ai-ml-dl

What is AI?

Source: IBM SkillsBuild Learning

1. Introduction

Artificial Intelligence, or AI, is the science of making machines smart.

2. Can a Computer Really Think?

The Turing test directly measures “how well a computer can imitate human conversation”

3. Computers Help Humans

To have a conversation about artificial intelligence (AI), we need a practical definition of human intelligence (HI)

Human intelligence is the ability toreason, solve problems, and learn.

These activities involve a complex interaction between cognitive functions like perception, memory, language, and planning.

People do these things naturally because human intelligence enables us to learn from past experience, adapt to new situations, and handle abstract ideas.

Humans can use learned knowledge to adapt to, shape and change their environment.

AI can process data and make certain kinds of predictions faster and more accurately than humans, but it isn’t magic and it isn’t all-powerful

Weak AI

Many companies use Weak AI to automate tasks to get results quickly and at lower costs.

Weak AI outperforms humans in narrowly defined tasks.

Samples

A chatbot that answers customer service questions.

Facial recognition on Facebook

Buying recommendations on Amazon

Apps that can convert voice to text

e.g. Alexa, Google Assistant, Siri

Augmented Intelligence

Augmented intelligence supplements human intelligence, helping humans make better decisions.

It doesn’t replace humans. Instead, it boosts their expertise and improves their productivity.

Samples

IBM Watson for Oncology helps health professionals identify key information in a cancer patient’s medical records, and it recommends several possible treatments along with estimates of how each one might work.

Its recommendations are excellent, based on constantly updated research in cancer science.

But it doesn’t choose the patient’s treatment. That’s up to the health professionals.

In other words, augmented intelligence helps human become faster and smarter at the tasks they’re performing.

Genaral AI

General AI doesn’t exist yet. But when it does, it will be a form of “whole brain emulation,” where a machine can think and make decisions on many different subjects.

It will drive the computers you see on science-fiction video, talking to humans about many subjects, while operating entire cities or starships.

Today, General AI is a goal rather than a practical technology. It will require decades of additional research and more powerful computers to achieve.

AI - ML - DL

Artificial Intelligence: ability to do tasks requiring human intelligence

Tone and Sentiment

Robotics

Visual Recognition

Voice Recognition

Natual Language Processing

Machine Learning: machines learn by experience and acquire skills without human involvement

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Deep Learning: algorithms inspired by the human brain

Writing Algorithms

Creating Models

Artificial Neural Networks

4. Other Technologies Help AI

Cloud

Enable businesses to operate new training algorithms and data management required by AI

Visual Recognition

Natural Language Processing

Predictive Analytics

Internet of Things (IoT)

5. Careers in AI

6. Learn the Jargon

Algorithms

Algorithms are mathematical instructions written by data scientists that tell the machine how to go about finding solutions to a problem. When a small selection of data (called training data) is run through an algorithm repeatedly, each time tweaked by a data scientist until its results are reliably accurate, the result is a model that the machine can use for additional learning by itself.

Chatbot

A chatbot is a computer program designed to simulate conversation with human users, especially over the internet. It is an assistant that communicates with us through text messages or voice and integrates as a virtual companion into websites, applications, or instant messengers.

Data

Data fuels AI. It allows AI systems to reveal patterns, trends, and associations with confidence. Some data is structured, which means it’s been organized into a format that computers can easily read and analyze, such as a database or an Excel file. Other data is unstructured, like tweets, PDFs, and video files.

Deep Learning

Deep learning (DL) is a group of neural networks (which are, in turn, groups of machine learning models). Deep learning can find patterns in complex data structures like images, video, and sound. Many of its models need no explicit training to find a solution, making them excellent for solving problems too big and complex for humans to engineer. Deep learning has been used to train self-driving vehicles, detect fraud, and even make “Deepfake” videos of popular celebrities.

Machine Learning

Machine learning (ML) is the engine of an AI system. It describes machines that learn without explicit instructions on how to perform tasks. It often depends on models: trained artifacts that guide machines when interpreting new data. Models represent patterns of data and help a machine learning system make predictions without being told how to do so.

Natural Language Processing

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. NLP can train computers to process large amounts of human text, like newspapers or conversations, comprehending the intent and meaning of that data. With NLP, a machine can then reply to humans with nuance and understanding. A common example of NLP is a customer service chatbot.

Neural Networks

Neural networks are groups of machine learning models. They simulate the human brain’s densely interconnected brain cells. They can learn things, recognize patterns, and make decisions without having to be explicitly programmed. Neural networks are capable of finding patterns within data that’s so complex that no human could program its analysis.

Reinforcement Learning

Reinforcement learning is a type of machine learning model that doesn’t give the machine any data at all, labeled or unlabeled. Instead, the machine tries different actions and receives reward signals (like cookies for a dog!) when it makes correct moves. In this way the system is trained solve a problem, with no human work required.

Speech Recognition

Speech recognition is technology that can recognize spoken words, which can then be converted to text or carry out a spoken command. A subset of speech recognition is voice recognition, which is the technology for identifying a person based on their voice.

Supervised Learning

Supervised learning is a type of machine learning model that provides the machine with a set of highly accurate data that’s been labeled by a human. The machine uses this model to recognize related things in untrained data sets.

Unsupervised Learning

Unsupervised learning is a type of machine learning model that doesn’t give the AI any labeled data. Instead it gives the AI unlabeled data, and the AI suggests various ways to cluster and organize it. This is valuable when the data is so large or complex that humans can’t identify its patterns themselves.

Visual Recognition

Visual recognition, also known as computer vision, is an AI subfield focused on training computers to understand and interpret pictures and video. Visual recognition models learn how to identify objects, people, or individual attributes in an image. For example, a model could help evaluate automobile accidents, identify the type of vehicle involved and its damage, and then estimate its cost to repair.

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