Ultimate Guide To Learning Artificial Intelligence
By definition, artificial intelligence is the ability held by an electric machine (for example, computer, a computer-controlled robot, mobile phone, etc.) to carry out functions similar to that of an intelligent being i.e. humans. These said abilities include reasoning, learning, problem-solving, perception, decision making, and linguistic intelligence.
When we talk about artificial intelligence we talk about a machine that can acquire information and at the same time apply it into various scenarios. In other words, a machine that gains information and knowledge through exposure and experience.
Artificial intelligence is developing rapidly, from mobile applications like SIRI and ALEXA to Tesla’s self-driving cars. As technology evolves so does artificial intelligence.
There are two core parts to AI; knowledge engineering and machine learning. Machines can only start thinking and reacting like humans when they have knowledge regarding the world and how it works. Knowledge engineering involves familiarizing AI with objects, their properties, and categories and how things are linked together. Machine learning involves using sensory inputs and deducing various aspects, objects, and situations of the real world.
History of AI
Before the 1950s artificial intelligence was something only heard in myths and stories. It wasn’t until the late 1950s that scientists for various fields (psychology, mathematics, economics, and political sciences) started discussing the possibility of an ‘artificial brain’. The whole concept of artificial intelligence came into existence when mathematician Alan Turing asked a simple question in his research paper, “Can machines think?” This became the basis of all future research of artificial intelligence.
The term ‘artificial intelligence’ was coined in 1956 during the Dartmouth summer research project. The project, directed by John McCarthy, not only generated the term AI but also set the basis and goals of all future work on artificial intelligence.
Early researches explored various components of AI such as problem-solving and basic reasoning (why, when, what). This early research paved the way for today’s AI which is used by millions of people around the world. These applications include Google’s AlphaGo, Siri, self-driving cars, etc.
The importance of artificial intelligence (AI)
In the modern world, artificial intelligence has become efficient to the point where it can make carry out various functions for human beings. Many industries have started using AI in several departments to perform day-to-day functions. Artificial intelligence is mainly used so that computers or machines can come up with solutions to complex tasks (similarly as humans do). Furthermore, by using their knowledge and information they can advise users on any given subject.
How is AI implemented?
Artificial intelligence comes under two categories: (1) Narrow AI, and (2) AGI (artificial general intelligence)
Narrow Artificial Intelligence
Narrow AI, also known as Weak AI, is a type of AI that can outperform humans in specific narrow tasks. These narrow tasks are highly defined and very specific (unlike general AI). Narrow AI focuses on a singular task at a time rather than performing multiple tasks at once. Examples of narrow AI include Siri, spam filters, Facebook newsfeed, image recognition software, etc. The basic underlying principle of narrow AI is that computers are only a simulation of human intelligence. They may seem to have cognitive functioning like humans but in reality, they are not conscious beings in any sense.
Artificial General Intelligence
AGI, also known as Strong AI, is defined as a type of AI that can mimic functions that are primarily carried out by the human brain. AGI is very common in science-fiction movies – a machine that can carry out all thought-process that a human can and solve a problem like an intelligent human being. AGI, at this moment, is more of a concept than reality. Humans have not yet discovered an algorithm that would completely make a machine like a human. Meaning that the machine would have its own perception, beliefs, and be intelligent in every sense.
Uses of Artificial Intelligence in the real world
Artificial intelligence has proved to be a huge breakthrough in computer science. It has made a noticeable impact on many industries, so much so that many have moved to artificial intelligence to minimize their work. Artificial intelligence has started to spread into many sects of the world such as healthcare, banking, the construction industry (and all heavy industries), and gaming. According to researchers and scientists, AI will replace many human jobs in the future – that means that AI might soon be seen in almost every industry.
Artificial intelligence has completely changed the face of medical science and healthcare. In recent years, scientists with the help of AI have introduced healthcare assistants, commonly known as health bots that offer constant healthcare to patients. Moreover, AI has improved the way medical research is conducted.
Artificial intelligence isn’t only helping banks carry out regular tasks but are also helping in creating a strong customer base. By using AI, banks are now able to make more informed decisions regarding credit and loans. Moreover, several banks have now started to use chatbots that offer assistance to customers 24/7. This not only makes the overall banking process more efficient but also reduces operating costs due to the avoidance of human errors.
Numerous construction agencies and heavy industries use artificial intelligence to transfer various objects from one place to another. Moreover, industries use AI to cut or shape objects. Not only does this save effort but also time, this means that these industries complete their tasks on time and efficiently. Furthermore, all important data can be stored and extracted whenever it may be needed.
Another industry where AI has completely changed its course in the gaming industry. Artificial intelligence has introduced virtual reality into games which has change user experience greatly. AI has been used in video games for a long time in order to produce a response and various behaviors in non-player characters.
Where AI has countless benefits in almost every field you can imagine, there comes some risk as well. One of the greatest risks AI can bring about is that it could be used for potential destruction or harmful activities. If AI is used for the wrong reasons it can have devastating consequences. However, all of this can be avoided, as scientists focus more on AI safety to regulate AI systems and behaviors.
About This Artificial Intelligence Guide
This guide offers the most insightful articles, educational videos, expert insights, specialist tips and best free tutorials about artificial intelligence from around the internet. The learning guide is split into four levels: introduction, basics, advanced and expert. You can learn at your own pace. Each item shows an estimated reading or watching time, allowing you to easily plan when you want to read or watch each item. Below you’ll find a table of contents that enables you to easily find a specific topic you might be interested in.
What is Artificial Intelligence?
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.
Artificial Intelligence (AI) Explained in 10 Minutes
This video on Artificial Intelligence will help you understand the concept of AI and how it is used in the real world to solve complex data driven problems. The following topics are covered in this video:
1. What Is Artificial Intelligence?
2. Types Of Artificial Intelligence
3. Applications Of Artificial Intelligence
Moving Beyond Linearity
It can be argued that artificial intelligence isn’t really a technology, per se, but is instead, at any given point, a set of future facing technologies that usually manifest near the end of an ascending business cycle. Some, like fully certified Level 5 autonomous vehicles, quantum communication systems and even artificial general intelligence (AGI) are years or decades in the future. However, seeds for these are being planted now, and it is likely, even if there is an economic downturn, that research will continue during the quiet periods.
The History of Artificial Intelligence
It’s considered by many to be the first artificial intelligence program and was presented at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in 1956. In this historic conference, McCarthy, imagining a great collaborative effort, brought together top researchers from various fields for an open-ended discussion on artificial intelligence, the term which he coined at the very event.
Types of Artificial Intelligence
There are two ways in which AI is generally classified. One type is based on classifying AI and AI-enabled machines based on their likeness to the human mind, and their ability to “think” and perhaps even “feel” like humans. According to this system of classification, there are four types of AI or AI-based systems: reactive machines, limited memory machines, theory of mind, and self-aware AI.
Understanding The Types of AI
The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.
Artificial Intelligence vs. Machine Learning: What are the Differences?
Artificial intelligence is exceptionally wide in scope. According to Andrew Moore, Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.”
Machine learning (ML) is a branch of artificial intelligence, and as defined by Computer Scientist and machine learning pioneer Tom M. Mitchell: “Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.”
What It Means to Give AI a Theory of Mind
An AI armed with theory of mind could simulate the mind of its human companions to tease out their needs. It could then determine appropriate responses—and justify those actions to the human—before acting on them. Less uncertainty results in more trust.
Machine Theory of Mind
Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans’ ability to represent the mental states of others, including their desires, beliefs, and intentions. We propose to train a machine to build such models too. We design a Theory of Mind neural network – a ToMnet – which uses meta-learning to build models of the agents it encounters, from observations of their behavior alone. Through this process, it acquires a strong prior model for agents’ behavior, as well as the ability to bootstrap to richer predictions about agents’ characteristics and mental states using only a small number of behavioral observations.
Self-Aware Artificial Intelligence: What if AI Would Become Conscious?
As artificial intelligence systems take on more tasks and solve more problems, it’s hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that “By 2029, computers will have emotional intelligence and be convincing as people.”
We don’t know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they’re conscious, does that mean they actually are?
Artificial Intelligence, Consciousness and the Self
To help answer the question whether machines will become conscious we must go back to the question of the nature of reality. Is the world a machine described by its parts and their interconnections, or is it fundamentally knowledge? The first view is called ontic (from ontological, that is related to structure), and the second is called epistemic (from epistemological, related to knowledge). In philosophy, these are the positions of two different schools, one believing that reality is being, and the other that it is becoming. The conception of the world as being is associated with materialism, while that of becoming assigns a more significant role to the observers.
The Ethical Challenge of Artificial Intelligence
The big problem is that the complexity of the software often means that it is impossible to work out exactly why an AI system does what it does. With the way today’s AI works – based on a massively successful technique known as machine learning – you can’t lift the lid and take a look at the workings. So we take it on trust. The challenge then is to come up with new ways of monitoring or auditing the very many areas in which AI now plays such a big role.
The Ethics of Artificial Intelligence
The possibility of creating thinking machines raises a host of ethical issues. These questions relate both to ensuring that such machines do not harm humans and other morally relevant beings, and to the moral status of the machines themselves. The first section discusses issues that may arise in the near future of AI. The second section outlines challenges for ensuring that AI operates safely as it approaches humans in its intelligence.
The third section outlines how we might assess whether, and in what circumstances, AIs themselves have moral status. In the fourth section, we consider how AIs might differ from humans in certain basic respects relevant to our ethical assessment of them. The final section addresses the issues of creating AIs more intelligent than human, and ensuring that they use their advanced intelligence for good rather than ill.
The Basics of AI for Business
AI pathfinder Philipp Gerbert dispels the myth of AI as a complex and mysterious tool for business. In reality, he says, even those of us outside Silicon Valley can have an intimate understanding of AI and put it to work today. Gerbert walks us through the ABC’s of AI and what it can mean for your organization.
Examples of Artificial Intelligence and Machine Learning
We distinguish between AI and machine learning (ML) throughout this article when appropriate. Think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI. All machine learning is AI, but not all AI is machine learning.
Our enumerated examples of AI are divided into Work & School and Home applications, though there’s plenty of room for overlap. Each example is accompanied with a “glimpse into the future” that illustrates how AI will continue to transform our daily lives in the near future.
The Future of Artificial Intelligence and Cybernetics
It’s clear that connecting a human brain with a computer network via an implant could, in the long term, open up the distinct advantages of machine intelligence, communication, and sensing abilities to the individual receiving the implant. Currently, obtaining the go-ahead for each implantation requires ethical approval from the local authority governing the hospital where the procedure is performed. But looking ahead, it’s quite possible that commercial influences, coupled with societal wishes to communicate more effectively and perceive the world in a richer form, will drive market desire.
How AI Can Save Our Humanity
AI is massively transforming our world, but there’s one thing it cannot do: love. In a visionary talk, computer scientist Kai-Fu Lee details how the US and China are driving a deep learning revolution — and shares a blueprint for how humans can thrive in the age of AI by harnessing compassion and creativity. “AI is serendipity,” Lee says. “It is here to liberate us from routine jobs, and it is here to remind us what it is that makes us human.”
Using AI to Give Doctors a 48-hour Head Start on Life-Threatening Illness
Streams is a mobile medical assistant for clinicians, and has been in use at the Royal Free London NHS Foundation Trust since early 2017. The app uses the existing national AKI algorithm to flag patient deterioration, supports the review of medical information at the bedside, and enables instant communication between clinical teams. Shortly after rolling out at the Royal Free, clinicians said that Streams was saving them up to two hours a day.
Responsible AI Practices
The development of AI is creating new opportunities to improve the lives of people around the world, from business to healthcare to education. It is also raising new questions about the best way to build fairness, interpretability, privacy, and security into these systems.
These questions are far from solved, and in fact are active areas of research and development. Google is committed to making progress in the responsible development of AI and to sharing knowledge, research, tools, datasets, and other resources with the larger community.
How to Build Your Own Neural Network From Scratch in Python
Most introductory texts to Neural Networks brings up brain analogies when describing them. Without delving into brain analogies, I find it easier to simply describe Neural Networks as a mathematical function that maps a given input to a desired output.
Neural Networks consist of the following components
An input layer, x
An arbitrary amount of hidden layers
An output layer, ŷ
A set of weights and biases between each layer, W and b
A choice of activation function for each hidden layer, σ. In this tutorial, we’ll use a Sigmoid activation function.
Basic Classification Tutorial: Predict an Image of Clothing
This guide trains a neural network model to classify images of clothing, like sneakers and shirts. It’s okay if you don’t understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as you go.
Best Practices for Machine Learning Engineering
This document is intended to help those with a basic knowledge of machine learning get the benefit of Google’s best practices in machine learning. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming. If you have taken a class in machine learning, or built or worked on a machine-learned model, then you have the necessary background to read this document.
Further Reading: Best Books About Artificial Intelligence
Machine Learning For Absolute Beginners: A Plain English Introduction. This book has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.
Applied Artificial Intelligence: A Handbook For Business Leaders. Applied Artificial Intelligence is a practical guide for business leaders who are passionate about leveraging machine intelligence to enhance the productivity of their organizations and the quality of life in their communities.
Superintelligence: Paths, Dangers, Strategies. Superintelligence asks the questions: What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life.
How to Create a Mind: The Secret of Human Thought Revealed. Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse-engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines.
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner–the Master Algorithm–and discusses what it will mean for business, science, and society. If data-ism is today’s philosophy, this book is its bible.
Further Learning: Best Artificial Intelligence Courses
Artificial Intelligence for Business. Solve Real World Business Problems with AI Solutions, Master the General AI Framework, Implement Q-Learning, Save and Load a model, Build an Optimization Model, Implement Early Stopping, Maximize Efficiency and much more.
Artificial Intelligence A-Z: Learn How To Build An AI. Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications. Understand the theory behind Artificial Intelligence, Make an AI to beat games, Solve Real World Problems with AI, Master the State of the Art AI models and much more.
Introduction to Artificial Intelligence (AI). In this course, you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks.
IBM Applied AI Professional Certificate. Whether you’re a student, developer, or a technology consultant, understanding AI and knowing how to create AI-powered applications can give you an edge in your career. This Professional Certificate is designed to arm you with the skills to work as an AI Application Developer.