Aug 28 Exciting and Hopeful Beginning: 

I went ahead and set up my linkedin profile to begin with. It was my first time making a Linkedin account and profile, but I caught on pretty quickly. I established the account, connected with people I knew, and set up my description. It was a new experience for me and I learned how to use linkedin for the first time. 

Next, I chose ImCreator to make my website, which I eventually linked to my Linkedin profile. I first struggled with the organization and color scheme, seeing the net for inspiration and the colors that produce the meaning to my topic. Being AI, I chose to use a gray and blue color scheme. These colors represented AI and gave a clean look. Next, usability, I had to make sure my website used nice pictures and inputs, that's easy on the eyes without anything difficult and overwhelming to go through. I wanted something simple, neat, and easy for the viewer. The website would be a huge part for my future relationship with my mentor and the better I do, the better mentor I could find. With all this in mind, after a few hours, I finally completed the Website. I hope to change the transitions and effects though, I am not entirely sure if the clean look I envisioned is brought to reality. But, I was happy with the result, so only time will tell. Idid face a few struggles, I had to search up how to use the website and a lot of things didn't work the way I wanted it to. The website fits my topic and theme and I meet the requirements, yet I feel a missing piece that I hope to find in the future and improve upon. 

All in all, week 1 was solid, I finished my website and linkedin setup and finalized my topic on Artificial Intelligence. 

Sep 4 Research and Realizations:

I started researching artificial intelligence, which was the thing I was very eager for. Right off the start, I had found many sources but most were not credible enough or didn’t fit my research. After some time I dove deeper into the net and found a perfect article that captured my interest in artificial intelligence and the potential effect on its economy. 

I learned that there was more to AI than just its introduction, more importantly the diffusion. How will the diffusion impact businesses more in depth. Right now AI is developing rapidly and it's a growing industry but more importantly if it wants to make a significant impact on the economy, whether its jobs or profits, many big companies other than silicon valley need to be pushing AI and it must become global. I dove deeper outside of the article and saw the diffusion of smartphones, when they came out there was a competitive area where every major company wanted to make ones. Now, huge companies are barely pushing the boundaries of AI and there is no room for significant change, but when the new innovations come, they will diffuse rapidly and it'll split the market. Jobs will be created and destroyed, although this article didn’t dive in too indepth to job loss. It explained the potential jobs and outlook. I learned about “complex query”, it’s a type of job that chatgpt would pay around 2$ for a line of code that could be prescribed to make the technology better. It's basically a syntax of code, that you could give to chat gpt and those codes could help other users with help in code. So whatever the situation is like coding for a loop, a user could upload the code for creating a basic loop in java and get paid by chatgpt for it. According to the article, doing 20 of those would give you the average American salary, which was mind blowing to me. Although could AI potentially create a few lines of code with its own knowledge? Instead of humans uploading it, I need to research about this further, because for this week I’ve created substantial progress.

Sep 11 Understanding the Limitations of AI Diffusion:

I started diving deeper into AI and researched that Recent research from YouGov in mid-March 2023 reveals that 42% of Americans are neutral about the impact of AI on jobs, with only 12% believing it will increase jobs and 29% thinking it will decrease jobs. However, a follow-up poll by the Los Angeles Business Journal in late April 2023 shows a significant shift, with 33% now expecting an increase in job loss due to AI, while 67% believe it will have a negative effect, indicating a noteworthy change in public perception during this period. As my own theory In response to this the US government later in a few weeks has major control to cover the diffusion of AI technologies. Although through social media and the internet, diffusion of AI is much faster, the regulations for public use of AI is in the hands of the government, which seem to have a slightly negative standpoint on AI. The government wants the technology to improve to actually integrate with the public, and they are holding off on the technology so it improves and while that happens, they plan to regulate and create laws on Artificial Intelligence. The US government doesn’t want a widespread diffusion of artificial intelligence as in the past with smartphones. The US wants to make it limited and take it step by step due to the danger of AI. 

This research I gathered shows the boost in AI and recently, Netflix posts a job opening for 900K per year to be an AI technical advisor. AI gets to a point where salaries are worth triple the average surgeon salary. As a huge company that Netflix is, giving that much money to an employer occupation opens up so many doors for people who want to invest time into AI. This puts AI in a step towards many job opportunities and success, while the government is limiting AI capabilities, as my total research findings. 

Sep 24 Finding new Ideas

I stumbled upon a new article this week and it was very long but it gave me good background information. The number of AI patents is used as a proxy for AI innovation in the study's analysis of a panel dataset of nations from 1970 to 2019 in order to evaluate this hypothesis. In a public database, text search queries are used to separate AI patents from other types of patents. The study discovers a favorable relationship between AI and economic growth using fixed effects and generalized method of moments (GMM) analysis. Notably, this influence on economic growth is larger than the overall effect of patents. The study also shows that the effect of AI on economic growth is stronger in advanced economies and gets more prominent in the later years of the dataset.  AI and machine learning are two advancements in computer science and digital technology that have found that the economy grows as the jobs grow. 

The old input-based manufacturing model has changed over the last 60 years to capital-intensive instruments based on information and communications technology (ICT). Production processes have changed as a result of the emergence of contemporary computers, the Internet in the early 1990s, and more recently, AI. For instance, Zeira's economic growth model from 1998 includes technology advancements that lower the need for labor but increase the capital requirement.

These technologies have also been identified in recent empirical research as possible sources of economies of scale, suggesting that they have the potential to increase total productivity and positively impact economic growth. The emergence of ICT has profoundly changed how industries function and offers hope for further increases in productivity and economic growth.

AI and associated technologies have significantly altered corporate operations during the past 20 years. While global corporations rely on AI and machine learning for things like supply chain efficiency and predicting maintenance needs, e-commerce businesses employ AI to predict customer behavior and increase sales. This study examines data from 1970 to 2019 to determine how AI innovation impacts long-term economic growth.

Through this article's findings, I am able to see how AI is diffusing. Additionally, I worked a bit on my website and changed the colors, but I may entirely change the site because it's hard to use and doesn't look as professional as I wanted it to. I hope to find time so I can maybe change the existing one or create a whole new one. Furthermore I worked on interview questions a bit more and made a topic proposal with my new research and findings. I studied deep enough to have a topic proposal I would say. The topic proposal just justified what I hope to do in ISM and how my research is inspiring me to choose the topic I chose. But that was a lot of work for the week, but I am still eager to explore more.

Oct 2: Refining and Defining

I redid my whole website on google sites, now it has a glow and something that felt missing had finally been filled. I chose brighter and more attractive colors, previously it was a very dim gray and navy blue. The website had no life, but now I made the color panel be futuristic gray and a light blue that really contrasts well with the design and my topic. I had also done research on an article on more AI related topics, which was something that has been ongoing for the past weeks but it's still a new excitement learning deeper into the AI world. Although I had interesting research which was helpful, the highlight for me was rebuilding my website, and I am even more excited how I will integrate it into my colors that define me in my linkedin which I have already changed and my future projects that I plan to incorporate the color scheme. It’s also better for mentors and people who research about me to see my website and remember those colors that define me and my topic. All in all, I am very happy with my website and my research.

Oct 9: First Professionals & Interviews

In the ISM business symposium this week, our goal was to interview professionals for the first time and get interviewed as well. We had to dress appropriately and present ourselves on a professional scale. As I saw many professionals, my ideas had opened up to a lot of people working in that space. I quickly noticed that many people had very similar interests in the AI technological field as I did, but after meeting with professionals I quickly realized I was the only one they met that took the economic side of AI and how I am the one measuring the impact of AI while everyone else is trying to be a part of the impact. This is strikingly a field many professionals think is more government focused or business focused which I agree with but I didn’t expect this much of a direction to that topic as I assumed. My biggest weakness was putting the words and ideas I had in my head and speaking them fluently in a professional manner. I could primarily memorize most phrases and questions, but in the midst of the conversation, I had a little struggle to professionally and deeply ask my question that I was looking for. There was one point where we all sat in a room with three professionals that discussed AI with us, understanding that it's a booming industry and it may take away many of the jobs that a machine is capable of. These three professionals urged that AI is something that is drawing upon us and acting quickly will ensure our career in the future. After I spoke with a professional that was in the IT management field, he was more on the unethical side of AI technology and didn’t really think AI is something that should be diffused. In contrast I spoke with another highly positioned manager in an IT field that had told me AI is going to diffuse and put nobody to rest until everyone is affected by it. What I primarily learned from this is that in reality, nobody actually knows what AI is capable of in the future, and they are all people like me struggling to answer that question, even the mentors and professional people that I’ve met, even the people that use AI everyday, are unsure. So will I ever be sure? I need to find a mentor that can guide me through that uncertainty, and I need major key evidence to prove the point I am making. I am not on any side of AI, but I wanna be on the side that's right.

Oct 16:  Finding professionals

I used my LinkedIn and researched potential mentors for my ISM original work and final product. I researched ideas for my original work, this is work that I must develop on my own. I personally felt the weeks have been going by quickly, especially since the start of the school year. Due to this reason, I actually wanted to get a head start on many of the parts I must complete for ISM as well as my journey in my topic on artificial intelligence. I researched the basics and not just what it is, but where it comes from. Rather than my knowledge, I thought this would be useful for the original work. Additionally, I gathered a few contacts that I hope to talk to or email in order to interview and get an appropriate mentor. I had prepared my script and developed my ideas to use for the calls and finding a mentor. Tedious task, but it's crucial for my progress.

Oct 23: Basic Learning

I covered the basic learning of AI because many people don't know or understand what artificial intelligence actually is. They think it's just a machine that's a robot. I researched further into AI and wanted to have a document of the basic fundamentals of AI and how its effects may be presented. I wanted to back track, and built a strong foundation to build more off of. I plan to research more on the exact fundamentals of AI but I explored the section of coding technology in creating AI. Python is a computer programming language that most artificial intelligence associates with. The easy and highly oriented language allows AI to make human-like decisions. There are also languages like swift and other programming languages that I hope to further study, but python is so vast as I already know the basics of Python. I wanted to learn more about the language, that was my main focus. I also conducted lots of research and gathered information on AI testing and learning these new ideas causes me to see the reasoning behind so many people claiming Artificial intelligence to be creating jobs. Learning and exploring deeper is always interesting and new. I'm keen to learn more about everything I have explored in the AI field, which is vast and holds many surprises.

Oct 30: Understanding AI Through My Research

As I began my research, I came to a realization that the field of AI has an endless amount of possibility and complexity. My exploration in AI is more focused on machine learning, neutral networking, and data analysis. I think my exploration has led me to a complete understanding of the background work of AI and its application across different fields. This can vary from an intricate job like the medical field to education, which are two different sectors.

Furthermore, I have really gained a wholesome understanding of the AI principles, coding algorithms and just basic algorithms for AI. Being able to comprehend these models help me make decisions and also help me understand the mechanism that functions the desired outcome. I have been talking a lot about just the technical aspect of approach, but it also expands into the ethical aspect or perspective. It not only affects companies, but it impacts society and its responsibilities. From what I have grasped, the crucial balance between technology and ethical requirements is paramount to the field of AI and the benefit of our future. Stay tuned for my next blog as I talk more about how the research is a helping hand in narrowing down to my original work and the brainstorming process within my research field. 

Nov 6: Narrowing Down Original Work Through AI Research

AI is a wide range of factors to encounter, It is endless with endless amounts of innovation. Finding my original work and narrowing down my ideas have been hard. My exploration within this field has led me to brainstorm innovative solutions, where my focus is primarily on maintaining the safety and innovation rate somewhat equal and safe for the users. As I learn more and more about AI, I think I will have a more clear understanding of where I could and can have the most impactful contribution. I have harnessed this knowledge from my past research.

Safety within these developments is a risky factor and that is something I feel like that needs to be addressed. Brainstorming sessions have become a fundamental part of this process. Collaborating with peers, exchanging ideas, and exploring different perspectives on this field has been also helping me a lot. Even though I have not got a full idea, I think I might make a website and focus on safety and information about Ai or maybe code an algorithm. I am not sure and I am still trying to weigh the pros and cons of each but also my experience doing those as my original work. My original work should captivate the audience through the vast information I present them.

Nov 13: Preparing for an AI Research Speech

I was introduced to my research speech this week. This was the moment I had to take all my knowledge I had gathered and find a way to deliver the safety, innovation and pros and cons of AI.  I also have to keep in mind that I need to keep my speech engaging to my audience because AI can get bored really easily if it's too technical. I had to find a way to connect to my audience but also my tone. In order to make sure my speech was impactful, I began by taking complex algorithms, concepts and my journey so far and making it easy to understand content and engaging subjects of matter. I took information from my research findings and highlighted the transformative potential AI has while addressing ethical connections and implications.

Practice was super important. I rehearsed my speech 5-6 times and reviewed my slides to make sure it was professional and engaging. Making sure i had not too many words.I incorporated visual aids to not overwhelm the audience but also help aid the comprehension within the audience. Finding the balance within technical depth and ethicality and societal implications was challenging but was something I needed to play around with and figure out. In my opinion, I think my knowledge and the way I put things together really helps open a new perspective within the audience and an understanding of our future.

Nov 27: Struggles of Cold Calling in the AI Field

Cold calls. I have been intimidated but these cause of the rejection. I love to help people, but being rejected and understanding the world it's been hard to accept rejections. I began my cold calls a month ago. It was a slow process. Very slow I would like to say. Navigating the topic of AI involves getting insights from people who work in the field. The cold-calling process has been so stressful for me, especially because most IT people do not stay on linkedin and are more occupied with meetings. I have been trying to network with my dad’s colleagues and their mutual connections. I have been cold calling three main sectors of AI.

AI analysts, data scientists, and UTD professors within the computer science field. Cold-calling has taught me the idea of perseverance. I think initially if it were me last year I would have given up with the first failed call. I have struggled to find people to connect with, because one I either get a response, or I get ghosted. One thing I realized is most AI based companies are start-up companies which means they have two times the amount of work because of the sole-reason that it is a start up. I think an important factor all the people in this field look for is knowing the algorithm but also not wasting their time, because they do not have time to waste. They like to be efficient. I am continuing to make connections and in hope to find more conventions and maybe a mentor soon.

Dec 4: Setting Up AI Interviews: Challenges 

I am still working on connecting with AI analysts and data scientists. I think both these career paths are something I would like to get more into. One company I have looked into is Digit7. Like mentioned in my last blog, it is a start-up company. I have not gotten a hold of them, but I think they are a company that I would be interested in working with. They focus on AI based products embedded with machine learning. They work on drones, self automated check outs and more.  Like mentioned before, securing and booking interviews with these individuals is hard because one analyst is constantly studying the data and outcome of the usage. However the challenge is not just scheduling the interviews but also the limited time frame and the commitment. They just do not have time to dedicate themselves to the students in their busy schedule. WHich is understandable, but I think despite these challenges I will find at least 3 individuals willing to help. I am still connecting to individuals on linkedin and messaging them. I think with my perseverance and being open to anytime for an interview and showing the passion will help me find my interviews. I plan to work harder and continue my research simultaneously to increase my knowledge.

Dec 11: Original Work Paper

Starting to write an original paper was a tough but good experience. The process needed hard work, strength to keep going and a real desire to learn. This pushed me past what I was used in school. Starting this job needed a lot of study. I looked into different places, like smart papers and books. I learned a lot that made my work strong. Putting together ideas, making a clear order and improving reasons needed careful work and sharp thinking. The secret to doing well was a strict routine. It was very important to balance doing research, making an outline, writing a first draft and then fixing it. Each part had its own troubles, but the feeling of success after getting past each barrier was great. The last parts were adjusting the material, making sure it was clear and meeting rules for sending it in.Checking very carefully, adjusting ideas well and making sure references are correct were necessary for the finished nice result. The hand in was the end of weeks hard work. But, more than just achieving a goal, the trip was full of learning. This process not only made me know more about the topic, but also helped improve my skills in doing research, thinking carefully and managing time. In the end, doing and sending my first work wasn't just a school achievement. It showed how much I have grown, kept going even in tough times and gained new knowledge too! This work has given me valuable skills and knowledge that will stay with me through school and my job.

Jan 15: Filling in the holes

After Christmas break I had to get back to work, I needed a mentor. Setting up interviews is the first priority and I ended up contacting many professionals and setting up an interview yet the weather had decided otherwise as it became freezing temperatures all across the United States which caused me and the professionals to come to a conclusion to postpone the interviews.

I continued meanwhile with research and a few questions I had in between regarding the specific roles Artificial Intelligence has on certain fields. For example, I read that Artificial Intelligence was used in construction. The use for AI came to be the prioritization in waterlogging, scaffolding, and protective equipment. It is obvious that AI is a great tool for prioritization, but many machine algorithms are great as well for digitally displaying difficult architectural designs to help enhance the visual representation to investors or even managers. The use of AI varies in many fields but its practicality it's the most important in considering the automation benefits which replaces the need of a human.

Lastly, I changed a few parts of my website to make it appealing, since I had found a few tabs and structure to be off putting. I took some inspiration from friends whose website I saw and found interesting. I didn't copy his style or structure but made a modification with my work based on the inspiration I took from him. This website will be a big part of my future in ISM with the mentor especially. Finally, I checked my linkedin and made sure everything looked appealing and made changes to my bio.

Jan 22: AI in Medical Industry

AI applied to medical imaging is one the most fascinating features of healthcare and its potential to dramatically change the diagnosis of pathologies. AI algorithms, notably the deep learning-based ones, have proved their ability to be accurate in image analysis, and these imaging methods include X-rays, MRIs, CT scan, and the pathology slides that are examined.

The use of AI is not only swift and precise but also valuable as it helps detect abnormalities, detect pattern and help health practitioners in condition like cancer which may also include fractures and other related diseases. In several instances AI algorithms have proved with an overwhelming superiority over the human expert in the diagnostic task, making a diagnosis quicker and more rightly done.

On the top of that, automated medical imaging systems can be utilized to render health care systems more broadminded and well-defined, primarily in low-income regions where there is a problem of shortage of radiologists or specialists. Through the automation of image analysis as well as their interpretation, AI is able to assist patients in being managed more effectively, those cases which are urgent taken care of first, and good treatment related choices made quickly.

As a whole, there is a great possibility that AI application in medical imaging will bring about a significant change in patients outcomes, reduce healthcare costs and obviously augment the quality of care that is available given this situation. Yet, as continuous research and validation becomes a must in this context, one would need to ensure the accuracy, security, and ethical applicability of these AI systems in clinical practice.

This is important research to determine the future of AI in Healthcare which is a specific domain I covered in my Original Work, yet I want to expand what AI can do not just in healthcare but all domains, because recently I have discovered there are links to each domain in certain applicability in AI. Which I wish to add to my final product.

Jan 29: First Interview

I had an exciting interview over the weekend with Ram Nandayala who is the vice president  of the company smart folks inc. His role in the company is to look and analyze data trends to help his company grow in the market. He has a successful business he claims, with his wife as the president and CEO. The company was a small business which took off in the way he marketed the it, which was one of his biggest obstacles. It was difficult to reach out to IT companies and offer them help as they were a small business. Now they place clients and make placements for companies such as Wipro and other IT companies. WE had a good discussion on AI in the market. Everyone says "AI is the new next big thing" but looking at data and analyzing trends we see that AI is far from perfect, he stated the chatgpt technology today has been around for a while now, but making it user friendly was the challenge that was the problem. We talked over how data is analyzed, and how to measure some frequencies and determine the potential outcomes. This interview really opened my eyes on the different aspects of AI and how to measure data using a broad outlook.

Feb 5: More Minor Research

I looked at an article discussing AI implications in modern society and summarized what I found most interested in a few sources combining the ideas and getting to understand what realistic implications we may see.

In education, AI can personalize learning experiences for students by adapting curriculum and teaching methods to individual needs and learning styles. AI-driven tutoring systems can provide real-time feedback and support to students, helping them grasp complex concepts more effectively and fostering a deeper understanding of subjects. Additionally, AI-powered tools can automate administrative tasks for educators, freeing up time for more personalized interaction with students.

In transportation, AI technologies such as autonomous vehicles can improve safety, reduce traffic congestion, and enhance mobility for individuals with disabilities or limited access to transportation. AI algorithms can optimize traffic flow, predict traffic patterns, and coordinate vehicle routes to minimize travel time and energy consumption. Furthermore, AI-enabled ride-sharing and delivery services can increase efficiency and convenience while reducing environmental impact.

In the workplace, AI automation can streamline repetitive tasks, increase productivity, and enable employees to focus on more creative and strategic activities. AI-powered tools can enhance decision-making by providing data-driven insights and recommendations across various industries, from finance and marketing to manufacturing and customer service. Additionally, AI-driven virtual assistants and chatbots can improve customer service interactions, enhancing satisfaction and loyalty.

In environmental sustainability, AI can help address pressing challenges such as climate change and resource management. AI algorithms can analyze environmental data to identify patterns, predict trends, and optimize resource allocation. For example, AI-powered systems can optimize energy usage, reduce waste, and facilitate the transition to renewable energy sources.

Overall, AI has the potential to revolutionize society by improving healthcare, education, transportation, work, and environmental sustainability. However, it is essential to address ethical, privacy, and equity concerns to ensure that AI benefits society as a whole, so most of these technologies are far from diffusion. This can help dictate my diffusion graph regarding AI. 

Feb 12: Actual Coding

I have covered Artificial Intelligence in most topics and done broad research. I wanted to slow the research down a bit regarding AI diffusion, and focus more on AI language models. I wanted to learn some additional coding with my existing python skills. I saw a few languages that involved the learning of machine learning, one in particular that stood out to me was ruby. Ruby is a coding language that, while easier than python, is much more efficient and faster. The efficiency and simplicity is admired by most programmers who use AI in their programs. The great thing about learning new languages is that it is very easy to transition from language to language if it's Object Oriented Programming and it gets simpler. Because java is the harder OOP language, learning that makes python easier and then ruby even easier. Although learning ruby may be harder during implication, the learning is quite similar to python. Additionally looking at the research apart from programming, I was able to learn a few things regarding AI in the military.

One significant aspect about AI in the context of the military is an ever growing AI’s autonomy use in the new generation of weapon systems. This peculiarity is made possible by AI algorithms which recognize patterns in real time and act on given situation without requiring human participation directly. The creation of "loitering munitions" or "kamikaze drones" is one of the marks of that progress. These drones, like their human counterparts, are trained and capable of maneuvering intelligently around unknown airspace, during surveillance, and open fire on identified potential threats. Moreover, the sensor technology included in these machines allows them to be integrated seamlessly with AI, which gives them the ability to autonomously loiter in a defined location, identify threats and take appropriate action. Completely contrary to traditional missiles or bombs, which carry fixed coordinates and are launched to one particular spot, loitering munitions are capable of modifying combat tactics and hitting many targets in a reloaded capacity for a prolonged time period. This grew interest in me because I haven't looked at military use of AI yet.

Feb 26: An enlightening Interview

I had an interview with Mr. Nagaraj, head of product development in an innovative artificial intelligence company called Digit 7. In this interview I experienced amazing future innovations of AI. I was honored to see the testing facility in which they test products and prototypes for a few of their AI products. One of 4 being the digit mart which is a shopping store that uses machine learning and sensors to track every item a consumer takes. They are tracked through the store and data is collected to ensure that whatever item is picked up is actually correct. All the consumer has to do, is scan their credit card, go inside the store, pick up what they want, and exit the store with a single tap on the screen. Once you go in the store, the AI tracks you using an identification number which is different for all people, then you pick an item. For example, a gatorade bottle and based on the weight after the item is picked up, along with the plentiful cameras and motion sensors, the item is assigned to the person. The interesting factor here is that, the sensors are able to pick up if the item is misplaced and tell who misplaced it. The advantage of having an all robotic and machine store is to help reduce costs for companies with no employees. The amazing thing is, it only cost 1 dollar per day for the company to add this smart store to their stores. While employee costs are much higher, these prices for installation are only a one time price that doesn't need anything but cleaning. Overall, the experience was fantastic and I really got a glimpse of the future of AI technology in a physical way. I am honored to have interviewed Mr Nagaraj and had the opportunity to see testing facilities in DIgit7.

Mar 18: Machine Learning and Predictive Analytics

As I was further engrossed in the study of predictive analytics, the idea of learning the technique and methods of machine learning was daunting as it was an intriguing adventure for me towards the frontier of data-driven decision making. Every algorithm, ranging from linear regression to random forests, individually possesses abilities and peculiarities within them, thereby empowering us with a variety of methods by which we can seek to tease out important discoveries. Having been acquainted with the principles that the gradient descent and decision tree algorithms, I stood on a firm ground to brace application. From the practical assignments and projects I conducted, I was able to enhance my proficiencies in data preprocessing, model selection, and evaluation, and enhanced my skills to approach the dilemma of limiting bias and variance so as to attain the best predictive performance. While I started to incorporate myself into the machine-learning world, I came across difficulties which included overfitting, feature engineering and interpretation of models. This made me design different solutions and perfect my analytical thinking/data. Working closely with teammates and sometimes interns, as well, extended the learning curve and made a living environment. We shared our experiences and mutual insights. Each application I worked with, and every model I tailored, let me understand better the importance of data and the unlimited capability of predictive analytics to foresee futures creatively through sound decision-making regarding any field and sector. Equipped with a sharp know-how of the machine learning algorithms, I could not be more enthusiastic to solve critical real-life challenges and bring out more value to the emerging industry of data science and analytics.

Mar 25: Continuing Research For My Final Product

While adopting a new machine learning concept of predictive analytics and automation has been a pivotal time for me because it brings together the fields of data science and technology and moulds them. Taking up this challenge, self-funded hands-on learning adventure became a means for me to get information from online resources, online courses and practical projects to deepen my understanding. At first, I plunged into the essence, of the algorithms and perfected their operations such as the regression, the classification and the clustering. While I have been talking a little bit about the basics, I will start to dive into more advanced topics such as neural networks, ensemble methods, and natural language Practical projects were also game-changer in my academic career that helped me to assimilate all the theory into the real-world situations including the predictive analysis of customer churn and classification of customer' sentiment. Applying different tools like TensorFlow, scikit-learn, etc. reduced the time for model building and made the process of model development and deployment iterative and rapid. Through this journey, I met with roadblocks and minor stumbling blocks, but each time I have used these circumstances to learn more and polish up my mastery. Collaborating with my fellow students in the online community made my mind broad and trained my problem-solving capability. Also, I participated in the hackathon, which also broadened my perspective and trained my problem-solving ability. Data wrangling and feature engineering were the foundations of my abilities in modeling and deployment, which taught me data preparation and model assessment. Throughout this expedition, it was more than pure programming skill I picked up, I was endowed with a zeal for innovation and a need to explore the potential of this amazing technology to help steer disparate fields in the right path.

Apr 1: April fools, Learning Python Tools  

In the midst of working on my final product, I wanted to excel my knowledge working with machine learning in order to create the graph that I envision to capture the true prediction of the foreseeable future for AI. Since gathering information was completed, I decided to work on a google spreadsheet with all the data I collected which I could then transfer to a graphical representation. While gathering the data and identifying the trends, I arrange the information and depict the predictive model using analytical graph. Through plotting of a few variables against time, I identify the trends and cyclical movements that may be corresponding upon new knowledge or insights which I have never considered. I select the most impactful visualization, fully trace each axis, and generally write the information with substantial meanings. Throughout the construction I evaluate the possible outliers and anomalies and give them the right credibility so as to make the prediction model practically valuable. Ultimately, I diagnose the essence and present real-lived knowledge to help in future strategy-forming. I want to finish this final product and proudly showcase my achievements in learning artificial intelligence and machine learning. One step at a time, I am reaching my goal for ISM.

Apr 15: Finishing Up The Final Product

From the machine learning algorithms I have encountered, I can say that they are such multi-purpose tools for discovering important relationships and trends contained within the data. What attracts me most is an interesting application to which I’ve dedicated myself and it is the using machine learning for the generation of graphs from data with high complexity. Primarily, I process the data with ensuring it is clean and following the specified format. Next, after choosing a specific algorithm that suits data and is suitable to build a graph.

For example, if I am working with numeric data and I need to show some time-based trend, a regression-based approach will be especially suitable. Subsequently, if I am dealing with categorical data and want to identify the connection or groupings then clustering algorithm like Kmean could be suitable. Subsequently, having trained the model to supply the predictions, I use Matplotlib or seaborn libraries in Python to visualize the graph. This step consists of the mapping of the input features onto the graph axes and the representation of the values that are predicted as points or lines. In the end, I add labels, titles, and other aesthetic components which give the graph the nature of being informative and appealing to the eyes. 

This, in turn, has made me realize how it is not only for analysis but also for creating interesting and meaningful visualizations which help the decision making process and communication be established.