US Secretary of State Antony Blinken has emphasized the need for unified global action on artificial intelligence (AI). Speaking on the importance of regulation, Blinken stated, "We must use our collective strength to establish, update, and ultimately implement international AI standards, which are essential to lasting security."

Dubai, December 19, 2024: The second edition of the Global AI Show, organized by Web3 and AI consulting giant VAP Group and powered by a leading media network Times of AI, wrapped up on a high note at the Grand Hyatt Exhibition Centre, Dubai on December 12 and 13, 2024.

Held under the official support of the United Arab Emirates Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications Office and with the Cyber Security Council as its strategic partner, the event was a resounding success, bringing together over 3,000 in-person attendees and about 110,000 online participants from across the globe.

With the theme, ‘AI 2057: Accelerating Intelligent Futures,’ the show witnessed thought-provoking discussions and groundbreaking announcements from C-suite executives, government leaders, and industry pioneers. These luminaries shared their insights on how AI is shaping industries and revolutionizing economies worldwide.

A keynote session by Ahmed Bin Sulayem, Executive Chairman & CEO, Dubai Multi Commodities Centre highlighted how AI and virtual economies are shaping commerce in the Metaverse, while Pujya Brahmavihari Swami, Spiritual leader, BAPS led the headliner on sacred intelligence and aligning AI with universal values for the greater good. 

The headliner panel discussion on ‘Your new Chief AI Officer’ was a special feature at the Global AI Show, consisting of prominent industry experts such as H. E. Mubaraka Ibrahim, CEO-AI, Emirates Health Services; Lt. Col. Dr. Essa Al Mutawa, Chief AI Officer, Dubai Civil Defence H.Q.; Dr. Marwan Alzarouni, CEO – AI, Dubai Economy and Tourism; Abdullah Al Jaziri, Chief AI Officer, DEWA; and Awadh Almur, Chief AI Officer, Federal Authority of Nuclear Regulation, along with the moderator Loubo Siois, Executive Producer & Host, The Dubai Talk Show.

Other government officials and C-level executives that were present at the event and provided in-depth insights included H.E Dr Mohamed Al Kuwaiti, Head of Cyber Security, United Arab Emirates Government, UAE; Honorable Nate Glubish, Minister of Technology and Innovation, Government of Alberta, Canada; Ilaria Buonpane, Director of Customer Experience, Talabat; Dr. Hakim Hacid, Executive Director & Chief Researcher, TII – Technology Innovation Institute; Kevin Ferguson, Senior Systems Technical Advisor, Center of Excellence & Development (CoE), UAE Armed Forces; Dr. Noah Rafford, Futurist-in-Chief, Dubai Future Foundation; and many more.

“The Global AI Show 2024 brought together an unparalleled gathering of minds to address the opportunities and challenges of an AI-powered future. It was a privilege to witness ideas that will shape the next decades unfold on our stage,” said Vishal Parmar, Founder and CEO of VAP Group, reflecting on the event’s impact.

Over 20 side events were a part of Dubai AI Week, such as EmpowHER: Women in AI and AI Capital Connect, that brought together distinguished experts and leading professionals from the AI industry, fostering meaningful discussions and knowledge exchange.

PulseAI, by the Global AI Show, focused on the transformative power of AI in healthcare, where C-suite stakeholders in healthcare, including Saqr Alhemeiri, Chief Innovation Officer, Ministry of Health and Prevention – UAE, and Veneeth Purushothaman, Group Chief Information Officer​, Aster DM Healthcare, discussed powerful use cases from around the globe on the safe, effective and democratic adoption of AI. 

While SurgeXL enabled startups to connect with the top 1% of VCs, angel investors, and sovereign wealth funds through exhilarating pitch competitions and AI-driven matchmaking, an official awards ceremony took place on December 13 to honor the visionaries and trailblazers in AI. 

The Global AI Show, sponsored by Airia, Salesforce, Wand, Saal, Seez, Multiverse Computing, Exotel, among others, concluded with an afterparty at the Soho Garden Meydan in Dubai. 

For those who couldn’t attend, highlights are available at www.globalaishow.com. Stay tuned for announcements on the next edition!

About VAP Group:

VAP Group, established in 2013, is a Blockchain and AI consulting giant as well as a leading force in Web3 and AI solutions, offering services in PR, advertising, recruitment, content development events and media management. Flagship events organized by VAP Group include the world-renowned Global Blockchain Show, Global Games Show and Global AI Show. VAP Group drives innovation through strategic PR and influencer marketing, bounty campaigns, and global events that showcase the brightest minds in the transformative fields of Web3, AI and Gaming.

For media enquiries, exclusive interviews, or press passes, please reach out to: media@globalaishow.com.

The post VAP Group’s Global AI Show Explores the Future of AI with Over 3,000 Participants appeared first on Cryptopress.

Artificial intelligence (AI) is revolutionizing the financial markets, redefining the way trades are executed, risks are managed, and strategies are designed. Once limited to traditional methods and human expertise, the trading is now shaped by advanced AI-driven systems that promise speed, precision, and scalability.

Willy Chuang, Chief Operating Officer (COO) of WOO X and a long-time advocate for innovative applications of AI in trading, shared a nuanced perspective on the opportunities and challenges posed by AI’s integration into trading platforms.

Smarter Tools for Faster Decisions

One of the biggest advantages AI offers in trading is the ability to process large amounts of data instantly. With AI, platforms can analyze a variety of sources — market data, financial news, and social media trends — to predict price movements and identify opportunities.

High-frequency trading algorithms take this a step further, executing thousands of trades in less than a second — achieving a speed and precision that human traders simply cannot match. 

“AI has transformed the world of trading, moving beyond simple neural networks to advanced LLM-based models that can process a variety of inputs from the market, social media, and other sources. Quant funds are now using these sophisticated tools to uncover deeper market insights and allow for smarter decisions,” Chuang explained.

To understand the growing focus on AI technologies in trading, US patent filings provide a clear picture. Since the introduction of large language models (LLMs) in 2017, the share of AI-related content in patent applications for algorithmic trading has jumped from 19% in 2017 to over 50% annually since 2020, reflecting a sharp increase in innovation in this area.

AI Adoption in Trading Applications. Source: IMF

This evolution has also made trading more precise. Advanced tools now analyze patterns in market behavior and adjust strategies dynamically as conditions shift. Machine learning models continuously improve by learning from historical data, enabling them to adapt more effectively to new situations.

But Chuang is quick to point out that these tools don’t replace humans — they complement them. This partnership ensures that traders can focus on making big-picture decisions while letting computers handle the nitty-gritty.

“Human traders aren’t being replaced here but are instead evolving their roles. They now focus more on creating and overseeing AI-driven strategies, managing risks, and ensuring ethical practices. This ‘partnership’ between AI and human-in-the-loop enhances decision-making and fosters collaboration across different expertise areas,” he said. 

AI Is Tackling Unpredictability in Trading

However, even the most advanced trading technology faces challenges when markets behave unpredictably. Rare events, like the COVID-19 pandemic in 2020, caused massive market disruptions that many systems weren’t prepared to handle. These “black swans” can lead to massive losses if trading platforms fail to respond effectively.

According to Chuang, ensuring AI systems remain adaptable during volatile conditions requires two key strategies. First, enhancing model explainability is critical — transparent AI decisions allow traders to understand and isolate the factors driving market volatility more effectively. This often involves a hybrid approach, where humans collaborate with AI to create experimentation frameworks capable of quickly adapting to new information.

Second, adaptability can be improved by integrating reinforcement learning, enabling systems to continuously refine their strategies and respond more effectively to unexpected changes.

“For example, deploying two AI agents to collaborate in managing incidents that cause volatility allows the system to fine-tune its responses in real-time. The agents can analyze the situation, adjust strategies, and store valuable insights for future reference, ensuring the AI continuously learns from each unexpected event,” Chuang shared.

Another critical challenge is ensuring the quality of the data used by platforms. High-quality, reliable data is essential for AI-driven trading, but sourcing and maintaining it is no small feat.

One of the biggest obstacles is consolidating data from various exchanges and order books into a single, consistent source while minimizing delays. Any inconsistency or lag can significantly impact trading decisions, especially in fast-moving markets.

“The sheer volume of real-time data demands a robust and flexible infrastructure capable of processing and storing information quickly and accurately. Creating versatile SDKs that work smoothly across various platforms adds another layer of complexity, as they need to balance speed, compatibility, and security,” he added.

Addressing these hurdles is key to realizing the full potential of AI in trading. With precise and timely data, trading platforms can equip users to make smarter decisions and remain competitive in dynamic financial markets.

Opening the Door for All Traders

For years, advanced trading tools were available only to large financial institutions with deep pockets and specialized teams. Smaller traders were often left out, relying on outdated methods or basic tools that couldn’t compete.

Today, that’s changing. Many platforms now offer affordable or even free tools that simplify complex trading processes. For instance, apps provide automated trading bots, market analysis, and personalized recommendations for traders at all levels of experience. These features allow small-scale traders to compete in ways that were unimaginable just a few years ago.

“It’s something we at WOO are committed to addressing. Our vision is to make advanced AI trading tools accessible to everyone, including smaller traders who may feel left out. We’re focused on creating personalized experiences that fit traders of all levels, simplifying complex AI technologies so that traders can focus on their goals without needing deep technical knowledge” Chuang stated.

But accessibility isn’t just about cost — it’s also about usability. In the past, products often missed the mark by catering only to either new traders or advanced ones, leaving many users feeling left out.

To address this, platforms are offering tutorials, webinars, and user-friendly interfaces that make it easier for traders to get started. This focus on education ensures that more people can take advantage of the opportunities that trading technology offers.

“User education is key for helping traders make the most of AI-powered tools. Our vision is to create hyper-personalized experiences that cater to each individual’s unique needs, regardless of their experience level. Focusing on personalized education and support helps to ensure that all traders can confidently navigate AI-driven trading,” he noted.

Building Trust Through Transparency

Regulatory compliance and ethical considerations are critical focus areas as AI becomes a core component of trading platforms. Keeping pace with financial regulations is particularly challenging for developers and platforms due to the complexity and constant evolution of the rules.

To operate effectively in this environment, platforms must follow the rules while maintaining transparency about the strategies and technologies they use. Clearly explaining how AI systems function and recognizing their limitations helps build trust with both regulators and stakeholders.

“Equally important, aligning the AI initiative closely with legal and compliance teams can make a significant difference. By collaborating, teams can share valuable ideas on how regulations can evolve to better fit an AI-heavy trading environment,” Chuang said.

Ethical considerations are just as vital. One major issue is the “black box” problem, where it’s hard to understand how AI systems make decisions. To fix this, AI needs to be more transparent so traders and others can clearly see how results are reached.

Protecting personal data is another top priority. Strong security measures must be implemented to safeguard sensitive information and ensure user privacy. The data sources used by AI must also be transparent and ethical, ensuring accuracy and eliminating biases that could lead to unfair or distorted results.

“Clear ownership of AI models is also important. This prevents intellectual property disputes and ensures that creators receive proper recognition for their work. Addressing these ethical issues allows developers to create AI-driven trading platforms that are powerful, efficient, trustworthy, and respectful of user rights,” he summed up.

The Path Forward

The future of trading lies in striking the right balance between technology and human expertise. Despite the growing role of automation, human intuition and decision-making remain essential. 

While technology can handle routine tasks and identify opportunities in real time, humans provide the strategic oversight, creativity, and judgment that technology cannot replicate. Advanced tools may perform much of the heavy lifting, but humans are still needed for big-picture thinking, creativity, and decision-making.

“Humans remain essential as the orchestrators of these AI agents. This collaboration ensures that AI operates effectively and aligns with traders’ goals. AI can handle much of the heavy lifting, but the strategic oversight and creative problem-solving that humans bring to the table are irreplaceable,” Chuang shared.

Either way, the combination of blockchain and AI is unlocking new possibilities. Blockchain strengthens data security and safeguards user privacy while streamlining processes like onboarding, allowing advanced tools to offer personalized insights and more efficient operations. For traders, it promises a future with secure, accessible systems that make financial markets more inclusive and resilient.

“Imagine a seamless onboarding experience where blockchain reduces friction and safeguards your information, while AI personalizes your journey and provides tailored insights. This synergy not only enhances the efficiency and security of trading operations but also makes cutting-edge technology accessible to everyone. The fusion of AI and blockchain is paving the way for a more innovative, inclusive, and resilient financial ecosystem,” he concluded.

As trading platforms work to solve problems like unpredictable markets and data issues, the opportunities for traders will keep growing. The mix of fast, efficient technology and human expertise is building a trading world that is more reliable, accessible, and forward-thinking.

Abu Dhabi, United Arab Emirates – December 19, 2024: VeraViews, a leader in blockchain-based advertising transparency, has partnered with AIREV’s revolutionary AI platform OnDemand to strengthen its fight against ad fraud and improve transparency in the digital advertising industry. The collaboration will leverage OnDemand’s advanced AI technologies to support VeraViews’ mission.

Source

According to Cointelegraph, the Organisation for Economic Co-operation and Development (OECD) has highlighted the potential benefits and risks of adopting artificial intelligence (AI) tools in the tourism sectors of the Group of Seven (G7) countries, which include Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. The OECD's policy paper, titled “Artificial Intelligence and Tourism: G7/OECD Policy Paper,” released on December 18, emphasizes the growing importance of AI in fostering innovation and sustainability within the tourism industry.

The OECD's analysis identifies several advantages of integrating AI into tourism, such as enhancing visitor experiences, improving accessibility and audience engagement, and automating internal processes and customer service. The policy paper states that AI can aid in promoting sustainable tourism practices by efficiently managing resources, including energy use, waste reduction, workforce allocation, and optimizing tourist flows. Additionally, AI tools have the potential to reshape tourism processes and policies, benefiting local communities by better managing tourism flows. However, the OECD stresses the need for continuous evaluation and adaptation of AI technologies to ensure their successful implementation.

The use of granular data collected from AI tools can significantly enhance tourism policy-making processes. These data sets can also be used to train AI models for specific tourism-related applications. Despite these benefits, the OECD warns of risks associated with AI adoption in tourism, such as concerns about data quality, security, and environmental impacts. The organization advises policymakers to address key issues when implementing AI technologies, including robust data protection, consumer safeguarding measures, job impact analysis, and AI training and education for all stakeholders in the tourism industry.

The OECD also cautions that legal and regulatory frameworks for AI will have a substantial impact on tourism businesses and future policy-making. The paper concludes with a recommendation for the G7 Tourism Working Group to facilitate knowledge sharing on specific issues among the seven economies. In related news, on December 17, Abdullah bin Sharaf Alghamdi, president of the Saudi Data and Artificial Intelligence Authority (SDAIA), announced that Saudi Arabia ranked third in the OECD’s AI Policy Observatory, following the United States and the United Kingdom. This achievement positions Saudi Arabia as a leading destination in the Middle East for developing trustworthy AI tools and policies.

Skynet, Decentralized AI Payment Protocol, Secures $1.2 Million Skynet, a leader in decentralized artificial intelligence (AI), has secured $1.2 million in pre-seed funding. The investment round was led by GitHub, Polygon, and Veracode. This new capital will be used to continue developing Skynet's proprietary blockchain and expand its team. "We're excited to partner with such a strong group of investors who share our vision of a decentralized future for AI. This funding will enable us to accelerate the development of our platform and bring our groundbreaking technology to the world," said David Hanson, CEO of Skynet. ```

Artificial Intelligence (AI) could transform the tourism sector for the better, particularly in G7 countries. This is by driving innovation, sustainability, and inclusivity, according to the OECD’s latest policy paper, “Artificial Intelligence and Tourism.” 

In the report, the Organisation for Economic Co-operation and Development (OECD) emphasized that AI offers a transformative force in addressing tourism’s most pressing challenges, including managing visitor flows and optimizing resource usage. 

“AI applications offer highly personalised travel experiences through customised recommendations and virtual assistant services, analysing individual preferences to tailor suggestions for each traveller,” OECD stated.

AI-driven applications are already making headway in the industry, as tourists can now get tailored travel recommendations, automate operational tasks, and facilitate real-time traffic management. 

The policy paper also notes that AI has the potential to enhance visitor experiences with more interactive and personalized offerings while ensuring that tourism benefits are distributed equitably, especially in G7 countries comprising Germany, France, the US, Canada, the UK, Japan, and Italy.

Enhancing sustainability and accessibility

AI’s role in promoting sustainable tourism practices is a focal point of the OECD’s recommendations. By leveraging real-time data, AI can help destinations manage visitor flows to avoid overcrowding, preserve cultural heritage sites, and minimize environmental impacts. 

OECD mentioned Barcelona Zoo’s AI chatbot, Zoobot, as an example of how technology can enhance accessibility for visitors with disabilities. Similarly, it highlighted Notre Dame Cathedral’s use of AI-driven digital twins for restoration efforts, demonstrating AI’s capability to aid in the preservation of historical landmarks. 

Additionally, per the report, AI can improve the socio-economic value of tourism by fostering green mobility options, reducing carbon footprints, and promoting local economies. 

OECD also encouraged international collaboration among G7 nations to align AI research and policy efforts, which could make it easier to address global challenges collectively.

OECD: AI supporting small and medium enterprises (SMEs)

In the paper, the OECD echoes the importance of supporting SMEs in tourism as they adapt to AI. These smaller enterprises often struggle to keep pace with rapid technological advancements, facing barriers such as high implementation costs, limited expertise, and regulatory complexities. 

According to the OECD, the divide in AI adoption risks exacerbating existing inequalities within the tourism sector, favoring large, tech-savvy enterprises over smaller players. The organization deems tailored support programs and training initiatives imperative to bridge this gap and ensure inclusive growth. 

However, for SMEs to leverage AI effectively, governments and industry bodies need to focus on accessible innovation hubs, practical training opportunities, and financial support mechanisms.

Addressing risks and challenges

Despite its benefits, the OECD cautions against the risks of AI adoption. Data privacy concerns, algorithmic bias, and workforce displacement require careful management. While AI has the potential to automate routine tasks and create more complex, rewarding jobs, there is also a risk of job displacement, particularly in roles reliant on manual labor or repetitive tasks. 

OECD shed light on the misuse of AI for manipulative marketing, such as deepfake tourism videos or fake reviews, which can erode trust and harm destination reputations. To address these concerns, the organization calls for international cooperation in developing ethical AI standards and ensuring compliance.

“Existing regulatory and legal frameworks can promote responsible AI use in tourism by providing clear guidelines for developers and operators, and facilitate the safe and ethical integration of AI technologies. It is also important to provide a safe environment for businesses to test and pilot innovation including AI applications,” the paper outlined.

The OECD report concludes that while AI adoption in tourism is still in its early stages, its potential to revolutionize the sector is immense. 

Land a High-Paying Web3 Job in 90 Days: The Ultimate Roadmap

San Francisco, California, December 19th, 2024, Chainwire

Constellation Network, a Web3 ecosystem validated by the US Department of Defense, today announced the launch of a customized blockchain developed in partnership with the Common Crawl Foundation, to create the industry's first cryptographically secure, immutable archive of internet data for AI training and development.

The collaboration introduces a new approach to validating and securely accessing 17 years of internet crawl data—spanning nearly  9 petabytes which 80% of Large Language Models (LLMs) use to train AI—through an immutable, cryptographically secured blockchain network built on Constellation. This innovative application-specific network, or Metagraph, addresses pressing concerns in AI development while exploring vast new use cases for blockchain technology in emerging industries: data provenance, privacy, and ethical sourcing. Furthermore, the network will utilize Constellation's DAG utility asset to secure the archived internet crawls. This represents a significant advancement in utilizing cryptocurrency as a mechanism for businesses to notarize data, shifting the focus from consumer costs or gas fees typical of many other layer-one networks to an operational expense.

Key Technological Innovations

Comprehensive Data Archiving: A fully immutable copy of internet history, providing unprecedented transparency and traceability for AI training datasets

End-to-End Encryption: Cryptographic security that ensures data integrity throughout the AI development lifecycle

Ethical AI Framework: A robust solution for addressing concerns around data collection, storage, and usage in large language models

"This integration is a critical step forward in securing the future of AI development," said Alex Brandes, CTO of Constellation Network. "By ensuring cryptographic integrity and immutability of training data, we are addressing one of the most pressing challenges in the field today: trustworthiness and provenance of datasets. We believe our platform will grow to become a cornerstone in the field of responsible AI development, setting new standards for data integrity and trust.” 

Industry Applications

The blockchain-enabled data archive is already attracting attention from advanced AI research initiatives. TraceAI, a project developed through the National Science Foundation (NSF) and SBIR program, is in testing stages in the development of their own application-specific network, built on Constellation, to add immutability, auditability, and proof of authorship to its training models and to develop advanced watermarking technologies. TraceAI will also leverage  Common Crawl’s Constellation-built solution to further extend their work in blockchain encrypted AI to include tracking the source origin of data.

Kevin Jackson, Vice President of Space Domain Communications & Commercialization for Forward EdgeAI, emphasizes the significance of this breakthrough: "This represents the natural evolution of AI and machine learning model development—transforming data management from a technical challenge to a trusted business tool that drives global standardization and verification."

Looking Forward

Over the coming months, Constellation Network and Common Crawl Foundation will work together to expand on solution sets for AI developers and further integrate the distribution of the cryptographically validated access to the crawl as part of the standard release process.   

“For users of the Crawl who are concerned about the provenance of the data, especially those using it for AI models, Constellation and their hypergraph blockchain provides an elegant solution”, said Rich Skrenta, Executive Director of the Common Crawl, “we are looking forward to adding the ability to securely validate the crawl as part of our standard distribution by partnering with Constellation”.

Evidence of this integration can be found on Constellation’s transaction viewer, called the “DAG explorer,” and developers can get started using verified historical crawls for AI applications. Please follow along for further solutions to be developed by Constellation, Forward Edge-AI, and Common Crawl. 

About Constellation Network Constellation is a leading blockchain network advancing innovation through on-chain data security, partnering with critical global stakeholders, including the U.S. Department of Defense, to deliver transformative, next-generation technologies.

About Common Crawl Foundation The Common Crawl Foundation is a 501(c)(3) non-profit organization dedicated to providing a copy of the internet to the public, free of charge. Their web archive consists of petabytes of data collected over years of web crawling, serving as a critical resource for researchers, businesses, and developers worldwide.

About Forward Edge-AI Forward Edge-AI is at the forefront of a revolution in responsible and inclusive Artificial Intelligence (AI) for the betterment of humanity. Since its foundation in 2019, our goal is to become the dominant player in Artificial Intelligence and lead the revolution in augmenting edge technology with human intelligence.

About Common Crawl Foundation

Contact

Email: press@constellationnetwork.io 

Website: https://constellationnetwork.io/ 

Twitter: https://x.com/conste11ation 

GitHub: https://github.com/Constellation-Labs/tessellation

DAG Explorer: https://mainnet.dagexplorer.io/

Disclaimer. This is a paid press release.

In the cryptocurrency market, several key narratives have been shaping the landscape in 2024, influencing investor sentiment and market trends. Here's an overview based on recent insights:

1. Real-World Assets (#RWA ): The tokenization of real-world assets has gained traction, aiming to bridge traditional finance with blockchain technology. This involves tokenizing assets like real estate, bonds, and art, providing new avenues for investment with increased liquidity and accessibility. The narrative suggests a future where traditional and decentralized finance (#DeFi ) converge. $ONDO, $QNT, $OM, $CHEX

2. Bitcoin Financialization (BTCfi): There's an emerging focus on financial products built around Bitcoin, including Bitcoin-backed bonds, loans against $BTC , and its inclusion in asset baskets. This narrative suggests Bitcoin's evolution from a mere store of value to a fundamental component of financial systems.

3. AI and Big Data Tokens: With the rise of #AI in tech, there's a growing narrative around tokens that power AI-related blockchain projects. The integration of AI with blockchain technology is seen as a way to enhance efficiency, security, and innovation within crypto. $NEAR, $FET , $VIRTUAL, $PAAL

4. Gaming (#GameFi ) and NFTs: The narrative around NFTs has evolved, with a focus on utility within gaming and other digital ecosystems. #GameFi, where players can earn crypto by playing games, continues to be a hot topic, blending gaming with financial incentives. $SAND, $RON, $MANA, $NOT

5. Bitcoin ETFs and Institutional Adoption: The approval of spot Bitcoin ETFs has been a major narrative, signaling broader institutional acceptance and potentially leading to increased mainstream investment in crypto. This is seen as a step towards legitimizing the asset class.

6. Decentralized Physical Infrastructure Networks (#DePIN+AI ): This narrative revolves around blockchain networks that incentivize the creation of physical infrastructure through token rewards, aiming to decentralize services like Internet connectivity or data storage. $RENDER, $TAO, $GRT , $GLM

7. Layer 1s and Layer 2s: Scalability solutions are still at the forefront, with Layer 1 (#L1) blockchains like $Solana making significant comebacks and Layer 2 (#L2) solutions like Optimistic Rollups and ZK Rollups addressing Ethereum's scalability issues. These narratives are driven by the need for faster, cheaper transactions in blockchain networks.
L1: $INJ, $AVAX, $FTM;
L2: $MNT, $ARB, $STX

8. Memecoins: These have continued to be a significant narrative, with tokens like $Bonk, $Pepe, and others capturing substantial attention due to their viral nature and community engagement. Memecoins often see rapid rises and falls in popularity, driven by social media trends and community enthusiasm. $AI16Z, $GOAT, $FARTCOIN

These narratives are not just stories but reflect the ongoing evolution of technology, market dynamics, and investor behavior within the crypto space. They highlight areas of growth, innovation, and potential investment opportunities while also serving as a reminder of the speculative and volatile nature of the market.

As the cryptocurrency market evolves, established giants like Solana and Ethereum continue to dominate discussions around scalability and smart contract capabilities.

Both blockchains have carved out their niches—Solana with its high-speed transactions and Ethereum as the leading platform for decentralized finance (DeFi) and NFTs. However, the market is now witnessing the emergence of AI-driven tokens like Lightchain AI (LCAI), a project combining artificial intelligence with blockchain technology.

Priced at just $0.003 during its presale, this AI token is gaining attention for its transformative potential. With features like the Proof of Intelligence (PoI) consensus mechanism and the Artificial Intelligence Virtual Machine (AIVM), analysts are predicting that Lightchain AI could deliver massive returns, challenging even the dominance of Solana and Ethereum in the coming years.

Solana’s Strengths High-Speed Transactions and Network Scalability

Solana has established itself as a top blockchain platform due to its impressive transaction speeds and low fees.

Leveraging its Proof of History (PoH) consensus mechanism, Solana can process up to 65,000 transactions per second, making it ideal for applications that demand high throughput. This scalability has attracted numerous DeFi projects, NFT marketplaces, and Web3 applications, solidifying its reputation as a high-performance blockchain.

However, Solana’s rapid growth has not been without challenges. Network outages and centralization concerns have raised questions about its long-term reliability. While Solana remains a strong player in the blockchain ecosystem, its vulnerabilities highlight the need for alternative platforms that combine scalability with resilience and innovation—qualities embodied by emerging AI tokens like Lightchain AI.

Ethereum’s Position Dominance in Smart Contracts and DeFi

Ethereum remains the cornerstone of the cryptocurrency market, powering the majority of decentralized applications and smart contracts.

Its transition to Ethereum 2.0 and the Proof of Stake (PoS) consensus mechanism has addressed scalability and energy consumption concerns, further strengthening its position. Ethereum’s vast ecosystem, supported by thousands of developers, makes it the go-to platform for DeFi protocols, NFT projects, and enterprise solutions.

Despite its dominance, Ethereum faces competition from newer blockchains offering faster and cheaper alternatives. High gas fees, even post-merge, and limited scalability compared to platforms like Solana have left room for innovative projects like Lightchain AI to capture market share. Ethereum’s focus on smart contracts may also limit its versatility in addressing emerging trends like AI integration, where Lightchain AI excels.

AI Token to Watch Why It’s Poised for a Massive Surge

Lightchain AI (LCAI) is quickly becoming a standout project in the cryptocurrency landscape. Unlike Solana and Ethereum, which focus primarily on scalability and smart contracts, Lightchain AI integrates artificial intelligence into its core framework. Its Proof of Intelligence (PoI) consensus mechanism incentivizes nodes to perform AI tasks, ensuring that network activity contributes to advancements in artificial intelligence.

Another game-changing feature is the Artificial Intelligence Virtual Machine (AIVM), a computational layer that allows developers to execute AI-specific tasks within a decentralized environment.

This innovation positions Lightchain AI as a versatile platform capable of addressing challenges across industries such as healthcare, logistics, and finance. Priced at just $0.003 during its presale, Lightchain AI offers a low entry point with high upside potential, making it an attractive investment for those seeking exponential returns.

Key Technological Innovations Behind the AI Token’s Potential

Lightchain AI’s technological innovations are the foundation of its massive growth potential. The PoI consensus mechanism replaces traditional validation methods with AI-specific computations, such as model training and optimization. This approach not only enhances network utility but also opens up new opportunities for developers and enterprises.

The AIVM is another transformative feature, enabling real-time execution of AI tasks with high efficiency and scalability. Its compatibility with popular AI frameworks like TensorFlow and PyTorch ensures seamless adoption, making Lightchain AI a go-to platform for AI-powered decentralized applications. These innovations set Lightchain AI apart from traditional blockchains, positioning it as a leader in the next wave of crypto evolution.

Analysts’ Predictions What’s Driving the Bullish Sentiment for This AI Token

Analysts are increasingly optimistic about Lightchain AI’s future, citing several factors driving bullish sentiment. The growing demand for AI-driven solutions across industries creates a natural market for platforms like Lightchain AI.

Its $0.003 presale price provides early investors with an unmatched opportunity to participate in a project with transformative potential.

The platform’s roadmap, which includes a testnet launch in January 2025 and a mainnet activation in March 2025, underscores its commitment to delivering on its promises. Strategic partnerships and ecosystem expansion efforts are expected to further enhance its adoption and credibility. Analysts project that Lightchain AI could reach significant price milestones, with some forecasting a potential $20 price target by 2025, representing exponential returns for early adopters.

Future of AI Tokens and Their Role in the Crypto Landscape

While Solana and Ethereum remain key players in the blockchain space, the emergence of AI-driven tokens like Lightchain AI (LCAI) signals a new era of innovation and utility. Combining blockchain scalability with artificial intelligence, Lightchain AI addresses real-world challenges and unlocks new opportunities for decentralized applications.

With its affordable presale price, cutting-edge technologies, and clear roadmap, Lightchain AI is positioned to deliver massive returns, challenging the dominance of established platforms. For investors seeking high-growth opportunities in the evolving crypto landscape, Lightchain AI represents a compelling choice that could redefine the market in the years to come.

👉 Visit the Lightchain AI Website 👉 Read the Lightchain AI Whitepaper 👉 Follow Lightchain AI on Twitter/X 👉 Join the Lightchain AI Community on Telegram

Disclaimer: This is a sponsored press release and is for informational purposes only. It does not reflect the views of Crypto Daily, nor is it intended to be used as legal, tax, investment, or financial advice.

Inaccurate, duplicate, and incomplete data continues to plague industries. Artificial intelligence is leveraged to mitigate these issues, but it has inherent limitations. AI datasets can contain mislabeled or irrelevant data.

Fraction AI is pioneering a new approach to data labeling by combining the efficiency of AI agents with human insights. The company recently completed a $6 million pre-seed funding round co-led by Symbolic and Spartan alongside strategic investments from Illia Polosukhin (Near), Sandeep Nailwal (Polygon), and other outstanding angel investors.

Fraction AI tackles the increasing challenge of producing high-quality data. Traditional methods depend solely on AI or humans. Fraction AI aims to use human understanding as guidance for AI agents. Funds from the round will go toward in-depth exploration and infrastructure upgrades to scale the cutting-edge hybrid approach, whose effectiveness is confirmed by research.

Introducing Gamified Adversarial Prompting

Data scientists have demonstrated that the datasets created using GAP, or gamified adversarial prompting, enhance the performance of the latest AI models. The GAP framework involves crowdsourcing high-quality data to fine-tune large multimodal models, turning data collection into an engaging game. It encourages players to provide complex, fine-grained questions and answers that fill gaps in the models’ knowledge.

In lay terms, Fraction AI incentivizes AI agents to create high-quality data through real-time competitions. Developers set up and launch agents using detailed instructions to guide their actions and achieve the best possible outcomes, while ether is staked as the economic foundation. Participants get economic incentives in what facilitates a continuous stream of valuable training data.

Current issues with data quality

Inaccurate data costs organizations tens of millions of dollars a year. Banal examples include misspelled customer names, customer addresses with errors, and incorrect data entries in general. Whatever the cause, inaccurate data cannot be used because it causes deviations throughout any data analysis.

When one imports data from multiple sources, it is not uncommon to end up with duplicate sets. Using retail as an example again, you might import customer lists from two sources and find a few people who bought things from both retailers. Duplicate records become a problem because you only want to count each customer once.

When data is combined from two different systems, inconsistent formatting can arise. Cross-system inconsistencies can cause major data quality issues unless they are identified and rectified swiftly.

Incomplete data and dark data are two additional problems. Some records are missing key information, such as phone numbers without area codes or demographic details without the age entered. Dark or hidden data is data that’s collected and stored but not actively used. IBM estimates that 90% of all sensor data collected from IoT devices remains unused. Many organizations aren’t even aware of this wasted resource, which accounts for more than 50% of the average organization’s data storage expenses.

Human understanding facilitates improvement

As an educational tool, GAP motivates humans to challenge the limitations of AI models, leading to notable improvements in performance. It encourages error detection by tasking players to identify inaccuracies or inconsistencies in datasets or AI outputs. Their diverse backgrounds can bring varied perspectives, making it easier to spot biases that a single development team might overlook.

Gamification encourages innovative thinking through challenges or puzzles designed to stretch the limits of a dataset or model. Players can uncover novel use cases, detect biased outputs or inputs, and propose more inclusive alternatives. This reduces systemic biases in data and models, creating a more equitable foundation for all kinds of applications. Additionally, participants will flag previously unnoticed data anomalies because they’ll be rewarded for uncovering flaws. Rewards for identifying significant flaws could conceivably be higher, reducing the risk of unexpected failures or vulnerabilities in real-world applications.

As the technology scales, more and more people can play games simultaneously, enabling exponential improvements as the sheer volume of input accelerates the identification of weaknesses.

The dark side of creativity

Creative problem-solving doesn’t have to be for the public good. The rewards would be the primary motivation for some users, leading to an excessive focus on them. Taking this a step further, it’s not unreasonable to expect malicious actors to try and game the system, and platforms will need to deploy mechanisms to detect and block harmful activities. An example is using AI and statistical models to monitor user behavior patterns, flagging anomalies that indicate spamming or unusual submission patterns. Unusually high submission rates or repetitive patterns from a single user could be flagged for review.

The GAP framework could assign reputation scores to participants based on their contribution history. Ideally, new users would have limited influence until they establish credibility to reduce the risk of initial exploitation.

Finally, there will be users flagging issues randomly. Platforms leveraging GAP will need to involve human experts or AI to deter participants from flagging accurate and valuable data.

Taking data quality mainstream

Risks aside, humans will be encouraged to spot mislabeled or irrelevant data in AI datasets, improving the quality of machine learning and AI models. Beyond AI, gamified contributions can enhance the accuracy and completeness of free, publicly accessible datasets like Wikipedia or OpenStreetMap. Flagging misinformation in real time will lead to more reliable repositories.

GAP will also impact harmful, biased, or inappropriate content. Platforms like Reddit or YouTube could adopt it to identify and remove such content faster.

 

Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.

 

World Bank Advocates for Balanced Approach in AI for Education

The World Bank has acknowledged artificial intelligence's (AI) growing influence in education, highlighting its potential to bring both opportunities and challenges.

In a recent article by Jaime Saavedra, Human Development Director for Latin America and the Caribbean, and Ezequiel Molina, Senior Economist, the organisation emphasized that when implemented effectively, AI can enhance learning outcomes, improve teacher training, and provide specialised support for students.

A notable example comes from Ecuador, where an AI tutoring programme successfully improved students' math skills at a cost of just $18 per student.

While public and institutional concerns about AI are valid—and even necessary for responsible oversight—AI's capacity to drive positive change in education remains promising.

What does AI mean for the developing world? Listen to the #DevelopmentPodcast to learn more, hear from:

➡️@LongaNaomi
➡️Brigitte Hoyer Gosselink @Googleorg
➡️@_PMolnar
➡️@WBG_Digital's Christine Zhenwei Qiang
➡️ and more! https://t.co/eljC2oZsB9 pic.twitter.com/tpHVVrBuYO

— World Bank (@WorldBank) December 16, 2024

Worries about AI's Impact on Education

Critics have raised important concerns about AI's impact on equity, effectiveness, and the nature of learning itself.

However, the World Bank argues that AI is already integral to many aspects of society, making it crucial to address these challenges now.

The World Bank contends that promoting AI is not irresponsible, even in schools lacking the infrastructure to fully support it.

While acknowledging the need for basic resources and well-qualified teachers, the World Bank highlights that AI, when properly implemented, can bridge these gaps more quickly.

It can scale teacher training and deliver educational content to remote areas, ensuring that students who need additional support can access it more effectively.

AI can help learning... when it isn't a crutch.

There are now multiple controlled experiments showing that students who use AI to get answers to problems hurts learning (even though they think they are learning), but that students who use AI as a tutor perform better on tests. pic.twitter.com/nBulAIbvYY

— Ethan Mollick (@emollick) October 11, 2024

Addressing the concern of whether AI will serve humanity or the other way around, the World Bank acknowledges the risks, particularly given that AI development is largely concentrated in the Global North.

To mitigate this, the World Bank advocates for strategic engagement, including cultivating local AI talent through scholarships, developing culturally relevant content, ensuring data sovereignty, and strengthening teachers' digital skills.

Uruguay provides a strong example, having developed regulatory guidelines that respect local teaching methods while embracing AI, ensuring the technology supports rather than dictates their educational goals.

Will AI Promote Complacency & Laziness?

Since the rise of generative AI tools like ChatGPT, a common concern among teachers and parents has been their potential impact on student's work ethic.

While it is true that such tools could encourage intellectual complacency and superficial learning, the World Bank argues that the key lies in how they are used.

Lazy students are just handing in work generated by AIs, say critics. But is it as simple as that? And even if they are, why? As @strategistmag discovers, the truth is more complex and nuanced than authorities understand. https://t.co/iJ3BdE9sFx #AIinEducation pic.twitter.com/co22pIdw8C

— diginomica (@diginomica) December 17, 2024

AI tools can act as a high-IQ partner for students, aiding them in brainstorming or completing assignments.

However, as the World Bank points out, AI did not invent cheating; it merely amplifies the choices students already face.

Ultimately, it is up to individuals to decide how to use these tools constructively.

Rather than stifling learning, AI presents an opportunity to rethink education—focusing less on rote tasks like summarising texts, which AI can handle, and more on developing higher-order skills like critical thinking, creativity, and analysis.

In Nigeria, for example, World Bank pilot programmes revealed that when students use AI tools thoughtfully, they engage with the material on a deeper level.

Instead of simply asking students to summarise content, teachers can encourage them to critique AI-generated analyses, compare interpretations, and collaborate with AI to brainstorm innovative ideas.

The World Bank emphasizes that great teachers can foster critical thinking through guided conversations, and with AI, they have powerful tools to design lessons and activities that address complex skills.

Responsible AI Implementation in Schools

In education, scientific breakthroughs that translate directly into classroom practice have been rare, in contrast to fields like medicine where research quickly leads to tangible innovations.

The World Bank believes AI can change this by applying the evidence-based methods used in medicine, pharmaceuticals, and food safety—pushing forward without letting fear of innovation hinder progress.

However, just as no new medical treatment or food product reaches the public without rigourous testing, AI in education requires a systematic, careful approach.

The World Bank argues that true success in integrating AI will come when we can harness its potential while keeping the human element at the heart of the learning process.

Education, which revolves around human interaction, has the opportunity to use AI as a powerful tool to enhance this interaction, but not to replace it.

By adhering to this principle, AI can serve educational values rather than dominate them.

AI tutors are already better than human tutors

"By the numbers: Students who were given access to an AI tutor learned more than twice as much in less time compared to those who had in-class instruction, according to a study by two Harvard lecturers of 194 Harvard Physical…

— Chubby♨️ (@kimmonismus) October 29, 2024

The article concluded:

“The future of education will be inevitably influenced by technological changes. With intelligence and creativity, we can use these tools to help schools, teachers, and students who need the most support. Success depends on making smart investments while keeping the human element at the center. Remember: education is fundamentally about human interaction. AI should make this interaction more effective and enjoyable; it won’t replace it. By focusing on this principle, we can ensure that technology serves our educational values, not the other way around.”

San Francisco, USA, December 19th, 2024, Chainwire

Fraction AI, the company pioneering a new approach to data labeling by combining human insights with AI agents, announced the completion of its $6 million pre-seed funding round. The round was co-led by Spartan and Symbolic, with participation from Borderless, Anagram, Foresight, and Karatage, alongside strategic investments from prominent angels, including Sandeep Nailwal (Polygon) and Illia Polosukhin (Near).

Fraction AI tackles the growing challenge of producing high-quality data at scale. Traditional methods depend solely on humans or AI. Fraction AI blends human insight with AI's efficiency, leveraging human understanding to guide AI agents. Funds from this round will fuel research expansion and infrastructure upgrades to scale this innovative hybrid approach.

“The data layer has been a critical yet often overlooked bottleneck in advancing AI,” said Shashank Yadav, CEO of Fraction AI. “Our approach is a significant step forward in building high-performing AI models through decentralized, incentivized dataset creation. With this funding, we aim to scale our efforts and reshape data labeling. Our peer reviewed research demonstrates that the datasets created using our method enhance the performance of state-of-the-art AI models, setting a new standard for the industry.”

Fraction AI’s competitive framework encourages AI agents to generate high-quality data through real-time competitions. Builders set up and deploy AI agents using well-thought-out instructions to guide their actions and achieve the best results, while Stakers provide the economic foundation by staking ETH. This dynamic ecosystem produces a continuous stream of high-quality training data while offering economic incentives for participants. It is currently live on a closed testnet with over 60,000 users and plans to launch their public testnet in January 2025.

Fraction AI’s novel method has been backed by open-source research, validating the effectiveness of its hybrid labeling process. As the protocol continues to grow, it aims to redefine how training data is created, paving the way for the next generation of AI innovations.

About Fraction AI

Fraction AI is a protocol redefining data labeling by combining human insights with AI agents. The approach enables humans to guide AI agents in labeling tasks, achieving higher accuracy with the efficiency of automation. Through a competitive, real-time framework, Fraction AI generates high-quality training data while rewarding participants, fostering innovation, and advancing the AI ecosystem.

Website | X | Whitepaper | LinkedIn

Disclaimer. This is a paid press release.

The world of crypto is booming, but so are crypto hacks. As prices soar and adoption spreads, hackers are having a field day. With centralized platforms, private keys, and even advanced AI in their sights, the battle between cybersecurity and hackers rages on. Here’s what you need to know.

Why Crypto Hacks Are Surging

Crypto thieves are busier than ever. In 2024 alone, they stole $2.2 billion, up 21% from the previous year. Centralized platforms are their favorite targets. These platforms handle massive funds, making them juicy prey for hackers. This year saw over 300 incidents, many involving private key compromises. Without these keys, users lose access to their crypto—forever.

Big names weren’t spared. DMM Bitcoin in Japan lost $305 million, while WazirX in India suffered a $235 million blow. Experts say the growing use of AI and quantum computing could make things even worse. The message is clear: crypto hacks are evolving, and they’re not slowing down.

The Role of AI in Crypto Hacks

Artificial intelligence is a game-changer—for hackers too. Cybercriminals now use AI to craft smarter phishing attacks, trick people into revealing private keys, and spread malware. Social engineering scams have also become more convincing, thanks to AI-generated content. Fake airdrops and phishing emails are flooding inboxes, often targeting unsuspecting crypto fans.

This isn’t just a problem for individuals. Companies are feeling the heat too. Centralized finance platforms reported a massive 1,000% increase in incidents this year. As AI keeps advancing, experts warn, hackers will find new and sneakier ways to steal crypto.

Quantum Threats Are on the Horizon

Quantum computing is another looming danger. While it’s still developing, quantum tech could one day break encryption systems that secure crypto wallets. That’s a scary thought for an industry built on blockchain security. Companies need to act fast, adopting quantum-safe protocols to stay ahead of the game.

Even now, quantum technology is shaking things up. Google recently unveiled a chip that can process data at incredible speeds. As this tech improves, crypto hacks could enter a whole new dimension.

Lessons From a Tough Year

This year’s wave of crypto hacks teaches us some crucial lessons. First, cybersecurity isn’t optional—it’s essential. Multifactor authentication can protect accounts, and cold storage wallets keep crypto safe from online threats. But even hardware wallets come with risks, as phishing attacks grow more sophisticated.

Second, vigilance is key. Users must beware of unsolicited messages and scams. Hackers are creative, and their schemes evolve constantly. Staying informed and cautious can make all the difference.

The Road Ahead for Cybersecurity

The crypto world is at a crossroads. As hackers get smarter, so must cybersecurity measures. Advanced tools like real-time threat detection and cross-chain monitoring are becoming necessary. And while quantum computing poses long-term risks, immediate actions—like education and better platform security—are critical.

Crypto hacks aren’t going away, but with proactive measures, the industry can fight back. The stakes are high, but so is the potential for a safer, stronger crypto future. Stay alert, stay informed, and keep your assets secure.

Fraction AI, a pioneering crypto artificial intelligence (AI) startup, has secured $6 million in pre-seed funding. The investment round was co-led by renowned venture capital firms Spartan Group and Symbolic Capital, with participation from notable investors including Borderless Capital, AMALGAM, Foresight Ventures, and Caratage. The funding will fuel Fraction AI's mission to advance the integration of AI into the crypto ecosystem. The startup aims to develop innovative AI-powered solutions that optimize trading strategies, enhance risk management, and streamline crypto asset management. The company's team of experts in both AI and cryptocurrencies brings deep knowledge and experience to the project. Fraction AI has gained recognition for its cutting-edge AI algorithms that provide valuable insights and predictions within the crypto market. The startup's platform offers real-time data analysis, trading recommendations, and risk assessment tools, empowering traders and investors with a competitive edge. With this seed funding, Fraction AI plans to expand its team, enhance its AI capabilities, and forge strategic partnerships to further its impact in the crypto space. The startup's vision aligns with the growing demand for AI-driven solutions in the rapidly evolving crypto industry.