Basics of AI and Machine Learning
Artificial intelligence and machine learning are the most disruptive technologies in the 21st century. These two technologies have made major impacts in the business landscape making processes in organizations more effective, efficient and productive.
Artificial intelligence and machine learning have lead to the creation of intelligent systems that have helped run the day to day business activities. Although they look similar and can sometimes be used as a synonym for the other, they are different in design and function.
Difference Between Artificial Intelligence and Machine Learning
By definition, artificial intelligence is a computer science responsible for making a computer system that assumes the intelligence of a human being. In simpler terms they are extremely intelligent systems that mimic human intelligence; they use algorithms such as deep learning and reinforcement learning algorithm to make an intelligent decision and do they do not need be preprogrammed.
There are three types of artificial intelligence; weak AI, general AI and strong AI. Currently, weak and strong AI is the ones being used as research and development for strong AI continues.
Machine learning on the other hand can be seen as knowledge extraction from data. It is a sub-branch of artificial intelligence that allows machines to learn from historical data and experiences. In this case, accurate predictions are made using data. With rich structured and semi-structured data, machine learning extracts and analyzes insight using algorithms that enables it to make an accurate prediction based on the data.
Machine learning can be divided into three categories: supervised machine learning, unsupervised machine learning and reinforcement machine learning.
Comparison Between Artificial intelligence and Machine learning
|Artificial Intelligence||Machine Learning|
|It is the technology behind human intelligence simulation.||Machine learning is a sub-branch of AI which allows machines to learn for experience without the need to be programmed.|
|The main aim is to make sophisticated computer that will solve complex problems.||The main aim is to allow machine to learn from experience and make accurate outputs.|
|Involves the making of intelligent machine to perform human actions.||Involves training of a machine to perform specific task to get desired outcomes.|
|Its two main subjects are machine learning and deep learning.||Its main subject is deep learning.|
|Its scope is very broad.||Its scope is limited.|
|Artificial intelligence maximizes the chances of success.||Machine leaning is only interested in accuracy and patterns.|
|Its application include customer support using catboats, Siri Online, intelligent humanoid robot, Expert System, game playing, etc.||Its application include Online recommender system, Facebook auto friend tagging suggestions, Google search algorithms, etc.|
|Its capabilities have three categories: Weak AI, General AI, and Strong AI.||It has three types; Supervised machine learning, Unsupervised machine learning, and Reinforcement machine learning.|
|AI uses three types of data; Structured data, semi-structured data, and unstructured data.||Machine learning only uses two types of data: Structured and semi-structured data.|
Machine Learning for Humans
Does machine learning matter?
Artificial intelligence is disrupting industries. It is now necessary more than know the ins and outs of artificial intelligence at your fingertips. After a couple of trials and errors, the success of machine learning is now evident as big cooperates are embracing the new and ever-growing technology.
Here are examples of organizations using machine learning:
- Google’s conversational agent AI interacts with users as a support desk. It also discusses morality answers general questions and expresses opinions
- OpenAI has agents that invented their language to communicate and collaborate efficiently to achieve better results.
- Facebook has also trained its agent to conduct negotiations and lie.
The semantics tree: artificial intelligence and machine learning
To understand machine learning, using a semantics tree is helpful. As you have seen above, artificial intelligence stays at the top of the tree. It is an intelligent system that mimics the human’s mind. Below it; is machine learning which is a sub-branch of artificial intelligence, it allows machines to learn from historical data and experiences. Further, at the bottom of the tree are the three categories of machine learning; supervised machine learning, unsupervised machine learning and reinforcement machine learning.
Probabilistic Machine Learning and Artificial Intelligence
Probabilistic machine learning avails a framework to know what learning is. It describes how to manipulate and represent uncertainty about predictions and models. It also plays a critical role in scientific data analysis, robotics, machine learning, artificial intelligence and cognitive science.
Probabilistic machine learning can therefore be seen as learning that brings about reasonable models to explain observed data; models are used to make predictions and rational decisions. Uncertainty plays a key role in all of this.
Prediction about future consequences and future data can be uncertain, here, probability machine learning provides a framework for guidance.
There is research on current frontiers of probabilistic machine learning with focus on; Probabilistic programming Bayesian optimization, Probabilistic data compression, automating the discovery of interpretable and plausible models from data and Hierarchical modeling.
While key challenges are present, based on the probabilistic framework there will be substantial advances in machine learning and artificial intelligence in future. Among the problems are; scientific model discovery and interpretation, optimization, data compression, decision making, and personalization.
Often probabilistic machine learning defines ways to solve a problem in principle; the challenge is finding how to put it in practice and by doing so in a computationally acceptable manner.
Machine learning has played a huge and important role in the acceleration of creation of artificial intelligence. Now, many areas of business are enjoying efficiency and productivity and companies are reaching ROI with these technologies faster. As more research is done. With inventions like probabilistic machine learning and deep learning, the future for artificial intelligence is bright.
Topics in Machine Learning
- Algorithms Used in a Machine Learning System
- Artificial Intelligence and Machine Learning
- Automated Machine Learning Platform
- Big Data and Machine Learning
- Customer Segmentation Using Machine Learning
- Data Warehouse and Machine Learning
- Designing a Learning System in Machine Learning
- Ethical Machine Learning
- Facebook Machine Learning Platform
- Machine Learning Consulting
- Machine Learning in Embedded Systems
- Machine Learning in IoT Devices
- Machine Learning Servers
- Product Recommendation System in Machine Learning
- Reinforcement Machine Learning – Definition, Types, Algorithms, Examples and More
- Scalable Machine Learning
- Sentiment Analysis Using Machine Learning
- Top Machine Learning Companies