Tdavis_Deep Neural Networks_03192021
docx
keyboard_arrow_up
School
RMU *
*We aren’t endorsed by this school
Course
MIS 548
Subject
Information Systems
Date
Jan 9, 2024
Type
docx
Pages
6
Uploaded by timothy.davis2
Running head: DEEP NEURAL NETWORKS (DEEP LEARNING)
1
Deep Neural Networks (Deep Learning)
Timothy E. Davis
Rasmussen College
Author Note
This paper is being submitted on March 19, 2020 for Devan Shepherd’s CTS4557CBE Section 01CBE Emerging Trends in Technology.
DEEP NEURAL NETWORKS (DEEP LEARNING) 2
Deep Neural Networks (Deep Learning)
Deep neural networks is the technology that is built to simulate the activities within human brain, Specifically being the pattern an understanding the pathway what that is put through layers that simulated with neural connections. Experts say that deep neural networks is networks that have input layers, in output layer and one hidden layer between the three. Each of the layers have their own specific performance type to sort and order within a process that is referred to by many “feature hierarchy.” A key feature in using this technology of neural networks is dealing with unlabeled data. Deep learning is the phase in which is used to describe deep neural networks which is specified as a form of machine learning. This is where technologies use different aspects of artificial intelligence to classify the order of information to go beyond the simplicity of input/output protocols ("https://www.iiste.org/Journals/index.php/JEP/article/view/47622", 2019). Deep neural networks and the impacts on society has a vast range of positive in negatives. Initially when you think of the capabilities that deep neural networks give humans you
think of how enhance the simple senses of the brain can be to the complex problems that it will help you solve ("Deep Neural Networks Help to Explain Living Brains", 2021). This technology being mass deployed within our society can have deep effects on how we learn and the number of efforts that is given based on the reliability deep neural networks. The technology enhances neural connections and allow humans to visually recognize things that the normal eye cannot identify or minutely distinguish. The amount of power that is gives to individuals within our society makes you think that this is overly exciting, and everyone will have the ability to make precise and genius like decisions. However, this obviously will come at a cost. This can be very disruptive to our society. At this point our society will start to see a divide. More of a divide than
DEEP NEURAL NETWORKS (DEEP LEARNING) 3
what we see today with the rich and poor. It would be the smart rich against the poor dumb. The way that you can have your sensory information sore an relay will enhance art. It would enhance art with colors sizes edges and shapes. Also, it will enhance the ability 2 make financial decisions
for the benefit of one's wealth. Deep neural networks and the impacts on business operations. Organizations and every level of growth from the startups to Fortune 500 companies use machine learning, AI, as well as deep learning technologies for vast range of applications ("Deep Neural Networks Help to Explain Living Brains", 2021). Deep learning happens to be the fastest area of growth within the AI segment. It is very empowering within every class of emerging markets and it is very instrumental in the number of abilities that it gives organizations ("AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference?", 2021). It's already enabling technology applications with things like autonomous vehicles, personal smart assistance, and medical precision. It is greatly shaping innovation around many industries. It is impacting AI applications with fraud detection and supply chain optimization within some of the largest companies in the world. Some business use cases is with social media. For example, the way that
Pinterest manage their business media pictures and how you can zoom in to pinpoint and visually
discover different patterns colors and objects. The engineers add Pinterest use deep learning to teach the system how to recognize features in the image by using richly annotated data sets. We also see deep learning in the financial industry with performing things such as E discovery ("Deep Neural Networks Help to Explain Living Brains", 2021). This is where large companies like JP Morgan Chase use deep learning analytics to identify insider trading detection as well as government regulatory compliance. Also hedge funds use analytical text to closely identify massive document repositories to obtain insight for new investment performance. This was a
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help
DEEP NEURAL NETWORKS (DEEP LEARNING) 4
recent disruption within the business market operations with his funds utilizing deep learning to anticipate a stock performance. Although, many investors bet it against the stock market and now
new policies and regulations are being discussed to manage business practices. The methodology to use for evaluating the impact of emerging technologies on society would be the fundamental approach to conducting lifecycle assessments (LCA). Emerging as well as existing technologies pose unique challenges for analyst ("Life Cycle Assessment (LCA) explained - PRé Sustainability", 2021). These challenges are associated with LCA's within emerging technologies these issues are related to the lack of data, a lack of incumbents against the comparable, scale up, as well as uncertainties with how emerging technologies will be deployed. There are stages to help target technology development which influenced the design to
ensure environmental goals and innovations are being met. Today, many funding agencies have required developers to report LCA results four emerging technologies ("Life Cycle Assessment (LCA) explained - PRé Sustainability", 2021).
The methodology to use for evaluating the impact of emerging technologies on business operations would be the governance approach. It is important for these new technologies to be ethical, objective, trustworthy, and overall useful. It is important to have unbiased research and data so that we can understand how business plan to apply this technology and risk to our livelihoods or how detrimental it can be. Due to malpractice technology research, there has been permanent damages and loss of life in the past in our economy form government an institutional study. Belmont report requires the implementation of higher degree of government over the research of metrologies with businesses. This is to apply a trusted research pathway with ethical considerations an integrity.
DEEP NEURAL NETWORKS (DEEP LEARNING) 5
The strengths of deep neural networks are the impact that it would have on enhancing an individual’s neural sensory. This would elevate the overall thinking and reasoning based on the clear identification of these elements. Businesses are stronger in the way that they assess information that is transcribed from people. They are able to find adaptive responses for enhanced imagery to fraud detection. Furthermore, both businesses and society will have an heightened response and accuracy too complex problems. Weakness is of deep neural networks is that it requires a substantial amount of data in order to get the outcome that the technology is intended for. Another drawback is that it is awfully expensive to train, because of the complex data models. It also is not as easy to understand the outputs on just learning. It will require classifiers to help you understand the different algorithms to help perform task. Some opportunities up deep neural networks is within the medical field. This type of technology can help in cases of less than normal sensory. For instance, if there is an individual that cannot hear, this technology would be idea. This technology would also be great for businesses to heighten the response of their employees as well as the ability to reverse problems in advance. Threats that deep neural network is black box. Black box nature has the capabilities to approximate any event and study the formation however it does not give any insight on the type of structure that the formation is being approximate. Another threat is security and cyberattacks. With all new technology there has been malicious attacks on the technology. With deep learning and neural networks being used to enhance this can also be the same with the type of cyberattacks that already exist. Neural networks can be use to replicate the target’s writing style in a phishing scam.
DEEP NEURAL NETWORKS (DEEP LEARNING) 6
Reference
AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference?. (2021). Retrieved 20 March 2021, from https://www.ibm.com/cloud/blog/ai-vs-machine-
learning-vs-deep-learning-vs-neural-networks
Deep Neural Networks Help to Explain Living Brains. (2021). Retrieved 20 March 2021, from https://www.quantamagazine.org/deep-neural-networks-help-to-explain-living-brains-
20201028/
https://www.iiste.org/Journals/index.php/JEP/article/view/47622. (2019).
Journal Of Education And Practice
. doi: 10.7176/jep/10-12-05
Life Cycle Assessment (LCA) explained - PRé Sustainability. (2021). Retrieved 20 March 2021, from https://pre-sustainability.com/articles/life-cycle-assessment-lca-basics/
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help