University Hackathon Showcase: The Future of Tech

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2024 Hackathons

AI Cal Hacks

June 22-23

Intel collaborated with the University of California, Berkeley to cohost this event. Over 1,000 attendees joined to create AI-focused projects using AI PC developer kits from Intel and the Intel® Tiber™ Developer Cloud. Intel hosted workshops and an Intel- focused lab during the event to assist students with their hackathon project.

Best Use of Intel® AI Winners

Out of the 32 hackathon project submissions for the Best Use of Intel AI category, the following projects were winners:

Dispatch AI, the first-place winner, is an empathetic agent eliminating 911 wait times during critical emergencies. Dispatch AI fully used the Intel Tiber Developer Cloud for project development and demonstration and extensively integrated Intel AI tools, particularly Intel Extension for Python to optimize their project. Additionally, they implemented a fine-tuned Mistral LLM for specialized emergency response using the Intel Tiber Developer Cloud.
Batteries by LLM, the second-place winner, predicts the structure of new lithium-ion electrolytes from text description and optimizes using first- principle modeling to fight climate change. Using an Intel® Gaudi® card from the Intel Tiber Developer Cloud, they fine-tuned a Llama 2 7B model that converts natural text to modeling input files that reflect atomic positions (POSCAR file).
Accel, the third-place winner, is an empathetic chemistry tutor. To build the core capabilities of Accel, the team used Intel Gaudi software and an AI PC from Intel. Intel Gaudi software allowed them to distill a model ('selinas/Accel3') by fine-tuning it with synthetic data they generated from the Llama 70B model to 3B. This enabled them to successfully run their app on the AI PC. The team found the prospect of distributing AI apps with local compute to deliver a cleaner and more secure user experience very exciting, and they also enjoyed thinking about the distributed systems implications of NPUs.

For more information, see Innovative AI Projects.

HackDavis

April 20–21

Intel collaborated with University of California, Davis to cohost this event. Over 800 attendees joined to code for social good. Intel hosted workshops before and during the event to get students started on Intel® technology and assist with their hackathon projects.

Best Use of Intel® Tiber Developer Cloud Winners

Out of the 11 hackathon project submissions for the Best Use of Intel Tiber Developer Cloud category, the following projects were winners:

Mad Molecool, the first-place winner, is an all-in-one LLM-powered electronic lab notebook for molecular biologists to easily access common biochemical information. The LLMs were fine-tuned using personally curated datasets with self-annotated data from web-scraped journal articles alongside the cleaner pubmedQA and BiosQA datasets. The main technical stack was a Python* and Flask back end with the LLMs communicating from Intel® Tiber™ Developer Cloud through a separate Flask server.
Midas Green, the second-place winner, helps democratize access to agricultural technology by identifying plant diseases. Midas Green uses a convolutional neural network (CNN) and fine-tuned Gemma model optimized with int8 quantization from Intel and retrieval augmented generation (RAG) to ensure accurate responses. With the CNN, they can classify 32 plant diseases from images captured in any garden. The back end, hosted on Intel Tiber Developer Cloud, efficiently processes the data, while the development environment on Intel Tiber Developer Cloud ensures seamless integration and scalability. The model outputs disease classifications, which are then analyzed through a RAG to suggest effective treatment options for farmers.
Doggo AI, the third-place winner, is an interactive child’s companion that shows emotional responses for children to have an actual conversation with. The team used Intel Tiber Developer Cloud to develop their project for the back end.

For more information, see Innovative AI Projects.

Anohka

April 19

Intel collaborated with Amrita University to host a hackathon at Anohka 2024. Around 1,200 developers joined to develop innovative AI projects using Intel® tools and the Intel Tiber Developer Cloud platform. Students also learned from 30 mentoring sessions hosted by Intel.

Best Use of Intel Tiber Developer Cloud Winners

Out of the 11 hackathon project submissions, the following projects were winners:

Comic-ify, the first-place winner, is an LLM-based application that converts mundane PDFs into visually engaging comic-style content for a better reading experience. Its fine-tuned generative AI (GenAI) model generates imaginative text and images based on text. The team used the Intel Tiber Developer Cloud platform and optimized Python* libraries provided by Intel® Distribution for Python*.
DeepFakeDetective, the second-place winner, is a solution that employs advanced machine learning models to detect and mitigate deepfake media through image classification, video classification, audio analysis, and a combination of these techniques. The AI frameworks used in the project include Intel® Extension for PyTorch*, TensorFlow* optimizations from Intel, and Intel® oneAPI Deep Neural Network Library (oneDNN). The team also used accelerated hardware resources available on Intel Tiber Developer Cloud.
Traffic-signal-AI dynamically adjusts traffic signals to prioritize the passage of emergency vehicles while effectively managing the traffic flow. It uses Intel Extension for PyTorch, Intel® Extension for TensorFlow*, and high-speed CPUs on Intel Tiber Developer Cloud for real-time video analysis, audio processing, and optimized machine learning algorithms. AI Tools from Intel help perform faster data processing, model training, and inference in the project.

For more information, see Interesting AI Projects.

LA Hacks

April 19–21

Over 1,000 students joined LA Hacks at the University of California. Students had the chance to learn from Intel experts through multiple workshops based on GenAI, ITRex, and Stable Diffusion*.

Best Use of Intel Tiber Developer Cloud Winners

Out of the 16 hackathon project submissions for the Best Use of Intel Tiber Developer Cloud category, the following projects were winners:

Soundscape, the first-place winner, is an application that generates real-time adaptive music through an AI-driven composition engine. It performs real-time location tracking and user preference analysis to generate soundtracks dynamically. The team used Intel Tiber Developer Cloud for back end optimization to ensure responsiveness and scalability of the application. The project was ranked third among the overall LA Hacks winners.
Aide-n, the second-place winner, is an application that can help people with common injuries. It uses an image-to-text generation technique to convert the input image of an injury into a textual description, which is then fed into an LLM that generates a potential treatment. The team used Intel® architectures and Intel Extension for PyTorch to build an optimized LLM on Intel Tiber Developer Cloud.
Scribe, the third-place winner, is an AI tool to empower doctors with data-driven, accurate diagnosis of patients' health conditions. It prevents contradictions such as medication errors and misdiagnoses. The team used the computational power of resources available on Intel Tiber Developer Cloud to fine-tune an open source LLM.

For more information, see Innovative AI Projects.

HooHacks

March 23–24

More than 650 students attended the University of Virginia's HooHacks. Competing for a chance to win a Lenovo* AI PC, students used the Intel Tiber Developer Cloud to create their projects with the help of Intel experts. Students also had a chance to participate in a fun GenAI activity where they took home a custom tumbler that they built using Stable Diffusion for a Jupyter* Notebook.

Best Use of Intel Tiber Developer Cloud Winners

Out of the 14 hackathon project submissions for the Best Use of Intel Tiber Developer Cloud category, the following projects were winners:

MosaicHealth AI won first place. It is a solution that efficiently filters massive personalized medical data from fitness wearables in real time to help doctors provide critical insights during patient consultations. It helps expedite the diagnosis process by generating comprehensive medical reports. The team extensively used:
- Intel Tiber Developer Cloud to build and deploy its solution
- Intel® Distribution of Modin* for accelerated data analysis
- Intel® Extension for PyTorch* for faster inference.
  
  The team also fine-tuned the Phi-2 Transformer-based model using Intel Tiber Developer Cloud and used a Prediction Guard (an Intel® Liftoff member) LLM APIs for compliance and reliability.
Mind River AI took second place as an innovative AI-based meditation and emotional support assistant that prioritizes user privacy and personalization. The web application creates tailored conversations and meditations for each user, securely storing all interactions and personal information in a private database. Using the LangChain* framework, VectorDB package (powered by Intel’s embeddings), and applying a fine-tuned open source LLM model on a medical dataset on Intel Tiber Developer Cloud, MindRiver offers a unique experience enhanced by neuro-inspired art designed to evoke specific emotions.
EcoSense secured third place as an AI-powered IoT solution that enhances energy efficiency and sustainability by replacing traditional thermostats with advanced environmental sensors. The system collects data on volatile organic compounds, carbon dioxide, humidity, and temperature to identify human activities using machine learning algorithms. It then adjusts room temperature and air quality dynamically for optimal energy consumption and comfort. The team performed model fine-tuning on Intel Tiber Developer Cloud.

For more information, see Innovative AI Projects.

Daksh AI

March 8–10

Intel collaborated with SASTRA University to host a GenAI-focused hackathon at Daksh AI. Students participated in three workshops that focus on GenAI and LLMs that are run by Intel innovators and professors. As a part of their hackathon participation, students accessed the Intel Tiber Developer Cloud to build their projects using Intel technology.

Hackathon Winners

Out of the 27 hackathon project submissions, the following projects were winners:

Stylist AI, the first-place winner, is a generative AI-based solution for personalized outfit recommendations that can improve user experience on e-commerce websites. The team employed Intel Extension for PyTorch, Intel Extension for TensorFlow, oneDNN, and Intel Tiber Developer Cloud.
Llama Hunt, a second-place winner, is an LLM-based web application that helps find job openings tailored to an applicant's resumes and preferences. The team used Intel Extension for PyTorch for faster inference and Intel Tiber Developer Cloud for accelerated deployment of the project.
Find My Doctor, also a second-prize winner, is a mobile application designed to help users find the best doctors available near them based on symptom analysis and patient and hospital classification, ensuring prompt access to healthcare. The team used oneAPI optimizations of machine learning libraries and Intel Tiber Developer Cloud for faster running on GPUs.
EmergAI, the third-place winner, is a call management solution that enables timely responses to critical situations for efficient emergency services. Using oneAPI optimized machine learning and natural language processing (NLP) frameworks and algorithms, it prioritizes emergency calls in the absence of call handlers by tracking the caller's location and analyzing the call's keywords.

For more information, see Innovative AI Projects.

TreeHacks at Stanford University

February 16–18

In the 10th TreeHacks, 1,500 students participated in Intel workshops, learned from the founder of Prediction Guard, Daniel Whitenack, and explored the Intel Tiber Developer Cloud to compete for a chance to win a Lenovo AI PC.

Best Use of Intel Tiber Developer Cloud Winners

Out of the 30 hackathon project submissions for the Best Use of Intel Developer Cloud" category, the following projects were winners:

Meshworks, the first-place winner, combined mesh-based radio technology with cutting-edge AI processing to build a resilient information network for emergency responders in disaster-struck areas. The project enables connections between different device nodes and the command node in a field, followed by visualizing the summarized reports of fields on a map. The team trained a neural network on Intel Tiber Developer Cloud to perform binary image classification to distinguish damaged and undamaged buildings. Intel Extension for PyTorch was used for model optimization, Whisper for speech-to-text conversion, and Prediction Guard LLM for summarization.
ScratchML secured second place with a no-code platform designed for teaching machine learning and data analysis principles to students through an easy-to-use GUI and drag-and-drop functionality. The project uses the Prediction Guard LLM API to provide real-time insights into user decisions and outcomes within lessons using Neural-Chat-7B. The team used Intel Tiber Developer Cloud to construct and test the sandbox model and to take advantage of PyTorch Optimizations from Intel.
Memory Playground achieved the third-place position by creating a web app that boosts memory recall and attention through a memory palace technique (a psychology-based technique of associating mnemonic images in mind to some known places). It helps senior citizens and those with Alzheimer's disease and dementia by generating images of objects that are related to a given setting or environment, as well as related words to the categories. Intel Tiber Developer Cloud was used to use the OpenAI API with GPT-4* and the Stable Diffusion* model.

For more information, see Next-Gen AI Developers Take Advantage of Intel Resources at TreeHacks.

Hacklytics at Georgia Institute of Technology

February 9–11

Intel started 2024 by attending Hacklytics. Over 1,200 students had the chance to use Intel Tiber Developer Cloud, learn from Intel experts on GenAI and LLMs, and compete for a chance to win a Lenovo AI PC.

Best Use of Intel Tiber Developer Cloud Winners

Out of the 37 hackathon project submissions for the Best Use of Intel Tiber Developer Cloud category, the following projects were winners:

Robotic Registers won first place with its Graph Generative Adversarial Network (GAN) model designed to synthesize logic circuits using a PyTorch GPU environment on Intel Tiber Developer Cloud. The project helps generate datasets crucial for deep learning models in the logic circuit domain. It augments training data for machine learning algorithms, enhancing the robustness and diversity of datasets in electronic design automation. Training models on the generated logic circuit graphs enables advanced anomaly identification within real circuits. Furthermore, the synthetic logic circuits serve as valuable benchmark datasets for testing the performance of various EDA algorithms, facilitating comprehensive exploratory data analysis to unearth patterns, trends, and irregularities in logic circuit design.
Easy Deep Learning secured the second position in the hackathon. The team developed an innovative, no-code platform to simplify the deep learning model fine-tuning process. The user-friendly platform empowers users to start with just a few images and leverages synthetic data generation through Stable Diffusion and various data augmentations to build and fine-tune models quickly. The unique project architecture integrates a locally developed frontend with Firebase* for real-time data synchronization. It used a Jupyter Notebook with Stable Diffusion on Intel Tiber Developer Cloud as the back end engine to craft a sophisticated synthetic data pipeline, seamlessly followed by a custom-written training loop within the same notebook environment.
Estate Edge, the third-place winner project, uses satellite imagery and deep learning to predict real estate trends by analyzing development over 10 years. It identifies growth areas, advising on smart investments by distance learning. The team used code from one of Intel’s workshops based on wildfire prediction using Intel Extension for PyTorch to get the dataset from Google Maps* mapping service. It used Intel Tiber Developer Cloud as the platform for project development and execution.

For more information, see Top AI Projects Built on Intel Developer Cloud at Hacklytics