## Inspiration

As busy ~~hakerrs~~ hackers, spelling, sentence structure, or grammar are seldom luxuries that we have time to indulge. We decided to face our writing demons for one last time. The result? Polish. In providing a closed-sourced alternative for many of the same mechanical functions as other text processing algorithms such as Grammarly, we can simultaneously provide instant feedback while protecting information and increasing user transparency. With increasing reliance on and availability of technology, cyber security is more important now than ever before; we hackers need a reliable, intuitive interface ~~which~~ that can protect our data and save us time. We keep Polish running local on the user's device, which allows our users to maintain control over their own data, and their own writing.

(Disclaimer: This paragraph was not written with Grammarly)

## What it does

Polish is here for your writing– or reading– needs. Our summarization feature will slash the time you spend dealing with verbose writing. Polish provides clear, concise, and reliable highlights of the text you need to get through. If you found the above paragraph too long, here is a summarized version created by Polish:

"This product was inspired by Grammarly: we saw great benefits in fixing its closed-sourced nature, because we consider transparency of our product one of the most important features. With safety being an ever growing issue, we know we need our users to trust the solution that we provide. "

Polish also works to finish sentences through its AI-generated recommendations. These can provide creative and useful endings for any sentence starters you desire. Writer's block? Just allow Polish to find a sentence ending that works for you. Not enough time? All you need is a few sentence starters to generate whole paragraphs of text in a fraction of the time. Whether you need something to cure boredom, spark creativity, or save time, Polish has an array of features to work for your specific needs.

## How we built it

One of the biggest goals for this project is to move all computationally expensive and data handling components off of the cloud and onto the user's device for enhanced control over the model and user data privacy.

To achieve this we first identified some datasets, namely [ASSET](https://huggingface.co/datasets/asset) for summarization and [JFLEG](https://huggingface.co/datasets/jfleg) for grammatical correction and combining both datasets to create a sentence completion dataset.

Then, we chose T5 for its multitasking capabilities (and existing pretraining on summarization) to finetune on this dataset.

Following this, we began to build the webapp. As this space of running Transformer models on the edge is very poorly documented/not documented at all, we ended up translating inference and tokenization code from a variety of sources from python to js. To save on time, our frontend is built on vanilla HTML/CSS/JS.

## Challenges we ran into

* Many of our team members are new to building web applications, and had to learn HTML/CSS/JS from scratch
* There is very little exploration into running natural language on the edge (ore even with tensorflowjs). However, this didn't keep us from managing to create a working model that works on laptops and (select, non-apple) mobile devices!

## Accomplishments that we're proud of

We are proud that Polish is so user friendly. We created this product with user's interests in mind, hoping to ease their worries in the domain of safety while maximizing speed and visibility into what the model is actually doing by moving traditionally server-side data directly into users' hands in the browser. Furthermore, we made sure to focus on creating an accessible product by making the text size adjustable, adapting to various screen sizes with ease. With a smooth zooming feature, the smaller text can be enlarged for users. We believe that creating the best experience will not only provide a good technical site for writing amelioration but also a product users will love.

## What we learned

As our team is majority beginners, we struggled acclimating ourselves to JavaScript. However, this has been a wonderful learning opportunity, as we familiarize ourselves with JavaScript, HTML, cleaning data, and training our model. We learned a lot about web development, and about all the parts that come together to create this product.
Another very important lesson we learned is task prioritization (even without systems such as boards to keep track of them). We were often sidetracked building fun and quirky features or sharing a laugh over the dumb things the model generated.

## What's next for Polish

As great of a product that Polish is after one continuous sleep-deprived coding session, we believe that it has a lot of room to grow. We are hoping to do more extensive debugging, resolving fundamental architecture flaws, and moving on to more industry standard frameworks, such as react native. We then want to optimize Polish to work better on cell phones by creating a mobile app and taking advantage of the more robust frameworks, such as tensorflow lite. This way it will be more distributable and have a wider market. We also believe in constantly bettering our model, because we know that we can never be perfect. Optimizing the model inference speed and memory efficiency would be the natural next steps for making Polish the natural option for customers. We hope that you will love Polish as much as it loves to help you support you in your writing!
## Inspiration

Care.ai was inspired by our self-conducted study involving 60 families and 23 smart devices, focusing on elderly healthcare. Over three months, despite various technologies, families preferred the simplicity of voice-activated assistants like Alexa. This preference led us to develop an intuitive, user-friendly AI healthcare chatbot tailored to everyday needs.

## What it does

Care.ai, an AI healthcare chatbot, leverages custom-trained Large Language Models (LLMs) and visual recognition technology hosted on the Intel Cloud for robust processing power. These models, refined and accessible via Hugging Face, underwent further fine-tuning through MonsterAPI, enhancing their accuracy and responsiveness to medical queries. The web application, powered by the Reflex library, provides a seamless and intuitive front-end experience, making it easy for users to interact with and benefit from the chatbot's capabilities. Care.ai supports real-time data analytics and critical care necessary for humans.

## How we built it

We built our AI healthcare chatbot by training LLMs and visual recognition systems on the Intel Cloud, then hosting and fine-tuning these models on Hugging Face with MonsterAPI. The chatbot's user-friendly web interface was developed using the Reflex library, creating a seamless user interaction platform.

For data collection,

* We researched datasets and performed literature review
* We used the pre-training data for developing and fine-tuning our LLM and visual models
* We collect live data readings using sensors to test against our trained models

We categorized our project into three parts:

* Interactive Language Models: We developed deep learning models on Intel Developer Cloud and fine-tuned our Hugging Face hosted models using MonsterAPI. We further used Reflex Library to be the face of Care.ai and create a seamless platform.
* Embedded Sensor Networks: Developed our IoT sensors to track the real-time data and test our LLVMs on the captured data readings.
* Compliance and Security Components: Intel Developer Cloud to extract emotions and de-identify patient's voice to be HIPAA

## Challenges we ran into

Integrating new technologies posed significant challenges, including optimizing model performance on the Intel Cloud, ensuring seamless model fine-tuning via MonsterAPI and achieving intuitive user interaction through the Reflex library. Balancing technical complexity with user-friendliness and maintaining data privacy and security were among the key hurdles we navigated.

## Accomplishments that we're proud of

We're proud of creating a user-centric AI healthcare chatbot that combines advanced LLMs and visual recognition hosted on the cutting-edge Intel Cloud. Successfully fine-tuning these models on Hugging Face and integrating them with a Reflex-powered interface showcases our technical achievement. Our commitment to privacy, security, and intuitive design has set a new standard in accessible home healthcare solutions.

## What we learned

We learned the importance of integrating advanced AI with user-friendly interfaces for healthcare. Balancing technical innovation with accessibility, the intricacies of cloud hosting, model fine-tuning, and ensuring data privacy were key lessons in developing an effective, secure, and intuitive AI healthcare chatbot.

## What's next for care.ai

Next, Care.ai is expanding its disease recognition capabilities, enhancing user interaction with natural language processing improvements, and exploring partnerships for broader deployment in healthcare systems to revolutionize home healthcare access and efficiency.
## Inspiration

With the recent Corona Virus outbreak, we noticed a major issue in charitable donations of equipment/supplies ending up in the wrong hands or are lost in transit. How can donors know their support is really reaching those in need? At the same time, those in need would benefit from a way of customizing what they require, almost like a purchasing experience.

With these two needs in mind, we created Promise. A charity donation platform to ensure the right aid is provided to the right place.

## What it does

Promise has two components. First, a donation request view to submitting aid requests and confirm aids was received. Second, a donor world map view of where donation requests are coming from.

The request view allows aid workers, doctors, and responders to specificity the quantity/type of aid required (for our demo we've chosen quantity of syringes, medicine, and face masks as examples) after verifying their identity by taking a picture with their government IDs. We verify identities through Microsoft Azure's face verification service. Once a request is submitted, it and all previous requests will be visible in the donor world view.

The donor world view provides a Google Map overlay for potential donors to look at all current donation requests as pins. Donors can select these pins, see their details and make the choice to make a donation. Upon donating, donors are presented with a QR code that would be applied to the aid's packaging.

Once the aid has been received by the requesting individual(s), they would scan the QR code, confirm it has been received or notify the donor there's been an issue with the item/loss of items. The comments of the recipient are visible to the donor on the same pin.

## How we built it

Frontend: React  
Backend: Flask, Node  
DB: MySQL, Firebase Realtime DB  
Hosting: Firebase, Oracle Cloud  
Storage: Firebase  
API: Google Maps, Azure Face Detection, Azure Face Verification  
Design: Figma, Sketch

## Challenges we ran into

Some of the APIs we used had outdated documentation.
Finding a good of ensuring information flow (the correct request was referred to each time) for both the donor and recipient.

## Accomplishments that we're proud of

We utilized a good number of new technologies and created a solid project in the end which we believe has great potential for good.

We've built a platform that is design-led, and that we believe works well in practice, for both end-users and the overall experience.

## What we learned

Utilizing React states in a way that benefits a multi-page web app
Building facial recognition authentication with MS Azure

## What's next for Promise

Improve detail of information provided by a recipient on QR scan. Give donors a statistical view of how much aid is being received so both donors and recipients can better action going forward.

Add location-based package tracking similar to Amazon/Arrival by Shopify for transparency