Ultimate Guide To Learning 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.

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

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.

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.

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.

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 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.”

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.

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.

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?

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 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 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.

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.

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.

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.

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.”

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.

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.

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.

This short TensorFlow introduction uses Keras to:

1) Build a neural network that classifies images.

2) Train this neural network.

3) And, finally, evaluate the accuracy of the model.

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.

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.

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.

Machine Learning & AI Skills Program by Pluralsight. Build AI and machine learning skills with courses and assessments on Python, TensorFlow, R, Neural Networks, Microsoft Cognitive Services and others to create more engaging experiences for your customers.

Artificial Intelligence Specialist Program by Udacity. Learn specialized skills in the most transformative AI fields; Computer Vision, Natural Language Processing, Deep Reinforcement Learning, or core AI Algorithms. Each of these programs are advanced topics, building on your existing skills in programming, deep learning, and machine learning.

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.