Already, neural networks have come into being, utilizing artificial intelligence to eliminate the strain on human workers and optimize certain processes. While these networks are designed to alleviate some of the unnecessary exertion that plagues human workers, there are some potential issues that accompany this innovative technology. Read the blog: http://jackfitzpatrick.io/the-tragic-flaws-of-neural-networks/

1. THE TRAGIC FLAWS OF
NEURAL NETWORKS
B Y J A C K F I T Z P A T R I C K

2. With the integration of artificial intelligence into nearly
every industry and avenue of communication, it should
come as no surprise that developing complementary
technology to promote intuition and machine learning
is the next logical step.
While these networks are designed to alleviate some of
the unnecessary exertion that plagues human workers,
there are some potential issues that accompany this
innovative technology.

3. B L A C K B O X N A T U R E
Black box refers to an unclear process that occurs within the network that produces an
unpredictable outcome based on the input.
In some regards, an algorithm or process that enables human intervention, override, and
insight can allow for more straightforward and accurate deductions.
Neural networks are often limited in their application as well as their clarity and accuracy.

4. Success with these tools largely results from trial and error which takes time,
money, and other resources. Identifying the proper structure, assessing best
practices, and determining the most effective applications of neural networks will
take time.
We have not yet reached a point that neural networks are standardized or
perfected enough for regular use.
UNCERTAINTY

5. Unlike other machine learning
programs, neural networks require
an immense amount of data in order
to function.
Because they are designed to
operate independently of human
technicians (with leeway for
hardware issues), neural networks
must have access to the most
accurate and relevant information in
the largest volume possible to ensure
the outputs are as comprehensive as
they can be.
The success of the neural network in
question is entirely dependent on
the quality and quantity of the data
it receives.
DATA

6. T E C H N O L O G I C A L A D V A N C E S I N T H E
F I E L D O F A . I . H A V E E N A B L E D A
M A S S I V E S H I F T I N C O M M U N I C A T I O N ,
P R O B L E M S O L V I N G , A N D T I M E
M A N A G E M E N T O N T H E I N D I V I D U A L
A N D S O C I E T A L L E V E L .
H O W E V E R , T H E N E U R A L N E T W O R K
T E C H N O L O G Y I S N O T Y E T P E R F E C T E D ,
S O I T S W I D E S P R E A D I N T E G R A T I O N I S
S T I L L O U T O F S I G H T .

7. F O R M O R E I N F O R M A T I O N O N
C Y B E R S E C U R I T Y A N D T H E
D I G I T A L A G E , P L E A S E V I S I T :
T H A N K Y O U !
J A C K F I T Z P A T R I C K . I
O