Artificial Intelligence research is developing intelligent ‘super machines’ that can learn, compute and adapt like the human brain. By Michael Megarit
How Does Artificial Intelligence Technology Mimic the Human Brain?
Have you ever wondered how Facebook is able to recognize your friends on pictures you publish?
Or how cars are able to drive themselves and avoid collision?
The short answer is obvious: science.
The longer, more accurate answer is less obvious: algorithmic software powered by sophisticated Artificial Intelligence machines.
The Cambridge Dictionary defines Artificial Intelligence (AI) as a machine or system that possesses certain qualities of the human mind such as understanding language, recognizing images, solving problems and learning independently.
From simple chess engines to fully autonomous vehicles, computer scientists are capable of designing machines with various levels of intelligence and abilities.
However, their ultimate goal is designing machines capable of mimicking the human brain and performing complex tasks faster and more efficiently than humans.
This scenario is far from futuristic.
In fact, it’s already become a reality.
Deep Learning is creating ‘super machines’
Machine Learning is the field of AI concerned with designing intelligent machines capable of performing specific tasks and learning from data without being regularly updated or improved.
However, despite their sophistication, these systems behave like computers, not like humans.
Deep Learning, which is a sub-field of Machine Learning, takes the concept one step further.
It reproduces the architecture of the human brain so that machines can perform tasks that conventional AI algorithms – including Machine Learning – struggle to do.
How does it do this?
By using a complex network of artificial neurons that mimic the flexibility and computing power of the human brain.
Artificial Neural Networks Mimic the Human Brain
Artificial Neural Networks (ANNs) are computing systems made up of connected units called artificial neurons, which are modeled on the actual neurons of the human brain. These neurons are made up of dozens – sometimes hundreds – of layers of interconnected algorithms.
Each layer receives and interprets information sent by the previous layer. They process data, identify patterns, compare new data to past data, analyze solutions and try to solve problems in the most efficient way possible.
Every step of the way, wrong answers are eliminated and resent to the previous layer to adjust the algorithm. The marvel of this technology is that the algorithmic adjustments are done automatically, without any human interference.
This process enables the machine to become “more intelligent” over time.
How Are ANNs Used In The Real World?
ANNs have the ability to solve many complex problems more efficiently than humans could ever dream of. Thus, they are being applied in a growing number of industries.
In our day to day lives, ANNs are widely used in the speech and facial recognition software of our Smartphones. That’s how your phone recognizes you even if you get a haircut or speak in a different accent. It is also used in robotics to ensure that smart appliances react appropriately to certain situations: for example, a smart fridge will emit a signal if the door is left open or if the compartments have abnormal temperatures.
In business and industry, ANNs have many useful applications, such as:
- Automobile industry: Self-driving cars and robo-taxis.
- Financial services: Forecasting future stock prices and algorithmic trading.
- Banks: Refine credit scoring methods and screening loan applicants.
- Advertising: Targeted recommendations.
- Risk-management: Predicting the likelihood of a particular event.
- Fact-checking: Cross-checking multiple sources of information.
- Surveillance and security: Vocal and facial recognition.
We could go on and on listing innovative ANN use cases.
The simple fact is that they are being adopted across all sectors of the economy and society.
And it’s easy to understand why.
Their flexibility and scalability make them ideal for computing vast amounts of data from multiple inputs.
The question now is will human brains become obsolete?
Let’s hope not, but only time will tell…
About the Author
Michael Megarit is a partner with Cebron Group. With over 25 years of domestic and international corporate finance experience, he provides M&A and capital advisory to high-growth technology companies.