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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

Profound Nihilism?

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Normally, “nihilism” means the belief that life is without objective meaning, purpose, or intrinsic value.  Trump was elected by mean-spirited idiots, but they could hardly be called “nihilists.” For example, they believe very strongly in white supremacy, the dismantling of the federal government, saving people from the lethality of vaccinations, etc.

Now, there is a secondary meaning to the word, i.e., those who reject established social systems.  In this sense, I suppose they are indeed nihilists, in that they reject lawfulness, honesty, human rights, truth, science, tolerance, and compassion.

Profound Nihilism?

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Renewable Energy

Vestas Shares Jump 20%, UK Blocks Ming Yang Factory

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Weather Guard Lightning Tech

Vestas Shares Jump 20%, UK Blocks Ming Yang Factory

Vestas doubles second quarter profit and adds €4.7 billion in market value overnight. Plus EnBW finishes He Dreiht after a V236 blade break, the UK blocks Ming Yang’s Scottish factory, and India rules turbines are movable goods.

The Uptime Wind Energy Podcast is brought to you by Weather Guard Lightning Tech, creators of the StrikeTape Ultra LPS retrofit. Subscribe to Uptime’s Substack newsletter. And check out Rosemary’s “Engineering with Rosie” Youtube channel. Have a question we can answer on the show? Email us!

The Uptime Wind Energy podcast, brought to you by StrikeTape. Protecting thousands of wind turbines from lightning damage worldwide. Visit StrikeTape.com. And now, your hosts

Allen Hall: Welcome to the Uptime Wind Energy podcast. I’m your host, Allen Hall, and I’m here with Rosemary Barnes, Matthew Stead, and Yolanda Padron. And three out of the four of us will be in Melbourne Australia talking to a number of operators and interested parties about WOMA 2027. Matthew, where will we be the couple of days we’re in Melbourne?

Matthew Stead: So, um, first of all, we’ve got the Pullman, uh, East Melbourne, which is, uh, where the venue will be for, for 2027. Um, so that’ll be our home base. Um, we’ve got around about eight meetings planned already. So what we’re doing is we’re talking to the operators and a few other industry, um, players about [00:01:00] what we need to talk about, how we’re gonna move the industry forward in Australia.

Uh, so it’s gonna be jam-packed, but there’s a little bit of time left on the Friday afternoon if there’s any late-minute, um, people that wanna get in contact and catch up with us, um, for next Thursday, Friday, or actually Friday. Uh, so yeah, it’s gonna be a, a jam-packed time. I think we’re gonna be tired, too many coffees, and talking to all the key, all the key operators, uh, about what they wanna hear about and how we can move the, the industry forward.

Allen Hall: And if someone wants to put an input into the WOMA panel about what will be discussed at WOMA 2027, Matthew, how would they do that? How do they get ahold of you?

Matthew Stead: Well, we have a wonderful website, and that’s got all the details you could ever want. Um, you can also register on the website, so please register.

Otherwise, um, I’m sure we’re gonna be a sellout this year for sure. So woma2027.com.

Rosemary Barnes: I just wanna add that when people talk to [00:02:00] me about the event, they always say how they love that the topics are so relevant, and the reason why that they’re so relevant is because we make sure to go around to operators and find out what are the issues that they’re really dealing with.

So anybody that’s thinking of attending, even if you can’t, you know, meet us up, meet up with us in Melbourne, get in touch and tell us what are the, yeah, what are the topics that you’re struggling with that you’re not, um, you’re having trouble finding enough information, having trouble finding the people that can help you.

And y- yeah, like we take all of that information, and that’s how we come up with our agenda each year. And yeah, I mean, for us, that’s the, the main thing is that this has to be really relevant, up-to-date information for the industry, and we need your help to make sure it stays that way. I

Matthew Stead: mean, that’s what we’ve done the last two years, so this is– we’re just repeating the formula, um, listening to the operators and getting the good topics and the good speakers.

Allen Hall: Well, Vestas has had a good quarter. Uh, the, for the last couple of years, honestly, s- [00:03:00] Vestas has been really thin on margins. There was questions about it continuing on. Rising costs mostly, uh, supply chains, especially during COVID, were bad. Uh, and, uh, but for the most part, the shareholders stayed attached.

Well, that story is changing rapidly. The world’s largest turbine maker posted second quarter operating profits of $400- €46 million, more than double what the analysts had expected, and it’s raised its full-year margin guidance alongside half-year results for the first time in a decade. The shares climbed about 20% in Copenhagen, adding roughly €4.7 billion of market value in a single session.

Now, the chief executive, uh, Henrik Andersen, ha- put it plainly to, uh, uh, in a couple of news sources that something much bigger is happening and Vestas is gonna be the, the leader in wind. That’s how I read it, that everybody [00:04:00]at Vestas was super happy with the, the change in direction and things were moving up steadily.

But a 20% jump in a day is remarkable. You don’t see that in large industrial businesses like wind energy. Matthew, this has real implications on what happens next for Vestas because success like this usually means more orders.

Matthew Stead: Yeah, I wonder what’s going on under the hood there. Um, I mean, Vestas is a quality company, although, although can I just do a quick segue?

How many turbines were installed in Denmark in the last, uh, two years? Like last year and the year before?

Allen Hall: I don’t know. How many?

Matthew Stead: I believe it was eight turbines installed onshore in Denmark last year, and the year before it was 12. So, you know, maybe, maybe Vestas needs to focus on their own backyard a little bit as well.

Allen Hall: I’m not sure there’s a lot of opportunity there. Yeah, onshore.

Matthew Stead: How can you ever be full? I mean, there’s always, um, [00:05:00] uh, you know, um, you know, resiting or, um, you know, upgrades and-

Rosemary Barnes: You know what? Allen and I are probably gonna get some time in Jutland, uh, later this year, um, and that area and the old wind turbines there was actually the inspiration for my whole YouTube channel.

It just, ’cause there’s, you know, there’s turbines there from, the earliest one is, um, from the ’70s and still going. I think it’s one and a half megawatts, actually huge for, for that time. Um, and it was like community made, um, at Tvind. But anyway, I’m interested to revisit the site and have a look and see are these, you know, all these old turbines still there.

It’s only, like six years since I went through and did the experience but for the most part, they don’t seem to be yet pulling down the, the small old ones and putting up big ones. There’s a lot of, a lot of them are community owned. Um, and yeah, I mean, Danish people love wind turbines, but there’s only so many that you can have onshore.

Like, people are happy to live near them by, you know, the standards of people in other countries, but you don’t want [00:06:00] one in your literal backyard. I think that there is, there, there is a, a limit to how many more onshore wind turbines that you can get in that area and offshore expansion is the more likely way to go.

Um, and also I think it’s, it’s, it’s good to recognize that if you have a domestic only or a domestic first strategy, that will only get you so far and then you have to expand, and I think Denmark did that really well. I think Germany a little bit less. I think that Enercon were a bit surprised, um, by their strategy.

It, uh, they had a real hard time anyway when they had to transition away from mostly Germany to getting overseas. And obviously, like if you look at China, they have most of their installations are in China. They are trying so hard to get outside of China because it’s not, like even a market as big as China, it’s got decades to go before it will be full.

Um, you can still recognize that that’s not your, like long-term strategy for growth has to involve expansion, I think.

Allen Hall: I think Vestas, regardless of what happens in Denmark, is making a play for the United States. That seems to be [00:07:00] where a significant effort is happening at the moment and on offshore. Their– Vestas seems very excited about the offshore opportunities.

Of course, there’s a ton of wind turbines gonna be installed in the UK and, and all around Northern Europe. Offshore, the opportunities to buy turbines, there’s only a couple that you could get today. Uh, uh, the GE Vernova offerings I, I don’t think are gonna fit the mold, and I don’t know if GE’s even actively selling.

So their competitor realistically is Siemens Gamesa, which does seem like the smaller player at the minute versus Vestas, which is heavily pushing the V236 and will fill order books like crazy, I think, uh, just based upon the, the history they’ve had and everybody knowing who they are. So Also on the move in Australia, right?

Vestas is huge in Australia right now.

Rosemary Barnes: I think it’s really good that their, um, yeah, finances, uh, are [00:08:00] looking a bit better ’cause it’s been funny. Like, I tried early on in my wind career to invest in, you know, wind turbine manufacturers knowing that there would be immense growth, and I was right. There, there was immense growth.

Not that that was so hard to figure out that there would be, but it did not lead to any kind of, um, return on, on anything, you know. Like, that did not keep pace with the just general market. Um, so I, I stopped trying to, stopped trying to invest to that. But it has been really, really hard for the companies to, you know, raise money or y- you know, do any of the things that they need to do because they’ve always, like, they’re growing, growing, growing, but finances has been so tight that it has been a real constraint on the amount of engineering that they could do, and I really hope that Vestas are gonna take this opportunity that they’ve got compared to, you know, a lot of the other manufacturers.

Vestas do have really strong, um, innovation and, yeah, engineering capabilities for doing– you know, developing new technologies and improving them, and I really hope that they’re taking this opportunity to build that up. There are a lot [00:09:00] of very good engineers with a lot of experience in the industry in that area that are working in other fields at the moment because, you know, there’s been a lot of contraction in Denmark.

So I don’t know, it seems like a really good time to hire back some of that really in-depth knowledge and, yeah, get a- get ahead of, you know, some of the future quality problems. We’re going through such a hard time at the moment from the fast development that happened in the 20-teens when there wasn’t a whole lot of money around.

We’re dealing with quality problems now, so, you know, maybe we can get ahead and not have the next round of them if we can invest in just a lot more, uh, engineering capacity.

Allen Hall: When you have success like Vestas has, usually the upper level management and some of the executive team starts getting pilfered, that they’ll get offers to repeat that success at another company, and it sounds like that process has started already.

There’s a couple of executives that have recently departing or are in the midst of departing from Vestas. [00:10:00] I would see that continuing f- at least for the next six months, uh, because everybody wants to repeat that, right? If you can get a 20% increase in your valuation overnight, uh, I can, I can list a number of companies, regardless of industry, that would love to participate.

Even in a 5% increase, that would be remarkable. So, um, Vestas is gonna have a hard time holding onto this. That’s just the nature of the business where things are successful, people will wander. And Rosemary, I, I think they’re– And Yolanda In, in my book, Vestas should sort of s-stand down and just make quality products.

I’m not sure you sh-should tinker too much at the time being and just make the good stuff better. That seems like a way to really increase profits.

Yolanda Padron: Yeah, I mean, solving a lot of the issues that– And, and that’s not just a Vestas exclusive thing, right? All of these OEMs have some sort of issue that maybe– I know Rosie’s touched a lot on, on it, where [00:11:00] you build this version A and then version B solves one of the small little issues, but now it creates another little problem, and then you have version C, and then everything just kinda has its own niche little issue, um, that really expands over time.

So if they could solidify what they already have in, in a, in a model that, that would help them just even keep a lot of their customers, I think that’d be great, and it would help, certainly help them, um, not continuously, like, rotate around the customers, ’cause it almost feels like, at least in the States, right, you, you get GE to be really, really strong and have a huge market share, and then GE starts focusing more on gas turbines, so then they all go onto Vestas, and then they all go onto Ontara now.

Um, and then just, you know, just kind of everybody starts cycling through them because they just kind of want something that’s better quality than what they’re getting in the long haul.

Matthew Stead: Allen, you, you talked about you think there’s something big under the hood. I think you, you [00:12:00] thought that maybe Vestas was angling towards something or being quite bullish.

Do you think that they might take over GE Vernova?

Allen Hall: I don’t think they’re gonna grab Vernova, and I don’t think Vernova is for sale at the minute, but I wonder if Siemens Gamesa is, or Nordex. I mean, Nordex has done terrific the last couple of quarters and is making inroads in places that I didn’t think possible three, four years ago.

Uh, the European marketplace is be- becoming really unique in that sense that there’s a lot of money being put out. But is there a sole perfect solution for Europe? Not at the minute, ’cause you got two competitors there, and then China trying to, to work its way in. Will the Europeans come together and form something more united, even if it’s just a partnership, a loose partnership, versus letting China on the shores?

We’ll see. 64 of the largest machines that Vestas has builds are standing off the German coast, but one blade is missing a [00:13:00] piece. We’ll talk about that when we come back.

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Allen Hall: Well, Germany’s largest offshore wind farm is now fully installed, and EnBW confirmed this, uh, past week that all 64 of the Vestas V236 15-megawatt turbines are s- standing at the He Dreiht wind farm about 85 kilometers northwest of Borkum. Uh, 960 megawatts, [00:14:00] 2.4 billion euros invested. Man, these offshore projects are expensive to get installed.

Uh, so it’s power for roughly 1.1 million households, and there’s no state subsidy behind any of it. And so this is a little bit of a u- unique situation. Uh, th- well, the one footnote about the wind farm is they had a V236 blade break and fall into the North Sea, and they had fished it out and I think I passed along s- pictures that I saw online of, uh, one of the police boats pulling the shear web out of the water I don’t know what to think anymore about some of these offshore blade issues.

Obviously, Vestas is very conscientious about it and will be doing RCAs and engineering reviews and all the above to go identify what the problem is. But it does just lead to a little bit of a pause of do– what is going on for some of these offshore [00:15:00] wind blade installations or, or whatever’s causing these blades to break?

Do we have a good handle on it? Yolanda, is– are we following up on all the design details so that we can prevent these things in the future?

Yolanda Padron: I mean, I’d, I’d hope you’d be following up on the design, right? Like, and, um, but I think there is still a little bit of a disconnect from, from what we’ve seen, and again, not just Vestas exclusive, um, between the people who are designing and the people who are manufacturing, the people who are in operations, right?

So, uh- The, from what we’ve heard, uh, this could have potentially been a, um, partially because of a transportation issue, which is what happens a lot in onshore. It’s a lot more common than we would like it to be. Um, and so that even goes beyond what would go on in the design studio and what would go on in the manufacturing and what would [00:16:00] go on even just for the people that are running the site, right?

So, so some sort of, um, in between, uh, EPC error. Um, but yeah, I just think that, like in a lot of industries, there should be a lot more communication between all of these teams on the lower level, so that way a lot of these problems can, can be avoided.

Allen Hall: I’m wondering if it’s actually an issue on the, the testing side.

And, uh, the one question that just popped up, and we saw from the ORE Catapult, uh, survey that’s being conducted at the moment, and if you haven’t participated in that, you just visit ORE Catapult and answer some of the survey questions. But torsion on a blade, which is very difficult to test for, and it really isn’t tested for today, but does happen during the move and the transportation of these big offshore blades.

Is it one area that we need to do a little more work in or maybe spend some more time focusing on it to see what is happening as blades are [00:17:00]moved?

Rosemary Barnes: The thing about te- torsion is that it is much more significant as blades get longer. I can’t, I can’t remember the equation off the top of my head, which is, um, bothering me.

But I think it scales with, like, the fourth power or something of, of length. And so whilst it was always a bit of a problem, it’s much more of a problem as it gets, as blades get bigger. I mean, they’ve never, like, fully tested a blade, and there was always a lot of reliance on, hey, y- you know, like we’ve tested certain things that is possible to test in a test facility on the ground.

But they also rely on their decades of experience of how blades actually behave in the field. But, you know, remember, that’s a real lagging, lagging indicator because y- you know, their decades of experience is mostly with lots smaller blades. Now, blades are really different because they’re longer and different effects are, are taking over.

It’s not just, uh, torsion, but it’s also the laminates get much thicker, and then y- you know, you, you have issues with the way that they’re curing, [00:18:00] and there’s a lot more just space for, um, defects to be present in a really thick laminate All of those things add up. Oh, yeah, then add in addition, like new materials, carbon fiber is new, and then new ways of producing it, you know, pultrusions, um, all kinds of different materials like balsa’s being replaced with foams and, um, like, you know, 10 times that number of what sounds like a small innovation, but all of these things have the potential for damage and don’t have a really long track record in the field to be able to kind of calibrate.

We do need to remember that, like, when you do something new, things are gonna break, uh, sometimes, they’re gonna fail sometimes. If they don’t, then you’re definitely being too conservative, and your product is costing more than it should, and nobody wants more expensive wind energy, right?

Matthew Stead: Rosie, Rosie, I, I know you’re doing some, some excellent work on, um, industry studies around erosion and temperature and so forth.

Um, I just wanted to let a little secret out of the bag that, um, in the future there will also be some [00:19:00] other studies on torsion and blade twist and blade dynamics. So, um, just a few things are in, in train at the moment, which I can’t share, share, but, uh, watch this space around better understanding blade twist.

Allen Hall: The Hydride wind farm runs on European turbines, but the next one might not. Two governments with two very different answers on who gets to build Europe’s wind fleet.

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Well, two countries and two decisions, one question. In Scotland, the UK government blocked plans for the Chinese manufacturer Mingyang to build a turbine factory, uh, near Inverness on national security grounds. 1.5 billion pounds of investment, up to about 1,500 jobs. And First Minister John Swinney has asked the new prime minister to reconsider.

And the UK energy secretary minister called that request irresponsible. Meanwhile, up in Denmark, Vattenfall has just won two offshore wind farms and will not say whether it will buy European turbines. Danish suppliers are not taking that quietly. So [00:21:00] the Scotland question about the Mingyang factory is at least being discussed again with the new prime minister in the UK.

It does seem like there’s a lot to do and get the government formed and make all this stuff happen. But I don’t see a Burnham administration changing the outcome for Mingyang, but I could be wrong. At the, the same time, Vestas is pushing for a more Eurocentric focus and to really keep out the Chinese.

Uh, something has to give here pretty soon.

Matthew Stead: I actually think Mingyang should, um, set up a factory in Scotland. I, I mean, what’s wrong with that? I mean, uh, why is that a security issue?

Rosemary Barnes: Set up the factory and put the, like, whatever you’re worried about, put protections in place for it, require it to be a local joint venture or whatever.

You know, we’ve seen the blueprint in many of what used to be, you know, less rich countries. That’s how they, you know, got a head start on some of these technologies. It’s not like, I don’t think that China [00:22:00] has a head start on wind, wind turbine technology, but they certainly have different ways of doing things that, um, yeah, we could, we could learn from.

But I think across the board, wind turbines, batteries, solar panels, whatever, let them set up factories, put the rules in place that mean that your country benefits from it and you’re getting the, you know, the information transfer.

Yolanda Padron: Do you think that’ll, like, impulse a lot of these more established European companies to maybe start fixing some of the issues that they’ve known about for, for a while, um, particularly regarding the blades and everything that we’ve talked about earlier?

Like, there’s enough competition there, so maybe they need to start looking a little bit more deeply into their problems.

Allen Hall: Do we think that Chinese operations have been out front, forward, honest, I’ll even use, about their blade issues?

Rosemary Barnes: No, but this is a good way to find out, isn’t it?

Allen Hall: Governments decide who is allowed to build a turbine after a discussion on Scotland.

Uh, but, but [00:23:00] occasionally, a court decides what a turbine legally is. India has just settled that question, and the reasoning should be of interest to anybody who ships machines across a border right after this. As wind energy professionals, staying informed is crucial and let’s face it, difficult. That’s why the Uptime Podcast recommends PES Wind Magazine.

PES Wind offers a diverse range of in-depth articles and expert insights that dive into the most pressing issues facing our energy future. Whether you’re an industry veteran or new to wind, PES Wind has the high-quality content you need. Don’t miss out. Visit peswind.com today. A tax fight in India has produced a definition every turbine supplier should read.

Is a wind turbine bolted to a concrete foundation movable goods, or is it immovable property? State tax authorities argued immovable, which would have [00:24:00] taxed erection and commissioning contracts at 18% instead of 5%. The Andhra Pradesh, uh, High Court disagreed, and on the 12th of August, the Supreme Court declined to interfere.

The reasoning rests on something this whole industry takes for granted. A turbine can be taken down, moved, and put back up. So a turbine is a movable object, and it has less taxation. Bonus. So this is a really interesting discussion that’s happening in India because it’s probably symptomatic of things we’re seeing elsewhere across the world about taxation for wind turbines, right?

That, um, if there’s a way to tax a wind turbine, we’re gonna try to do it. This is a unique way, uh, that happens in India where depending on if it’s permanent or movable, the tax rates are different. I, I guess that would apply to a lot of components inside a wind turbine too, Matthew, don’t you? Like the, the generator, the, the big heavy things, [00:25:00] gearbox, generator, blades, rotors, tower sections, would be taxed at a, a lesser rate.

Matthew Stead: I agree with the court case that it’s all movable and, uh, you can actually buy turbines on the secondhand market, can’t you? I mean, if I wanted to buy, yeah, whatever, whatever, I could buy one and, and put it up in my backyard if I had a bigger backyard. Um, so yeah, I vote for movable. I vote for lower taxes.

Yolanda Padron: The way that it would work a lot of times in the US is, I mean, it’s, you pay, the company itself pays a lot less than they would’ve over time, right? Just by pure, the, the regular kind of tax laws. Um, but the community, there’d be just direct donations to the community, so then they’d get, uh, like money would actually come into the community where the turbines were being built instead of just distributed around the state, which I mean, in a state as big as Texas, it gets, um, but easier for that c- um, that county to get a lot more, uh, funding than they would typically get if it was [00:26:00] through a big enough area.

Um, but yeah, no, I agr- I completely agree with you guys that, that this should be a movable good. I mean, how many times have we seen, uh, even just a blade, um, that it looks like it’s, uh, just a, a failed blade that they have to go in and replace, and then they take it out, fix it, and then just bring it back to the same site or take it to another site across the country.

And, and to that point, like if you were to h- judge it as something that’s immovable, would then any blade replacement just not be taxed? Because then it’s, you’re moving that one component and two, but it’s essentially the same turbine. Like, I don’t know how that all would make sense.

Allen Hall: I think the Uptime Supreme Court agrees with the Indian Supreme Court that wind turbines are movable, and that’s good.

Well, that wraps up another episode of the Uptime Wind Energy podcast. If today’s discussion sparked any questions or ideas, we’d love to hear from you. [00:27:00] Reach out to us on LinkedIn. And if you found value in today’s conversation, please leave us a review. It really helps other wind energy professionals discover the show.

And don’t forget to subscribe so you never miss an episode. For Rosa, Yolanda, and Matthew, I’m Allen Hall. We’ll see you here next week on the Uptime Wind Energy podcast.

Vestas Shares Jump 20%, UK Blocks Ming Yang Factory

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Vermont and Florida: A Key Difference

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Can’t swear that the story here is authentic, but it sure rings true.

Vermont is a somewhat quirky state, but it protects its citizens very well. FWIW, this is where I want MY tax dollars going too.

Florida is a deeply red state that, true to form, wants as much ignorance as it can possibly produce. Educated people aren’t voting for people like Ron Desantis.

Vermont and Florida: A Key Difference

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