Name: Jagadesh Mudunuri
My Intention to enroll in Technology Research Methods subject is to gain a wide range of skills and methods that can be beneficial in researching various projects, Moreover, I will get an opportunity to learn about advantages and disadvantages in approaching into a Research project. Furthermore, this could help me to gain sound knowledge in leading a project for future work paced environment. Before enrolling in this subject I have gone through the content topics that grabbed my attention over this subject and the topics as follows: The Research Process, Overview of Research methods, research proposal, survey-based Research, and Data Analysis & Interpretation.
I have chosen my Interest in Machine learning for Research Project and my learning plan for this project is succeeded by attending the workshops and lectures on various tools and technologies like statistics, Introduction to R, Machine Learning, etc. As my Research project is on Machine Learning and Firstly, it discusses a wide range of technologies that are combined into Artificial Intelligence (AI). These consists of different fields like machine learning, Robotics is used for Artificial Intelligence(AI) systems to acquire a high level of intelligence when compared to the human brain.
Furthermore, the project is undertaken to develop a theoretical aspect of background in experiments on how robots undertaking on humans. The literature review is followed up to fill the drawbacks that are present on the Artificial Intelligence technologies, like the ethical implications of t machine learning technology is to become more valuable in the coming future. So I researched more about machine learning like how robots can replace the manpower equivalent to the human mind. Moreover, I am keen on robotic technologies that took under my Bachelor’s degree as a computer science graduate.
In the recent years, AI has seen rapid development. A number of products backed by AI have been developed and released. Speech recognition softwares like Cortana, Siri, and Google Assistant are examples of these pervasive technologies as they are easily accessible via an internet backed device. One of the most striking differences between these AI technologies and the industrial automation devices is that the former has the ability to teach itself new things. This poses a threat to humans as machines have abilities like faster processing speed, working tirelessly, infinite memory, and a significantly lower error rate with which humans can’t compare. Hence, as these AI systems become a part of our society in the future, they will displace a lot of people from their jobs. It’s not just menial tasks that are at risk. Even jobs requiring extensive mental processing like doctors and lawyers can be automated by smart AI robots.
In the report, the entities affected by the automation of jobs and its gradual overtaking of intellectual jobs in various fields will be discussed. Also, the related ethical issues will be discussed along with legal, social, cultural, business and organisational issues. Finally, the possible resolution around curbing the impact of automation on the general public will be analysed and studied.
Planned summary of Goals:
Workshops and lectures Class Timetable Useful for Project
Design Thinking : —- less
Intermediate R : 9/10/19, 10/10/19 N/R
Statistics : 12/8/19-2/9/19 less imp
Machine Learning : mid Sep Very Imp
Research Proposal Workshop : Sep 19 to Sep 30 Very imp
Research Publishing Workshop: 30/9, 3/10 Imp
Actual Learning plan:
My actual learning plan for this project is to attend the workshops and the lectures that I attend on machine learning in mid-September would help me to gain deep knowledge about this technology on the ongoing semester.
In summation to this, I can plan my schedule to learn more advanced machine learning techniques to get information on automation of robots on human works in industrial or in any other respective field.
I have one of my friend who is studying master’s in Automation, Further, he can guide me through this machine learning to know more detail about the programming languages like C++, Java, etc. If required to be used in this project, I can divide my work in to break-down structure to work more appropriately in each section to achieve my target to complete this project in a given time.
For the experimental part, I will attend the lab sessions that will be conducted in Technology research methods subject this semester and can recollect the programming skills that will help me in this project.
Finally, I would attend a Research proposal workshop to follow those instructions to avoid mistakes in the Research project and I can successfully reach my goals required to complete this task.
This report discusses various technologies that are commonly grouped under the term Artificial Intelligence (AI). It examines how recent advances in various AI subfields such as machine learning have enabled these AI systems to achieve a high level of ‘cognitive’ ability.
In addition, the ethical implications of this technology, and the threat that they pose to long term employment is examined. AI affects an increasingly significant portion of jobs and careers previously thought to be the exclusive domain of humans and so the ethical and economical implications of this technology are likely to become more acute in the future.
While the report suggests some potential solutions for these dilemmas, it also points to the urgent need for further consideration and investigation into the ethical implications of automation on employment, in order to transition into a new technological age.
Course components for Technology Research Methods:
1) Design Thinking
2) Intermediate R
4) Machine Learning
5) Research Proposal Workshop
6) Research Publishing Workshop
I) Design Thinking:
In a universe of progressively rapid change, the client is quickly moving to whoever best lives up to their desires. For some organizations, this is a risk as much as a chance—customary or built up a piece of the overall industry is never again assurance for future achievement. To flourish, organizations should be the snappiest to find out about client needs and make an interpretation of them into suitable items and administrations.
Design thinking is a compelling order that empowers associations to enhance and make new an incentive by concentrating on client needs. In any case, the key rules that make Design Thinking so incredible are beginning to wind up weakened as certain courses center more around procedure and organizations as opposed to exploratory experience.
Five Stages of Design Thinking:
2) Define the Problem
Stage 1- Empathise:
The primary phase of the Design Thinking procedure is to increase an empathic comprehension of the issue you are attempting to handle. This includes consulting specialists to discover progressively about the zone of worry through observing, connecting with it and relating to individuals to comprehend their encounters and inspirations, just as inundating yourself in the physical condition so you can increase a more profound individual comprehension of the issues in question. Empathy is essential to a human-centered structure procedure, for example, Plan Thinking, and compassion permits structure scholars to put aside their very own suppositions about the world to pick up understanding clients and their needs.
Depending on time constraints, a significant measure of data is assembled at this phase to use during the following stage and to build up the most ideal comprehension to the client’s needs, and the issues that underlie the advancement of the specific item.
Stage 2-The Problem:
The Define stage will help the Designers in your group to ultimate thoughts to set up features, functions and whereas other componentswill enable them to take care of the issues, or in any event, enable clients to determine issues themselves with the problem. In the Define stage will organize to advance to the third stage, Ideate, by posing inquiries which can enable you to search for solutions
In this third stage, the Designers start producing ideas, It is critical to get many thoughts or issue arrangements as could be allowed towards the start of the Ideation stage. You should pick some other Ideation methods before the end of the Ideation stage to enable you to research and test your thoughts so you can address the most ideal approach to either tackle an issue or give the components required to evade it.
Stage 4 –Prototype:
This is an exploratory stage, and the point is to distinguish the most ideal answer for every one of the issues recognized during the initial three phases. The arrangements are executed inside the models, and, individually, they are explored and either acknowledged, improved and re-examined or dismissed based on the client’s requests. Before the finish of this stage, the structure group will have a superior thought of the limitations innate to the item and the issues that are available and have a clear perspective on how genuine clients would carry on, think, and feel when collaborating with the final result.
Stage 5 –Test:
This is the final stage in five stage module. In this stage, the designers continuously test a product by using the best solutions that are noticed in the prototype phase. The results that are generated during the testing are often used to redefine one or more problems to understand the users in a better way than normal.
II) Intermediate to R:
R Programming Stage 1:
• Import data into R, manipulate it and clean it.
• Visualise the data and perform simple statistical tests.
• Extend R’s capabilities using free statistical software packages obtained from the comprehensive R archive network.
R Programming Stage 2:
• Navigate and clean large data files
• Interactively explore and visualise datasets to answer questions
• Develop analysis procedures that scale to large datasets
This course consists of five lectures followed by five labs.
• Lecture 1 is about the Purpose of Quantitative research and Descriptive Statistics ¬-6/8/19
• Lecture 2 is Hypothesis Testing and Statistical Significance – 13/8/19
• Lecture 3 Hypothesis Testing for comparing groups – 20/8/19
• Lecture 4 is about Contingency Analysis and Regression Analysis
• Lecture 5 Model Selection Methods.
IV) Machine Learning course:
Learned content from the Machine learning course follows below:
This is a technical course, and students should have a look over some of the git-hub links below to assess if they are able to appropriately benefit from the taught components.
Optimisation methods in general. not limited to just Deep Learning
basic neural networks and multilayer perceptron
Convolution Neural Networks: from basic to recent Research
detailed explanation of CNN, various Loss function, Centre Loss, contrastive Loss, Residual Networks, Capsule Networks, YOLO, SSD
Word2Vec, skip-gram, GloVe, Fasttext
Deep Natural Language Processing
RNN, LSTM, Seq2Seq with Attention, Beam search, Attention is all you need, Convolution Seq2Seq, Pointer Networks
Deep Reinforcement Learning
basic knowledge in reinforcement learning, Markov Decision Process, Bellman Equation and move onto Deep Q-Learning, Policy gradient, TRPO and PPO.
V) Research Proposal Workshop:
• In 32144 Technology Research Preparation (TRP), I worked on developing a literature review on my topic of study.
• In TRM I have learned in developing the understanding of the literature into a Research Proposal.
• Traditionally the proposal forms the first chapter of my thesis, while the literature review forms the second chapter.
• This workshop allowed me to start to develop my research proposal and understood the importance of this type of document in my future research.
How to do Research ?: By following the Feynman Algorithm is followed by writing down the problem, thinking very hard to get the task done, writing down the solution to it. And in Polya Principles you have to make sure to understand the problem, coming up with a plan for solving it, checking planned works.
Forms of the proposal:
• You may come across different types or forms of a research proposal.
• Thesis proposal – A outline of a topic, the problem you wish to address and the importance of doing so. Typically written in the first phase of your research, for initial candidature confirmation.
• Thesis proposal [post hoc] – a version of your proposal written for publication as part of your final thesis submission. Typically written with the experience of having done the work proposed in the outline proposal
• Grant Research Proposal – a proposal aimed at securing research funding.
Thesis Approach 1: Focusing on Research Question
“A statement or theory that is put forward as a premise to be maintained or proved.”
This is a useful starting point for your introductory chapter, and will often come at the end of your introductory paragraph.
A broad topic can be described in perhaps three of four words.
There will be too many sources for us to consider
The information you want is buried within a mountain of information you are not interested in and Difficult to draw anything other than general statements.
Developing a Question:
• The history of your topic
• The structure and composition of your topic
• The categorisation of your topic
• Develop negative questions
o Why has EIT not been carried out with RF fields?
o Develop speculative questions.
o Extend questions posed in the literature
Structuring a Research Question:
- Topic: I am trying to learn about/working on/studying _
- Question: because I want to find out who/what/when/where/whether/why/how _
- Significance: in order to help my ‘reader’ understand___________
Thesis Approach 2: Formatting a thesis from topic and keywords.
Looking after the Thesis statement you need to constructa simple thesis statement combining the keywords and systems.
Background Aims and Significance, objectives and Readers are to be include on your Research proposal.
VI) Research Publishing Workshop:
Publishing research Information (through submitting to online repositories, portals or aggregators) has emerged as a key part of the scholarly communication process because it promotes transparency, reproducibility and the validation of research methods.
Publishing research data refers to a set of practices that are more robust and sustainable than emailing files to your colleagues. These practices include submitting data to an online repository, portal or aggregator. These services undertake to make the research data, and metadata about the research data, findable, accessible, reusable and persistently available into the future. Most research institutions have their own repositories, and many disciplines have an established practice of contributing to a subject based repository
Research data can be published in numerous ways:
• Through sharing information about research datasets using rich metadata records in repositories, most often at institutions.
• syndicating those metadata records to discipline-specific portals or repositories
• formally published in Data Journels.
• Informal publication such as via personal or commercial repositories or websites.
Why should I publish my data?
There are a lot of good reasons for sharing and enabling the reuse of data:
• Encouraging scientific enquiry by enabling the validation of research methods
• Maximising transparency and accountability through scrutiny of research findings
• Promoting new opportunities for collaboration
• Reducing the cost of duplicating data collection
• Providing credit to the researcher as a research output in its own right.
Finally, I can confidently say that by attending the workshops and lectures learned more information about the literature review, research proposal, Researching publishing, Research presentation, these skills would help me in my future to grow as a better expertise person in my core industry.