Artificial intelligence (AI) is currently one of the hottest buzzwords in tech and with good reason. The last few years have seen several innovations and advancements that have previously been solely in the realm of science fiction slowly transform into reality.
Experts regard artificial intelligence as a factor of production, which has the potential to introduce new sources of growth and change the way work is done across industries. For instance, this PWC article predicts that AI could potentially contribute $15.7 trillion to the global economy by 2035. China and the United States are primed to benefit the most from the coming AI boom, accounting for nearly 70% of the global impact.
This Simplilearn provides an overview of AI, including how it works, its pros and cons, its applications, certifications, and why it’s a good field to master.
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What Is Artificial Intelligence?
Artificial Intelligence is a method of making a computer, a computer-controlled robot, or a software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems.
A Brief History of Artificial Intelligence
Here’s a brief timeline of the past six decades of how AI evolved from its inception.
1956 - John McCarthy coined the term ‘artificial intelligence’ and had the first AI conference.
1969 - Shakey was the first general-purpose mobile robot built. It is now able to do things with a purpose vs. just a list of instructions.
1997 - Supercomputer ‘Deep Blue’ was designed, and it defeated the world champion chess player in a match. It was a massive milestone by IBM to create this large computer.
2002 - The first commercially successful robotic vacuum cleaner was created.
2005 - 2019 - Today, we have speech recognition, robotic process automation (RPA), a dancing robot, smart homes, and other innovations make their debut.
2020 - Baidu releases the LinearFold AI algorithm to medical and scientific and medical teams developing a vaccine during the early stages of the SARS-CoV-2 (COVID-19) pandemic. The algorithm can predict the RNA sequence of the virus in only 27 seconds, which is 120 times faster than other methods.
Types of Artificial Intelligence
Below are the various types of AI:
1. Purely Reactive
These machines do not have any memory or data to work with, specializing in just one field of work. For example, in a chess game, the machine observes the moves and makes the best possible decision to win.
2. Limited Memory
These machines collect previous data and continue adding it to their memory. They have enough memory or experience to make proper decisions, but memory is minimal. For example, this machine can suggest a restaurant based on the location data that has been gathered.
3. Theory of Mind
This kind of AI can understand thoughts and emotions, as well as interact socially. However, a machine based on this type is yet to be built.
Self-aware machines are the future generation of these new technologies. They will be intelligent, sentient, and conscious.
How Does Artificial Intelligence Work?
Put simply, AI systems work by merging large with intelligent, iterative processing algorithms. This combination allows AI to learn from patterns and features in the analyzed data. Each time an Artificial Intelligence system performs a round of data processing, it tests and measures its performance and uses the results to develop additional expertise.
Ways of Implementing AI
Let’s explore the following ways that explain how we can implement AI:
Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic a human brain’s neural network. It can make sense of patterns, noise, and sources of confusion in the data.
Consider an image shown below:
Here we segregated the various kinds of images using deep learning. The machine goes through various features of photographs and distinguishes them with a process called feature extraction. Based on the features of each photo, the machine segregates them into different categories, such as landscape, portrait, or others.
Let us understand how deep learning works.
Consider an image shown below:
The above image depicts the three main layers of a neural network:
- Input Layer
- Hidden Layer
- Output Layer
The images that we want to segregate go into the input layer. Arrows are drawn from the image on to the individual dots of the input layer. Each of the white dots in the yellow layer (input layer) are a pixel in the picture. These images fill the white dots in the input layer.
We should have a clear idea of these three layers while going through this artificial intelligence tutorial.
The hidden layers are responsible for all the mathematical computations or feature extraction on our inputs. In the above image, the layers shown in orange represent the hidden layers. The lines that are seen between these layers are called ‘weights’. Each one of them usually represents a float number, or a decimal number, which is multiplied by the value in the input layer. All the weights add up in the hidden layer. The dots in the hidden layer represent a value based on the sum of the weights. These values are then passed to the next hidden layer.
You may be wondering why there are multiple layers. The hidden layers function as alternatives to some degree. The more the hidden layers are, the more complex the data that goes in and what can be produced. The accuracy of the predicted output generally depends on the number of hidden layers present and the complexity of the data going in.
The output layer gives us segregated photos. Once the layer adds up all these weights being fed in, it'll determine if the picture is a portrait or a landscape.
Example - Predicting Airfare Costs
This prediction is based on various factors, including:
- Origin airport
- Destination airport
- Departure date
We begin with some historical data on ticket prices to train the machine. Once our machine is trained, we share new data that will predict the costs. Earlier, when we learned about four kinds of machines, we discussed machines with memory. Here, we talk about the memory only, and how it understands a pattern in the data and uses it to make predictions for the new prices as shown below:
AI Programming Cognitive Skills: Learning, Reasoning and Self-Correction
Artificial Intelligence emphasizes three cognitive skills of learning, reasoning, and self-correction, skills that the human brain possess to one degree or another. We define these in the context of AI as:
- Learning: The acquisition of information and the rules needed to use that information.
- Reasoning: Using the information rules to reach definite or approximate conclusions.
- Self-Correction: The process of continually fine-tuning AI algorithms and ensure that they offer the most accurate results they can.
However, researchers and programmers have extended and elaborated the goals of AI to the following:
Logical ReasoningAI programs enable computers to perform sophisticated tasks. On February 10, 1996, IBM’s Deep Blue computer won a game of chess against a former world champion, Garry Kasparov.
Knowledge RepresentationSmalltalk is an object-oriented, dynamically typed, reflective programming language that was created to underpin the “new world” of computing exemplified by “human-computer symbiosis.”
Planning and NavigationThe process of enabling a computer to get from point A to point B. A prime example of this is Google’s self-driving Toyota Prius.
Natural Language ProcessingSet up computers that can understand and process language.
PerceptionUse computers to interact with the world through sight, hearing, touch, and smell.
Emergent IntelligenceIntelligence that is not explicitly programmed, but emerges from the rest of the specific AI features. The vision for this goal is to have machines exhibit emotional intelligence and moral reasoning.
Some of the tasks performed by AI-enabled devices include:
- Speech recognition
- Object detection
- Solve problems and learn from the given data
- Plan an approach for future tests to be done
What is Artificial Intelligence: Advantages and Disadvantages of AI
Artificial intelligence has its pluses and minuses, much like any other concept or innovation. Here’s a quick rundown of some pros and cons.
- It reduces human error
- It never sleeps, so it’s available 24x7
- It never gets bored, so it easily handles repetitive tasks
- It’s fast
- It’s costly to implement
- It can’t duplicate human creativity
- It will definitely replace some jobs, leading to unemployment
- People can become overly reliant on it
Let us continue this article on What is Artificial Intelligence by discussing the applications of AI.
What is Artificial Intelligence: Applications of Artificial Intelligence
Machines and computers affect how we live and work. Top companies are continually rolling out revolutionary changes to how we interact with machine-learning technology.
DeepMind Technologies, a British artificial intelligence company, was acquired by Google in 2014. The company created a Neural Turing Machine, allowing computers to mimic the short-term memory of the human brain.
Google’s driverless cars and Tesla’s Autopilot features are the introductions of AI into the automotive sector. Elon Musk, CEO of Tesla Motors, has suggested via Twitter that Teslas will have the ability to predict the destination that their owners want to go via learning their pattern or behavior via AI.
Furthermore, Watson, a question-answering computer system developed by IBM, is designed for use in the medical field. Watson suggests various kinds of treatment for patients based on their medical history and has proven to be very useful.
Some of the more common commercial business uses of AI are:
1. Banking Fraud Detection
From extensive data consisting of fraudulent and non-fraudulent transactions, the AI learns to predict if a new transaction is fraudulent or not.
2. Online Customer Support
AI is now automating most of the online customer support and voice messaging systems.
3. Cyber Security
Using machine learning algorithms and ample sample data, AI can be used to detect anomalies and adapt and respond to threats.
4. Virtual Assistants
Siri, Cortana, Alexa, and Google now use voice recognition to follow the user's commands. They collect information, interpret what is being asked, and supply the answer via fetched data. These virtual assistants gradually improve and personalize solutions based on user preferences.
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Different Artificial Intelligence Certifications
1. Introduction to Artificial Intelligence Course
Simplilearn's AI for Beginners is designed to help learners decode the mystery of artificial intelligence and its business applications. The course provides an overview of AI concepts and workflows, machine learning and deep learning, and performance metrics. You’ll learn the difference between supervised, unsupervised and reinforcement learning, be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.
2. Machine Learning Certification Course
Simplilearn’s Machine Learning course will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You'll master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of a Machine Learning Engineer.
3. Artificial Intelligence Engineer Master’s Program
Simplilearn's Artificial Intelligence course, in collaboration with IBM, gives training on the skills required for a successful career in AI. Throughout this exclusive training program, you'll master Deep Learning, Machine Learning, and the programming languages required to excel in this domain and kick-start your career in Artificial Intelligence.
4. Simplilearn’s Artificial Intelligence (AI) Capstone Project
Simplilearn’s Artificial Intelligence (AI) Capstone project will give you an opportunity to implement the skills you learned in the masters of AI. With dedicated mentoring sessions, you’ll know how to solve a real industry-aligned problem. You'll learn various AI-based supervised and unsupervised techniques like Regression, Multinomial Naïve Bayes, SVM, Tree-based algorithms, NLP, etc. The project is the final step in the learning path and will help you to showcase your expertise to employers.
Reasons to Get an Artificial Intelligence Certification: The Key Takeaways
Here are the top reasons why you should get a certification in AI if you’re looking to join this exciting and growing field:
1. Demand for Certified AI Professionals will Continue to Grow
The McKinsey Global Institute predicts that approximately 70 percent of businesses will be using at least one type of Artificial Intelligence technology by 2030, and about half of all big companies will embed a full range of Artificial Intelligence technology in their processes. AI will help companies offer customized solutions and instructions to employees in real-time. Therefore, the demand for professionals with skills in emerging technologies like AI will only continue to grow.
2. New and Unconventional Career Paths
A Future of Jobs Report released by the World Economic Forum in 2020 predicts that 85 million jobs will be lost to automation by 2025. However, it goes on to say that 97 new positions and roles will be created as industries figure out the balance between machines and humans.
Because of AI, new skill sets are required in the workforce, leading to new job opportunities. Some of the top AI roles include:
- AI/machine learning researcher - Researching to find improvements to machine learning algorithms.
- AI software development, program management, and testing - Developing systems and infrastructure that can apply machine learning to an input data set.
- Data mining and analysis - Deep investigation of abundant data sources, often creating and training systems to recognize patterns.
- Machine learning applications - Applying machine learning or AI framework to a specific problem in a different domain and for example, applying machine learning to gesture recognition, ad analysis, or fraud detection
3. Improve Your Earning Potential
Many of the top tech enterprises are investing in hiring talent with AI knowledge. The average Artificial Intelligence Engineer can earn $164,000 per year, and AI certification is a step in the right direction for enhancing your earning potential and becoming more marketable.
4. Higher Chances of a Discussion
If you are looking to join the AI industry, then becoming knowledgeable in Artificial Intelligence is just the first step; next, you need verifiable credentials. Certification earned after pursuing Simplilearn’s AI and ML Courses will help you reach the interview stage as you’ll possess skills that many people in the market do not. Certification will help convince employers that you have the right skills and expertise for a job, making you a valuable candidate.
Artificial Intelligence is emerging as the next big thing in technology. Organizations are adopting AI and budgeting for certified professionals in the field, thus the growing demand for trained and certified professionals. As this emerging field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries.