Fall 2019 Technical Writing ENG2575 E270 with Professor Ellis

TO: Professor Ellis
FROM: Amir Radoncic
DATE: 09/17/2019

SUBJECT: 500-Word Summary on how “cryptographers scramble to protect the internet from attackers”

This article is by Adrian Cho who explains how quantum computers are a huge importance in the future. This article also covers the importance of how we can build and protect these future quantum computers. He explains how hackers are going to be a big problem to quantum computers etc.

The main points of the article are about how we can create ways of protection for quantum computers such as Cryptographers are looking for ways to ensure the protection of public and secret keys between two senders so that valuable information is not stolen by an outside entity. For example, there is a popular public key scheme called RSA, which scrambles a message by multiplying it by itself a number of times. Researched has been conducted on ways to develop algorithms and schemes in order to ensure the protection of the information on the quantum computers.  There is no guarantee that quantum computers are un-hackable, so it is extremely important that cryptographers implicate new ways in combination with current schemes, to ensure the safety and security of users.

The article is solid on why we need quantum computers and why. Although in my opinion quantum, computers could be the technological breakthrough that we have been looking for. There has been educated guesses such as NIST could standardize two or three algorithms each for encryption and digital signatures as early as 2022, says Dustin Moody, a mathematician at NIST in Gaithersburg, Maryland. The agency wants options, he says. “If some new attack is found that breaks all lattices, we’ll still have something to fall back on.” There are a bunch of educated/professional people who have tried to invent new algorithms which are really smart although not enough.

ChoAug, A., MalakoffAug, D., EscobarAug, H., NordlingAug, L., PennisiAug, E., Reilly, S., … GalvisAug, S. (2019, August 21). Cryptographers scramble to protect the internet from attackers armed with quantum computers. Retrieved from https://www.sciencemag.org/news/2019/08/cryptographers-scramble-protect-internet-hackers-quantum-computers

TO:Prof. Ellis
FROM:Fernando
DATE:09/17/19
SUBJECT:500-Word Summary of Weinberg’s “Law and Technology- Biometric Identity”

Jonathan T. Weinberg discusses about the biometric technology being implemented in some places like India and Pakistan and how it’s had a positive impact. Trying to make slight improvements with keeping data organized, secured, and easier for the government and people. Though that is the case for these places, the U.S. made the decision to drop the idea on biometrics and Weinberg then goes on about what improvements India and Pakistan have started, that could have benefited the U.S and possibly some issues. In the article, “Law and Technology- Biometric Identity” by Jonathan T. Weinberg, he gives valid points that could change the use of ids to biometric technology.

Biometric id card would link the person’s biometric data such as fingerprints, iris scans, and a photograph making it difficult to replicate that information. To ensure the card belongs to the card holder, a biometric verification test would be done to guarantee the card indeed belongs to the card holder. India plans to use their people’s biometric data to then link that to any governmental data given in a card for uses in work, ATMs, and health benefits keeping all that data in their protected databases. This policy would be much helpful for the U.S. to link biometric id to the people to check whether the person has any criminal record, has work authorization, and reduces identity fraud. 

According to Weinberg, “talks about Pakistan using the biometric data in voting registration, to track down people that have voted more than one time in voting election to then de-duplicating them.”  In addition, mentions about uncovering many workers that are “ghost worker”, government having more of an organized place, and being able to reduce less fraud and trust between people. Another positive talked about biometrics is having an unborn baby registered since many poor countries don’t get paper documentation it would be beneficial to use biometric technology instead. 

Issues mentioned in the article would be the U.S. having people frightened on the idea of biometrics because it would contain vulnerable information on them of whether being noncitizen or citizen. Meaning that noncitizen people would be limited from doing anything like traveling, working, or even having health insurance and having the information whether they can be arrested or deported. Even the idea of the government taking control of people’s personal biometric data, is a problem since that limits them to anything because people have in trusted the government with all their sensitive information. Furthermore, something much worse would be the databases not being secured as people would of think that it would have been, hackers having very sensitive data very much like India databases.

Biometrics is a topic that can be easily understood and can make life’s easier for traveling, for keeping something protected, and make things faster. It is a topic that is interesting that Weinberg made valid points to believe in the biometrics technology and keep on using it. It is something that can lead to a brand-new technology or idea if done correctly, just like technology that help humanity.

References

Weinberg, J. T. (2016). Biometric Identity. Communications of the ACM, 59(1), 30–32. https://doi-org.citytech.ezproxy.cuny.edu/10.1145/2846082

TO: Prof. Jason W. Ellis

FROM: Hector Dextre

DATE: September 17, 2019

SUBJECT: 500-Word Summary of Katare, Padihar and Qureshi’s “Challenges in the Integration of Artificial Intelligence and Internet of Things”

To understand better the challenges in the integration of Artificial Intelligence (AI) and Internet of Things (IOT), it is important to know what these technological terms are. According to the authors, “Internet of Things (IOT) (‘Thing’ refers to a device which is connected to the internet and transfers the device information to other devices.)” and “Artificial Intelligence (AI) is the intelligence which is not natural but used by machine in some order based on instructions” (Katare, Padihar and Qureshi, 2018, p.10). IOT, usually located at an early stage of development due to the limited capacities of its processor, needs an AI’s speed injection to allow the increase of the capabilities of IOT. Knowing more about IOT and their applications, AI and its uses cases and their challenges will permit individuals to comprehend the rapid development of technology.

Internet of Things are the access to objects with the use of internet. The authors stated, “the main goal of development of IOT is to connect the internet with physical world and the environment with wireless networks” (Katare, Padihar and Qureshi, 2018, p.10). In other words, IOT is basically a network of physical gadgets and it is a system of apparatuses equipped with information gathering technologies so they can interact and interchange data with one another. There are many applications of IOT, but just to indicate some of them that are applicable in agriculture, poultry, farming, health care, environment, education, technology, manufacturing, housing, finance, sports, energy and transportation.

Artificial Intelligence, according to John McCarthy, the father of AI, is “the science and engineering of making intelligent machines, especially intelligent computer programs” (Katare, Padihar and Qureshi, 2018, p.11). In other words, AI is the method of making a robot controlled by computing machine or software that thinks intelligently. The applications of AI are machine learning and deep learning with the capacity to automatically acquire and enhance performance from experience. The correlation between these two applications is that deep learning is a subcategory of machine learning which is made up of algorithms that train it to do every task like face recognition using large amounts of data. AI uses cases, just to indicate some of them, are image recognition, warehouse optimization, credit verification and medical diagnosis.

When IOT and AI connect, the challenges become more complex. First, in terms of security, it is indispensable to make sure that data is secure and in reliable hands. Second, in terms of compatibility and complexity, many devices that have many different technologies may cause many difficulties after merging all these devices in one. Third, in terms of artificial stupidity, the incapacity of AI program to do basic tasks perfectly. Fourth, in terms of lack of confidence, the concern of consumers and businesses about security to protect IOT devices. Fifth, in terms of cloud attacks, the unwished attention from detrimental viruses has been drawn by the quick growth of cloud computing technologies. Finally, in terms of technology, the biggest challenge is the competition of all technologies; however, we have many more challenges and having these challenges and giving competition to every technology is a very tough operation.

The integration of AI and IOT tends to arise rapidly, which will make the internet more useful. AI and IOT will change the future of humans but it requires the support and patience of us. Connecting them together will give a valuable innovation and experimental technology which will benefit companies and user by supplying good and efficient products.

References Katare, G., Padihar, G., Qureshi, Z. (2018). Challenges in the Integration of Artificial Intelligence and Internet of Things. International Journal of Systems and Software Engineering, 6(2), 10-15. Retrieved from http://search.ebscohost.com.citytech.ezproxy.cuny.edu/login.aspx?direct=true&db=aci&AN=134633037&site=ehost-live&scope=site

TO: Prof. Jason W. Ellis
FROM: Jeremy Corona

DATE: 09/17/2019
SUBJECT: 500-Word Summary of Wiedemann’s “Research for Practice: The DevOps Phenomenon”

If you are in the realm of Information Technology, then you most likely have heard of the term “DevOps”. DevOps stands for Development Operations. A lot of people even IT professionals have a hard time defining this term. Is it a career? Is it a concept? What is a DevOps Engineer? DevOps is all of those and more, it is best to think of it as a culture. DevOps is a method of software development and delivery. It is method organizations are taking advantage of in order to improve the efficiency of their software development, deployment pipeline. In this article “Research for Practice: The DevOps Phenomenon” by Wiedemann, Forsgren et al. takes a closer look at this new methodology on producing stable, feature rich software applications with high customer satisfaction.

The traditional “Waterfall” method of delivering a software product has been around for years. While it does have its advantages there is a giant gap between the software developers and the operations team. DevOps is the methodology to bridge that gap. During the Waterfall method once the project is done, the application is handed off to the Operations team. They are responsible for the day to day maintenance and stability of the application. They are on the forefront when interacting with customers and bugs are being found. Developers do not usually see this going on in the background because as far their concerned they have delivered the product. This can cause a conflict within the two teams because when the new features roll out, the operations team is worried about more instability and bugs.

With the DevOps methodology, organizations bring those two teams together to develop and produce software that continuously creates value. There are many ways to implement this concept. Collaboration is key. Operations people will start doing some development work to see how things get done and how the teams’ function. Developers would start maintaining some of the products that have created as well. Some organizations implement cross training and job shadowing. This puts employees on the same page when brainstorming new products or developing and delivering new meaningful features to an existing product. “For organizations hoping to capture market share and deliver value faster (or even just deliver software more safely and securely), DevOps promises both speed and stability.” (Forsgren,  2018, p. 45.)

This doesn’t mean that DevOps is easy to implement in an organization. Organizations may be hesitant to change their software development cycle. Implementing DevOps may cause some employees to gain more responsibility, and that can always be alarming. Strong leader-ship is needed to adopt this mind set. DevOps isn’t a strict structure. It is a very flexible concept that organizations implement in their own ways. DevOps teams doesn’t just only have to include developers and operations members, some organizations include stakeholders as well. The goal is for the organization to not fall short in deploying fast, high quality software products.

DevOps has many different definitions to different organizations. To some it’s a position to bridge the gap between two teams, to others it is a collaborative team with one common goal. DevOps It is a guideline, and a set of principles for organizations to follow. Organizations across the globe are having great success with this methodology. Implementing DevOps can be challenging, but with strong leadership and inclining employees’ organizations can reap the benefits of DevOps. 

References:

Wiedemann A., Forsgren N.,  Wiesche M., Gewald H. & Krcmar H. (2019).Research for Practice: The DevOps Phenomenon. Communications Of The ACM, 62(8), 44-49.

TO: Prof. Jason W. Ellis

FROM: Huzaifa Anas

DATE: September 17

SUBJECT: 500-Word Summary of Hassabis et al “Neuroscience-Inspired Artificial Intelligence”

Hassabis et al in Neuron argues that the field of neuroscience and AI (artificial intelligence) have a symbiotic relationship, but it’s in jeopardy, because of decreasing communication and collaboration. The contention states neuroscience provides a productive source of inspiration for algorithms and architecture, which is “independent of and complementary to the mathematical and logic-based methods and ideas that have largely dominated traditional approaches to AI” and “neuroscience can provide validation of AI techniques that already exist.” (Hassabis et al, 2017, p. 1). Moreover, they believe the progress in AI will eventually pay dividends to neuroscience by being a good test field. Within this article, past breakthroughs are examined to support this argument, while looking at how continued collaboration and communication can benefit both fields.

Two of AI’s backbones originate from neuroscience, which’s deep learning and reinforcement learning. Deep learning has revolutionized AI through dramatic advances in its neural and capable networks of learning freely from unstructured or unlabeled data. Reinforcement learning, the second pillar of modern AI, is a powerful tool enabling AI researchers to create software agents that act in an environment maximizing some sort of reward. In the 1940s artificial neural networks were developed, which could compute logical functions and ultimately “learn incrementally via supervisory feedback (Rosenblatt, 1958) or efficiently encode environmental statistics in an unsupervised fashion” (Hasabis, 2017, p. 2). This is the foundation for deep learning. Soon after backpropagation algorithms were made, which allowed learning to occur in networks of multiple layers whose value was recognized in 1986 by cognitive and neuroscientists working on Parallel distributed processing or PDP, which better-represented human-like behavior than serial logical processing, which AI researchers were focusing on. PDP has been applied to machine translation through the idea that “words and sentences can be represented in a distributed fashion (i.e., as vectors)” (Hasabis, 2017, p. 2). Deep learning ultimately became a field independent of PDP. Reinforcement learning comes from animal learning research, which Pavlov and Skinner pioneered. Reinforcement learning is used in robotic control, skillful play in backgammon and go.

If someone looks closely, AI research is still heavily inspired and guided by Neuroscience through AI work on attention, while eventually pivoting towards efficient learning and more independent behavior like transfer learning and imagination. The goal of AI is to form human-like behavior, and it’s practical an accurate biological framework as a reference. Attention is a critical issue currently because not all information is equal and therefore unlike before where all information was treated equally in neuroscience now information is being given different values, which allows for more efficient computing power usage. For the future, we want to decrease the computing power and a large amount of data needed for AI as currently. Humans can learn from a few examples, which AI can’t, and researchers are trying to apply developmental psychology ideas here. For imagination and transfer, learning neuroscience is still pioneering this part, but in the future, it’ll hopefully provide practical insights for AI work. All things considered, both fields can provide feedback to each other by having neuroscience provide ideas, and AI proves as a testing ground for these ideas. This isn’t compulsory, but just an effective and logical symbiotic relationship.

Article Cited APA format

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.

I’m not sure if restructuring definitions is considered plagiarism.