The Tragic Flaws of Neural Networks | Jack Fitzpatrick
0 likes36 views
The document discusses the challenges and limitations of neural networks in the context of artificial intelligence integration across industries. It highlights issues such as the black box nature of these networks, the dependency on high-quality data, and the significant trial and error involved in their successful application. Despite advancements, neural networks are not yet perfected, which hinders their widespread adoption.
The Tragic Flaws of Neural Networks | Jack Fitzpatrick
- 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