Artificial Intelligence and Machine Learning are in trend everywhere, including newspapers and blogging sites. Thanks to this widespread coverage, these two terms are used interchangeably.
Most people still get AI and ML wrong, even professionals in the IT domain. However, with the increased demand for ML and AI professionals, it is vital that people understand these two concepts.
Professionals could even take an online AI course to learn the principles of Artificial Intelligence. For the rest of us, this post aims to shed light on the difference between AI and ML to help readers understand what these terms actually mean.
What is Artificial Intelligence?
Artificial intelligence is a computer program or a group of complex statistical models that perform smart functions. From a computer program playing chess to a voice recognition system, AI plays an important role. AI has three groups—Narrow AI, artificial general intelligence (AGI), and superintelligent AI.
Narrow AI refers to AI that is efficient in performing just one particular task. For example, in 1996, IBM’s Deep Blue defeated chess grandmaster Garry Kasparov in the game. Similarly, in 2016, Google DeepMind’s AlphaGo, defeated at Lee Sedol at Go.
Artificial General Intelligence is the ability of machines to perform different tasks that human beings carry the capability to perform. AGI computers are equal to humans in many ways.
These machines are capable of performing the same level of intellectual tasks that humans can perform successfully. Steve Wozniak, Apple’s co-founder, created the Coffee Test as an AGI indicator. A robot under the test has to enter a home and attempt to make coffee. This involves finding the right tools, checking out how they run and then performing the task.
On the contrary, Superintelligent AI takes things a step further. According to Nick Bostrom, it is an intellectual skill better than human brains and involves creativity, social knowledge, and wisdom.
When machines start to outshine human intelligence, that’s when you can believe that superintelligent AI has arrived. However, this has a long way to go. The AI technology that is currently present is quite ‘dumb,’ according to many industry experts.
Machine learning has done a lot for businesses. From detecting customer fraud to using sentiment analysis to plan market growth, machine learning is everywhere. So, how is it done?
Machine learning refers to algorithms that help machines learn using data, experiences, and, observations. ML algorithms carry the power to develop archetypes that can accurately predict future events and warn humans of potential catastrophe in advance. These machines are able to make intelligent decisions on the basis of their past experiences.
However, this technology has a long way to go. There is Nautilus, a supercomputer that predicts the future; or so is said. This machine uses sentiment analysis and other ML algorithms to predict the future. While the technology is promising, it has a long way to go.
Scientists, after years of research, have developed algorithms to help machines recognize problems and solve them with novel approaches. But yet, there are miles to go before machines can learn on their own and present solutions without human intervention.
How do neural networks help in Machine Learning?
A computer system that is specifically designed to mimic a human brain is called a neural network. Neural networks work by classifying data into different buckets.
With the help of neural networks, machines can understand the world in the same way a human does. Neural networks meet this objective while keeping up inherent advantages like accuracy, speed, and unbiased behaviour.
Neural networks integrate a probability system. It uses the data fed to it to make statements, predictions, and decisions with certainty.
Neural Networks can recognize images and classify them accordingly to the elements they contain, but only if provided with the right guidance. Machine learning applications can read any information, and identify whether the person who wrote it has made a complaint or offers praise. Moreover, they can judge from a piece of music whether the person is sad or happy.
There are many other opportunities and applications provided by systems that depend on machine learning and neural networks. Machine Learning helps machines understand the vital nuances of human language, rather than mechanically responding to questions without understanding its meaning.
Deep learning and machine learning are related to each other. Deep artificial neural networks have different algorithms leading to high accuracy, for example, image recognition, sound recognition, recommender systems, etc. For machines to fully utilize deep learning algorithms, they must first be trained using a massive database, so they can understand the many parameters that affect real-life situations.
For example, to use deep learning algorithms in understanding handwriting, scientists need to run these algorithms through huge volumes of handwriting samples so the machine understands the different styles of writing. To say this has been a tough task would be an understatement.
Machines are an important part of the human race, thanks to Machine Learning and Artificial Intelligence. AI use Machine Learning techniques, and it’s accepted widely.
Nowadays artificial intelligence is part of every application which we use. These technologies have made it possible to shift intelligence from computational programs to real-life models. However, a lot of research is required to achieve human-level intelligence.
About the author – Danish Wadhwa is a Fountainhead and CEO Growth Marketing Agency Fly.Biz and has invested his marketing skills on SAAS startup MSGWOW for a growth.