Difference between Narrow AI and General AI.
                     DIFFERENCE BETWEEN 
NARROW AI AND GENERAL 
AI
What Is Narrow AI?
• Narrow AI (ANI) is described as a particular kind of artificial 
intelligence that beats human technology in a very strictly 
described mission.
•  Unlike the general artificial intelligence, a single subset of 
computational capacities and developments in the artificial 
intelligence are based on.
Source: www.springboard.com
What Is Narrow AI?
• Every day, there are many examples of narrow AIs, 
illustrated by devices such as Alexa, Google Assistant, Siri 
and Cortana. Including:
• Automobile vehicles
• Tools for facial recognition in images
• Customer support offers information on a webpage
• Page-listing technology from Google which decides the sites at the top of 
the search engine
• Recommendation services for posts focused on reading history which 
may be valuable additions to your cart
• Spam filters that clean your inbox by automatic sorting
Source: www.usmbusinesssystem.com
ADOPTION OF ANI
• Many organisations are actively investing in and adopting 
ANI to increase performance, minimise costs and simplify 
activities, but there are significant restrictions on ANI. 
Source: fourandhalf.com
OBSTACLES
• In order to obtain reliable results, ANI needs a vast volume 
of high level data, which is not suitable to all environments.
• The research curve to better institutionalise AI can be steep. 
Companies have modern systems and technology to set up 
and prepare their workers.
Source: www.arrkgroup.com
OBSTACLES CONT.
• If a mission varies, an ANI device loses its efficacy because it 
is designed for a particular reason.
• Replacing people with regulatory machines often leads to 
greater dissatisfaction and consumer loyalty – in the 
entertainment industry.
• When overcoming these problems, and opening new AI 
implementations, we are heading into a new model — 
general artificial intelligence
Source: www.springboard.com
What Is General AI?
• General AI enables a computer, in various contexts, to use 
experience and knowledge. 
• This closely resembles human intellect by offering 
autonomous learning and problem solving opportunities
Source: www.unite.ai
Switch from ANI to AGI
• The challenge now is to switch from ANI to AGI in advanced 
areas such as computer vision and processing of natural 
language. 
• Computer hardware must raise processing power to do 
more cumulative calculations per second in order to achieve 
AGI. 
Source: www.ediweekly.com
Comparison with Human Brain
• Tianhe-2, a Chinese national security technology 
supercomputer, currently has a record 33.86 petaflops 
(quadrillions of cps). 
• Though the human brain sounds amazing, it is predicted 
that it will exaflop (a billion cps). Technology must also keep 
up.
Source: www.itproportal.com
Approach of AGI
• One of AGI's key methods is actually dubbed the "full brain 
emulation," which is used to transfer the brain's memory 
and mental status to a computer. 
• The brain is similar to a computer architecture and they can 
all function in a neural network system. 
• If the correct steps are taken, the transistor links in the firing 
paths are improved. 
Source: www.aidaily.com
Machine Learning and Deep 
Learning on the Road to AGI
• Machine learning explains how computational devices can 
actually learn themselves, and identify patterns and make 
decisions without guidance or pre-programming. 
• Deep learning is a branch of machine learning that learns 
from unmonitored and unstructured data processed by the 
neural networks.
Source: www.oracleblogs.com