AI, Machine Learning, Deep Learning Explained Simply

Supervised ML, Unsupervised ML, Reinforcement Learning

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People talk about “Artificial Intelligence” as if it’s still in the future. Today, in 2019, Artificial Intelligence is already proliferating in our lives. From robotic pets that we buy as the latest toy for our kids to the robotic surgeon that’s performing our family member’s next scheduled surgery, to the recommendation systems that learn our preferences for music, movies, and ads, we are in fact already in the Age of Artificial Intelligence.

As “Artificial Intelligence” become more intelligent and prevalent, there’s a natural fear that grows within us. We can fear the dystopia brought on by not implementing AI correctly in our society. We can fear that AI will replace all of our jobs. We can fear our addictions to these technologies. Alternatively, we can try to understand it all and step back to really evaluate the cost and benefits of implementing AI in our workplaces. Using understanding, each one of us can help to shape our own future with AI.

What is Artificial Intelligence?

Historically, both Alan Turing’s “Thinking Machines” and John McCarthy’s “machines that could think autonomously” were the definitions used for AI. As AI Systems evolved, we are now referring to AI as “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.”

Artificial Intelligence is just as the word implies, the intelligence is artificial, programmed by humans to perform human activities. This artificial intelligence is incorporated into computer systems to create AI Systems that ultimately functions as units of “thinking machines”.

General AI Systems can solve problems intelligently. (Example: AI-powered Stock Trading System)

Narrow AI Systems can perform specific tasks very well. (Example: AI-powered Manufacturing Arm)

According to Darrell M. West’s report for Brookings Institute, these systems have three qualities: intentionality, intelligence, and adaptability.

Intentionality — Humans design AI systems with the intention of making decisions from historical or real-time data or both. These AI systems contain predetermined responses.

Intelligence — AI systems often incorporate machine learning, deep learning, and data analytics with artificial intelligence that enable intelligent decision making. This intelligence is not human intelligence. It’s the machine’s best approximation to human intelligence.

Adaptability — AI systems have the ability to learn and adapt as they compile information and make decisions. As AI systems learn from real-time data, AI systems can refine their decision-making capabilities to improve the outcome.

Artificial Intelligence, Machine Learning, Deep Learning by Jun Wu

Artificial Intelligence, Machine Learning, Deep Learning

AI Systems often incorporate artificial intelligence, machine learning, and deep learning to create a sophisticated intelligence machine that will perform given human functions well. Increasingly, all three units are individual pieces of the entire AI System’s intelligence puzzle.

Machine Learning — It is an application of artificial intelligence that provides the AI System with the ability to automatically learn from the environment and applies that learning to make better decisions. There are a variety of algorithms that Machine Learning uses to iteratively learn, describe and improve data in order to predict better outcomes. These algorithms use statistical techniques to spot patterns and then perform actions on these patterns.

Deep Learning — It is the next generation of Machine Learning. It’s a subset of Machine Learning. Deep Learning models can make their own predictions entirely independent of humans. Machine Learning models of the past still need human intervention in many cases to arrive at the optimal outcome. Deep Learning models use artificial neural networks. The design of this network is inspired by the biological neural network of the human brain. It analyzes data with a logical structure similar to how a human would draw conclusions.

Supervised Machine Learning vs Unsupervised Machine Learning vs Reinforcement Learning

The basics of machine learning comprise of learning from the environment, then applying that learning to make decisions. In order to do this effectively, there are categories of machine learning algorithms that make this possible.

Supervised Machine Learning — In supervised learning, the objective is to come up with a mapping function (f) that will best describe the input data (x) to conclude the output data (Y). We know x and we know Y. But, we have to find the mapping function (f) that will achieve a certain level of performance. Then, we can apply the mapping function (f) to new data to gain similar results. Training data is used to find the function f.

Y = f(X)

There are two types of Supervised Machine Learning problems: Classification and Regression depending on the type of output variable. If the output variable is categorical, then it is a classification problem. (Example: Color can be red, blue, purple, etc…) If the output variable is a real value, then it is a regression problem. (Example: Height can be on a scale of 0ft to 10ft)

A List of Supervised Machine Learning algorithms include:

Linear Regression

Support Vector Machines

Logistic Regression

Naive Bayes

Linear Discriminant Analysis

Decision Trees

K-nearest neighbor algorithm

Unsupervised Machine Learning — Unlike Supervised Machine Learning, unsupervised machine learning does not assume a correct set of output “Y”. There are no outputs. The objective here is to present the most interesting structure that best describes the input data.

There are two types of Unsupervised Machine Learning problems: Clustering and Association. Clustering problems are when you discover groupings inside the input data. (Example: grouping voting behaviors by gender) Association is when you discover rules inside the input data. (Example: female voters tend to vote for female candidates)

A List of Unsupervised Machine Learning algorithms include:

Hierarchical Clustering

K-means Clustering

Mixture Models

DBSCAN

Local Outlier Factor

Neural Networks

Expectation-maximization algorithm

Principal Component Analysis

Non-negative matrix factorization

Reinforcement Learning — Unlike supervised ML and unsupervised ML, reinforcement learning is focused on finding the best path to take in a situation to maximize reward in a situation. The decision is made sequentially. Along each step the algorithm takes on the path to total reward, it will either have a positive or a negative reward. The total reward is the sum of all positive and negative rewards along the path. The goal is to find the best path that maximizes the reward. (A good example of this is an AI-enabled stock trading system.)

Q-Learning

Policy Iteration

State-Action-Reward-State-Action (SARSA)

Deep Q Network

Deep Deterministic Policy Gradient

Deep Learning is the Next Generation of Machine Learning

Deep Learning is the next generation of machine learning algorithms that use multiple layers to progressively extract higher level features (or understanding) from raw input. For instance, in image recognition applications, instead of just recognizing matrix pixels, deep learning algorithms will recognize edges at a certain level, nose at another level, and face at yet another level. With the ability to understand data from the lower level all the way up the chain, a deep learning algorithm can improve its performance over time and arrive at decisions at any given moment in time.

The power of deep learning algorithm lies in its ability to take on both supervised learning tasks as well as unsupervised learning tasks. It also approximates many brain development theories of the human brain.

Deep learning algorithms are now used by computer vision systems, speech recognition systems, natural language processing systems, audio recognition systems, bioinformatics systems and medical image analysis systems.

Learning more about the fundamentals of Deep Learning Algorithms:

Convolutional Neural Networks

Artificial Neural Networks

Feedforward Neural Networks

Multiple Linear Regression

Gradient Descent

Logistic Regression

Real Life Applications

In real life, problems are rarely simple. AI is best suited to solve certain problems over others. More often than not, AI is best suited to perform certain steps at solving a problem while leaving the rest to a human being. For instance, AI enabled chatbots might be able to follow up with employees about their projects to receive updates on statuses, but, managers still have to build teams, inspire teams and steer the teams toward the right directions.

Problems best suited for AI to solve:

Repetitive Tasks — Manual tasks that follow logical steps to lead to a conclusion. (Example: packaging goods to be ready for delivery at a warehouse)

Data Intensive Tasks — Tasks involving analyzing large amounts of data looking for patterns and anomaly. (Example: detection of fraud from financial records.)

Super Human Tasks — Tasks that require superhuman capabilities and speaks to the limitations of human sensory skills and fine motor skills. (Example: Robot surgeon can use the most precise movements to perform non-invasive surgeries. Finely tuned computer vision can spot tumors on an MRI when human eyes can not see it.)

As AI Systems advance, we are challenged by confronting our own limitations as human beings. While AI brings more efficiency into our lives, we are confronted with new problems created by integrating AI into our lives. Only with more understanding and less fear, can we empower ourselves to move forward in the Age of Artificial Intelligence.