Space Summary
The Twitter Space Deciphering the Role of AI in Crypto’s Evolution hosted by graphdex_io. The evolving landscape of cryptocurrencies is increasingly intertwined with the transformative potential of Artificial Intelligence (AI). As AI continues to revolutionize various aspects of the crypto industry, from trading strategies and security measures to user experience enhancements and scalability solutions, its role is paramount in driving innovation and efficiency. By leveraging AI-driven technologies, the crypto space is witnessing advancements that enhance decision-making, security protocols, and operational efficiencies. Embracing AI in cryptocurrency ecosystems not only optimizes processes but also paves the way for future developments in decentralized finance and blockchain technology.
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Questions
Q: How does AI benefit cryptocurrency trading strategies?
A: AI optimizes trading by analyzing vast datasets, identifying patterns, and executing trades efficiently.
Q: What role does AI play in enhancing crypto security?
A: AI enhances security by detecting anomalies, preventing fraud, and reinforcing blockchain networks against cyber threats.
Q: In what ways does AI impact DeFi platforms?
A: AI transforms DeFi with efficient lending processes, risk assessment models, and personalized financial services.
Q: How do AI-driven chatbots improve user experiences in crypto exchanges?
A: Chatbots offer instant support, personalized assistance, and efficient query resolutions, enhancing user satisfaction.
Q: Why is AI crucial for scalability in blockchain technology?
A: AI presents innovative solutions for scalability challenges, enabling blockchain networks to handle increased transaction volumes efficiently.
Highlights
Time: 00:14:28
AI in Trading Strategies Exploring how AI optimizes cryptocurrency trading strategies for better outcomes.
Time: 00:26:45
AI-driven Security Measures Highlighting the role of AI in enhancing security and fraud detection in the crypto space.
Time: 00:38:19
DeFi Revolution with AI Discussing how AI is reshaping decentralized finance platforms with innovative solutions.
Time: 00:44:55
User Experience Enhancement Examining how AI-driven chatbots improve user interactions and experiences on crypto exchanges.
Time: 00:57:10
Scalability Solutions Exploring AI's impact on overcoming scalability challenges in blockchain technology.
Key Takeaways
- AI plays a significant role in optimizing trading strategies and risk management within the crypto market.
- Machine learning algorithms enable efficient data analysis for improved decision-making in cryptocurrency investments.
- AI enhances security measures by detecting fraudulent activities and enhancing blockchain network protection.
- The integration of AI algorithms in decentralized finance (DeFi) platforms revolutionizes lending and borrowing processes.
- Predictive analytics powered by AI offer insights into market trends, aiding in informed investment decisions.
- AI-driven chatbots provide personalized assistance and enhance user experience on crypto exchange platforms.
- AI applications in crypto mining streamline operations, increasing efficiency and reducing energy consumption.
- Automated trading bots leverage AI technology for executing trades at optimized timings, minimizing human error.
- AI-powered sentiment analysis tools help investors gauge market sentiment and make data-driven trading choices.
- AI is instrumental in developing innovative solutions for scalability challenges in blockchain technology.
Behind the Mic
Welcome and Introduction
Hello, everyone. Greetings from Hong Kong. Hello, everyone. Guys, we're just waiting for the last speakers to join us and we'll begin our panel discussion. Hey, crypto insider, how you doing? Hey, guys, I just can't see some of our last speakers. If you can request the mic, I'm trying to find you in the whole list of listeners. Currently, I can only see two people joining. For now, please request the mic so we can streamline this process. All right, guys. GM, welcome to everyone. Welcome to today's panel discussion. Today we're going to be talking about deciphering the role of AI in Crypto's evolution. And today we're entering a very fascinating era where artificial intelligence and crypto are converging into one, creating all these possibilities for innovation, scalability and efficiency.
Exploration of AI and Crypto
And as you know, intersection of AI and blockchain has this potential to reshape the decentralized finance and AI's capability to analyze large data sets, detect patterns, and even predict market trends. It's this one application in the world that could significantly change the drive of the next wave of transformation. And today, joining us, there are some top leaders in the space who are at the forefront of integrating AI into the crypto ecosystem. I'm super happy to present with you today, co-founder of Vexor. Hi there, Saad. Pleasure to have you here today. Hello, hello. Thank you for having me. Thank you so much for joining. If you could give a brief intro about who you are, would you do that? Be perfect. Awesome. Amazing. Well, first of all, I'm Saad, also known as crypto banks, co-founder of Vixer.
Introduction of Vixer
So super quick intro of Vixer. We're building AI based play reputation engine for web three games. So basically focusing on the scalable and sustainable user acquisition and retention for any tokenized campaigns. So in a way, we're like the fairy godmother of web three games. That's like many projects refer to us. And the main idea is like we're transforming the typical super high cost, low retention play to airdrop model, or any quest platform as a matter of fact, into something that's a lot more smarter, a lot more scalable. So we're actually integrating AI into this reputation modeling to start understanding the behaviors and predictive analytics of any user that's coming in through their on-chain activity, through their off-chain activity, on social media, on different platforms.
User Engagement Strategy
So we are able to strategically work with different projects on that front to really help them target the right audience, the right players, convert them, and ultimately keep them engaged in the long run. Thanks for having me and super excited for today's conversation. Thank you so much, Saad. Pleasure to have you here. We're moving on to our next speaker for Today, which is Peter. Peter is the head of BD at Mind network. Hi, Peter. How are you? Peter, you have to unmute yourself so we can hear you. All right. Peter is not. Maybe there are some technical issues on the other side. For now, we're just going to talk to Niels. Niels from Okee network.
Niels from Okee Network
Hi, Niels, how are you? Hi. Greetings from Hong Kong. Thanks for having me. Thank you so much for joining. Tell us a little bit more. Aukey is a decentralized machine perception network. Our focus is not to help AI look at the blockchain, but use the blockchain to help AI look at the physical world. We observe that 70% of the world's economy is still tied to physical locations and physical labor. But AI is unable to be helpful to humanity in those places because the physical world is not digital and AI can only act on digital information. So we built a deep pin for AI perception so that the AI can look at the physical world, borrowing sensor data and spatial reasoning from other devices on the network.
Continuing the Discussion
Awesome. Thank you so much for joining us today. I'm excited, very excited to have you here. Let's check on Peter now if Peter can hear us. Peter, is there any way you could unmute yourself? Yeah. Hey, Peter, what's up?
Introduction and Personal Background
How are you? Yeah, doing great. Feels great to be here. Hello, guys. I am Peter. I am Peter Gabriel here in Twitter, but elsewhere in web three, I am using the web three name Nonin and I am the community manager of Fambam. So we are the. We are a depend based reputation layer for professional networking. So basically what we're doing is we have a platform wherein users can create their profile and build the reputation. And then through the help of AI, we can achieve user profiling, segmentation of credentials. And we are also an ecosystem wherein more marketing campaigns will be more targeted and will be more effective with the help of AI and tepping. Yeah, and feels great to be here. Awesome. Thank you so much for joining. Guys, let's dive right into our discussion.
AI in Decentralized Finance
Let's talk about how AI is being used today in the decentralized finance. What challenges lie ahead and what the future might hold as these technologies are evolving from day to day. Peter, to start us off, how our AI technologies are currently being utilized in Defi platforms today. And looking ahead, how do you see the integration of AI impacting the future of crypto markets and blockchain ecosystems as a whole. All right, thank you. And so, yeah, so in the current landscape of DeFi, AI is mostly used in features like for example, on the user side, AI is commonly used in providing them with more personalized recommendations or tailor DeFi strategies. This can also include like analyzing the on chain activities and I behaviors, like what are the tokens that they are holding and how long have they been holding those kind of assets.
AI's Role and Efficiency in DeFi
So, yeah, so it also includes aggregating prices and strategies across the different DeFi protocols or platforms. And also based from that, AI can also provide predictive analysis on the returns or on the positions based on the market sentiments, market trends, or any potential major changes along the way in the market. Yeah, so in, also in some platforms, DeFi platforms are also implemented implementing something like credit scores were in. This is very common in platforms where in they are lending or there are providing crypto loans. So AI is helping the platform create or profile users based on their activities, based on their behavior, based on their risk management profiles. So, for example, in a crypto loan platform, when a user who has an acceptable credit score will be automatically approved by the AI, he or she will be automatically approved or given the loan.
Efficiency and Smart Automation
And all of this will be done through the help of AI. So with that being said, AI is adding more efficiency and smart automation to the whole process. It adds more efficiency and creativity when it comes to the already decentralized components of the DeFi protocol. And on the other hand, for builders, for the builders, or for the creators of the platform, it also helped them by detecting early potential threats or any security risk in the platform. It also provide them real time insights on the performance, on the overall uptime of the platform and any other areas. And, yeah, because these are very critical and very fundamental when it comes to the sustainability and reliability of the platform. So, yeah, given disadvancements in DeFi right now and the big benefits of AI on the decentralized platform financing, I'm pretty bullish that over the next months or years, we can see more smart integrations on AI in the overall web three space because, yeah, so, because AI, the thing is, AI is evolving, blockchain is evolving as well.
Expectations for the Future
So there's no other way. For me, there's no other way but to expect and to see more smarter AI driven apps and any other future integrations that can make the most use of the best of both worlds. Thank you. That's all. Thank you. So much. Peter Saad has one follow up. Yeah, honestly, I love the take that Peter took, because I do have a similar perspective. Because one of the biggest use cases that we see today with AI and overall analytics is the ability to tailor an entire experience and tailor an entire onboarding, let's say, for different protocols, for different users. Because right now, the way everything works in web three is regardless of who you are and what you do or what assets you have or whatever, your overall reputation, your entire experience is the same everywhere.
AI and Personalized Experiences
So you go to a protocol, you want to do lending, it's going to be black and white areas, which makes sense, right, just for risk assessment and for overall risk assessment. But then what we can use with AI and what Peter really touched on perfectly, is adding this on chain analytics to ensure basically real time integrations and real time personalized experiences for these protocols. So, looking for a DeFI lending protocol, what could be? It is the AI will analyze the wallet history, the on chain behavior, even some off chain data that can bring it in and keep the privacy through, keep the privacy overall through ZK proofs. And with that, the protocol can provide, could lower collateralization ratios, could lower interest rates, depending on who you are, what kind of assets you have, what kind of behavior you had in the future.
Risk Assessment through AI
And we can look at this as like the credit score that we see on web two by bringing in web three and using this AI for the analytics part, because AI could really help us figure out the transactions, give them meaning, and really start digging deeper into it. So on that front for risk assessment, I fully align with Peter, and I just wanted to add one additional use case for AI, which is on the trading aspect for users overall. And it's all about this algorithmic trading, because what this goes hand in hand is these AI agents that we've been able to create, because right now we see AI as a means to do some analysis, as a means to answer some questions, as a means of to dig deeper, but not necessarily do actions. What we've seen today with fetch AI, with a lot of different web three protocols that use AI, is the creation of these AI agents that will be able to perform actions for you on your behalf.
Future of AI in Trading
And one of the main use cases, I imagine, is going to be algorithmic trading, where you may have AI powered bots all over DeFi markets, doing a lot of arbitrage, doing a lot of different executions on different strategies. And this could be really cool and we'll see how everything will evolve. But yeah, I do think the future for AI is massive. The AI agents will be very, very big. So it could be through AI advisory, it could be through analytics, could be direct use cases. But yeah, super excited for it. Thank you so much, Saad. That's definitely a very, very bright future for algorithmic trading that we see here with AI developing. Niels, I'm going to pass over the mic to you, and I'm going to ask you about some of the things that aren't maybe working out as well for now, and maybe there is a room for improvement there.
Challenges of AI in Crypto Integration
So while the integration of AI and crypto platform is very promising, there are certainly challenges and limitations. So what challenges do you foresee in the adoption of AI within the crypto space, and what do you think are the steps that we should be taking right now in order to address these hurdles? I think there's, in general, a lot of misplaced optimism about how much blockchain and AI will naturally fit together. A lot of it is stemming from a misunderstanding of how the blockchain works, but some, unfortunately, also about how AI works.
The Future of AI and Blockchain
There is a distant target that we can aim at as a civilization to try to make sure that training data is equitably owned, for example, using blockchain, but we're pretty far from being there. There's a lot of decentralized AI focusing on things like decentralized access to GPU's, but I'm pretty bearish on that in the long term, too. What I think is smart to do is to focus on the realization that over the next two decades or so, our civilization will grow to maybe 100 billion decision making entities. And most of those entities will be artificial intelligence themselves. And a lot of the world's transactions, I believe the majority of the world's transactions will be carried out by artificial intelligences, and they'll be doing things that we don't naturally think about today.
AI and Urban Challenges
But, for example, in 2014, Naval Ravicant wrote this very interesting thought piece about how he thought that the Internet really needs a native money layer, so that in the future, things like robots and self driving cars can negotiate with each other about how to use the physical space. Like, for example, I used to live in Beijing for seven years, and in Beijing there are more cars on the road than there are people in Los Angeles. And as a result, about the time it takes or it took to build the pyramids is lost in traffic in Beijing every week. The scale of modern asian cities is kind of boggling. Let me repeat this just so you don't miss it. The amount of human productivity lost in Beijing traffic every single week would have been enough to build the pyramids.
The Role of Self-Driving Cars
Now, if we want to imagine that something like self driving cars is going to help solve this issue by coordinating with each other and making traffic flow more smoothly, and we don't need red lights anymore, then we need to imagine a world where the cars can communicate with each other and can negotiate with each other about who gets to go first, who's in a hurry and whatnot. And naval ravicant predicted that one of the use cases of blockchain will be as a native money layer for the Internet, so that the AI's themselves can negotiate with each other. And as we approach 100 billion decision making entities, a lot of those will be cars and drones and humanoid robots. And as a blockchain industry, we should focus more on what is AI going to help us buy?
AI's Purchasing Needs
Like, how can they help us with trading? Instead, think about what will AI want to buy when there's 90 billion AI and 10 billion humans, what do the AI want to buy? And to me, it's very clear that what AI will want to buy is the necessary information for them to be able to navigate the world and solve their tasks. So digitally, they will want to be able to buy information to solve tasks, and physically, they will want to buy spatial data to be able to carry out tasks. So I think that the real promise of blockchain and AI is building out the things that will allow AI to buy what they want.
Short-Term vs Long-Term Applications
And only in the short term is it going to be interesting to have AI help us buy what we want. Thank you so much. These are definitely very great insights. Saad, I want to talk to you about something a bit more technical because AI is often discussed in terms of its ability to optimize processes. Do you think AI can contribute to the scalability and the efficiency of blockchain networks and consensus mechanism, or are there any current developments in that area that stand out to you? That's a bit of a different thing that everyone is talking about right now.
AI's Role in Blockchain Scalability
Yeah, I mean, let's look at how AI helps scale web three networks overall. One of the main strengths of AI, I do believe that it's the ability to optimize processes overall. But when you bring that into the web three space, it has a lot to offer in terms of scaling the ecosystem efficiently. So first of all, AI plays a massive role in user onboarding and engagement overall. And we know that user experience in web three can be intimidated, right? Like, we have wallets, gas fees, protocols, and AI can make this journey far smoother because like imagine an AI powered onboarding system that basically helps new users understand concepts in real time, guides them through setting up a wallet, even creates this wallet behind the scenes suggests the first defi protocol to interact with.
Optimizing User Experience
By breaking down these complex processes, I do believe that AI can help onboard users more effectively, which is crucial when we talk about scaling adoption or when we talk about mass adoption. But then there's the network optimization. And what AI can do is basically analyze blockchain activity in real time and start predicting these bottlenecks, because that's one of the biggest value add of AI. It's these predictive modelings and for example, projects like Chainlink, the oracle, they're already experimenting with integrating AI for predictive scaling. Basically, they want to ensure that the oracles, or even transaction processing, can dynamically adjust based to demand.
The Traffic Control Analogy
And we see this in web two projects when we look at supply chain processes, they already use this, they already have their own models, they already integrated AI, and now we're doing the same with web three. So think of AI, going back to a similar analogy as Nils. Think of AI as like a traffic control system, but for blockchain. So diverting resources, anticipating peaks and managing capacity a lot more effectively to avoid this congestion and these super high gas fees that we already experienced so many times in different bull runs in different like specific days or events. So, yeah, so there's that.
Governance and AI Integration
And then another area is potentially automated governance for DAosh, for DaO scalability overall. So in daos, decision making can sometimes be slow, especially as a number of participants grows, and as well, the entire model itself can be questionable, where right now most of the DAO models is just based on who holds the most tokens will have the greater say, but then it's literally pay to vote. So what can AI can come in is they can help streamline this process. One, by suggesting optimized proposals based on previous voting behavior and clustering similar types of votes together for better efficiency.
Enhancing Decision-Making
But then on a second, which is a point that Peter, as well mentioned in the very beginning, is looking into this reputation based profiling. So we can use AI for reputation profiling, help understand who has the greatest value to add within the protocol, and give that person, for instance, more power than someone that holds just the tokens and doesn't necessarily care about the projects. The project's future in more of a technical and hands on approach, let's say. So, yeah, so I think that there's a lot of different ways to see it as well, through DeFi it can help scale managing liquidity, for instance.
Risk Assessment and Efficiency
So on the processes front, there's a lot that AI can do to really help and improve the entire back end of web three protocols. And we can see that through as well, the risk assessment, where AI can help us understand, okay, which user can have malicious activity, and there's anomaly detection. There's a lot of different things that we can do to really help, and AI agents, again, can really help on that front to automate it, where we don't necessarily need humans to come and interact with the AIH, but AI can be self sufficient. All it needs is the model that we feed it and then become selfish, efficient, and makes everything optimized.
Envisioning the Future
Thank you so much, Saad. I feel like there is this very natural question that flies out of your answer, and I'm just going to address it to the entire panel. So I'm hoping you guys are going to raise hands and we can answer it all together. So, looking to the future, how do you all guys envisioned this collaboration between AI and blockchain evolving in the coming years? Do you think there are any applications or solutions that might emerge from the synergy, something that we don't see yet? I'd love to hear answers from Peter and Niels if you guys have anything to add to this.
The Deep In Movement
I think to really understand blockchain and AI, you also need to look at the deep in movement, because deep in ultimately is about building the infrastructure that will become the nervous system for AI out in the world. AI needs to perceive the physical world. It needs to understand what is happening. And I think that the final destination for the deep in movement, and maybe even for the blockchain movement, is to become this nervous system for AI. I believe in the fullness of time, we will see that Satoshi's legacy was starting, the decentralization movement that led to decentralized infrastructure, that led to decentralized machine perception, that led to AI that can help us in the physical world.
Excitement for AI Discussion
And I'm really excited about that. 100%, 100%, very much supporting you on that. Guys, there's one more thing that I want to talk to you about, because when we talk about AI, we obviously talk about large data sets. And when we talk about large data sets, there's always the regulatory framework that's coming into the play. What kind of frameworks you think might, we might need to have in order to both eliminate risks, but also allow the space for innovation to happen while promising the consumer protection? Peter, maybe we can start with you and maybe you could talk about your idea of this perfect world that we might be heading to.
AI Impact and Data Security
Hello? Yeah, sure. So, yeah, so I'm just gonna make it quick because. Yeah, so, with Aihdem, ever since, AI has been, has taken the world by storm. With the popularity of chat, GPD and other and mid journey, of course, it. One of the biggest concern has been the privacy and the security of data. So, yeah, so we all know that whenever we are interacting with AI, it is being stored or the AI is, the information is somehow being stored somewhere and some data can be used. And some people around the world are very much concerned on how the servers or how the builders of these AI apps are going to use everything that they are gaining from us, for example, our information or our other data. Like that we are somehow teaching the AI.
Need for Regulatory Frameworks
So, yeah, so when it comes to regulatory framework, I guess we have to focus more on the rules or the regulations concerning the privacy and the security of data. The data should not be used without our consent. It should not be you. It should only be used for the sole purpose of machine learning. And also we have, in the context of blockchain plus AI, we have to have a framework that has something to do with the transparency as well, because, yeah, AI, it's used in a blockchain environment. It's going to use lots of resources and data sets. Right. So we have to have something that will have us that will somehow ensure that each model or each AI model is being, I mean, can be audited.
Importance of Security and Transparency
And because if that is audit, if that can be audited, we can have like peace of mind or like a sense of security that these AI models are not being planted with. We can say bias machine learning or machine, or inaccurate information or something like that. So we have to. Those two aspects are very important, security and privacy. And of course, transparency. Those are very critical points if there will be regulatory framework, because right now there is no consensus yet. There is no widescale consensus yet for the regulations. All we have is like region based policies, and each region might have different sentiments towards the AI and of course, the blockchain plus AI combination. So we really have to focus those on those two, because those two aspects are the common grounds or the common denominators of AI.
Insights on Data Quality and ZK Proofs
Thank you so much. Saad, do you have any opinions on this? Yeah, actually I wanted touch on as well, piggybacking on what Peter said. I mean, there are two things or two critical aspects of regulation for AI and web. Three that I do believe is very important to focus one is these zero knowledge proofs, and two is what Peter, as well mentioned, is the data quality and integrity. So starting with ZK proofs, there are a huge leap forward in ensuring privacy for AI data processing. So basically, ZK proofs allows us to use data without actually revealing it, which is essential in the decentralized, privacy focused space like web three. So imagine basically a system where AI can analyze user behavior to make decisions, but without actually knowing any personal details or revealing any personal details.
Future of AI Regulation
So I do believe that regulation will move or push for ZK proof integration as a standard requirement for AI systems. Just because it's going to allow, it's going to put a lot of focus and privacy to be respected by design. Right, which is one of the key things, because right now with AI, we're getting so much data, as you mentioned, that if any of it gets leaked or any of it gets published, it's going to be hell for web three. Overall with GDPR, everyone like GDPR, frameworks, protocols will fumble. But yeah, so basically this helps maintain consumer trust. It as well helps us maintain the trust with different governments, with the SEC, with whomever wants to come and get involved into web three while still leveraging the AI's capabilities.
Role of Data Integrity
And then on the second front, which is as important of an issue, is the data quality and integrity. So AI models are only as good as the data they're being trained on. And without real quality control, the risks are significant. So what we've seen today is a lot of projects, a lot of people, a lot of different users want to create AI models and don't necessarily focus on making sure that the data that's being inputted is quality data. So if an AI model basically uses poor quality data or any biased data, it can lead to directly faulty decision making. So, which can be like right now, if we see the future of AI with AI agents, it can be a massive impact, just because you'll fully put your trust into AI, which a lot of people blindly trust AI, and then at the end it's going to be completely flawed, it's going to be incorrect and lead to a lot of issues.
Consequences of Poor Data
So that could lead to, like for example, on the loans example. So loans can be given to users who are unlikely to repay or denying loans to people that are extremely well qualified. So this doesn't just damage the credibility of a platform, but it directly impacts the user's financial outcome on that example. So, yeah, so data integrity, data quality and privacy. I do believe there are three fronts that are critical to look into when we talk about regulatory frameworks. Thank you so much, guys.
Closing Thoughts
These are very useful points that I've heard here today, and I'm sure that our listeners loved listening, too, in my books. That brings us to the end of our discussion. I want to say a huge thank you to our panelists for showing up, for sharing your insights into how AI is passed to basically change the crypto space. And as we've heard today, it's not only improving the current operation, but it's also opening new doors into how we're going to see crypto ecosystem changing. So once again, thank you so much for joining all of us today. We hope that this conversation has provided you with some valuable insights into the intersection of AI and crypto. We're going to be here for more educational, informational spaces here within the next couple of weeks, so don't be hesitant to join.
Invitation for Future Discussions
Same place, same time, guys, once again, thank you so much for your expertise. And until the next time, thank you very much.
