June 19

Blog post 7-Reflecting on presenting in front of parents

My experience when presenting for the parents was a good experience for me. I felt comfortable when I presented.

 

I felt comfortable when I was presenting because I decided to not follow my script, and I engaged the audience more. I first asked if people know what AI is, and a lot of people didn’t know, so I decided to create more of a basic structure so that people can understand my project. If a lot of people knew what AI was, I would go into more of the techs parts, without explaining it in a basic manner. I asked the audience more Heston’s which made them feel included, and made me feel more comfortable. The structure of my presentation pleased me, and made me feel natural when I was presenting.

 

Overall, I was very happy with how I presented, and I look forward to these types of knowledgeable presentations in the future. Here is my performance of my capstone project:

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June 15

Blog post 6-working on my final project

My experience well working on my final project is hard to explain. I didn’t feel that pressurized, but when I looked at the sheet of when it was due, I did. I wanted to memorize it so well that I would 100 percent not fail, but later I developed a new strategy that I would have a general idea of what I was going to speak about on the slide, and because I personally went through the experience of research, and I am very informed about what I want to talk about, so I can improvise a little bit. This strategy made me feel every confident about my success rate, and made me focus more.

 

I also felt motivated by the means of how the project commenced, and how i get to share my knowledge with other people. I feel like this is my chance to share what I know, and be me, without so many guidelines. I love talking and presenting, so being able to geek out about technology, with a lot of speaking and in a presenting format, was a great experience for me. I feel very motivated and I love how the teachers conducted this project.

 

This is all I experienced. I developed a strategy that really helped me, and I am very excited to share my knowledge with other people.

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June 6

Main Inquiry Essay

 

How Has Artificial Intelligence (A.I.)  

Revolutionized Healthcare, and

How Will A.I. Continue to Improve Healthcare?

 

Simply put, artificial intelligence (A.I.) is a machine learning system that is able to adapt and learn all by itself. AI is incredible. Now, because AI is such an advanced platform, there are so many uses. One of the ways that AI is used is for healthcare is image processing technology, which has improved diagnosis in healthcare. Another way AI has revolutionized healthcare is through an app called Sugar IQ, which helped patients with Diabetes through deep learning. But finding ways that AI can help healthcare are ongoing. A way that AI is contributing to the future is by that diagnosis is becoming more efficient, which means we can diagnose more problems, diseases, and people.

 

How AI uses Image Processing Technology, to improve Healthcare

 

One way AI is revolutionizing healthcare is through image processing technology. Image processing technology is one of the things ran by a system called a neural network. Image processing technology has improved healthcare by having research data that is critical to diagnosing patients. Here are several examples of how image processing technology is being used to diagnose medical symptoms:

 

  • Neural Network Review of X-Rays: After a patient has an x-ray taken, the film is scanned through a neural network machine, and the neural network software assesses the results of the X-Ray.
  • Facial recognition software: Facial recognition software is being used to diagnose harder-to-find symptoms. For example, diagnosing genetic disorders. It uses a machine learning algorithm to complete this, which means in other words that the
  • Chatbots: Companies are using AI-chatbots with speech recognition to identify patterns in patient symptoms. This helps form a potential diagnosis.

 

How AI, Through Sugar IQ, Has Improved Healthcare:

Sugar IQ:

Sugar IQ is an application that is designed to keep people’s glucose sugar levels level. (This app is just for diabetic patients) The app also helps you figure out the snacks that are challenging for your blue one levels based on hardware technology called the Guardian CGM Connect. The Guardian CGM connect is a tiny device that you tape to yourself, and every 5 minutes it inducts such a small needle into your body, that you can’t even feel it. The device then uses data that you insert about what you have eaten, then, the AI kicks in. The AI processes what you have eaten, and learns about how your body developed overtime. Then, the AI software recommends what food for you to eat, to the point to keeping your sugar glucose levels at a normal rate. Sugar IQ is revolutionizing people with diabetes through deep learning.

 

How AI has diagnosed problems in Healthcare:

Watson is IBM’s machine learning platform for AI. Watson’s first project was trying to use AI to detect cancer. For Watson to do this for the first time, it took 3 years. For the second type of cancer, it took 1 year. A big improvement. The third type which is where Watson is right now, took 4 months. Overtime, Watson adapted and got faster at detecting cancer, because it developed new ways to identify, and diagnose cancer.

How Will AI Impact healthcare In the Future:

Diagnosis is the future of AI. It is developing a fast rate. I mean so fast. Diagnosis is a big part of healthcare, and now since healthcare has many more problems, the AI is discovering how to diagnose more diseases and problems, based on the fact that there are more problems that are developing.

Conclusion:

These two reasons show how AI has revolutionized healthcare, and the example of how we can improve AI in the future shows how we can contribute to a brighter future. AI has made such an impact on the world in general, and in healthcare. Who knows, maybe one day your life can change due to AI.

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May 31

Blog Post #4-Capstone Interview

I really loved my interview with Pooja Kumar. Pooja is an investor for clients in Healthcare, and she knows a lot in the industry of AI in Healthcare. She was very helpful with centering where I should drive my research, and giving more detailed examples of how AI is improving Healthcare, and how that can improve, and how we can centralize AI in Healthcare with humanity. Here are some examples of what Pooja thought about how AI is revolutionizing Healthcare, and where and how we should adapt to improve the technology for the future:

 

  • AI is starting to revolutionize healthcare, it hasn’t revolutionized Healthcare yet. AI will most likely develop statistically over the next couple decades. The context is that she works with doctors and investments for Healthcare, some are AI, so in the field of statistics is where she is an expert on.
    • Examples of ways that AI will advance and revolutionize healthcare in the future:
      • Hospital Systems, doctor advancements and analysis, and Institutions of AI research is where AI in Healthcare statistically is predicted to evolve.
      • Can technology replace the less clinical tasks that people can do
      • Technicians are wondering if AI can read radiology waves, and transfer them and analyze them to create an algorithm or a suggestion using core ML and Deep Learning platforms.
  • Generally in 10 or 20 years, AI will be at a level that is incomparable to humans
  • AI hasn’t had a major impact on patients, it is more at the level of watching over things that are in place
  • Natural Language processing is good to pull information like clinical notes for AI Finance in Healthcare. Generally the Finance, numbers, and Natural Language processing are the main components and more advanced aspects of AI as of now, in Healthcare.
  • Diagnostics of AI:
    • AI in Diagnostics is going to speed up in the next few years, because their is more and more data that is going to become a major input into AI platforms and they can learn, we can learn, and look for platforms like Processing of Language and Analytics of Medicine, etc., that will allow us to see, and teach these platforms to perform tasks that humans do. X-Rays are a good example of diagnostics.Here was the first historical breakthrough: 20 to 30 years ago, stays were on films,with bad memory drives, and you had to get them processed and printed out to view them. The pixels were also very bad quality, which gives the painter it a negative start to their diagnostics. And comparing it to now and our time, we have advanced technologies that perform these amazing tasks,and AI will get up to that even, and center all of the other 3rd party hardware and software establishments that we use currently to gather the information, but the problem is that they are all on separate platforms. If these components were part of one of the main platforms of AI, we can futuristically have a great way of diagnostic get problems to AI in Healthcare towards the future.
    • Neural Networks drive the diagnosing, and will improve in the future and be able to process more RAM (Random Access Memory) data in the future as our technologies improve.
  • How will AI have an impact on patients in the future?
  • Sun Microsystems AI predicted is going to replace most doctors in the that is the way that the future is going to continue in their future, but Pooja doesn’t think that tat will happen. Machines she thinks should be a companion to humans in the future, and we should collaborate our minds and not act like it is slavery. This should be a beneficial aspect of AI collaborating with humans.

 

I found all of these examples very interesting and informative, and I was able to better base my research. Here are some ideas that I got from Pooja about where to futuristically conduct my research:

  • Hardware technology powering Neural Networks
  • How we can make Hardware smaller and more effective
  • Platforms for AI pros and cons
  • Natural Language processing Neural Networks and how they contribute to AI in Healthcare

 

Over all this interview had me realize the importance of us wrong with these technologies because of the massive amount of data that these machines can discover out of the ordinary thing that we can’t.

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May 30

Blog post 3-Site Visit (Capstone)

Generally speaking, I really loved my capstone site visit. It really was a great experience and it inspired me to be a part of AI development in the future. I met two people that were named Matt Calgary Kathy McGrody both very inspirational, and showed me how AI can contribute and make Healthcare data more accurate, and better. Humans have been working with this technology for years, and having a kid like me visit IBM, and see Watson and AI software, well, that is pretty big.

    I had a great time their. Unfortunately there was no photo of video content recorded, because I was not allowed to. I will break up the next two paragraphs into each person that I met, and what I learned from each of them.

First, i met someone named Kathy McGrody. I learned about the basic Architecture, and how the place that i am visiting is so special to IBM, and their success. What she said is that the Architect that designed the building, his name was Wallace Eckert. He was modern by the means of how it was built in 1964, and the long glass panels represent the true beauty of the building.

Here are some pics:

Anyways, that’s not the point. I mainly learned from Kathy that how AI has contributed to Healthcare is really big because of how the insights, and the data that Watson is feeder is so accurate, that Watson comes up with a great solution, that has came in handy many times, and still continues to. Here are the full notes that I have: Link

 

What I learned from Matt Calgary is the development, and actual Software of the new app SugarIQ for iOS devices. It is not yet out on the App Store as of the date 5/29/18, but it will come out in the next month or two. SugarIQ has a built in Watson software to it, and it is for people with diabetes. Their is a wireless device called Guardian Connect. Guardian connect is a tiny device that injects a needle into your skin every 5 minutes. Don’t worry, you don’t even feel it. Anyways, then the data that it collects about your blood pressure, sugar, etc., and then Sugar IQ makes references and can see what you ear, and it asks questions a to know you, and using deep learning platforms of AI, is able to get to know you better and make references to you better. For example, if the app recognizes that you play soccer every Saturday afternoon, it will remind you to pack a snack with a lot of calories such as a granola bar, so your blood sugar goes too low. Also, Sugar IQ will notify you on your iOS compatible device if your blood pressure or sugar is too high, or low. I found this pretty cool. Here are a couple of videos on Sugar IQ:

After that, I really enjoyed the site visit. It was fun, I got to see some really cool parts of AI in Healthcare, I learned a lot, and most of all I was inspired to be part of AI in Healthcare In the future, and to make the world better where it needs to be improved on.

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April 30

Blog Post #2- Choosing a Main Inquiry Question and sub Questions:

One reason why capstone has really helped me be organized which is essential for me to organize my life is because Capstone tests stamina, and I need to organize my thinking in an innovative and structured way so that the project can be a success in the long term will be a success and so that the other students can connect to my final product by understanding and brainstorming the final product, but in order to present all of those traits that Capstone requires, I need to be very organized and be on top of everything.

    And although being organized relates to my overall capstone success, me choosing a main inquiry question and sub question related to my research so far being organized. Honestly, I had absolutely confirmed what my main inquiry question was, bt the thing that was very complicated for me was the sub questions. I knew I had to gain a lot of research to be able to complete this project, but at the same time I had to be organized to generate these sub questions. To start developing the sub questions, I knew I had to create a variable so that I can organize the strategic process for generating good sub questions. The strategy for me was to think about the components of robotics. The main components are:

  • AI Robotic learning
  • Hardware innovation
  • RAM transactions
  • Coding/programming
  • Economics vs. Ethics

 

Now ever since I thought of these 5 elements, I thought that I could brainstorm further and generate stub questions that relate to my main inquiry question. After a lot of persistence and more research, I came up with more organized and at the same time creative sub questions. After that process, I finally had revised and final sub questions. Here are the sub questions reflecting my main inquiry question:

  • What types of Robotics perform these medical needs, and how are each one of them designed?
  • What is the energy source that powers these Robots?
  • How do they transfer analysis data to a hard drive, or some other physical hardware, or some other physical place to store?
  • Can the Robotics feel based on AI deep enhanced learning?
  • How does AI affect the more human like reactions and actions?
  • Is AI really a big factor of the Robotics in healthcare? Or can Robots advance in other ways?
  • How do the Economics spread out compared to the Ethics, and can companies generally afford it?

 

Generally speaking, the main inquiry question and sub questions had to be generated by me organizing my thinking, generating a ton of research, and then at the same time being able to install creative and bold thinking, and for me to use these aspects to generate solid main and sub inquiry questions.

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April 19

Post #1-Choosing my topic

Finding my topic was generally hard, mainly because I had so many ideas. Since I am so interested in Technology, I decided that my topic would revolve around Technology. I had 4 topics that I wanted to do. My first one that I wanted to do is generally how AI powered robotics improved humans, but that was very broad, and although Ms. Edwards said to start broad, it was way too broad. The topic was interesting though and came to my advantage because then I later realized how the idea was useful because I was generally motivated to learn, and that I had as topic that I was passionate about, so it would be easier to find a topic by using a factor of me being passionate about topics.

Later on, I brainstormed topics that were related to AI Robotics, and then I came across some more: Data and research gaining from AI based computers medically related topics, and also how AI chess computers are programmed, and also how they have affected humans play better chess, and generally gain more knowledge about the chess industry, including history. I thought these 2 newly developed topics were very interesting, and I wanted to more deeply research them. I then used the same method that I used before, determined to see what can even further motivate me, and generally what I would be the most passionate about in the long term. What I decided I would be the most passionate about in the long term is AI data gaining from computations medically related topics. This translates into AI based computers gaining knowledge from historical cell actions made by scientists, which really interests me because is it technology related, but also generally improves humans and is more efficient which I also deeply care about.

And then after that, I finally used all of the information from research and attempts in choosing my topic to bring the medical topic down a little bit. I then choose as my final topic which is about the robotics doing surgery for humans, and not the robotics that do the surgery are the main focus, Burt the main focus is then the robots that do the surgery physical of the surgery, then robots that do the surgery collect data from the surgery, computers analyze the information, and the humans then program the Robots to do better based on the data that the computers collect from the robots in the first place. I also was wondering how can we improve this formula of surgery, so this ended up to be my topic: the data that is collected from robotic surgery, and how the data provided by the robots that do surgery can be improved so that the robots can do better surgery.

Generally speaking, there were many complications revolving around my topic selection, but I used my failures to motivate me and to better pick a topic. It was stressful, but I have to stay optimistic if I am going to survive this long journey.

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