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Wolves Summit AI Hackathon

Making a difference with AI

It is our pleasure to welcome you to the Wolves Summit AI Hackathon. In partnership with Wolves Summit, we are giving the lablab.ai community an opportunity to develop an application to fight for a great cause - Saving Planet Earth. This is a preliminary event of the Wolves Summit conference that will take place in Vienna, Austria on 20-21 October 2022. Current world events show us that actions towards environmental safety are necessary. Do you have what it takes to protect the planet using Artificial Intelligence? Join us and innovate to build a better future for everyone!
Wolves Summit AI Hackathon event thumbnail

Wolves Summit

Wolves Summit is committed to fostering deep and productive collaboration between angel investors, VC funds, corporations, and the most promising startups in the CEE region invested in creating real, positive value in the world.

Founded in 2015 in Warsaw, Poland, the conference has grown to become the largest tech event in Central and Eastern Europe.

Today, Wolves Summit is best known for its matchmaking platform helping its attendees move the needle through meaningful connections.

Every edition attracts more than 2500 participants from 80 countries providing 100+ hours of immersive educational content, keynotes, and startup pitches.

The Challenge

The aim of the hackathon is to prepare a project that could potentially save planet Earth from environmental struggles.

Come up with an AI-based solution to a current (or future) problem you are passionate about. As you work on your project, be sure to document your progress so that you can present your final product to your audience. This will help you communicate your ideas more effectively and ensure that your project is on track.

Examples

Shayp

Shayp is a leading specialist in water efficiency and monitoring. The company uses machine learning and real-time data to report when water is unnecessarily wasted due to leakages or system discrepancies in buildings.

Gilytics

Gilytics helps energy and engineering companies automate infrastructure planning, routing, and monitoring, saving time, money, and CO2 with better data, visuals and communication to accelerate the energy transition.

WePower

WePower platform connects corporate energy buyers and energy retailers directly with green energy generators so that all businesses, no matter the size, can easily purchase locally produced green energy at competitive rates and full transparency.

Minimum

Minimum lets organizations calculate, report on, and reduce the carbon emissions of the business, whether the enterprise is a corporate or an investment fund.

🏆 Prize

The winning team of this hackathon will receive a VIP ticket for their representative to attend Wolves Summit in Vienna, Austria on 20-21 October 2022. The VIP ticket offers:

  • 1x On-site conference pass with the Matchmaking App,
  • UNLIMITED meeting requests in the Matchmaking App,
  • Access to the Main Stage online,
  • Investors & Partners book prior to the event,
  • Feature on the Startup List,
  • Lunch included,
  • Networking Party,
  • VIP Networking Evening,
  • Main Stage Showcase Opportunity,
  • Dedicated Concierge service.

Additionally, every start-up project that wants to attend Wolves Summit conference in Vienna, Austria can sign-upand receive a 30% discount for tickets (other WS discounts apply and add up 😊).

Wolves Summit Hackathon details

🗓️ Where and when

The hackathon starts on October 6th and ends on October 8th. We will provide you with workshops, keynotes and mentoring sessions hosted by AI experts during the event.

🦸🏼‍♂️ Who should participate?

Previous experience in AI is not required but welcomed. While many participants are industry experts, we also welcome people with other types of domain knowledge that want to understand & explore how AI can be used in their fields.

🛠️ How to participate in the hackathon

The hackathon will take place online on lablab.ai platform and lablab.ai Discord Server. Please register for both in order to participate. To participate click the "Enroll" button at the bottom of the page and read our Hackathon Guidelines.

Speakers, Mentors and Organizers

  • Mathias Asberg
    Mentor

    Mathias Asberg

    Founder New Native

  • Paweł Czech
    Speaker

    Paweł Czech

    Founder New Native

  • Jakub Zakrzewski
    Organizer

    Jakub Zakrzewski

    Event Manager at New Native

  • Anastasiia Strakhova
    Organizer

    Anastasiia Strakhova

    Community Manager at New Native

  • Olesia Zinchenko
    Organizer

    Olesia Zinchenko

    Social Media Manager at NewNative

Hackathon FAQ

Who can join the Hackathon?

We welcome domain experts from all industries, not just AI or tech. Successful AI solutions require a combination of technical expertise and domain knowledge. Coding experience is recommended.

Do I need a team?

You are welcome to join as a team or solo. If solo, we still encourage you to look for a team before the event, but ultimately it is your choice. We recommend you to join the Deep Learning Labs Discord channel: https://discord.gg/gCuBwBB35k and posting in the #looking-for-team channel to get to know your potential future team members.

Do I need a Github account?

It is recommended. At least one team member should have a Github account. You can create one for free if you don't already have one.

I have other questions.

Feel free to reach us on social media, or through our Discord channel.

Event Schedule

  • To be announced

Winner Submissions 🏆

Chase The Fire

Chase The Fire

Our solution consists of 2 parts, before the forest fire and after the forest fire. Before the forest fire First protocol (software solution): We used M.L. To solve the problem in particular library scikit-learn inside Python and with a dataset containing scripts we used NLP to convert the string to an integer so that the algorithm could handle the dataset and then split the data into partial training and partial test. They were divided into five classification algorithms and we compared the results on their own by examining the model to find the best algorithm for dealing with this data and then using the best algorithm in the metrics model. Then we saved the model and its weights. We have built an application with pyqt5 which receives potentially fire factors from datasets and sensors to predict the fire before it happens. After the forest fire Second protocol (hardware solution): The console makes the user feel extra confident. It is connected to a solar panel that automatically supplies power to the system. The controller will be positioned all over the forest which sends alarms and data to the server which throws the GSM system e.g., temperature, humidity, smoke, fire detection, fire location, rain detection, and wind direction for fire tracking. As well as making a plan for a safe path for animals in case of emergency by buzzing or whistling to keep the animals away from danger. This data is taken to create a new dataset through which the system is updated ml algorithms and the past dataset.

Chasers
medal
fire detector

fire detector

The app designed by team innovators is a wildfire detector and risk analysis. This project has two branches in which the first one is wildfire detector which is build using deep learning model. The detector asks for a forest image and predicts if there is fire or no fire in the image. The second branch is of risk analysis which is build using a machine learning model. In here we are predicting the next day wildfire spread. This app can be a great asset for researchers and climate change organizations to be prepared to fight the wild fires. Most of the description is provided in the presentation and the github repository. For demo of the project look into the repository on github we have added the demo links in the read.me. That's all! Thankyou!

Innovators
medal
SOILTREE

SOILTREE

The past decade has been onerous to global humanity. The COVID-19 pandemic and triple global crisis undermine decades of global progress. Nevertheless, these events drew global attention to the Sustainable Development Goals. Deforestation is definitely one of the world's major concern. However, we as human beings require wood for constructions and manufactures. Annually, deforestation timber also generated approximately $500 million in economic value. Since we can't stop the trees from being cut down, we can plant fast growing timbers on suitable soils. How should we know which type of timbers should we plant based on the soil? Here we introduce a timber guide system to recommend the best timber to be planted.

Crops Analysis
medal

Submitted concepts, prototypes and pitches

Submissions from the teams participating in the Wolves Summit AI Hackathon event and making it to the end 👊

Fake smile detection

Fake smile detection

Fake smile is an emotional sign on the face that can be used as information for non-verbal communication. One of its functions is for lie detection purpose based on the information of emotional sign generated on the face. The emergence of fake smile indicates that there are negative emotions, uncomfortable feeling, and something hidden in a person. This research aims to detect fake smile. In fact, real smile is characterized by the contraction of zygomatic major muscle on the edge of mouth corner and obicularis oculli muscle on the eyelids. However, on a fake smile, zygomatic major muscle experiences contraction, but obicularis oculli muscle doesn't contract. Contraction of the zygomatic major muscle is identified by the appearance of wrinkles on the cheeks corner of the mouth, whereas obicularis oculli contraction is identified by the feature value of eye elongation. On the test image, segmentation of RoI (Region of Interest) is done on cheeks and eyes. With the growing era of social media, it is difficult to identify the real from fake whether it is any news or face/video of any celebrity, politician, etc. Also, the fake or manipulated faces and videos are being generated enormously which are harder to detect by traditional means of software or methods.

Trojan Horses
Chase The Fire

Chase The Fire

Our solution consists of 2 parts, before the forest fire and after the forest fire. Before the forest fire First protocol (software solution): We used M.L. To solve the problem in particular library scikit-learn inside Python and with a dataset containing scripts we used NLP to convert the string to an integer so that the algorithm could handle the dataset and then split the data into partial training and partial test. They were divided into five classification algorithms and we compared the results on their own by examining the model to find the best algorithm for dealing with this data and then using the best algorithm in the metrics model. Then we saved the model and its weights. We have built an application with pyqt5 which receives potentially fire factors from datasets and sensors to predict the fire before it happens. After the forest fire Second protocol (hardware solution): The console makes the user feel extra confident. It is connected to a solar panel that automatically supplies power to the system. The controller will be positioned all over the forest which sends alarms and data to the server which throws the GSM system e.g., temperature, humidity, smoke, fire detection, fire location, rain detection, and wind direction for fire tracking. As well as making a plan for a safe path for animals in case of emergency by buzzing or whistling to keep the animals away from danger. This data is taken to create a new dataset through which the system is updated ml algorithms and the past dataset.

Chasers
medal
TrashCollector

TrashCollector

During recent years, people started to produce more and more trash, polluting the planet. Even with continuing recycling, there is one important issue – trash on streets. It can be in already packaged bags or individual wrappers and bottles on the group, with some of it later being in the waters of Earth. The idea of the project is to create a trash detector using computer vision networks that can be used in 2 ways. Firstly, it can be integrated with already installed security cameras and have some software for trash collectors to use, so they would know where the trash is. Second more improved strategy is to create an automated robot which would collect the trash.

EarthAI
SOILTREE

SOILTREE

The past decade has been onerous to global humanity. The COVID-19 pandemic and triple global crisis undermine decades of global progress. Nevertheless, these events drew global attention to the Sustainable Development Goals. Deforestation is definitely one of the world's major concern. However, we as human beings require wood for constructions and manufactures. Annually, deforestation timber also generated approximately $500 million in economic value. Since we can't stop the trees from being cut down, we can plant fast growing timbers on suitable soils. How should we know which type of timbers should we plant based on the soil? Here we introduce a timber guide system to recommend the best timber to be planted.

Crops Analysis
medal
fire detector

fire detector

The app designed by team innovators is a wildfire detector and risk analysis. This project has two branches in which the first one is wildfire detector which is build using deep learning model. The detector asks for a forest image and predicts if there is fire or no fire in the image. The second branch is of risk analysis which is build using a machine learning model. In here we are predicting the next day wildfire spread. This app can be a great asset for researchers and climate change organizations to be prepared to fight the wild fires. Most of the description is provided in the presentation and the github repository. For demo of the project look into the repository on github we have added the demo links in the read.me. That's all! Thankyou!

Innovators
medal
Sustainable Journey Assistant

Sustainable Journey Assistant

The automotive industry is one of the largest industries in the world by the revenue it generates. As technology advances the opportunities, advancement, and challenges increase multifold in the industry across the globe. .Traffic jams and congestion each year costs the world economy over a trillion dollar Engine Idling in jams contribute to harmful particle emissions that also get concentrated more near the location . Besides, The vehicles of today and the future require highly versatile onboard diagnostic systems that can detect and recognize errors and possible malfunctions. There is a huge demand for new and improved solutions that can help achieve this. Our goal is to create an IoT solution, which displays different methods of transportation, calculates their carbon emissions, and recommends the most eco-friendly routes – all in one application. With a touch of a button, commuters will get the chance to make smarter and more sustainable decisions when it comes to their travel journey. We aim to connect different modes of transportation in one app as a connected ecosystem and calculate carbon footprint based on the transportation journey. Using this we educate consumers on what their carbon footprint means and empower them to change towards more sustainable habits and enable payments towards different modes of transportation to happen seamlessly in one app. For the diagnostics section 3D Model of the car will have multiple points on it which will get highlighted based on the fault so as for the user to understand it. Historical data of users and the fault they faced will be stored using their consent which will help the future users .Helpers would be incentivized using reward system and with growing users the proximity of a user helping a stuck car would increase. While the car enters an area with low connectivity , it would prompt the user to download the essential data for offline use. We offer On Board Diagnostics Scanner to our user for FREE so that errors can be found more frequently. The idea is convert empathy into actions.Our app would arrive at various results and then convert it into actions.The first one being the speed optimizer.Traffic jams and congestion each year costs over a trillion dollar.Engine Idling in jams contribute harmful particle emissions.The app which would display an "IDEAL SPEED" for the vehicle(from varios factors) ,driving at which the driver won’t face any traffic jam thus preventing engine idling, less pollution and preventing mental stress.The app would take data like car’s speed,nearest jam/congestion using location data etc,keep updating itself on user actions.Further,our common payment plan linking public and private transportation on pay-later cycle gives user insights where he could save money,carbonfootprints and empathise on issue. The app arrives at meaningful facts to which user can act on to.It connects the user on points like lessening car congestions , car pooling etc.The cost of wasted fuel can add up to $70-650 a year, depending on fuel prices, idling habits and vehicle type).Idling actually increases overall engine wear by causing the car to operate for longer than necessary.For every 10 minutes our engine is off, we prevent one pound of carbon dioxide from being released (carbon dioxide is the primary contributor to global warming) 11 million tons of carbon dioxide, 55,000 tons of nitrogen oxides, and 400 tons of particulate matter are emitted into the environment from heavy-duty truck idling during rest periods.Emissions from engine idling are contributing to climate change and impacting air quality, which pose negative health risks for everyone. We introduce a “Pay Later” feature in the app where the user can sync all his transportation modes and we suggest methods to lessen the monthly cost based on the use pattern and where the person could have improved by adhering to the suggestions like car pooling , public transports etc. The user does the payment on a 15 Day cycle and we provide insights to the user based on his use and suggest the methods of lessening the carbon footprint. Further, if the user obeys the tips and suggestion , we reward him/her with third party offers/coupons .Revenue generation can be made at this node by partnering with brands on this.Further, our common payment plan linking public and private transportation on pay-later cycle gives user insights where he could save money , carbon footprints and empathize on issue. We can provide the Convenience of 1-Tap Check Out and give analysis to the users on their usage .

BharatVerse Innovations
Winners Announced
Band of Agents Hackathon

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Band of Agents Hackathon

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