Artificial intelligence and machine learning often appear simultaneously, especially in topics like big data and analytics. These terms are used interchangeably yet they are different from each other. This note aims to delineate the difference between AI and Machine Learning.
The Rise of Artificial Intelligence
Research labs are filled to the brim with Artificial Intelligence due to its extensive usage. As we have matured in this field, new locks to AI have been opened.
The birth of Artificial Intelligence goes back to the time of Aristotle when he introduced syllogism. Scientists from various fields – Mathematics, Economics, Engineering, Psychology, and Political Science, suggested the development of an artificial brain. Between the 1930’s and 40’s, Alan Turing, a leader in computing formulated techniques, laid the foundation for Artificial Intelligence today. That is how AI came into being.
Today, Artificial Intelligence is interpreted as the fusion of machines and humans. To put it simply, AI is an umbrella term for all processes that makes computers smarter and faster. AI is one of the essential fields of Computer Science that involves robotics, expert systems, general intelligence machine learning, and natural language processing. Of course, big data is an integral part of AI. In order to be able to process and analyze information, AI systems need to have access to vast fields of data, which is made possible by Big Data systems.
AI is listed as General or Applied. Applied Artificial Intelligence is common and is used in designing systems that trade stocks and shares. Further, the Generalized AI is capable of handling any task and rapid progress is being made in this field.
During its early years of development, AI helped people avoid tedious household chores by using machines like Dishwashers, Vacuum Cleaners, and Lawn Mowers. As the field progressed, AI has a significant hand in the development of the security systems. The national security system uses data on AI systems to help predict problems that the nation might face in the future. This helps solve criminal cases and help investigators can build criminal profiles.
Artificial intelligence also contributes to education and learning. AI can be used to make personalized tutoring to scrutinize the study pattern of students. The elderly and disabled have also benefited from it. Robotics also has its uses here. Robotics uses voice recognition systems to help people with speech difficulties make the most of speech therapy. Artificial Intelligence has also uplifted the transport industry. With the advent of software programmed cars, the risk of accidents and traffic jams have been significantly reduced.
Giants like Google, Netflix and Facebook have recognized the power of Artificial Intelligence and all have revamped themselves with its help. When combined with a broader subject, machine learning becomes confusing to most people. Let us now try to untangle the concept of machine learning.
The Rise of Machine Learning
There are two important breakthroughs that led to the rise of Machine Learning.
First came with Arthur Samuel who coined the term “machine learning” in 1959. He used analysis to teach a computer to learn on its own instead of having to input step-by-step instructions into it.
A more recent breakthrough was the rise in the use of the internet, and the sudden increase in the size of storage for digital data, which allowed data to be stored and analyzed to develop insights.
As soon as these innovations were in place, scientists realized that it would be far better to teach computers to think like human beings than to have to code tedious instructions every time computers had to perform an action.
Machine learning is all about the algorithms that enable machines to learn using data, evaluation, and experiences. Powerful machine learning algorithms help create various archetypes that are capable of predicting multiple future events accurately and can alert people in advance. It also helps machines make the right decisions based on their past experiences. After years of research in this field, new algorithms are being developed to help machines find problems and solve them using the right approaches. Researchers are still writing a great deal of code and extensive research is still required to get to a point where machines can understand problems and derive automatic solutions without human help.
If you are planning to launch a career in Machine Learning, then this Machine Learning certification is for you where you can get training from experts and learn to implement various algorithms.
Let us now look at some real time examples of AI and ML to draw a distinction between the two:
It is a bot that is shaped after human behavior and can hold human conversations. The responses are not programmed, rather it searches keywords from the phrases used and matches the input with the conversations (data) that are saved already.
Self Repairing Hardware
At Caltech, scientists made an integrated circuit through sensors and actuators that restores itself when damaged. The sensors perform multiple operations like reading temperature, current, voltage, and power. The chip is given a goal state, like the highest output power. When the chip (with the help of actuators) senses that it is nearing the goal, it immediately modifies itself. Previous researchers, for instance, D. Mange et al. used evolutionary algorithms in the creation of self-repair logic circuits.
With the help of traditional AI techniques like machine learning, speed recognition, classification and natural language processing, scientists are trying to make our phones smarter. These advanced methods give us robust applications, such as SIRI on iOS or Kinect from Microsoft.
Amazon Automates Employee Access Control
Amazon, leaders of machine-learning based recommendation engines, conducted a machine learning contest on Kaggle. The aim was to check if it was possible to automate employee access granting and revocation. Amazon has a detailed dataset of employee profiles with their roles and responsibilities and their access levels. Amazon wanted to develop a computer algorithm that will estimate which employees should be given access to which resources. According to Amazon’s report, “These auto-access models decrease human involvement when granting or revoking access and thereby reduce errors.”
Cornell University is trying to develop an algorithm that helps identify whales in an ocean to protect them from getting hit by ships. Additionally, Oregon State University is working on software that will inform which bird species is/are on a specific audio recording collected in field conditions.
Predictive & Prescriptive Sales
Machine learning allows companies to analyze their datasets to accurately identify which opportunities may or may not close in a given time period. Some solutions such as SalesChoice take it a step further to also offer insights on what sales teams can do to improve their odds of winning these deals. That mix of predictive and prescriptive insights allows machine learning to not only predict but also coach sellers in approaching their pipeline.
All in all, machine learning and artificial intelligence are now vital portions of the world’s ecosystem. People who are well-versed in the field of data science can do wonders in their field. As the influence of AI and machine learning in our lives continues to grow.
Fountainhead and CEO Growth Marketing Agency Fly.Biz