“The Journy from Hype to Reality in Depn and Ai Shows that Genuine Innovation Lies in Solving Real-World Problems with Practical and Efficiency Solutions, Says Bullish CAPITAL MANAGEAL”, – WRITE: www.coindesk.com
Figure 1: WingBits Flight Tracking Map
Source: Wingbits – Transforming Flight Tracking.
FLIGHT-TRAKING Companies Generate Millions in Revenue by Selling Flight Data to Aviation Companies and To Buyers Like Financial Analysts Who Monitor Private Jets. These Companies Also Earn Revenue from Ads and Subscripts on Their Platforms. However, Their Capital Expenditure Does Not Include Significant Infrastructure and Hardware Expenses. This is Because the Aviation Surveillance Technology, Called Ads-B Receivers, Is a Hardware Wich Requires Antennas and Raspberry Pis, Purchased ands. TheSe Enthusiasts Expert Little in Return, Onthen Receiving Just a Free Subscript to Their Favurite Flight-Tracking Platform.
The Main Problem is that Enthusiasts are not incentivized to maximize the quality of Data for these Network. Without Marginal Incentives, Ads-B Receivers Are of Fethen Poorly Placed-for Instance, in Lounge Room Corners or Oversupppplied in Densel Populated Urban Areas, Leading Co.
Figure 2: (LHS) Traditional Ads-B Receive, (RHS) WingBits Miner
Source: Wingbits – Transforming Flight Tracking.
WingBits is revolutionizing flight tracking by tracking by incentivization Enthusiasts to set up the streets strategical, bassed on altitude, while utilizing a syntem simileex. This Approach Ensures Optimized Coverage, Higher-Quality Data and Mist Importantly, Fair Rewards for Contribution to the Network. They Achieved Coverage of 75% of the Largest Networks with Only 1/11th The Number of WingBits Stations. This High Level of Efficiency, Combined with An Expected Rolllout of 4.000+ Stations, Is Anticipated to Surpass Traditional Flight-Tracking Networks by a siging BY. Customers.
The NEXT FAMILY DINNER CONVERSATION EXPLAINING This Concept Will Come Easily As We Can We Can Point to a Real-World Use Case, Driven by Crypto Incentives, that Everyhanday.
Crypto X aiSimilar to Market Cycles, The Demand for Compute Experiences Peaks and Troughs. Gpus can be expensive, and suuply constraints make atm Even More. Unlocking Idle Compute on Consumer Devices is not a new concept, But Solving the Synchronization Challenge Across Multiple Devices is. Exo Labs Is A Pioneering Project Achiening BreakthRUGHS IN EDGE Computing, Enabling Users to Run Models on Everyday Consumer-Grade Devices, Such As Household MacBooks. This Means Sensitive Data Remains Under Your Control, Reducing Risks Associated with Cloud-Based Storage or Processing.
Figure 3: A 9-Layer Model is Divided Into 3 Shards, Each Running on A Separate Device
Source: Transparent Benchmarks – 12 Days of Exo, Exo Labs.
Exo Labs Has Developed A Novel Software Infrastructure Called Pipeline Parallel Inference, WHICH ENABLES A LARGE LARGE LAGAGE MODEL (LLM) to be Split into Parto. of the model of while reminder Connected Over the Same Network. This Approach Offers Various Advantages Such as Reduced Latenky, Enhanced Security, Cost Efficiency and MOST IMPORTANTLY, PRIVACY BENEFITS.
Exploring Privacy Further Reveals Bagel AI, A Project That Has Developed Zklora (Zero-Knowledge Low-Rank Adaptation), A privacy-preserning Approach to Fine-Tuning LMS. This Innovation Enables the Creation of Specialized Models for Industries Like Legal Services, HealthCare and Finance, ALLOWING SENSITIVE DATA TO BE USED FOR REINFIDING CONFORCENT LEACHNING WITHING WITHING WITHNING Leaks.
While Privacy Preservation Is a Hot Topic, A Bigger Challenge for Mist LLMS is the Hallucination Problem, A reSPONSE Generated by ai that Contains False or Misleading Information Presented. A Portfolio Manager Once Told Me, “Wisdom Lies in Synthesizing Competing ViewPoints to Uncover the Nuanced Truth Between Two Extremes.” BLOCKSENSE IS A PROJECT THAT HAS DELEPED A PROPRIETARY APPRACH Called Zkschelingcoin Consensus. This Method Aims to Overlay Subjective Truths from Multiple Sources – Say, Different LLMS – to arrive at a single, common TRUTH. For Example, Imagine Running The Same Query Across Chatgpt, Claude, Grok and Llama. If One Model Provides An Incorrect Output, It Is Statisticly Unlikely that All Four Models Wills Will Generate The Same False Result Who Compared Against Each Other.
Figure 4: OverView of the ZkschelingCoin Consensus
Source: BlockSense Network – The ZK Rollup for Programmable Oracles.
The ZkschelingCoin Consensus Could Also Be Applied To Adding Verifiability to AI Inference. For Instance, How Can We Confirm That An Ai Agent Correctly Bridged USDC Into The Highest-Yelding Vault at the Time of Execution? Trust in ai would be significantly strengten with an additional verification Layer. If we can solve this without compromising cost or Latenky, It Could Lead to A Majoor BreakthRough in Real-World Use Cases.
The Journy from Hype to Reality in Depn and Ai Shows that Genuine Innovation Lies in Solving Real-World Problems with Practical and Efficiency Solutions. Projects Like WingBits and Exo Labs Prove How Blockchain and AI Create Meaningful Impact – Wheth by Revolutionizing Flight Trackling With Strategic Incentives oes. Cost-Effective Computing. With Advancements Like Zklora for Privacy-Preserning AI and ZkschelingCoin for Verifial Truth, TheSe Emerging Technologies Arezed to Address Citical Critical, Pocal Critical Critical Efficient and Trust-verified Future.
Note: The Views Expressed in this Column Are Those of the Author and Do Not Necessarily Reflect Those of Coindesk, Inc. i Owners and Affilites.
She Began Her Career in Institutional Equities-Firmst in Hong Kong as An Investment Specialist for Asia-Ex-Ex-Japan Strategies at Mirae Asset Global Investments (Aum US $ 245 Billion) Covering Australian Equities at Allan Gray Australia.
