Attention, developers, data scientists, and AI enthusiasts: the Worldwide AI Hackathon is coming in 2023! Get ready for the next generation of AI at the Worldwide AI Hackathon, where contestants will work on groundbreaking projects that could lead to the creation of the next ChatGPT or Stable Diffusion, as well as address pressing issues like data privacy and machine learning model quality.
The Worldwide AI Hackathon, co-organized by Wow AI and Transatlantic AI eXchange as a part of the WowDAO (the 1st decentralized autonomous organization for the AI community, in early development), is offering students, developers, AI engineers, data scientists and AI startup founders the chance to revolutionize the field of artificial intelligence. In this competition, participants will work to solve three of the toughest challenges faced by the industry as determined by over 30 incredible judges from large corporations like Google, Meta, Apple, Microsoft, SAP, Samsung, Oracle, IBM, Intel, Accenture, etc. These challenges were voted by the top AI executives include:
1. Generative AI applications
Generative AI is one of the most impactful and rapidly evolving technologies that brings productivity revolution and venture capitalists are betting hundreds of millions of dollars on startups that use AI to generate images, text, and more, Wired and Emerging Technologies and Trends Impact Radar for 2022 reported. The potential of generative AI is huge because this technology can learn to mimic any distribution of data. That means it can be taught to create worlds that are eerily similar to our own and in any domain.
2. Synthetic Data applications
Synthetic data is really important for business due to concerns on public sources because of privacy, restrictions or lack of details about how was obtained, as well as distribution of the samples. Synthentic data is generated so it doesn’t contain any private data, it can have the same distribution as the original, or even can improve on inbalanced data. The potential of synthetic data for industries where getting data can be really hard, or to create enviroments controlled for edge cases or uncommon ones to improve the models.The industry of the future rely on data, to train models and AI Applications, a data centric approach can improve accuracy with less data.
3. Self-supervised learning in the autonomous industry
Self-supervised learning in the autonomous industry refers to the use of unlabeled or semi-supervised large-scale collected data to train recognition models for real-world perception tasks in autonomous systems. This is a data-intensive approach, however, it holds great potential for the ever-growing field of autonomous systems, including drone research, autonomous exploration, and bio-inspired systems. The goal is to develop autonomous systems that can think and react independently in real-world situations.
Participants in the Worldwide AI Hackathon will receive mentorship from an accomplished lineup of data scientists, AI engineers, and software engineers from Meta, Amazon, Airbus, Shell, Starbucks, Heineken, and GE Aerospace among others, and have access to resources and networking opportunities with professionals in the field. The winners will receive cash prizes and the opportunity to present their projects in San Francisco, as well as support to commercialize their products.
Don't miss this opportunity to be at the forefront of AI innovation and join the Worldwide AI Hackathon in 2023. Register now!