12 January 2021
Satsyil’s insights on Big Data and AI/Machine Learning
Insights on Machine Learning and Big Data technologies
Artificial intelligence or AI enables machines to use algorithms to mimic human behavior, while machine learning or ML allows machines to improve using statistical methods. In contrast, deep learning or DL uses neural networks to function like the human mind. In relationship to one another, AI includes both ML and DL, and deep learning is a subset of machine learning. Software and hardware developers use these concepts to create applications that anticipate and respond to the inputs of customers and other users.
How Do These Technologies Work?
AI uses computer algorithms to allow decision making and ML algorithms create opportunities for programs to mimic behavior based on outcomes of previous iterations. Deep learning adds layers in a neural network that provide faster, more complex analysis of data for more nuanced reactions.
The most basic artificial intelligence algorithms use search trees and advanced mathematics to solve problems. this increases the chance of success without ensuring accuracy. With machine learning, programmers can use math and logic inputs to create algorithms that allow machines to "visualize" outcomes.
Machine learning allows for corrections that make these algorithms more accurate over time. For deep learning, developers need a very clear idea of the mathematics involved. They break functions into linear paths that overlap and digress. By adding more layers, deep learning functions somewhat like the human brain. To reach the highest levels of accuracy, DL requires huge amounts of data.
Examples of AI, ML and DL
You can see examples of all three types of machine intelligence and existing applications as follows:
- examples of AI applications that perform basic functions automatically include ridesharing apps such as Lyft and Uber, Google's AI-powered predictions and services, AI autopilot used on commercial flights.
- Examples of machine learning include Siri, Google and Alexa as well as Amazon SageMaker, which uses machine learning to create a workflow and prepare data as well as to optimize it and take action.
- Examples of DL applications include: Fraud detection algorithms, virtual assistants, facial recognition software, and investment modeling. Many customer relationship management systems also use this technology to improve customer satisfaction.
How Efficient Are Machine Intelligence Applications?
The efficiency of artificial intelligence depends on the complexity of the data, the amount of data available and the sophistication of the machine learning and deep learning algorithms and neural networks, respectively. Machine learning is less efficient than deep learning applications and isn't very useful for huge amounts of data. Deep learning is required for the best possible results given large data sets.
Satsyil helps small businesses develop solutions using AI/ML/DL, big data, data science, and advanced search solutions. Contact us today to set up a consultation or learn more about our services.