-aware artificial intelligence.

Classification based on the capabilities

AI can also be classified based on its capabilities into the following categories:

1. Artificial Narrow Intelligence

Narrow AI (also known as Weak AI) is a type of artificial intelligence designed to perform a specific task or function and cannot adapt to new tasks or situations. It is typically used in practical applications where it can be trained to perform a specific task with a high level of accuracy.
Examples of narrow AI include virtual assistants like Siri and Alexa, which are designed to answer questions and perform specific tasks, such as setting reminders or playing music. These systems are able to understand and respond to natural language input and can perform a wide range of tasks, but they are not able to adapt to new tasks or situations on their own.
Narrow AI is widely used in a variety of applications, including customer service chatbots, language translation systems, and image and speech recognition systems. It is particularly useful for tasks that require a high level of accuracy and repeatability but are not significantly impacted by changes in the environment or the need to adapt to new tasks.

2. Artificial General Intelligence

General AI (also known as Strong AI) is a type of artificial intelligence designed to perform any intellectual task that a human can perform. It can learn and adapt to new tasks and situations and exhibit human-like intelligence and decision-making abilities.
General AI is still in the research stage and has not yet been fully realized. However, it has the potential to revolutionize a wide range of fields by enabling machines to perform tasks that currently require human intelligence, such as problem-solving, decision-making, and social interaction.

3. Artificial Super Intelligence

Superintelligent AI, also known as artificial superintelligence or ASI, refers to a hypothetical future AI that would be significantly more intelligent than any human being. It would be able to perform tasks and make decisions that are beyond the capabilities of even the most intelligent humans.
There is currently no known way to predict precisely what such an AI would be capable of. Still, it would surely have the potential to revolutionize society and bring about significant technological advancements. Some experts have expressed concern about the potential risks of developing superintelligent AI, including the possibility that it could pose a threat to humanity if it were to be programmed with goals that are incompatible with human values. However, others believe that the development of superintelligent AI could bring significant benefits and help solve some of humanity’s most pressing challenges.

Types of Machine Learning

We learned that Machine learning is a subfield of artificial intelligence (AI) that involves the development of algorithms and models that can learn from data and improve their performance over time. These algorithms and models are able to learn without being explicitly programmed and can adapt to new data and situations. There are several types of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning. Let us take a look at a few of the Machine learning types.

1. Supervised

AI models are created using supervised ML. Experts in this subject matter train the models. Thus, they are known as SMEs or subject matter experts. The models study the newly input data and label them as responsive or unresponsive. They can tag the data as associated with several kinds of issues. With these ML models, you can expect to get more similar content
Usually, supervised machine learning models are used for predictive analysis. Based on the previous experience, to judge the decisions taken by SMEs regarding the documents which have been reviewed. It has been developed using loose artificial neural models designed like the human brain. The assessment they make helps in future prediction and forecasting.
A few examples of supervised learning algorithms are Decision Trees or Linear Regression.

2. Unsupervised

Unsupervised ML is also used for creating AI models. From the name itself, it becomes evident that it features more automation. The models are trained by software and processes mimicking the training offered by people. Unsupervised ML models can categorize the data you have input. It is also capable of identifying trends or patterns even without any training. The model can be used for classifying content or summarizing any content.
Some real-life examples of unsupervised learning are Customer Segmentation or Customer Groups around which a marketing campaign is developed.

3. Semi-Supervised

Semi-Supervised machine learning is the middle ground between the above two models. So, they are a combination of the two approaches. SMEs label a small amount of data for starting a training mode. The two previous models are combined to come up with a model that can be used for predictive as well as descriptive purposes.
Text document classifier is a common application of semi-supervised learning.

4. Reinforcement Learning

Reinforcement learning is machine learning in which an agent learns through trial and error by interacting with an environment and receiving rewards or punishments based on its actions. In reinforcement learning, the agent’s goal is to maximize the reward it receives over time.
The agent takes action in the environment, and the environment responds by giving it a reward or a punishment. The agent uses this feedback to adjust its behavior and improve its performance. For example, an AI agent playing a video game might receive a reward for defeating an enemy or completing a level, and it would learn to repeat actions that lead to these rewards.
Reinforcement learning algorithms use various techniques, such as value iteration and policy iteration, to learn the optimal behavior for a given task. These algorithms are used in a variety of applications, including robotics, autonomous systems, and games.

Machine Learning and Artificial Intelligence Innovation Will Drive Innovation in the Future

Machine Learning models can detect patterns to offer insights. Research by Statista showed that the global AI market is expected to increase to $126 billion by 2025. But AI doesn’t just come with growth opportunities. It can also lead to the disruption of many industries, as promised by machine learning.

AI and ML will give future business leaders better decision-making power. Thus, it will enable researchers to look at problems from various perspectives and offer insights all the time. It is something that humans can’t conceptualize. So, technologies are the best allies of humanity in the future.

AI also comes with great market opportunities. It can adjust itself to every wave of subsequent disruptions. About 52% of the companies, on the other hand, have accelerated their AI adoption plans due to the onset of the pandemic. These had a significant effect on workplaces and businesses across the world.

With the adoption of analytic technologies, organizations are learning more about their world and themselves. ML adoption is making people of every level ask questions that challenge what the organization believes it knows about itself.

Whether rocky or rosy, the future is coming, and AI will play a significant role in it. With the development of technology, the world will see new business applications, brand-new startups, and consumer uses. Indeed, it will lead to the displacement of many jobs, but it will also create some new ones. Along with IoT, AI and ML can potentially remake the economy. However, how the technologies will impact the world is yet to be seen.