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Pharos Network announces Prediction Markets and AI Research Project with University of Hong Kong's FinTech Academy
Pharos Network, the modular Layer-1 blockchain, has announced a research project in collaboration with the Hong Kong-Standard Chartered Foundation FinTech Academy. The goal is to deepen the ties between the academic and blockchain worlds while giving students a chance to get hands-on with technology they can use to aid their research into AI decision-making in the context of prediction markets. The joint research initiative is being operated in conjunction with the Master's Capstone Project running at HKU Business School, an open practicum program for students. Eight Master's students will participate in a three-month study that will delve deep into the capabilities of AI models when it comes to predictive decision-making - and Pharos will be supplying the onchain data for them to work with. In addition to supplying students with real onchain datasets, Pharos will be providing expert guidance and support throughout the program. Any projects that emerge from the three-month program showing real potential will be entered into the Pharos incubation program, fast-tracking their route to development. From validating the underlying technology to assisting with market implementation, Pharos will assist every step of the way. For students, the upside to this is significant, since it presents an opportunity to move beyond theoretical modeling to building systems that are activated on a live Layer-1 network. In the process, this will help to grow the Pharos ecosystem of applications and burnish its reputation as a leading blockchain built for AI and institutional finance. It's no secret that AI is rapidly infiltrating everything, transforming it in ways that are still being discovered. When it comes to prediction markets, there are clear applications for artificial intelligence, particularly when it comes to structured modeling of event probabilities. In theory, AI should be able to do this sort of stuff with greater accuracy than humans - but the only way to find out, of course, is to test this thesis. Which is exactly what the HKU students will be doing. Pharos' tech stack will be put to use in facilitating this including its Smart Access List Inference (SALI) Parallel Execution Engine, which can deliver up to 30,000 TPS. This is the sort of throughput that prediction markets demand, given their need to handle real-time settlement and advanced probability models. In addition, Pharos has deployed the X402 AI module that's been designed for agent interaction. This provides a framework for agents to participate in automated predictions while also handling payments between different agents. All combined, the end result is that Pharos' Layer-1 network has all the tools a Master's student could conceivably need to develop AI-based solutions focused on prediction markets. Ultimately, the collaboration between Pharos and HKU's FinTech Academy will give students a practical grounding in what AI can do and how its decision-making can be improved. The ability to tap into vast amounts of real-time data, all delivered onchain, should prove invaluable. As Dr. You Yang, Assistant Professor of Finance at the University of Hong Kong, succinctly puts it, "Our collaboration with Pharos Network offers students a unique opportunity to test these theoretical frameworks in a real-world tech environment. I anticipate that their rigorous empirical analysis will contribute valuable insights to this field." With the three-month program poised to commence, the results of all this research will soon become manifest - starting with real-world data and ending, for the most promising proposals, with live deployment on Pharos Network.
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Pharos Network Teams Up With University of Hong Kong's FinTech Academy to Launch Prediction Markets and AI Research Project
Layer-1 blockchain Pharos Network has announced a joint academic research project with the University of Hong Kong-Standard Chartered Foundation FinTech Academy. The collaboration will see participants explore prediction markets and AI's ability to make smarter decision-making. The initiative isn't merely limited to theoretical research, either - it's also got a practical component that will see promising academic projects incubated and incorporated into the growing . The ultimate goal is to demonstrate what AI is capable of when it comes to making critical decisions in the context of prediction markets and where its current limitations lie. In the process, the joint initiative will help to put Pharos on the map in Hong Kong and the broader Asian market, funneling the best academic talent working on these disciplines into its AI-focused ecosystem, where their ideas have the potential to be transformed into real-world applications.
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Pharos Network has partnered with the University of Hong Kong's FinTech Academy on a three-month research initiative exploring AI decision-making in prediction markets. Eight Master's students will analyze real onchain data to test whether AI models can predict event probabilities more accurately than humans, with promising projects fast-tracked into Pharos's incubation program for live deployment.
Pharos Network, a modular Layer-1 blockchain, has announced a joint academic research project with the Hong Kong-Standard Chartered Foundation FinTech Academy
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. The AI research project aims to bridge academic theory and blockchain practice while exploring how AI models for predictive decision-making perform within prediction markets. Eight Master's students from HKU Business School will participate in this three-month initiative, gaining hands-on experience with Layer-1 blockchain technology as they investigate whether artificial intelligence can outperform humans in forecasting event probabilities1
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Source: Analytics Insight
The collaboration operates in conjunction with the Master's Capstone Project at HKU Business School, an open practicum program designed for practical learning
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. Pharos Network will supply real onchain data alongside expert guidance throughout the program, enabling students to move beyond theoretical modeling toward developing AI-based solutions for finance that function on a live network. The initiative isn't merely limited to theoretical research—it includes a practical component where promising academic projects will be incorporated into Pharos's incubation program, fast-tracking their route to market implementation1
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.Pharos Network's technical infrastructure provides the foundation needed for high-performance prediction markets. The platform's Smart Access List Inference (SALI) Parallel Execution Engine delivers up to 30,000 transactions per second, the throughput necessary for handling real-time settlement and advanced probability models
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. Additionally, the X402 AI module has been deployed specifically for agent interaction, creating a framework for automated predictions and inter-agent payments. This combination equips students with the tools needed to test whether AI models can achieve greater accuracy than humans in structured modeling of event probabilities—a thesis that remains largely untested in real-world applications within prediction markets1
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Dr. You Yang, Assistant Professor of Finance at the University of Hong Kong, emphasized the value of this practical grounding: "Our collaboration with Pharos Network offers students a unique opportunity to test these theoretical frameworks in a real-world tech environment. I anticipate that their rigorous empirical analysis will contribute valuable insights to this field"
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. For Pharos Network and University of Hong Kong, this partnership serves multiple strategic purposes. It helps position Pharos within the Asian market while funneling academic talent into its AI-focused ecosystem2
. Students gain access to financial technology infrastructure typically reserved for institutional finance, while Pharos expands its ecosystem of applications and strengthens its reputation as a blockchain built for AI integration. The incubation program component means that successful student projects could transition from academic exercises to live deployments, validating underlying technology while demonstrating practical use cases for predictive decision-making powered by artificial intelligence1
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