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Symbolic Artificial General Intelligence(AGI)

Symbolic AGI: A Journey into Understanding Artificial Intelligence


What is Symbolic AGI?


Symbolic Artificial General Intelligence(AGI) is a type of artificial intelligence that relies on symbolic representations of knowledge and reasoning. 

Symbolic representations use symbols and rules to represent knowledge, and reasoning is performed by manipulating these symbols according to the rules.

Symbolic AGI has a long and rich history, dating back to the early days of AI research. It has achieved significant successes in a variety of domains, including chess playing, natural language processing, and robotics.

Symbolic Artificial General Intelligence(AGI)

History of Symbolic Artificial General Intelligence(AGI)

Tracing the history of Symbolic AGI reveals a fascinating journey of evolving ideas and relentless pursuit of artificial intelligence resembling human-like intelligence. Here are some key milestones:

Early Seeds (1950s-1960s):

  • The birth of AI: The formal field of AI emerges in the 1950s, with pioneers like Alan Turing and John McCarthy laying the groundwork for symbolic approaches.
  • Logics and rules: Formal logic systems like propositional logic and first-order logic become the foundation for knowledge representation and reasoning.
  • Expert systems: Early applications emerge in medicine, finance, and other domains, using rule-based systems to mimic the expertise of human specialists.

Golden Age (1970s-1980s):

  • Rise of knowledge representation: Languages like Lisp and Prolog are developed specifically for manipulating symbolic knowledge.
  • Planning and problem-solving: AI systems designed for chess playing and robotic control showcase the strengths of symbolic reasoning for planning and action selection.
  • Knowledge-based systems: Cyc, a massive knowledge base of common-sense reasoning and facts, is initiated, aiming to capture the breadth of human knowledge in symbolic form.

Challenges and Diversification (1990s-2000s):

  • The “AI winter”: Funding and enthusiasm for symbolic AI decline as limitations like brittleness and slow learning become apparent.
  • Rise of machine learning: Neural networks and statistical approaches gain popularity for their data-driven learning capabilities.
  • Hybrid approaches: Researchers begin exploring ways to combine symbolic reasoning with machine learning for greater robustness and adaptability.

Renewed Interest and Exploration (2010s-Present):

  • Symbolic reasoning for deep learning: Projects like neural theorem provers and neuro-symbolic systems aim to integrate symbolic logic into deep learning frameworks.
  • Focus on explainability and transparency: Concerns about “black box” AI models call for symbolic approaches that offer interpretable reasoning processes.
  • Rise of embodied AI: The need for intelligent robots interacting with the real world rekindles interest in symbolic reasoning for embodied cognition and planning.

The journey of Symbolic AGI is far from over. While true human-level intelligence remains elusive, the constant evolution of technology and research keeps the dream alive. The future holds the potential for breakthroughs in hybrid approaches, explainable AI, and embodied intelligence, paving the way for a future where humans and machines collaborate with symbolic and neural capabilities.

The history of Symbolic AGI is a testament to human ingenuity and perseverance. Through continued research, collaboration, and exploration, we can harness the power of symbolic reasoning and other approaches to build a future where AI empowers and benefits humanity.

Symbolic Artificial General Intelligence(AGI)

Key functions of Symbolic Artificial General Intelligence(AGI)

Here are the key functions of Symbolic AGI, illustrated with visual examples:

1. Reasoning and Inference:

  • Draw conclusions from incomplete or uncertain information.
  • Combine multiple pieces of knowledge to reach new understandings.
  • Solve problems logically and systematically.

2. Planning and Problem-Solving:

  • Set goals and develop strategies to achieve them.
  • Break down complex tasks into manageable steps.
  • Anticipate potential obstacles and devise solutions.

3. Learning and Adaptation:

  • Acquire new knowledge and skills from experience or instruction.
  • Update its knowledge base and reasoning rules based on new information.
  • Adjust its behavior to adapt to changing circumstances.

4. Natural Language Understanding and Generation:

  • Comprehend human language in all its nuances and complexities.
  • Engage in meaningful conversations with humans.
  • Generate fluent and coherent text and speech.

5. Knowledge Representation and Reasoning:

  • Store and organize knowledge in a structured and accessible way.
  • Manipulate knowledge using symbolic operations to draw inferences and make decisions.
  • Utilize knowledge to solve problems and generate new ideas.

6. Contextual Understanding and Adaptability:

  • Grasp the context of a situation, including relevant background information and social cues.
  • Apply knowledge and reasoning in a context-sensitive manner.
  • Adapt its behavior to different situations and social norms.

7. Creativity and Innovation:

  • Generate novel ideas and solutions.
  • Imagine new possibilities and explore alternative pathways.
  • Engage in creative activities like art, music, and literature.

8. Metacognition and Self-Awareness:

  • Reflect on its own thought processes and capabilities.
  • Monitor its own performance and identify areas for improvement.
  • Develop a sense of self and its place in the world.
Symbolic Artificial General Intelligence(AGI)

Symbolic Artificial General Intelligence(AGI): Challenge and Impact

Symbolic AGI has also faced challenges. It can be brittle and difficult to scale, and it can be slow to learn from new data.

In recent years, there has been renewed interest in symbolic AGI. This is due to a number of factors, including the following:

  • The limitations of machine learning approaches, such as the “black box” problem and the difficulty of generalizing to new situations.
  • The need for explainability and transparency in AI systems.
  • The rise of embodied AI, which requires symbolic reasoning for planning and decision-making in the real world.

The future of symbolic AGI is uncertain. However, there is potential for this approach to play a significant role in the development of artificial general intelligence (AGI).

Here are some specific areas where symbolic AGI could make a significant impact:

  • Explainable AI: Symbolic approaches offer interpretable reasoning processes, which can be essential for building trust and transparency in AI systems.
  • Embodied AI: Symbolic reasoning is essential for planning and decision-making in the real world, which is a key challenge for embodied AI.
  • Hybrid approaches: Combining symbolic reasoning with machine learning can lead to systems that are more robust, adaptable, and efficient.

By continuing to research and develop symbolic AGI, we can build systems that are more intelligent, capable, and beneficial to humanity.

Here are some of the key concepts and terms associated with symbolic AGI:

  • Logical atoms: The basic building blocks of symbolic representations, like objects, properties, and relations.
  • Production rules: Conditional statements used for reasoning, mapping states to actions or conclusions.
  • Inference engine: Software system that manipulates symbols and rules to draw logical conclusions.
  • Model-based reasoning: Simulating situations and scenarios in the world to guide decision-making.
  • Situation Calculus: A formal language for representing actions, their effects, and the resulting world states.
  • Planning and scheduling: Generating sequences of actions to achieve goals within constraints.
  • Natural language understanding: Interpreting the meaning and intent behind human language.
  • Natural language generation: Producing fluent and context-aware text in response to stimuli.
  • Propositional logic and First-Order Logic: Formal systems for representing and reasoning about logical relationships.
  • Reasoning agents: Autonomous entities that make decisions and act based on their knowledge and goals.
  • Bayesian Networks: Probabilistic models representing relationships between variables and their uncertainties.
  • Common-Sense Reasoning: Applying intuitive knowledge about the world for efficient understanding and decision-making.
  • Non-Monotonic Reasoning: Handling situations where new information may invalidate previous conclusions.
  • Embodied Cognition: The interaction between an AI system’s mental processes and its physical body.
  • Sensorimotor Control: Coordinating sensors and actuators to interact with the physical environment.
  • Multi-Agent Systems: Systems composed of multiple interacting agents, simulating social and collaborative scenarios.
  • Reinforcement Learning: Learning through trial and error, receiving rewards for successful actions.
  • Explainable AI (XAI): Making AI decisions and reasoning processes transparent and understandable to humans.
  • Moral and Ethical Considerations: Addressing the ethical implications of developing and deploying AGI systems.
  • Human-AI Interaction (HAI): Designing how humans and AI systems can interact effectively and safely.
Symbolic Artificial General Intelligence(AGI)

Type of Symbolic Artificial General Intelligence(AGI)

While Symbolic AGI remains a theoretical future for artificial intelligence, within it exists a fascinating diversity of potential approaches. 

Here are some key types of Symbolic AGI:

1. Logic-based AGI: This approach is centered around formal logic systems like propositional logic and first-order logic. Knowledge is represented using logical formulas, and reasoning happens through manipulating these formulas according to established rules of inference. Examples include theorem provers and expert systems relying on logic rules.

2. Model-based AGI: This type focuses on building internal models of the world, including objects, their properties, and relationships between them. Reasoning involves manipulating and simulating these models to predict possible outcomes or make decisions. This aligns with approaches like situation calculus and belief networks.

3. Language-based AGI: This emphasizes natural language as the primary tool for knowledge representation and reasoning. Sentences and their interrelationships form the knowledge base, and reasoning uses natural language inferences and semantic rules to navigate and understand the world. This draws inspiration from projects like Cyc and WordNet.

4. Hybrid AGI: Recognizing the strengths and limitations of each approach, hybrid AGI seeks to combine them. For example, logic might be used for high-level reasoning, while neural networks handle sensory perception and low-level learning. This approach is still in its early stages but holds great promise for achieving true AGI.

5. Embodied AGI: Beyond pure reasoning, this type emphasizes the importance of embodiment for realizing AGI. An embodied AGI would interact with the world through a physical body, using its senses and motor skills to gather information and act on its conclusions. This adds a crucial layer of grounding and interaction to the reasoning process.

Symbolic Artificial General Intelligence(AGI)

Specific Research into Symbolic Artificial General Ìntelligence(AGI)

While research into Symbolic AGI is widespread, finding projects explicitly labelled as “Symbolic AGI” is rare. This is because the field is still undergoing rapid development and terminology hasn’t fully solidified. 

However, several ongoing projects embody the principles of Symbolic AGI and its various approaches:

1. DeepMind and Neural Theorem Provers: DeepMind, known for its work in Go and StarCraft AI, is exploring the integration of neural networks and symbolic reasoning, particularly through neural theorem provers. These projects aim to train neural networks to manipulate logical formulas effectively, potentially accelerating mathematical and scientific discovery.

2. Project Cogito: This initiative by IBM Research focuses on building a cognitive architecture inspired by human brain structures. It uses a knowledge base represented in multiple formats, including symbols, and employs reasoning mechanisms informed by logic and cognitive psychology.

3. Cyc and OpenCyc: Cyc is a massive knowledge base developed by Doug Lenat, encoding common-sense knowledge and reasoning rules using symbols and logic. OpenCyc is a publicly available version of this project, encouraging researchers to add knowledge and explore its potential for various AI applications.

4. Soar: This cognitive architecture developed by John Laird combines symbolic reasoning with production rules and decision-making capabilities. Soar has been applied to various domains, including robot control, game playing, and medical diagnosis, demonstrating its versatility in symbolic AI tasks.

5. COMET: This project from SRI International focuses on building a common-sense reasoning system based on logical representations and probabilistic inference. COMET aims to develop robust reasoning capabilities for robots and other AI systems operating in complex, dynamic environments.

Symbolic Artificial General Intelligence(AGI)

Symbolic Artificial General Intelligence(AGI) Projects: A Glimpse into the Future of AI

While achieving true Symbolic AGI remains a fascinating yet distant goal, several exciting projects are actively exploring its potential and laying the groundwork for future breakthroughs. 

Here are a few noteworthy examples:

1. DeepMind and Neural Theorem Provers: Imagine AI that seamlessly combines the pattern recognition of neural networks with the logic and deduction of symbolic reasoning. DeepMind’s research in neural theorem provers aims to do just that. By training neural networks on vast datasets of mathematical proofs, they hope to accelerate theorem proving and unlock new discoveries in science and mathematics.

2. Project Cogito from IBM Research: Inspired by the human brain’s structure and function, Project Cogito builds a cognitive architecture using a multi-format knowledge base and diverse reasoning mechanisms. This allows for flexible handling of information, from symbols and logic rules to visual and sensor data, offering a promising pathway towards robust AI capable of interacting with the real world.

3. Cyc and OpenCyc: This vast knowledge base, developed by Doug Lenat, encodes common-sense knowledge and reasoning rules using symbols and logic. OpenCyc, its publicly available version, empowers researchers to contribute their own knowledge and explore its potential for various applications, from education and robotics to natural language processing.

4. Soar: A Cognitive Architecture with Teeth: This versatile system combines symbolic reasoning with production rules and decision-making capabilities. Soar has proven its mettle in diverse domains, from robot control and game playing to medical diagnosis, showcasing its potential for adaptable and intelligent AI systems.

5. COMET: Navigating the Uncertain Sea of Common Sense: This project from SRI International tackles the challenge of common-sense reasoning, crucial for real-world intelligence. COMET uses logic representations and probabilistic inference to build robust reasoning systems for robots and AI navigating dynamic and unpredictable environments.

Symbolic Artificial General Intelligence(AGI)

Institution focused on developing “The Symbolic Artificial General Intelligence(AGI)” 

There isn’t one single institution solely focused on developing “The Symbolic AGI.” Symbolic AGI is still a theoretical future for artificial intelligence, and research in this area is spread across diverse teams and institutions worldwide.

However, several research groups and institutions are actively contributing to research and development related to the different types and approaches of Symbolic AGI. 

Here are some notable examples:

1. DeepMind: As mentioned earlier, DeepMind is exploring the integration of neural networks and symbolic reasoning, particularly through neural theorem provers. They have achieved significant progress in areas like logical reasoning and mathematical problem-solving.

2. OpenAI: This research laboratory founded by Elon Musk and others is conducting research on various aspects of AI, including natural language processing, reinforcement learning, and robotics. While not explicitly focused on Symbolic AGI, their work on symbolic reasoning and knowledge representation contributes to the broader field.

3. Stanford University: The Stanford Artificial Intelligence Laboratory (SAIL) is home to numerous research groups working on different aspects of AI, including natural language processing, robotics, and machine learning. Some projects within SAIL, like COMET and Soar, directly contribute to research on symbolic reasoning and cognitive architectures.

4. Carnegie Mellon University: The Robotics Institute at Carnegie Mellon has a long history of research in AI and robotics, with projects exploring symbolic reasoning and knowledge representation for robot planning and decision-making.

5. International Joint Conference on Artificial Intelligence (IJCAI): While not an institution itself, IJCAI is a major conference and forum for AI research. It features diverse research on symbolic reasoning, knowledge representation, and other aspects relevant to Symbolic AGI, showcasing the breadth of ongoing work in this field.

These are just a few examples, and many other universities, research labs, and private companies are actively contributing to the field of Symbolic AGI. It’s important to note that research in this area is collaborative and open-source, with frequent exchange of ideas and knowledge between different institutions and researchers.

Therefore, instead of pinpointing a single institution solely responsible for developing The Symbolic AGI, it’s more accurate to see it as a collaborative effort across various research communities worldwide.

Symbolic Artificial General Intelligence(AGI)

Symbolic Artificial General Intelligence(AGI) Technology

Achieving Symbolic AGI is a complex puzzle with many pieces, and the technologies involved are diverse and constantly evolving. 

Here are some key technological pillars fueling the quest for human-like machine intelligence:

1. Knowledge Representation and Reasoning:

  • Symbolic languages: These languages, like first-order logic, encode knowledge using symbols and relationships, enabling formal logical manipulations for reasoning and inference.
  • Knowledge graphs: These interconnected web-like structures capture relationships between entities and concepts, offering a rich tapestry of knowledge for AI to navigate.
  • Reasoning engines: These software systems handle logical deductions and inferences, drawing conclusions from the structured knowledge base.

2. Machine Learning and Neural Networks:

  • Deep learning: These powerful algorithms excel at pattern recognition and data extraction, offering a valuable layer of understanding for raw sensory information and large datasets.
  • Neuro-symbolic systems: These hybrid approaches combine the strengths of neural networks and symbolic reasoning, allowing AI to learn from data while utilizing logical structures for efficient knowledge processing.
  • Probabilistic reasoning: Techniques like Bayesian inference offer ways to handle uncertainty and incomplete information, crucial for real-world decision-making.

3. Natural Language Processing:

  • Language understanding and generation: These technologies enable AI to comprehend human language nuances and generate fluent, context-aware communication, fostering natural interaction and knowledge sharing.
  • Dialogue systems: These AI systems engage in meaningful conversations, asking questions, clarifying ambiguities, and providing relevant information, paving the way for human-like interactions.
  • Semantic reasoning: Understanding the meaning behind words and sentences is crucial for AI to grasp the deeper intent and context of natural language communication.

4. Robotics and Embodiment:

  • Physical robots: Providing AI with a physical body opens doors to real-world interaction and experimentation. Sensory inputs and motor control capabilities allow AI to learn and adapt through embodied experiences.
  • Robotics control systems: These systems translate abstract reasoning and decisions into concrete actions for the robot to execute in the physical world.
  • Sensor fusion: Combining data from multiple sensors like cameras, lidar, and touch sensors provides a richer understanding of the surrounding environment for robust decision-making.

5. Hardware and Computing Power:

  • High-performance computing: Complex reasoning and knowledge manipulation require substantial computational resources. Advancements in hardware and software optimization are crucial for handling the demands of AGI.
  • Cloud computing and distributed systems: Sharing processing power across multiple machines allows for tackling larger and more complex tasks, accelerating the development and testing of AGI algorithms.
  • Neuromorphic computing: Inspired by the human brain’s architecture, these specialized hardware systems aim to improve efficiency and performance for artificial intelligence tasks.
Symbolic Artificial General Intelligence(AGI)

20 Terms in Symbolic Artificial General Intelligence(AGI)

  1. Logical Atoms: The basic building blocks of symbolic representations, like objects, properties, and relations.
  2. Production Rules: Conditional statements used for reasoning, mapping states to actions or conclusions.
  3. Inference Engine: Software system that manipulates symbols and rules to draw logical conclusions.
  4. Model-Based Reasoning: Simulating situations and scenarios in the world to guide decision-making.
  5. Situation Calculus: A formal language for representing actions, their effects, and the resulting world states.
  6. Planning and Scheduling: Generating sequences of actions to achieve goals within constraints.
  7. Natural Language Understanding: Interpreting the meaning and intent behind human language.
  8. Natural Language Generation: Producing fluent and context-aware text in response to stimuli.
  9. Propositional Logic and First-Order Logic: Formal systems for representing and reasoning about logical relationships.
  10. Reasoning Agents: Autonomous entities that make decisions and act based on their knowledge and goals.
  11. Bayesian Networks: Probabilistic models representing relationships between variables and their uncertainties.
  12. Common-Sense Reasoning: Applying intuitive knowledge about the world for efficient understanding and decision-making.
  13. Non-Monotonic Reasoning: Handling situations where new information may invalidate previous conclusions.
  14. Embodied Cognition: The interaction between an AI system’s mental processes and its physical body.
  15. Sensorimotor Control: Coordinating sensors and actuators to interact with the physical environment.
  16. Multi-Agent Systems: Systems composed of multiple interacting agents, simulating social and collaborative scenarios.
  17. Reinforcement Learning: Learning through trial and error, receiving rewards for successful actions.
  18. Explainable AI (XAI): Making AI decisions and reasoning processes transparent and understandable to humans.
  19. Moral and Ethical Considerations: Addressing the ethical implications of developing and deploying AGI systems.
  20. Human-AI Interaction (HAI): Designing how humans and AI systems can interact effectively and safely.
Symbolic Artificial General Intelligence(AGI)

The future of Symbolic Artificial General Intelligence(AGI)

The future of Symbolic AGI is shrouded in both excitement and uncertainty. While achieving true human-level intelligence remains a distant dream, the progress in recent years paints a promising picture for the years ahead. Here are some potential scenarios:

Optimistic Visions:

  • Breakthrough in Reasoning and Planning: New theoretical frameworks or computational architectures could unlock significant leaps in logical reasoning and planning capabilities, opening doors for AGI to tackle complex real-world problems.
  • Hybrid Approaches and Integration: Combining the strengths of symbolic reasoning with deep learning and other techniques could lead to robust and efficient AGI systems capable of both understanding and learning from the world.
  • Emergence of Artificial Creativity: AGI could surpass human limitations in certain domains, leading to advancements in scientific discovery, artistic expression, and technological innovation.
  • Enhanced Human-AI Collaboration: Seamless interaction and knowledge exchange between humans and AGI could revolutionize fields like healthcare, education, and governance.

Cautious Considerations:

  • Limited Understanding of Consciousness: Replicating the true essence of human consciousness may still be beyond our grasp, leading to AGI systems lacking genuine understanding and empathy.
  • Ethical and societal challenges: The vast capabilities of AGI necessitate careful consideration of ethical implications, bias in algorithms, and potential societal disruptions.
  • Control and Safety Concerns: Ensuring the safe and responsible development and deployment of AGI will be paramount, requiring robust security measures and regulations.

The Path Forward:

  • Continuous Research and Development: Continued investment in research, collaboration between diverse disciplines, and open exploration of new ideas are crucial for advancing the field.
  • Focus on Explainability and Transparency: Making AI decisions and reasoning processes transparent is essential for building trust and mitigating potential risks.
  • Public Discussion and Policy Development: Open dialogue about the potential impact of AGI on society and proactive policy development are vital for responsible implementation.

Ultimately, the future of Symbolic AGI depends on the choices we make today. By prioritizing safety, transparency, and responsible development, we can harness the potential of AGI to usher in a future of prosperity and collaboration for humanity.

Symbolic Artificial General Intelligence(AGI)

Conclusion for Symbolic Artificial General Intelligence(AGI)

Symbolic AGI is a type of artificial intelligence that relies on symbolic representations of knowledge and reasoning. 

Symbolic representations use symbols and rules to represent knowledge, and reasoning is performed by manipulating these symbols according to the rules.

Symbolic AGI has a long and rich history, dating back to the early days of AI research. It has achieved significant successes in a variety of domains, including chess playing, natural language processing, and robotics.

However, symbolic AGI has also faced challenges. It can be brittle and difficult to scale, and it can be slow to learn from new data.

In recent years, there has been renewed interest in symbolic AGI. This is due to a number of factors, including the following:

  • The limitations of machine learning approaches, such as the “black box” problem and the difficulty of generalizing to new situations.
  • The need for explainability and transparency in AI systems.
  • The rise of embodied AI, which requires symbolic reasoning for planning and decision-making in the real world.

The future of symbolic AGI is uncertain. However, there is potential for this approach to play a significant role in the development of artificial general intelligence (AGI).

Here are some specific areas where symbolic AGI could make a significant impact:

  • Explainable AI: Symbolic approaches offer interpretable reasoning processes, which can be essential for building trust and transparency in AI systems.
  • Embodied AI: Symbolic reasoning is essential for planning and decision-making in the real world, which is a key challenge for embodied AI.
  • Hybrid approaches: Combining symbolic reasoning with machine learning can lead to systems that are more robust, adaptable, and efficient.

By continuing to research and develop symbolic AGI, we can build systems that are more intelligent, capable, and beneficial to humanity.

https://www.exaputra.com/2024/01/symbolic-artificial-general.html

Renewable Energy

Sunrez Prepreg Cuts Blade Repairs to Minutes

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Weather Guard Lightning Tech

Sunrez Prepreg Cuts Blade Repairs to Minutes

Bret Tollgaard from Sunrez joins to discuss UV-curing prepreg that cuts blade repair time by up to 90% and has recently recieved OEM approval.

Sign up now for Uptime Tech News, our weekly newsletter on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard’s StrikeTape Wind Turbine LPS retrofit. Follow the show on YouTubeLinkedin and visit Weather Guard on the web. And subscribe to Rosemary’s “Engineering with Rosie” YouTube channel here. Have a question we can answer on the show? Email us!

Allen Hall: Brett, welcome back to the program. 

Bret Tollgaard: Thanks for having me again.

Allen Hall: So a lot’s happening at sunrise at the moment. Uh, there’s, uh, activity with sunrise materials on a lot of blades this year.

Over the last couple of years actually, ISPs, operators, OEMs, are realizing that UV curing is a huge advantage.

Bret Tollgaard: Turns out there’s a lot of value added, uh, to the entire process when utilizing UV cure, uh, pre-req.

Allen Hall: So the, the pre pres are, have been available for a couple of years. The qualification though was always the concern.

Has the OEM qualified this material? Are they gonna give you the blessing? Does this show up in the manual? If I call the OEM, are they gonna say they have talked to you guys? A lot of those hurdles have been cleared at this point.

Bret Tollgaard: Yeah, great question. And we are happy to announce that we have finally been approved by a large OEM for use on the epoxy blade for now all general kind of repairs.

We have several more OEMs that have already passed their phase one mechanical testing, and we’re iterating through now [00:01:00] their, uh, secondary and tertiary kind of tests. And so we do expect to be fully qualified by several OEMs before the end of the year, which should make the ISPs integration and utilization of our materials much, much easier.

Allen Hall: So the, the, the problem you’re solving is repairs in the field for the most part, or sometimes in the factory. Mm-hmm. But a lot of times in the field that those repairs. It happened quite a bit. They’re the same repair, the same area, the same kind of thing over and over and over again. And wetting out fabric on site takes time.

Particularly if you’re using standard materials, you have to bag it. You have to apply heat in some cases to get it to kick, and then you have to wait several hours for it to cure. So in the repair cycle time, most of your time is waiting.

Bret Tollgaard: It sure is. Uh, and on top of all that, we all know that there aren’t enough technicians in this industry to even do all the repairs, uh, that would like to be done.

Yeah. And so to really kind of streamline all of that, [00:02:00] uh, we’ve rolled out a couple of new things and we’ve had a lot more interest in some pre consolidated preki patches for customers. Uh, if a particular blade model has an issue that is a standardized kind of repair. We’re actually now building custom prepregs, or we will build the appropriate width length, stack it, consolidate it, uh, wrap it between our films.

So then all the customer has to do when they get on site is, uh, you know, do do the appropriate surface prep. Scarfing, apply a little bit of our UV surface primer to the backside of that patch. But now they can go up tower, single peel, stick, roll out, and then they’re cured.

Allen Hall: And that’s a. How many hours of saving is that?

It’s gotta be like six, 12 hours of saving, of, of

Bret Tollgaard: labor. It’s upwards of 80 to 90% of the labor that’s gonna actually need to be done to apply that. Otherwise, and then same thing too. We’ve had a couple instances where we took a several day repair down to one, to two to three hours. And these are multi-meter long repairs that were fast tracked because we pre consolidated preki [00:03:00] everything.

Some were in flat sheet forms, some were much longer on rolls, where you’re actually then rolling out with a team. Um, and so we’ve been able to demonstrate several times, uh, over the last 12 months, uh, the, the value that a UV cure preprint.

Allen Hall: Well, sure, because that, that would make sense. The issue about wetting out fabric in the field you just done in the back of a trailer or something, somewhere like that.

Usually it is, it’s that you’re never really sure that you got the fabric wetted out. The experienced technicians always feel like, have done it enough that they get very consistent results. But as you mentioned, getting technicians is hard and, and there’s so many repairs to do. So you’re doing those wetting out composite things takes practice and skill.

Just buying it, preki it, where you have control over it. And you guys sell to the military all the time. So that, and you’re, are you ass 91 qualified yet? You’re in the midst of that?

Bret Tollgaard: So we, I mean, a, we just got ISO certified, uh, at the end of last year in December. So our [00:04:00] QMS system and everything like that’s up to date, that’s huge.

Another big qualification for the OEMs that want to see, you know, true quality and output.

Allen Hall: That’s it. I, if I’m gonna buy a preki patch, so, uh, uh, that would make sense to me, knowing that. There’s a lot of rigor as a quality system. So when I get out the the site and I open that package, I know what’s inside of it every single time.

Bret Tollgaard: Well, and that’s just it. And like we got qualified based on the materials that we can provide and the testing that’s being done in real world situations when you’re wetting out by hand and you’re vacuum backing and you’re trying to cure. It is a little bit of an art form when you’re doing that. It is, and you might think you have a great laminate, you got void content, or you haven’t properly went out that glass ’cause humidity or the way the glass was stored or it was exposed.

The sizing and the resin don’t really bite. Well. You might think you have a great repair, but you might be prematurely failing as well after X cycles and fatigue. Uh, simply because it’s not as easy to, to truly do. Right? And so having the [00:05:00] pre-wet, uh, pre impregnated glass really goes a long way for the quality, uh, and the consistency from repair to repair.

Allen Hall: Well, even just the length of the season to do repairs is a huge issue. I, I know I’ve had some discussions this week about opening the season up a little bit, and some of the ISPs have said, Hey, we we’re pretty much working year round at this point. We’re, we’ll go to California. We’ll go to Southern Texas.

We’ll work those situations. ’cause the weather’s decent, but with the sunrise material, the temperature doesn’t matter.

Bret Tollgaard: Correct. And I was actually just speaking to someone maybe half hour ago who came by and was talking about repairs that they had to do in Vermont, uh, in December. They could only do two layers of an epoxy repair at a time because of the amount of the temperature.

Allen Hall: Yeah.

Bret Tollgaard: Whereas you could go through, apply a six or an eight layer pre-reg cure it in 20 minutes. Uh, you know, throughout that entire length that he had and you would’ve been done. That’s, and so it took several days to do a single repair that could have been done in sub one hour with our material.

Allen Hall: I know where those wind turbines are.

[00:06:00] They weren’t very far from, we used to live, so I understand that temperature, once you hit about November up in Vermont, it’s over for a lot of, uh, standard epoxy materials and cures, it is just not warm enough.

Bret Tollgaard: Yeah, we, we’ve literally had repairs done with our materials at negative 20 Fahrenheit. That were supposed to be temporary repairs.

They were installed four or five years ago. Uh, and they’re still active, perfectly done patches that haven’t needed to be replaced yet. So,

Allen Hall: so, because the magic ingredient is you’re adding UV to a, a chemistry where the UV kicks it off. Correct. Basically, so you’re, it’s not activated until it’s hit with uv.

You hit it with uv that starts a chemical process, but it doesn’t rely on external heat. To cure

Bret Tollgaard: exactly. It, it is a true single component system, whether it’s in the liquid pre preg, the thickened, uh, the thickened putties that we sell, or even the hand lamination and effusion resin. It’s doped with a, a variety of different food initiators and packages based on the type of light that’s [00:07:00] being, uh, used to, to cure it.

But it will truly stay dormant until it’s exposed to UV light. And so we’ve been able to formulate systems over the last 40 years of our company’s history that provide an incredibly long shelf life. Don’t prematurely gel, don’t prematurely, uh, you know, erode in the packaging, all those

Allen Hall: things.

Bret Tollgaard: Exactly.

Like we’ve been at this for a really long time. We’ve been able to do literally decades of r and d to develop out systems. Uh, and that’s why we’ve been able to come to this market with some materials that truly just haven’t been able to be seen, uh, delivered and installed and cured the way that we can do it.

Allen Hall: Well, I think that’s a huge thing, the, the shelf life.

Bret Tollgaard: Mm-hmm.

Allen Hall: You talk to a lot of. Operators, ISPs that buy materials that do have an expiration date or they gotta keep in a freezer and all those little handling things.

Bret Tollgaard: Yep.

Allen Hall: Sunrise gets rid of all of that. And because how many times have you heard of an is SP saying, oh, we had a throwaway material at the end of the season because it expired.

Bret Tollgaard: Oh, tremendously

Allen Hall: amount of, hundred of thousands of dollars of material, [00:08:00]

Bret Tollgaard: and I would probably even argue, say, millions of dollars over the course of the year gets, gets thrown out simply because of the expiration date. Um, we are so confident in our materials. Uh, and the distributors and stuff that we use, we can also recertify material now, most of the time it’s gonna get consumed within 12 months Sure.

Going into this kind of industry.

Allen Hall: Yeah.

Bret Tollgaard: Um, but there have been several times where we’ve actually had some of that material sent back to us. We’ll test and analyze it, make sure it’s curing the way it is, give it another six months shelf, uh, service life.

Allen Hall: Sure.

Bret Tollgaard: Um, and so you’re good to go on that front

Allen Hall: too.

Yeah. So if you make the spend to, to move to sun, you have time to use it.

Bret Tollgaard: Yes.

Allen Hall: So if it snows early or whatever’s going on at that site where you can’t get access anymore, you just wait till the spring comes and you’re still good with the same material. You don’t have to re-buy it.

Bret Tollgaard: Exactly. And with no special storage requirements, like you mentioned, no frozen oven or frozen freezer, excuse me, uh, or certain temperature windows that has to be stored in, uh, it allows the operators and the technicians, you know, a lot more latitude of how things actually get

Allen Hall: done.

And, and so if. When we [00:09:00] think about UV materials, the, the questions always pop up, like, how thick of a laminate can you do and still illuminate with the UV light? And make sure you curate I I, because you’re showing some samples here. These are,

Bret Tollgaard: yeah.

Allen Hall: Quarter inch or more,

Bret Tollgaard: correct. So

Allen Hall: thick samples. How did you cure these?

Bret Tollgaard: So that was cured with the lamp that we’ve got right here, which are standard issued light, sold a couple hundred into this space already. Um, that’s 10 layers of a thousand GSM unidirectional fiber. Whoa. This other one is, uh, 10 layers of, of a biox. 800 fiber.

Allen Hall: Okay.

Bret Tollgaard: Uh, those were cured in six minutes. So you can Six

Allen Hall: minutes.

Bret Tollgaard: Six minutes.

Allen Hall: What would it take to do this in a standard epoxy form?

Bret Tollgaard: Oh, hours,

Allen Hall: eight hours maybe?

Bret Tollgaard: Yeah. About for, for the, for the post cure required to get the TGS that they need in the wind space, right? Absolutely. And so yeah, we can do that in true minutes. And it’s pre impregnated. You simply cut it to shape and you’re ready to rock.

Allen Hall: And it looks great when you’re done, mean the, the surface finish is really good. I know sometimes with the epoxies, particularly if they get ’em wetted out, it doesn’t. It [00:10:00] doesn’t have that kind of like finished look to it.

Bret Tollgaard: Exactly. And the way that we provide, uh, for our standard, uh, you know, pre pprs are in between films and so if you cure with that film, you get a nice, clean, glossy surface tack free.

But as more and more people go to the pre consolidation method down tower, so even if they buy our standard prereg sheets or rolls, they’re preki down tower, you can also then just apply a pre, uh, a peel ply to that top film. Oh, sure. So if you wet out a peel ply and then you build your laminate over the top.

Put the primer and the black film over when they actually get that up on tower, they can then just remove that fuel ply and go straight to Sandy or uh, uh, painting and they’re ready to rock.

Allen Hall: Wow. Okay. That’s, that’s impressive. If you think about the thousands and thousands of hours you’ll save in a season.

Where you could be fixing another blade, but you’re just waiting for the res, the cure,

Bret Tollgaard: and that’s just it. When you’re saving the amount of labor and the amount of time, and it’s not just one technician, it’s their entire team that is saving that time. Sure. And can move on to the next [00:11:00] repair and the next process.

Allen Hall: So one of the questions I get asked all the time, like, okay, great, this UV material sounds like space, age stuff. It must cost a fortune. And the answer is no. It doesn’t cost a fortune. It’s very price competitive.

Bret Tollgaard: It, it really is. And it might be slightly more expensive cost per square foot versus you doing it with glass and resin, but you’re paying for that labor to wait for that thing to cure.

And so you’re still saving 20, 30, 40 plus percent per repair. When you can do it as quickly as we can do it.

Allen Hall: So for ISPs that are out doing blade repairs, you’re actually making more money.

Bret Tollgaard: You are making more money, you are saving more money. That same group and band of technicians you have are doing more repairs in a faster amount of time.

So as you are charging per repair, per blade, per turbine, whatever that might be, uh, you’re walking away with more money and you can still pass that on to the owner operators, uh, by getting their turbines up and spinning and making them more money.

Allen Hall: Right. And that’s what happens now. You see in today’s world, companies ISPs that are proposing [00:12:00] using UV materials versus standard resin systems, the standard residence systems are losing because how much extra time they’re, they’re paying for the technicians to be on site.

Bret Tollgaard: Correct.

Allen Hall: So the, the industry has to move if you wanna be. Competitive at all. As an ISP, you’re gonna have to move to UV materials. You better be calling suns

Bret Tollgaard: very quickly. Well, especially as this last winter has come through, the windows that you have before, bad weather comes in on any given day, ebbs and flows and changes.

But when you can get up, finish a repair, get it spinning, you might finish that work 2, 3, 4 later, uh, days later. But that turbine’s now been spinning for several days, generating money. Uh, and then you can come back up and paint and do whatever kind of cosmetic work over the top of that patch is required.

Allen Hall: So what are the extra tools I need to use Sunz in the kits. Do I need a light?

Bret Tollgaard: Not a whole lot. You’re gonna need yourself a light. Okay. You’re gonna need yourself a standard three to six inch, uh, bubble buster roller to actually compact and consolidate. Sure. Uh, that’s really all you need. There’s no vacuum lights.

And you sell the lights. We do, we, [00:13:00] we sell the lights. Um, our distributors also sell the lights, fiberglass and comp one. Uh, so they’re sourced and available, uh, okay. Domestically, but we sell worldwide too. And so, uh, we can handle you wherever you are in the world that you wanna start using uv, uh, materials.

And yeah, we have some standardized, uh, glass, but at the same time, we can pre-reg up to a 50 inch wide roll. Okay, so then it really becomes the limiting factor of how wide, how heavy, uh, of a lamette does a, a technician in the field want to handle?

Allen Hall: Yeah, sure. Okay. In terms of safety, with UV light, you’re gonna be wearing UV glasses,

Bret Tollgaard: some standard safety glasses that are tinted for UV protection.

So they’ll

Allen Hall: look yellow,

Bret Tollgaard: they’ll look a little yellow. They’ve got the shaded gray ones. Sunglasses, honestly do the same.

Allen Hall: Yeah.

Bret Tollgaard: But with a traditional PPE, the technicians would be wearing a tower anyways. Safety glasses, a pair of gloves. You’re good to go. If you’re doing confined space, work on the inside of a, a, a blade, uh, the biggest value now to this generation of material that are getting qualified.

No VOC non [00:14:00] flammable, uh, no haps. And so it’s a much safer material to actually use in those confined spaces as well as

Allen Hall: well ship

Bret Tollgaard: as well as ship it ships unregulated and so you can ship it. Next day air, which a lot of these customers always end. They do. I know that.

Allen Hall: Yeah.

Bret Tollgaard: Um, so next day air, uh, you know, there’s no extra hazmat or dangerous goods shipping for there.

Uh, and same thing with storage conditions. You don’t need a, a flammable cabinet to actually store the material in.

Allen Hall: Yeah.

Bret Tollgaard: Um, so it really opens you up for a lot more opportunities.

Allen Hall: I just solves all kinds of problems.

Bret Tollgaard: It, it really does. And that’s the big value that, you know, the UV materials can provide.

Allen Hall: So. I see the putty material and it comes in these little tubes, squeeze tubes. What are these putties used for?

Bret Tollgaard: So right now, the, the existing putty is really just the same exact thickened, uh, resin that’s in the pre-print.

Allen Hall: Okay.

Bret Tollgaard: And it’s worked well. It’s, it’s nice we’re kind of filling some cracks and some faring, some edges and stuff if things need to be feathered in.

But we’ve [00:15:00] been working on this year that we’ll be rolling out very, very soon is a new structural putty. Okay. So we’ll actually have milled fibers in there and components that will make it a much more robust system. And so we’ve been getting more inquiries of, particularly for leading edge rehabilitation.

Where Cat three, cat four, even cat five kind of damage, you need to start filling and profiling before any kind of over laminates can really be done properly. And so we’re working on, uh, rolling that out here very, very soon. Um, and so that will, I think, solve a couple of needs, um, for the wind market. Uh, and then in addition to some new products that we’re rolling out, uh, is gonna be the LEP system that we’re been working on.

Uh, the rain erosion testing showed some pretty good results. But we’re buying some new equipment to make a truly void free, air free system that we’re gonna it, uh, probably submit end of April, beginning of May for the next round, that we expect to have some very, very good, uh, duration and weather ability with,

Allen Hall: because it’s all about speed,

Bret Tollgaard: it’s durability.

Allen Hall: All about e

Bret Tollgaard: Exactly. And ease of use by someone in the [00:16:00] field. Yeah. Or OEMs on, you know, in the manufacturing plant. Um, there has yet, in my opinion, to be a true winner in the LEP space. That is just the right answer. And so by applying our materials with the really high abrasion resistance that we expect this to have and be as simple to do as it really appeal, stick and cure, um, we think it’s gonna be a bit of a game changer in this industry.

Allen Hall: Well, all the sunrise materials, once they’re cured, are sandal

Bret Tollgaard: correct.

Allen Hall: And I think that’s one of the things about some of the other systems, I always worry about them like, alright, they can do the work today, but tomorrow I have to come back and touch it again. Do I have a problem? Well, and the sun rests stuff is at least my playing around with it has been really easy to use.

It’s, it’s. Uh, things that I had seen maybe 20 years ago in the aerospace market that have they thought about using the material not only [00:17:00] in the factory, but outside the factory. How easy is it to adapt to, how easy to, to paint, to all those little nuances that come up? When you’re out working in the field and trying to do some very difficult work, uh, the sunroom material is ready to go, easy to use and checks all the boxes, all those little nuances, like it’s cold outside, it’s wet outside.

Uh, it’s, it’s hot outside, right? It’s all those things that, that stop ISPs or OEMs from being super efficient. All those parameters start to get washed away. That’s the game changer and the price point is right. How do. People get a hold of you and learn about the sun rose material. Maybe they, you can buy through fiberglass or through composite one.

Mm-hmm. That’s an easy way to do, just get to play with some samples. But when they want to get into some quantity work, they got a lot of blade repair. They know what they’re doing this summer or out in the fall or this winter come wintertime. How do they get [00:18:00] started? What do they do?

Bret Tollgaard: Well, one of the first things to do is they can reach us through our website.

Um, we’re developing a larger and larger library now for how to videos and install procedures, um, generating SOPs that are, you know, semi, uh, industry specific. But at the same time too, it’s a relatively blanket peel and stick patch, whether it’s a wind turbine blade, a corroded tank, or a pressure pipe. Um, and so yeah, www.suns.com Okay, is gonna be a great way to do it.

Uh, we’re actively building more videos to put on, uh, our YouTube channel as well. Um, and so that’s kind of gonna be the best way to reach out, uh, for us. One of the big things that we’re also pushing for, for 26 is to truly get people, uh, in this, in industry, specifically trained and comfortable using the products.

At the end of the day, it’s a composite, it’s a pre impregnated sheet. It’s not difficult, but there are some tips and tricks that really make the, the use case. Uh, the install process a lot easier.

Allen Hall: Sure.

Bret Tollgaard: Uh, and so just making sure that people are, are caught up on the latest and greatest on the training techniques will [00:19:00] go a long way too.

Allen Hall: Yeah. It’s only as good as the technician that applies it

Bret Tollgaard: e Exactly.

Allen Hall: Yeah. That’s great. Uh, it’s great all the things you guys are doing, you’re really changing the industry. In a positive way, making repairs faster, uh, more efficient, getting those turbines running. It’s always sad when you see turbines down with something that I know you guys could fix with sun.

Uh, but it does happen, so I, I need the ISPs to reach out and start calling Sun and getting in place because the OEMs are blessing your material. ISPs that are using it are winning contracts. It’s time to make the phone call to Sun Rez. Go to the website, check out all the details there. If you wanna play with your material, get ahold of fiberglass or composite one just.

Order it overnight. It’ll come overnight and you can play with it. And, and once you, once you realize what that material is, you’ll want to call Brett and get started.

Bret Tollgaard: A hundred percent appreciate the time.

Allen Hall: Yeah. Thanks Brett, for being on the podcast. I, I love talking to you guys because you have such cool material.

Bret Tollgaard: Yeah, no, we’re looking, uh, forward to continuing to innovate, uh, really make this, uh, material [00:20:00] splash in this industry.

Sunrez Prepreg Cuts Blade Repairs to Minutes

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Infringing on the Rights of Others

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I agree with what Ricky Gervais says here; I would only add that there are dozens of ways religion impinges on others.

In my view, the most common is that it impedes our implementing science in things like climate change mitigation.  If you believe, as is explicit in the Book of Genesis, that “only God can destroy the Earth,” you have a good excuse to ignore the entirety of climate science.

Infringing on the Rights of Others

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Could You Be Paid to Sew Disinformation into Our Society?

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99% of this totally incorrect.

But hey, who cares, right? There’s a huge market for disinformation, and I’m sure you were handsomely paid.

Could You Be Paid to Sow Disinformation into Our Society?

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