Tag: business

AI Tools, Artificial Intelligence, Blockchains, computer vision, Digital Currency, Environment, finance, Gold, Mining, Yogi Nelson

Is AI the New Geologist? Digging Into the Future of Precious Metals

Yogi Nelson

by Yogi Nelson

Welcome to the BlockchainAIForum

Discovery and extraction of precious metals—gold, silver, platinum, palladium—has relied heavily on manual geological interpretation, slow survey cycles, and trial-and-error drilling since about 1850. Making the “boring” process boring. lol. Today a new queen sits atop the golden throne of precious metals discovery and extraction! And she offers excitement—her name is artificial intelligence (AI)! Ms. AI is re-engineering the precious metals sector from the ground up—pun intended. lol!

Why has she ascended? For the simple, yet complex, reasons that as ore deposits become deeper, grades decline, and environmental expectations increase, AI offers solutions that improve efficiency, reduce costs, and minimize risk. Let’s “dig” into this phenomenon by “extracting” how AI is being deployed across the precious-metals value chain—from exploration and drilling to processing, sustainability, and blockchain-based supply-chain verification.

AI-Driven Exploration: Faster, Cheaper, and More Accurate

Predictive Geological Modeling

Machine-learning models analyze enormous geological datasets—satellite imagery, geochemical surveys, seismic readings, magnetic anomalies, and historical drilling logs. What’s the point of doing that? Answer—to identify high-probability mineralization zones! AI can detect “hidden patterns” in rock structure that human geologists cannot.

Studies in Canada, Australia, and South Africa show that AI-driven exploration can:

  • Reduce exploratory drilling by 20–40%
  • Lower costs by millions of dollars per project
  • Shorten timelines by months or years

Remote Sensing Enhanced by AI

AI-trained spectral models can identify alteration minerals associated with gold, lithological contacts, structural breaks, and soil anomalies. Drones equipped with hyper-spectral sensors now produce site maps with centimeter-level precision, allowing exploration teams to prioritize the highest-value drill targets. Before AI you couldn’t hit what you couldn’t see!

AI in Drilling and Extraction: Precision and Real-Time Optimization

Smart Drilling Systems

AI-enabled rigs adjust torque, rotation speed, and angle in real time based on rock hardness. This reduces equipment wear and improves penetration accuracy. Sensor arrays feed live data into machine-learning models that classify rock types instantly.

Also consider this: on-site AI analyzers evaluate ore quality without waiting for off-site labs. These systems can identify gold or silver grade, platinum-group concentrations, and the presence of impurities. This allows miners to instantly determine what should go to processing—improving profitability.

Autonomous Mining Equipment

These systems reduce accidents, operate around the clock, and enhance fuel efficiency. Major miners now use:

  • Self-driving haul trucks
  • Autonomous blast-hole drill rigs
  • AI-guided loaders
  • Smart conveyor systems

AI in Processing: Higher Recovery, Lower Costs, Smaller Footprint

Machine-Learning Process Control

AI continuously fine-tunes flotation chemistry, water usage, mill speed, and smelter temperatures. These optimizations typically increase recovery rates by 2–5%, generating millions in added annual revenue for mid-size precious-metal operations.

But that’s not all. AI can also predict energy demand and reduce consumption by 10–20%. Given that extraction consumes large quantities of energy, this is a meaningful efficiency gain. One more point, AI models also excel at identifying structural risks in tailings dams, detect seepage patterns, and monitor environmental indicators such as water quality and dust emissions.

4. Safety and Environmental Protection

Predictive Maintenance

AI forecasts equipment failures before they happen, preventing downtime and reducing catastrophic failures. Computer-vision systems identify workers near unsafe machinery, detect non-compliance with safety gear, and monitor underground instability.

Let’s not overlook the important improvements in environmental monitoring, including water contamination, air quality, noise levels, and habitat protection.

5. Sustainability: AI as the Engine of “Green Mining”

Perhaps at the top of the chart is precision mining. With AI, removing less rock, using fewer chemicals, reducing diesel combustion, and optimizing water recycling are all achievable. This is becoming a core requirement as governments and investors demand cleaner resource extraction.

6. Blockchain + AI: Transparent Precious-Metal Supply Chains

Now the blockchain part—and as fans of blockchain, I’m sure you already know that AI plus blockchain equals a tamper-proof digital record of:

  • Mine origin
  • Ore transport
  • Refinery steps
  • ESG compliance
  • Responsible-sourcing certification

Smart Refining Contracts

Of course, the blockchain benefits don’t end there. They also include automated payments via smart contracts when AI-verified ore quality reaches contractual thresholds. And AI can scan blockchain records for fraudulent patterns. That’s gold-medal-winner level!

7. Limitations and Challenges

Of course, nothing is perfect. That’s why, when using AI in precious-metal exploration and extraction, users should be aware that large amounts of data are required to train the models, integration of sensors and AI equipment is costly, models need constant monitoring, and staff training needs its own budget line item.

Conclusion

The coronation of the new queen of precious metal discovery and extraction is complete. Wearing her artificial intelligence corona, she is reshaping precious-metal exploration and mining into a faster, safer, and more environmentally responsible industry. From her throne, her message is: as AI continues to merge with blockchain, sensor networks, and robotics, the mining sector is entering a historic transformation where data—not geology alone—will define future success.

Until next time,

Yogi Nelson

Sources & Citations

  • McKinsey & Company – “The Role of Artificial Intelligence in Mining.”
  • IBM Research – AI for Geoscience and Remote Sensing
  • Deloitte – Tracking the Trends: The Top 10 Issues Transforming the Mining Industry
  • Accenture – AI and Digital Twins in Mining
  • Rio Tinto – Autonomous Mining Operations Reports
  • World Gold Council – Responsible Gold Mining Principles
  • Journal of Mining Science – Machine Learning Applications in Ore-Grade Prediction
  • U.S. Geological Survey (USGS) – Mineral Resources and Remote-Sensing Studies
  • MIT CSAIL – AI for Environmental Monitoring & Industrial Optimization
Artificial Intelligence, Blockchains, cryptography, Maritime, Uncategorized, Urban Planning, Yogi Nelson

Navigating the Future: AI’s Role in Maritime Innovation

Yogi Nelson

Welcome to the Blockchain & AI Forum, where your technology questions are answered. Today’s question: how will artificial intelligence impact the maritime industry?

Trade and tariffs are headlines across the globe.  Trade and tariff share a common feature—shipping. Hence, now is a perfect time to examine AI trends within the maritime industry. 

According to Spire Maritime, a firm dedicated to providing a wide range of services to the industry, maritime companies linger in Ai adoption. Spire Maritime claims the maritime industry is now moving fast, hopefully faster than a cargo ship, in incorporating AI, Large Language Models, and Natural Language Processing (NLP).  In a very recent report, Thetius, a well-known maritime consultancy, says the maritime AI market is valued at $4.1B.  The good news is $4.1B represents a substantial increase from just $1.4B two years ago.  Moreover, 36 shipping companies have implemented or plan to deploy AI-enabled tech this year. 

Let’s briefly examine possible AI use cases in the maritime industry.   

Automation.  For example, in the future could AI lead to autonomous navigation?  The labor savings could be significant.  Or perhaps more immediately, AI could be used to automate mundane tasks such as detection of vessel names, contact information, shipowner, and technical specifications.  With AI maintenance and repairs and anticipated well in advance thus reducing vessel down time. 

Replacement of Manual Inputs with Data-Driven Predictive Information.  Spire Maritime points to the extensive reliance of human, often error-prone inputs as pervasive in maritime.  Rather than manual inputs, AI could be used to determine route optimization based on weather, fuel consumption, speed, and safety considerations.  The fuel savings alone could be significant not to mention the safety of crews nor the favorable impact on the environment. 

Detection of Patterns or Anomalies Using Data.  The maritime industry generate billions of data points.  Consider for a moment the millions of containers processed at a major port, such as the Port of Los Angeles, where I worked for three years.  Now multiply that by the hundreds of ports around world.  The data is overwhelming.  Making sense of the data and turning it into actionable information is the power of AI. 

These are all great use case possibilities, so why the delay or hesitation?  The answer boils down to several considerations:

  1. Data Quality and Accessibility.  As in many industries, the maritime industry faces issues regarding inconsistent or incomplete data.
  2. Integration with Existing Systems.  The maritime industry is ancient and as are its legacy technologies.  Integrating the new without disruption is a daunting task.
  3. Data Standardization.  Worldwide industry are often replete with inconsistent standards, maritime is no exception.
  4. Industry-wide Collaboration.  Can you image the collaboration required to effectively implement AI in the maritime industry; an industry with dozens of stakeholders, including shipping companies, port authorities, tech providers, etc. 
  5. Trust.  Can the technology be trusted with safety decisions, automation, etc. 

Time to go, but first a proverb from a nation with an important maritime industry; the island nation of Malta where they say:

Until next time,

Yogi Nelson

https://spire.com/maritime/maritime-artificial-intelligence-and-machine-learning/#:~:text=What%20is%20the%20role%20of%20AI%20in%20fleet%20management%3F,costs%2C%20and%20maximizes%20fleet%20performance.

https://www.lr.org/en/knowledge/research-reports/2024/beyond-the-horizon/

https://www.mitags.org/ai-impact-maritime-industry/

https://www.adv-polymer.com/blog/artificial-intelligence-in-shipping

https://cmr.berkeley.edu/2024/12/utilizing-ai-for-maritime-transport-optimization/

AI Agents, Artificial Intelligence, Blockchains, Environment, Productivity, Railwlays, Uncategorized, Urban Planning, Yogi Nelson

Navigating AI Disruption in the Railway Sector

Yogi Nelson

Welcome to the Blockchain & AI Forum, where your technology questions are answered.   Harold asks, how will artificial intelligence affect the railway industry?

Harold, you came to right place.  I’ll answer your question by citing research reports from McKinsey & Company, International Union of Railways (UIC), and Startus Insights.  Let’s start with the McKinsey report, titled: The Journey Toward AI-Enabled Railway Companies.

Prominent AI Use Cases in the Railway Industry:  McKinsey Report Findings

According to McKinsey, railway companies are focusing their AI efforts on approximately 20 use cases.  It should be noted, the McKinsey identified over 100 potential use cases!  The top 20 use cases are related to four key performance indicators:  on-time performance, customer engagement, safety, and operational performance.  The table below summarizes the use cases by category:

Table 1
ARTIFICIAL INTELLIGENCE USE CASES IN THE RAILWAY INDUSTRY
Business ActivityDescriptionAI Use Case
Railway UndertakingsActivities that are directly & indirectly related to delivering the final productCrew trainingSafetyOnboard operationsRolling stock maintenanceTrain station operationsRolling stock procurement & inventory management
Infrastructure ManagementActivities that are directly & indirectly related to the infrastructure needed to deliver the final railway productNetwork planningNetwork traffic managementNetwork constructionNetwork maintenance Rail safetyNetwork procurement and inventory management
Passenger ExperienceCustomer facing activities related to the operations of the railway companyBooking and ticketingCustomer railway journeyPassenger informationIn-station additional revenue  
Support FunctionsEssential non-operational activitiesHRFinanceIT

Of course, not all use cases have equal priority.  In the table below, the five most common use cases are listed on the left, and the five least used on the right.  The other 10 are in the middle.

Table 2 ATable 2B
Top 5 AI Use Case in Railway IndustryBottom 5 AI Use Cases in Railway Industry
Crew and shift managementNetwork infrastructure digital twin
Rail predictive maintenanceReal time disruption management
Real time intermodal informationTalent training
Energy efficient managementAutonomous trains
Security fraudSoftware development

The Keys to Successful Incorporation of AI in the Railway Industry:  UIC Report

We now turn to the findings by the Union of International Railways; UIC per their French acronym.  The UIC researchers claims that the railways with the most successful AI abilities share certain characteristics. For example, these railway companies dedicate research and development teams to their AI efforts.  Furthermore, they establish cultures of innovation and investment in partnerships to develop new technology.  Yet a third reason is that they build capabilities to implement use cases; and take a business-driven approach (rather than solely relying on IT departments) to drive development.  UIC researches say, there are six specific keys to success: strategic roadmap, skills, agile operating model, technology, data, and adoption and scaling. 

Who Are the AI Disrupters in the Railway Industry:  StartUs Insights Report

According to StartUs Insights, a consultancy specializes in discovery of intelligence trends, innovation, and engagement with the world’s most promising startups, scaleups, and technologies, there are 20 AI startups impacting the railway industry.  I’ll refrain from listing them all, and instead highlight three that best exemplify the AI story in railways. 

Let’s start with rail safety and the startup called RMT.  RMT is based in Italy.  RMT specializes in automatic recognition of defects on rail infrastructure, and employing AI to detect physical defects.  Energy efficiency is the wheelhouse of RailVision Analytics, a Canadian startup dedicated to improving energy efficiency via algorithms that reduce unnecessary braking, throttles, and train handling adjustments.  Last, there is Cervello.  Cervello is an Israeli startup. Cervello says it provides railways with “… comprehensive security solutions and in-depth visibility of the operational network and infrastructure.” With Cervello misleading commands are identified.  Moreover, Cervello produces a contextual view of the affected assets and operational impact.

Time to go but not before ending with this famous lyric from the O’Jays:  …” People from all over the world join hands start a love train, love train …”

Until next time,

Yogi Nelson