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As AI continues to drive innovation, companies face bigger challenges in collecting and managing the right data. Data privacy, scalability, and high-quality datasets are at the top of companies’ minds when it comes to leveraging AI.
To talk about how to solve these challenges, CEOWORLD spoke with Dr. Max (Chong) Li, founder and CEO of OORT, a platform that combines AI and blockchain to build smarter and more secure cloud solutions for decentralized AI applications. With over 200 patents, leadership roles in 4G and 5G development at Qualcomm, and a teaching position at Columbia University, Dr. Li is no stranger to solving hard problems.
At OORT, he’s working on making AI more accessible through the PoH algorithm. We talked to Dr. Li to find out more about OORT and what it means for businesses today.
With growing concerns about data privacy and regulations like GDPR, how challenging is it for businesses to collect and use data for AI applications responsibly?
Following privacy laws like GDPR means companies have to be open and secure with personal data. It’s not just about collecting data – they need explicit permission from users first. This means putting strict policies in place and using tools to anonymize or encrypt data, which can be more complicated and costly.
At the same time, businesses have to walk a fine line. They need enough data to train effective AI models, but it must also be ethically sourced and unbiased. Finding datasets that meet these criteria takes a lot of time, effort, and resources.
The stakes are high, too. Breaking these rules can result in big fines and damage to a company’s reputation. So responsible data collection isn’t just about the law – it’s also about building trust with customers.
The good news? New solutions like decentralized data platforms and blockchain-based verification are making it easier for businesses to collect and use data responsibly and correctly.
How is the global push toward automation and AI impacting the demand for high-quality, diverse datasets? What trends do you foresee in this space over the next few years?
The move to automation and AI is driving the need for high-quality, diverse data. AI systems learn and make decisions from data so that data quality and diversity directly impact performance. When datasets are not diverse, AI can develop biases that not only affect accuracy but also lead to unfair or unreliable outcomes.
As AI goes into healthcare, finance, and retail, the need for datasets that are big and representative of many scenarios, regions, and demographics is becoming more and more obvious. Companies are trying to get real-time data and integrate it into their AI systems to predict trends, personalize user experience, and automate processes.
This data reliance has also changed how organizations collect and use it. Ethical considerations are at the top of businesses’ minds, with businesses focusing more on transparency, user consent, and compliance with privacy regulations like GDPR and CCPA. In response, decentralized platforms powered by blockchain are emerging as a way to share data securely and transparently.
At the same time, synthetic data (artificially created to mimic real-world scenarios) is becoming more popular as a way to train AI systems without compromising privacy. Efforts to reduce bias are in full swing, with companies recognizing the value of diverse datasets for accurate and fair AI outcomes. As automation becomes more real-time, the demand for real-time data streams is growing so AI systems can stay up-to-date and responsive.
These developments could lead to a future where businesses must invest in innovative and ethical data strategies to stay ahead.
OORT specializes in simplifying data collection for decentralized AI applications. How does your platform make it easier for businesses to gather the right datasets?
OORT solves data collection for AI by tackling the problems businesses face – cost, complexity, and compliance. Our platform has a decentralized global network of contributors, so businesses can get diverse, high-quality data that mirrors real-world conditions, all at an affordable price.
A big part of this is our use of blockchain technology. Every piece of data is trackable and verifiable. That’s important when building AI systems that need accurate and consistent data.
We have an incentive-based model that rewards contributors for data to keep the system dynamic. This creates an active and engaged network so businesses always have access to fresh, relevant data for their needs.
We’ve also prioritized privacy and compliance by pre-processing data on contributor’s devices. This protects sensitive information and meets global regulations like GDPR so businesses can be confident they’re meeting legal and ethical standards.
By combining decentralized technology, transparency, and privacy-focused practices, OORT makes data collection simple and gives businesses the data they need to build AI systems without the hassle.
In today’s competitive landscape, what measurable advantages can businesses achieve by using OORT’s platform, especially when compared to traditional data collection methods?
Using OORT’s platform gives businesses a real edge, especially compared to traditional data collection methods. One of the biggest advantages is how fast it is. Traditional methods can be slow and full of red tape, but OORT’s decentralized model cuts out those inefficiencies.
That means businesses can get high-quality, ready-to-use data much faster, helping them launch AI solutions quickly and stay ahead in a competitive market.
On top of that, the diversity of the data we provide is a game-changer. Since we use a decentralized network to collect data, we have access to diverse and unbiased datasets.
With our global network of contributors, businesses can access datasets that truly reflect real-world scenarios.
This is critical for building AI models that are reliable and perform well across different applications—something that’s often a challenge with older, more centralized approaches.
What really ties it all together is the transparency and security built into the platform. By using blockchain technology, every piece of data is verified and tamper-proof, so businesses know they’re working with trustworthy and compliant datasets. For industries like healthcare or finance, where data integrity is crucial, this is a huge advantage.
And let’s not forget the cost. Traditional methods can be expensive, but OORT’s decentralized approach keeps costs low while still delivering top-notch data. Plus, the platform is scalable, so whether a business needs data for a small test or a massive AI initiative, we can meet their needs seamlessly.
All of these benefits—speed, diversity, transparency, cost savings, and scalability—work together to make OORT an ideal choice for businesses looking to build powerful, effective AI solutions and stay ahead of the curve.
As businesses aim to integrate AI into their operations, what do you see as the biggest roadblocks to effective data collection?
As businesses get further into AI, one of the biggest challenges is making sure the data you collect is relevant and actionable. AI needs precision but searching through massive amounts of data to find the insights is overwhelming. This means you have to balance the need for lots of data with the data being high quality and useful.
Another problem is the inflexibility of traditional data collection methods. AI projects are rarely static; businesses uncover new needs or opportunities, and the projects evolve. But many existing approaches don’t adapt, so you end up with datasets that no longer align with your goals or the AI models you’re building.
Team collaboration can also be a roadblock. AI systems often need input from multiple departments, but when teams don’t work together, there’s a risk of duplicate efforts or missed opportunities. Misalignment can create gaps in the data that undermine the AI’s overall performance.
On top of that, outdated infrastructure makes it even harder. Many businesses are still using legacy systems that weren’t designed for AI, so it’s hard to process and manage the data for modern applications.
And finally, there’s a lack of focus on collecting contextual data. It’s not just about raw numbers or isolated facts—AI models perform best when trained on data that mirrors real-world scenarios, user behavior and industry-specific challenges. Without this deeper context, even the most advanced AI systems will deliver limited or shallow results.
To address these challenges, businesses need to adopt more flexible and collaborative tools and invest in systems that can scale and process data better. By prioritizing flexibility, teamwork and contextual understanding companies can build a solid foundation for their AI initiatives and unlock their full potential.
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