AI Agents, AI Tools, Artificial Intelligence, content creation, Healtlh, Patents, Productivity, Science, Shoes, Yogi Nelson

From Nancy (Sinatra) to Neutral Networks: These AI Boots Were Made for Walking

AI Tools, Artificial Intelligence, Blockchains, computer vision, Fine Art, Patents, tokenization, Yogi Nelson

Blockchain and the Fine Arts: A New Canvas of Possibilities

🎨 Introduction

🧩 Provenance & Authenticity: The Foundation

⚖️ Tokenization & Fractional Ownership

🛒 New Marketplaces & NFTs

✅ Benefits: Transparency, Trust & Efficiency

⚠️ Challenges & Risks

🌍 Case Studies: Platforms in Focus

🔍 Emerging Trends & Future Directions

🧭 Conclusion

Artificial Intelligence, Blockchains, cryptography, Decentralized, Patents, Science, Uncategorized, Yogi Nelson

Access to technology is a human right, not a copyright

  • Energy
  • Food Technology (air, water, soil)
  • Pollution (focus on clean-up)
  • Quantum Research
  • Top 30 Most Active Benefactor Wallets: 2x
  • Scientists who have contributed research: 1.5x
  • Scientists who have been game show finalists: 2x
  • Scientists who have won the game show: 3x
  • New scientists this season: 1.4x
  • New community members this season: 1.2x
  • Scientists with more than 1 year of participation: 1.1x
  • Community members with longevity: 1.0x
  • Admin votes: 1.0x
  • Wallets with transactions from to banned/suspended/muted users: 0.5x for the amount sent to them.
  • The first phase of a season qualifies proposals from scientists or requests from the community.
  • The second phase of a season announces approved proposals from Phase 1. Preliminary funding is requested by the scientist and given approval or adjustment by the judges handling this season. This funding is intended to give a scientist support for a Proof-of-Concept or Minimum Viable Product.
  • The third phase votes on which scientists will be funded to finish solving the problem. Not every team will be ready at the same time, and may delay their participation into future seasons whenever they are ready, without further qualification.
  • General Community
  • Scientists
  • Donors
  • Admins

General Community users can earn platform tokens for:

  • Watching videos
  • Liking videos
  • Commenting
  • Hitting milestones in discussion forums and on-site time
  • Consistent voting during live shows

Scientists can earn platform tokens for:

  • Uploading videos
  • Uploading documentation
  • Participating in peer review discussions
  • Being selected to participate in the game show (as contender or judge)
  • Advancing to the 2nd or 3rd round in the game show
  • Successfully voting out scams/fake content
  • General Community actions

Benefactors can earn platform tokens for:

  • Making contributions to donation pools
  • General Community actions

Admins can earn platform tokens for:

  • Removing spam/fake content
  • Being voted in as a game show judge
  • More General Community actions to be determined at a later date
  • ​Recerca​ – fundraising tool for research. They do many things very well, including winning 2nd prize at the Hedera X Filecoin Grant Program. The shortcomings Recera suffers is insufficient decentralization by design. Moreover, the Recera project does not feature tax incentives and they failed to solve the headaches of needlessly lengthy, dull and monotonous funding applications. Council still acts as gatekeepers to donation.
  • ​Experiment.com​ – fundraising tool for research. Donors can browse research proposals and causes, and donate in accordance with their concerns. This project resembles a kickstarter marketplace design. The project does not adequately solve centralization issues, nor application issues, nor is it built on web3 technology that can operate independently. Furthermore, there are no associated tax incentives.
  • ​Molecule​ – Similar to Experiment.com, but focused only on BioMed research. Raised a $13M seed.

Artificial Intelligence, Blockchains, Patents

How are DARPA, Explainable Artificial Intelligence and Nvidia Connected? 

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, comes from Susan in Irvine and she ask how are DARPA, Explainable Artificial Intelligence (XAI) and Nvidia all connected?

Susan, you came to the right place.  As a wealth manager I know you have a fiduciary duty to maintain current on investable technology on behalf of your clients.  Around 1990 a new technology was on the horizon–the internet–and it created trillions in new wealth!  Artificial intelligence (AI) is on a similar trajectory.  However, AI is not well understood–yet.  Let’s take a moment to acquaint ourselves with AI from the perspective of DARPA. In hindsight, DARPAs XAI program could have been used to foresee a tremendous investment opportunity—Nvidia the advanced computer chip maker essential to the AI business.

DARPA is an acronym that stands for Defense Advanced Research Projects Agency.  DARPA is part of the Department of Defense (DoD) and its mission is development of emerging technologies for military use.  DARPA created the internet in the late 1960s to facilitate control and command of military communications.  Under DARPA leadership, the US military maintains technological superiority over all other nations. 

In 2015 DARPA released an article titled, Explainable Artificial Intelligence (XAI).  https://www.researchgate.net/publication/356781652_DARPA_’s_explainable_AI_XAI_program_A_retrospective     Remember the XAI project was undertaken in 2015–years before the public had access to ChatGPT and other similar products.   Let’s examine the research findings.

According to DARPA in 2015, “…dramatic success in machine learning will lead to numerous AI applications.  It appears AI will eventually produce autonomous systems that will perceive, learn, decide, and act on their own”, DARPA predicted.  However, DARPA was concerned the effectiveness of AI systems would be stymied unless and until machines can creditably explain their decisions and actions to human users. Holy transparency, Batman! Therefore, the XAI program was intended to create a suite of machine learning techniques that would:

  • Produce more explainable models, while maintaining a high level of learning performance (prediction accuracy); and
  • Enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

XAI program was focused on the development of multiple systems by addressing challenges in two areas: 

  • machine learning problems to classify events based on multimedia data  
  • machine learning problems to construct decision policies for an autonomous system to perform a variety of simulated missions

DARPA selected these two areas because both represented major operational challenges.  The first relates to classification and reinforcement learning and the second is centered on intelligence analysis and autonomous systems.  In other words, DARPA was working on building AI machines that understand the context and environment in which they operate, and over time allow them to characterize real world phenomena precisely and in real time.  Basically, DAPRA was developing AI-Gen 2 before the public had access to AI-Gen 1! Holy Explainable AI, Batman! 

DARPA released XAI seven public findings of the XAI program and kept an unknown number of classified secret findings! LOL. Holy keep it on the down-low, Batman! LOL!

  1. Users prefer systems that include explanations rather than just answers (no surprise)
  2. To improve end results the task must be sufficiently difficult that the XAI explanation helps (makes sense)
  3. User cognitive load to interpret explanations can hinder user performance (in other words there are times when too much explanation is a negative)
  4. XAI is more helpful in edge cases
  5. Explanation effectiveness can change over time
  6. XAI combined with an advisor helps (no kidding, LOL)
  7. XAI is effective for aligning mental models

What’s are the two takeaways?  Obviously, DARPA did not leak military secrets on the internet and only disclosed the minimum. However, DARPA did leave an interesting information trail in 2015 that had we followed it, could have been a monster opportunity.  The XAI 2015 article was a glimpse into the future and we might have forecasted the rise of Nvidia.  Holy unicorn, Batman!  Nvidia stock has since soared, and we could have, should have…

I end with a Russian proverb: trust but verify!

Until next time,

Yogi Nelson

Artificial Intelligence, computer vision, Patents

IS SMOKEY BEAR USING ARTIFICIAL INTELLIGENCE TO FIGHT FOREST FIRES?

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your technology questions are answered. As a bonus, a proverb is also included!  Today’s question, submitted by Mike, a former Fire Chief, and he asks if Smokey Bear is using artificial intelligence to fight forest fires?

Mike, you came to the right place.  Let’s converse about how AI protects the forest using California as an example, but first we start with a definition.  According to CAL FIRE, California Fire Fighting Agency, wildfires are uncontrolled fires that rapidly spread through vegetation, such as forest, grasslands, etc. https://www.fire.ca.gov/  CAL FIRE notes wildfires can be natural occurrences or resulting from human activity.  Holy burning bush, Batman!

Wildfires in California are an enormous problem.  Between 2019 – 2022, wildfires caused more than $25B in property damage, millions of acres of forest were burned, thousands of homes were destroyed and hundreds of deaths!  Moreover, wildfires are increasing in numbers and intensity.  Bottom line—it’s an enormous problem. 

Firefighting is a mammoth human coordination challenge.  AI is, in part, a human coordination technology.  California created the Alert California Initiative to coordinate the wildfire challenge.  The initiative has numerous partners.  The purpose is to use AI technology to make data driven decisions regarding wildfires. 

Alert California had a predecessor–Cameras on Mountain Tops (CMT) program.  Program administrators wanted to fix the shortcoming of CMT.  What was the shortcoming?  Essentially, too much data!  CMT system covered 90% of high fire risk areas, as defined by CAL FIRE. The deluge of data generated from the 1,000+ cameras spread throughout the state generated 8 – 16M images per day!  CAL FIRE could not manage the data in time to take effective action.  A human coordination problem.  The solution?   Add artificial intelligence service partner, DigitalPath!

DigitalPath started by implementing computer vision to help humans deal with massive data sets.  Just imagine, cameras were picking up 100,000 smoke images per day.  California operations centers were inundated with 7K – 10K fire alert emails/text/voicemail per day.  The goal was one alert per fire.  Holy efficiency, Batman!

Using a combination of AI computer vision and algorithms, Alert California has dramatically augmented its ability to determine whether an image is relevant, timely, duplicative, etc. and to classify the importance. With AI, firefighters work more effectively, by for example, positioning equipment in ideal locations and of course, under safer conditions!  Here is how it works:

When the AI spots a potential fire on the Alert California network of cameras, the AI system alerts firefighters and provides a percentage of certainty and estimates the incident location.  If the incident is confirmed by trained personnel, firefighters respond. AI is also used to estimate what resources are required, location, and quantity.  Moreover, the AI is also deployed to map out evacuations!  Smokey Bear is ecstatic, Batman!

Ironically extinguishing fires early today means more fuel for blazing infernos tomorrow.  Think of it as a dam that burst at maximum capacity!  However, on the horizon there are positive developments.  For example, with computer vision 40% of all wildfires are being detected by the AI system prior to 911 alerts, and the number is increasing.  With AI, controlled fires become more manageable.  AI is also improving firefighters’ ability to identify where fires are likely to occur and/or when they are occurring.  Simulations are working faster thus increasing the reliability of predictive models to give warnings.  In other words, there is reason for optimism.

I conclude with an Estonian proverb: The stomach never gets full by only licking.

Until next time,

Yogi Nelson