The Elusive Dream: Artificial General Intelligence and the Future of Our Minds
Artificial general intelligence (AGI) – the concept of a machine capable of human-level intelligence and adaptability – has long captivated the imagination of scientists, philosophers, and science fiction enthusiasts.
It conjures visions of robots seamlessly integrated into our lives, assistants capable of independent thought and learning, and perhaps even conscious entities posing profound philosophical questions about the nature of intelligence itself.
But where are we on the path to realizing this dream? Despite impressive strides in narrow AI, creating a true AGI remains a formidable challenge. We lack a comprehensive understanding of how human intelligence works, and our current machine learning techniques often struggle with tasks that come naturally to us, such as common sense reasoning, adapting to novel situations, and understanding nuances of language and emotion.
The road to AGI is paved with hurdles:
- The data dilemma: AGI would require training on vast amounts of diverse data, encompassing the complexities of human experience, culture, and knowledge. But ensuring the quality and representativeness of this data is a significant challenge. Biases within data sets can lead to biased AI, and privacy concerns limit access to sensitive information crucial for comprehensive training.
- The learning gap: Our current AI models, despite their feats in pattern recognition and task automation, still struggle with genuine understanding and the ability to learn from limited data. Bridging this gap requires breakthroughs in understanding and emulating human cognition, including memory, reasoning, and the ability to transfer knowledge across domains.
- The ethical minefield: The widespread deployment of AGI raises crucial ethical questions about accountability, bias, and the potential for unforeseen consequences. Establishing robust ethical frameworks and ensuring responsible development of AGI will be critical to navigating this uncharted territory.
Despite these challenges, the pursuit of AGI holds immense potential. Breakthroughs in this field could lead to revolutionary advancements in healthcare, education, scientific discovery, and countless other areas. AGI could help us tackle complex global challenges like climate change and poverty, and even assist us in understanding the universe and our place within it.
While the timeline for achieving true AGI remains uncertain, it’s clear that the journey is as important as the destination. The research and development efforts aimed at AGI are already pushing the boundaries of artificial intelligence, leading to significant breakthroughs in areas like natural language processing, robotics, and computer vision. This constant innovation not only brings us closer to AGI but also yields practical applications that benefit society in the present.
The pursuit of AGI is a collective endeavor, requiring collaboration between scientists, engineers, philosophers, ethicists, and policymakers.
By working together, we can navigate the challenges, harness the potential benefits, and ensure that the future of AGI is one that serves humanity, not the other way around.
The question of whether we will one day create a machine that mirrors the human mind is not yet answered. But the journey towards AGI, with its intellectual challenges and ethical implications, promises to be one of the most fascinating and transformative of our time. So let us embrace the pursuit of this elusive dream, not just for the technological marvels it may bring, but for the deeper understanding it offers of ourselves and the potential it holds for shaping a better future for all.
A Journey Through the History of Artificial General Intelligence (AGI)
The quest for artificial general intelligence (AGI), a machine capable of human-level understanding and adaptability, has captivated thinkers for centuries. Though still a theoretical goal, its history reveals a fascinating tapestry of ideas, milestones, and ongoing challenges. Let’s embark on a historical tour:
Early Seeds (Pre-1950s):
- Philosophical Precursors: From Alan Turing’s “Computing Machinery and Intelligence” (1950) to Ada Lovelace’s visionary notes on Babbage’s Analytical Engine, theoretical groundwork was laid for the possibility of intelligent machines.
- Science Fiction Seeds: Fictional creations like Karel Čapek’s “R.U.R.” (1920) and Isaac Asimov’s Three Laws of Robotics (1942) popularized the concept of artificial minds and sparked ethical considerations.
The Dawn of AI (1950s-1970s):
- Birth of AI: The Dartmouth Workshop in 1956 marks the official birth of AI research. Early optimism flourished, fueled by successes in game playing and problem solving.
- Symbolic AI: This dominant paradigm focused on representing knowledge and reasoning explicitly using symbols and rules. Projects like Newell and Simon’s Soar aimed to build cognitive architectures mimicking human thought.
- AI Winter: By the late 1970s, limitations of symbolic AI and overzealous predictions led to a funding decline and skepticism, known as the “AI Winter.”
Resurgence and Diversification (1980s-2000s):
- Expert Systems and Connectionism: Expert systems thrived in specific domains like medicine, while connectionism, inspired by the brain, led to neural networks.
- Probabilistic Models and Machine Learning: Bayesian networks and statistical learning methods like decision trees gained prominence, laying the groundwork for modern statistical AI.
- AGI Rekindled: Interest in AGI resurfaced with efforts like Marvin Minsky’s Society for Mind and John Haugeland’s “Having Thought: Essays in the Metaphysics of Mind.”
The Era of Deep Learning (2000s-Present):
- Deep Learning Revolution: The rise of deep neural networks, powered by increased computational power and large datasets, led to breakthroughs in image recognition, speech recognition, and natural language processing.
- AGI Hype and Debate: Renewed excitement over deep learning’s potential fueled optimistic claims about imminent AGI, accompanied by cautious voices urging focus on understanding intelligence before aiming to replicate it.
- Multi-Agent Systems and Embodied AI: Research explores agent-based interactions and embodied intelligence in robots, moving towards more complex and real-world scenarios.
The Road Ahead:
The history of AGI is a tale of progress, setbacks, and continuous evolution. Today, we stand at a crossroads, balancing optimism with critical challenges:
- Bridging the understanding gap: Can we move beyond simply mimicking intelligence to achieving genuine understanding and reasoning?
- Data and bias: How can we ensure AGI systems are trained on representative, unbiased data to avoid perpetuating societal inequalities?
- Ethical considerations: As AGI capabilities grow, robust ethical frameworks and human oversight become crucial to address issues of responsibility, autonomy, and potential misuse.
Our journey towards AGI is far from over. The past offers valuable lessons, the present demands careful progress, and the future holds both promises and perils. It is through ongoing research, collaboration, and responsible development that we can navigate this complex terrain and shape a future where AGI serves to benefit and empower humanity.
Development of Artificial General Intelligence (AGI)
The development of AGI, a machine capable of human-level intelligence and adaptability, faces numerous challenges but also holds immense potential for the future. Let’s delve into the current state of AGI development, exploring the hurdles and promising approaches:
Challenges:
- Understanding human intelligence: We still lack a complete understanding of how human intelligence works, encompassing aspects like memory, reasoning, common sense, and emotions. Replicating these capabilities in machines remains a major obstacle.
- The data dilemma: AGI would require training on vast amounts of diverse data, reflecting the complexities of human experience. However, ensuring the quality, representativeness, and ethical sourcing of such data presents significant challenges.
- Learning beyond tasks: Existing AI models excel at specific tasks but struggle with generalizable learning and adapting to new situations. Bridging this gap requires mimicking human-like learning processes, not just data crunching.
- The embodiment gap: Current AI mostly operates in digital environments. Integrating intelligence with physical embodiment in robots adds another layer of complexity, impacting perception, action, and interaction with the real world.
- Ethical considerations: Issues like bias, accountability, and potential misuse of AGI necessitate robust ethical frameworks and responsible development practices.
Promising Approaches:
- Neuromorphic computing: Inspired by the human brain, this approach aims to build hardware and software architectures that mimic its structure and function, potentially leading to more human-like learning and reasoning.
- Artificial general learning (AGL): This area focuses on developing algorithms that can learn and adapt across diverse tasks and domains, resembling human cognitive flexibility.
- Hybrid human-AI systems: Combining human expertise with AI capabilities could leverage the strengths of both, addressing complex problems while mitigating potential risks of fully autonomous AGI.
- Symbolic and statistical AI integration: Bridging the gap between symbolic AI’s logical reasoning and statistical AI’s data-driven learning could create richer and more robust intelligence.
- Explainable AI (XAI): Developing AI systems that explain their reasoning and decision-making processes is crucial for transparency, trust, and debugging potential errors or biases.
The Future of AGI:
The path to AGI is long and winding, with no guarantees of success. However, ongoing research and development efforts are constantly pushing the boundaries of artificial intelligence. By addressing the challenges and exploring promising approaches, we can move closer to realTransforming educationizing the potential of AGI for:
- Revolutionizing healthcare: Personalized medicine, disease diagnosis, and drug discovery could be significantly improved.
- : Personalized learning experiences, adaptive tutoring systems, and access to education in remote areas are potential areas of impact.
- Addressing global challenges: Sustainable development, climate change mitigation, and disaster response could benefit from intelligent systems.
- Boosting scientific discovery: AGI could assist in data analysis, hypothesis generation, and scientific experimentation.
While ethical considerations and responsible development are paramount, the pursuit of AGI remains a fascinating and potentially transformative endeavor. By working together, we can shape the future of this powerful technology to benefit all of humanity.
Remember, the development of AGI is an ongoing process, and new advancements and approaches are constantly emerging. This is just a snapshot of the current state and potential future of this field.
Infrastructure for Artificial General Intelligence (AGI)
The realization of AGI, a machine capable of human-level intelligence and adaptability, requires not just advanced algorithms and models but also a robust and capable infrastructure to support its development and deployment. Let’s explore the key elements of this infrastructure:
Computational Resources:
- High-performance computing (HPC): AGI training requires immense computational power for processing massive datasets and running complex algorithms. Access to supercomputers and cloud platforms with efficient parallelization capabilities is crucial.
- Specialized hardware: Neuromorphic hardware and accelerators designed to mimic the brain’s architecture could provide significant performance boosts for specific AGI tasks.
- Energy efficiency: With the immense power consumption of training AI models, research into energy-efficient hardware and algorithms is essential to ensure sustainable development.
Data Management:
- Data storage and access: AGI training requires storing and efficiently accessing vast amounts of diverse data. Scalable, secure, and distributed data storage solutions are essential.
- Data curation and labeling: High-quality, labeled data is critical for training accurate and unbiased AGI models. Efficient data curation and labeling processes are vital.
- Data privacy and security: Protecting sensitive data used in AGI development and deployment requires robust security measures and ethical data governance practices.
Software Tools and Platforms:
- Open-source frameworks: Open-source libraries and frameworks for AI development facilitate collaboration and accelerate progress. Tools like TensorFlow and PyTorch play a crucial role.
- Model versioning and management: Tracking different versions of AGI models, their performance, and training data is essential for efficient development and debugging.
- Simulation environments: Simulated environments for testing and refining AGI capabilities in various scenarios before real-world deployment can be valuable tools.
Human Expertise and Collaboration:
- Interdisciplinary teams: Developing AGI requires collaboration between experts in various fields, including computer science, neuroscience, psychology, ethics, and social sciences.
- Public-private partnerships: Collaboration between research institutions, private companies, and governments can accelerate AGI research and development through shared resources and expertise.
- Global talent pool: Fostering a diverse and inclusive research environment that attracts talent from all over the world is crucial for advancing AGI in an equitable and responsible manner.
Challenges and Opportunities:
Building the infrastructure for AGI poses numerous challenges, such as the ever-growing demand for computational power, the ethical considerations surrounding data privacy and bias, and the need for skilled personnel. However, these challenges also present exciting opportunities:
- Advancements in hardware and software: New technologies like quantum computing and neuromorphic chips have the potential to revolutionize AGI development.
- : Open-source initiatives and global research collaboration can accelerate progress and ensure wider accessibility of AGI benefits.
- Evolving ethical frameworks: Continuous dialogue and ethical considerations throughout deveCollaboration and data sharinglopment and deployment can ensure responsible and beneficial use of AGI.
The future of AGI infrastructure:
As AGI research progresses, the infrastructure supporting it will continue to evolve. Building a robust, comprehensive, and ethically responsible infrastructure is crucial to realizing the full potential of this transformative technology. By investing in these essential elements, we can pave the way for a future where AGI serves to benefit humanity and address some of the world’s most pressing challenges.
Financial cost of developing Artificial General Intelligence (AGI)
Determining the financial cost of developing Artificial General Intelligence (AGI) is quite challenging due to several factors:
-
Uncertain timeline: We lack a concrete timeline for achieving AGI. Many experts have speculated about its arrival, ranging from “within the next decade” to “nevertheless a century away.” This ambiguity makes it difficult to estimate the total spending.
-
Diverse approaches: Several research paths are vying for success in AGI, each with its own resource requirements. Some approaches, like neuromorphic computing, demand significant investment in specialized hardware and infrastructure, while others might primarily rely on software advancements and existing computational resources.
-
Distributed efforts: AGI research is driven by various entities, including universities, research institutes, private companies, and government agencies. Estimating the cumulative spend across these diverse actors is inherently complex.
-
Hidden costs: Beyond direct research funds, the development of AGI carries indirect costs. These include the opportunity cost of researchers’ time dedicated to this challenging pursuit, potential economic disruptions caused by automation, and investments in mitigating any unforeseen ethical or societal consequences.
Despite these challenges, we can still attempt some cost estimations and consider different frameworks:
Current spending: Existing research in AI, a crucial stepping stone towards AGI, receives billions of dollars annually. In 2023, global AI investment was estimated to be around $422 billion. A significant portion of this goes towards fundamental research that could contribute to AGI in the future.
Projected budgets: Several reports have estimated the potential cost of reaching AGI. A 2016 study by the Global Catastrophic Risk Institute suggested a budget of $50 billion over 10 years could be sufficient, while other estimates range from hundreds of billions to trillions of dollars.
Cost comparisons: It’s helpful to compare AGI development to other large-scale scientific endeavors. The Large Hadron Collider project, for example, cost around $13 billion over decades. The Apollo program, which put humans on the moon, is estimated to have cost $250 billion in today’s dollars.
Future considerations: The financial cost of AGI will likely depend on the chosen approach, the speed of progress, and the unforeseen challenges encountered. It’s crucial to ensure these costs are justified by the potential benefits of AGI, which could range from revolutionizing healthcare and education to tackling global challenges like climate change.
Ultimately, while precise financial calculations remain elusive, the pursuit of AGI demands thoughtful consideration of both its costs and potential benefits. Open collaboration, responsible resource allocation, and continuous ethical assessments will be crucial for navigating this complex endeavor and shaping a future where AGI serves humanity in a positive and sustainable way.
The landscape of individuals and groups who could benefit from Artificial General Intelligence (AGI)
The landscape of individuals and groups who could benefit from AGI is vast and diverse, encompassing multiple fields and scenarios. Here are some potential users who could leverage AGI for their advantage:
Individuals:
- Professionals:
- Scientists and researchers: AGI could assist in data analysis, hypothesis generation, and scientific experimentation, accelerating research in various fields.
- Doctors and healthcare professionals: Personalized medicine, early disease diagnosis, and drug discovery could be significantly improved with the help of AGI.
- Educators and teachers: AI-powered tutors and personalized learning experiences could revolutionize education, catering to individual needs and learning styles.
- Artists and creators: AGI could inspire and collaborate with artists, musicians, and writers, fostering creative expression and pushing the boundaries of artistic possibilities.
- Individuals with disabilities: AGI-powered assistive technologies could enhance mobility, communication, and independence for people with disabilities, improving their quality of life.
Businesses and Organizations:
- Corporations:
- Product development and innovation: AGI could assist in designing new products, optimizing manufacturing processes, and predicting market trends, giving companies a competitive edge.
- Financial services and risk management: AGI could provide insights for personalized financial advice, fraud detection, and risk analysis, improving decision-making in the financial sector.
- Non-profit organizations and government agencies:
- Climate change mitigation and disaster response: AGI could optimize resource allocation, predict natural disasters, and develop effective response strategies.
- Social welfare and development: AGI could analyze data to identify poverty hotspots, optimize resource allocation for social programs, and personalize interventions for individuals in need.
Overall, the users who could take advantage from AGI extend far beyond specific professions or groups. Any individual or entity seeking to solve complex problems, optimize processes, or gain deeper insights in their field could potentially benefit from this powerful technology.
However, it’s crucial to consider the potential downsides and ensure equitable access to AGI’s benefits:
- Bias and discrimination: AGI trained on biased data could perpetuate existing societal inequalities. Careful data sourcing and development of unbiased algorithms are necessary.
- Job displacement: Automation powered by AGI could lead to job losses in certain sectors. Rethinking education and job training programs is crucial for preparing the workforce for this transition.
- Access and affordability: Ensuring equitable access to AGI tools and resources for all, regardless of socioeconomic background, is essential to prevent further widening of societal gaps.
By promoting responsible development, ethical considerations, and equitable access, we can ensure that AGI benefits all of humanity and becomes a force for positive change in the world.
Data used in Artificial General Intelligence (AGI)
The types and data used in Artificial General Intelligence (AGI) are both diverse and complex, reflecting the ambitious goal of creating a machine with human-level understanding and adaptability. Here’s a breakdown of the key points:
Types of Data:
- Textual data: This encompasses books, articles, web pages, social media posts, and any other forms of written language. Textual data provides insights into human knowledge, reasoning, and communication, crucial for training AGI models to understand and generate language.
- Numerical data: This includes sensor data, images, videos, audio recordings, and other forms of quantifiable information. Numerical data allows AGI models to perceive the world, learn from past experiences, and make predictions about future events.
- Symbolic data: This refers to structured representations of knowledge, such as graphs, ontologies, and databases. Symbolic data provides AGI models with a framework for organizing information and reasoning about relationships between concepts.
- Multimodal data: This combines various data types, such as text and images, or audio and video. Multimodal data allows AGI models to learn from the interplay of different senses, similar to how humans experience the world.
Types of Algorithms and Models:
- Deep learning models: These are inspired by the structure and function of the human brain, often consisting of artificial neural networks. Deep learning models excel at pattern recognition, feature extraction, and learning from large datasets.
- Symbolic AI models: These utilize logic rules and knowledge representations to reason and solve problems. Symbolic AI models provide explainability and transparency, which are crucial for understanding how AGI models arrive at their decisions.
- Hybrid models: These combine elements of deep learning and symbolic AI, aiming to leverage the strengths of both approaches. Hybrid models offer the potential for more robust and interpretable AGI systems.
- Reinforcement learning: This type of algorithm learns through trial and error, receiving rewards for desirable actions and penalties for undesirable ones. Reinforcement learning could enable AGI models to learn and adapt in real-world environments.
Challenges and Opportunities:
- Data bias: Biases within the data used to train AGI models can lead to biased and discriminatory outcomes. Ethical data sourcing and careful model development are necessary to mitigate this risk.
- Explainability and transparency: Understanding how AGI models make decisions is crucial for building trust and accountability. Research on explainable AI aims to address this challenge.
- Generalizability: AGI models should be able to learn and adapt across diverse tasks and situations. Bridging the gap between data-driven learning and adaptable reasoning is a significant hurdle.
- Ethical considerations: The development and deployment of AGI raise numerous ethical questions about bias, autonomy, and potential misuse. Robust ethical frameworks and responsible development practices are essential.
Despite these challenges, the potential benefits of AGI are vast and transformative. From revolutionizing healthcare and education to tackling global challenges like climate change, AGI holds immense promise for the future. By thoughtfully addressing the types of data and algorithms used, while carefully considering the ethical implications, we can pave the way for a future where AGI serves humanity in a positive and sustainable way.
Type of Artificial General Intelligence
There’s no single “type” of AGI yet, as it remains a theoretical concept. However, several theoretical frameworks envision different approaches to achieving AGI, each with its own strengths and weaknesses. Here are some prominent examples:
1. Human-inspired AGI:
- Biomimetic AGI: Mimics the structure and function of the human brain, using artificial neural networks inspired by biological neurons. This approach holds promise for mimicking human-like learning and adaptability, but faces challenges in replicating the complexity of the brain and efficiently scaling such models.
- Cognitive architectures: Attempts to model human cognitive processes like memory, reasoning, and problem-solving using symbolic AI techniques. This approach offers interpretability and explainability, but can be difficult to scale and adapt to diverse tasks.
2. Logical formalisms:
- Formal logic-based AGI: Utilizes axioms and logical rules to represent knowledge and reason about the world. This approach offers clarity and rigor, but can be inflexible and struggle with real-world uncertainties and complexities.
- Probabilistic reasoning: Employs statistical methods to reason under uncertainty and make predictions based on probabilities. This approach is more flexible and handles uncertainty well, but can be computationally expensive and require large amounts of data.
3. Hybrid approaches:
- Neuro-symbolic integration: Combines elements of neural networks and symbolic AI, aiming to leverage the strengths of both. This approach has the potential for more powerful and flexible reasoning, but is complex to implement and optimize.
- Evolutionary AGI: Uses evolutionary algorithms to create and select from a population of potential solutions, mimicking the process of natural selection. This approach can be effective for discovering novel solutions, but can be slow and unpredictable.
4. Embodied AGI:
- Robotics and embodied AI: Focuses on building AGI systems that interact with the real world through robots or other physical forms. This approach allows for grounding in the physical world, but faces challenges in integrating perception, action, and learning in a cohesive manner.
It’s important to remember that these are just some theoretical frameworks, and the actual path to achieving AGI may involve unforeseen approaches or a combination of these. Additionally, the “type” of AGI may eventually be less relevant than its capabilities and how it is used.
Regardless of the specific type, some key properties are often considered essential for AGI:
- Generalizability: Ability to learn and adapt across diverse tasks and situations.
- Embodiment: Interaction with the real world through perception and action.
- Self-awareness and reflection: Consciousness of its own state and ability to learn from its mistakes.
- Social intelligence: Understanding and interacting with other intelligent agents.
The pursuit of AGI raises numerous ethical and societal questions that need careful consideration before large-scale deployment. Ultimately, the type of AGI we develop will depend on our choices and priorities as a society.
Company involved in research and development related to Artificial General Intelligence (AGI)
Several companies are involved in research and development related to Artificial General Intelligence (AGI), though due to its theoretical nature, none have definitively achieved it yet. Here are some prominent players:
Large Tech Companies:
- DeepMind (Alphabet/Google): Focuses on deep learning and reinforcement learning, known for successes in game playing and protein folding.
- OpenAI (Microsoft/Elon Musk): Promotes open-source development of safe and beneficial AGI, notable for its GPT-3 and Codex language models.
- Meta (Facebook): Invests in AI research across various areas, including natural language processing and computer vision.
- Amazon: Research efforts span multiple AI aspects, including robotics and Alexa development.
- Apple: Focuses on applying AI to its products and services, particularly in Siri and machine learning features.
Research Institutes and Startups:
- The Alan Turing Institute (UK): Leading research center for AI and theoretical foundations, including AGI.
- OpenAI Five (AGI for StarCraft game): A collaboration between OpenAI and several universities, pushing the boundaries of AI in complex gaming environments.
- Anthropic AI: Founded by OpenAI researchers, focuses on safety and security aspects of AGI development.
- DeepMind Health: Applies DeepMind’s AI expertise to healthcare challenges like protein structure prediction for drug discovery.
- BenevolentAI: Utilizes AI for drug discovery and development, seeking to accelerate medical breakthroughs.
Noteworthy Initiatives:
- Partnership on AI: Consortium of tech companies and research institutions focused on ethical and responsible development of AI, including AGI.
- Global Catastrophic Risk Institute: Non-profit dedicated to mitigating existential risks, promoting safe and beneficial AGI research.
Important Aspects to Consider:
- While these companies contribute to AGI research, it’s crucial to remember that true AGI remains a long-term goal and the landscape is constantly evolving.
- Collaboration and open-source initiatives play a crucial role in sharing knowledge and accelerating progress towards safe and beneficial AGI.
- Ethical considerations and responsible development principles are paramount throughout the research and development process.
The pursuit of AGI is a complex and multifaceted endeavor, requiring diverse expertise and resources. By understanding the landscape of companies and initiatives involved, we can stay informed about advancements and engage in responsible discussions about the future of this transformative technology.
Universities to consider if you’re interested in learning about AGI:
As the quest for Artificial General Intelligence (AGI) heats up, universities around the globe are ramping up their offerings in this exciting field. Here are some of the top universities to consider if you’re interested in learning about AGI:
1. Massachusetts Institute of Technology (MIT):
- Renowned for its Computer Science and Artificial Intelligence Laboratory (CSAIL), a hub for cutting-edge AGI research.
- Offers undergraduate and graduate programs in Computer Science and Artificial Intelligence, with courses like “Introduction to Artificial Intelligence” and “Deep Learning for Natural Language Processing.”
- Boasts distinguished faculty like Rodney Brooks, known for his work on embodied AI, and Joshua Tenenbaum, a pioneer in Bayesian cognitive science.
2. Stanford University:
- Houses the Stanford Artificial Intelligence Laboratory (SAIL), another powerhouse in AGI research, focusing on areas like natural language processing, robotics, and machine learning.
- Offers undergraduate and graduate programs in Computer Science, with specializations in Artificial Intelligence and Machine Learning.
- Notable faculty include Fei-Fei Li, a leading figure in computer vision, and Andrew Ng, co-founder of Coursera and Landing AI.
3. Carnegie Mellon University:
- Home to the Robotics Institute, a world leader in robotics research, with strong connections to AGI development.
- Offers undergraduate and graduate programs in Computer Science and Robotics, with courses like “Introduction to Artificial Intelligence” and “Robot Learning.”
- Renowned faculty include Manuela Veloso, a pioneer in robot planning and learning, and Tom Mitchell, a leading figure in machine learning theory.
4. University of California, Berkeley:
- Established the Berkeley Artificial Intelligence Research (BAIR) lab, focusing on fundamental AGI research and its societal implications.
- Offers undergraduate and graduate programs in Computer Science, with specializations in Artificial Intelligence and Robotics.
- Notable faculty include Pieter Abbeel, a leader in deep reinforcement learning, and Shai Shalev-Shwartz, a prominent figure in machine learning theory.
5. University of Toronto:
- Houses the Vector Institute, a leading center for artificial intelligence research, known for its contributions to deep learning and reinforcement learning.
- Offers undergraduate and graduate programs in Computer Science, with specializations in Artificial Intelligence and Machine Learning.
- Renowned faculty include Geoffrey Hinton, co-inventor of the backpropagation algorithm, and Raquel Urtasun, a leader in self-driving car technology.
Choosing the right university for you will depend on your specific interests and goals. Consider factors like:
- Program curriculum and research focus: Does the university offer courses and research opportunities aligned with your specific interests in AGI?
- Faculty expertise: Are there professors whose research aligns with your interests and who can provide mentorship?
- Location and culture: Do you prefer a research-intensive environment in a bustling city like Boston or a more laid-back setting like Palo Alto?
- Financial aid and scholarships: What financial aid options are available to help you fund your studies?
Remember, the field of AGI is constantly evolving, so staying up-to-date with the latest research and developments is crucial. Attending conferences, workshops, and seminars can be a great way to network with other students and professionals in the field.
Potential positive impacts from Artificial General Intelligence (AGI)
Artificial General Intelligence (AGI), a machine capable of human-level intelligence and adaptability, holds immense potential for benefitting humanity across various fields. Here’s a glimpse into some potential positive impacts:
Revolutionizing Industries:
- Healthcare: AGI could assist in personalized medicine, early disease diagnosis, drug discovery, and development of advanced medical robots for surgery and care.
- Education: Personalized learning experiences, adaptable tutoring systems, and access to education in remote areas could be significantly improved with AGI-powered tools.
- Science and Research: AGI could analyze vast amounts of data, generate hypotheses, and accelerate scientific breakthroughs in fields like climate science, astronomy, and material science.
- Business and Economics: Optimized resource allocation, market predictions, and development of innovative products and services could be powered by AGI, enhancing efficiency and productivity.
Addressing Global Challenges:
- Climate Change: AGI could optimize energy usage, develop renewable energy sources, and predict natural disasters, aiding in mitigation and adaptation efforts.
- Disaster Response: AGI-powered robots could assist in search and rescue operations, analyze damage, and optimize resource allocation in disaster zones.
- Global Poverty and Inequality: AGI could analyze data to identify poverty hotspots, optimize resource allocation for social programs, and personalize interventions for individuals in need.
Enhancing Individual Lives:
- Accessibility and Assistive Technologies: AGI-powered tools could provide enhanced mobility, communication, and independence for individuals with disabilities, improving their quality of life.
- Creative Expression and Collaboration: AGI could inspire and collaborate with artists, musicians, and writers, pushing the boundaries of artistic possibilities and fostering creative expression.
- Personalized Assistance and Services: AGI-powered virtual assistants could handle complex tasks, manage schedules, and personalize services, catering to individual needs and preferences.
However, it’s crucial to acknowledge and address potential downsides and challenges:
- Bias and Discrimination: AGI trained on biased data could perpetuate existing societal inequalities. Careful data sourcing and development of unbiased algorithms are necessary.
- Job Displacement: Automation powered by AGI could lead to job losses in certain sectors. Rethinking education and job training programs is crucial for preparing the workforce for this transition.
- Ethical Considerations: The development and deployment of AGI raise numerous ethical questions about bias, autonomy, and potential misuse. Robust ethical frameworks and responsible development practices are essential.
Ultimately, the benefits of AGI can only be realized through responsible development, ethical considerations, and ensuring equitable access to its benefits. By promoting these principles, we can shape a future where AGI serves as a force for good, empowering humanity and addressing some of the world’s most pressing challenges.
Effect from Artificial general intelligence (AGI)
Artificial general intelligence (AGI), a hypothetical machine with human-level intelligence and adaptability, promises to significantly impact technology in numerous ways, both positive and negative. Let’s explore some potential effects:
Positive impacts:
- Technological advancement: AGI could accelerate innovation across various fields. For example, it could design new materials, create advanced robots, and optimize complex systems, leading to breakthroughs in fields like energy, medicine, and space exploration.
- Enhanced automation: AGI could automate complex tasks currently performed by humans, increasing efficiency and productivity in various industries. This could free up human time and resources for creative and strategic endeavors.
- Personalization and adaptation: AGI-powered technologies could personalize user experiences, tailoring services and information to individual needs and preferences. This could provide more intuitive and effective tools for communication, education, and entertainment.
- Problem-solving and decision-making: AGI could analyze vast amounts of data and identify patterns humans might miss, leading to better decision-making in areas like finance, logistics, and resource management.
- Human-machine collaboration: AGI could collaborate with humans on complex tasks, amplifying human intelligence and enabling us to tackle challenges beyond our individual capabilities.
Negative impacts:
- Job displacement: Automation driven by AGI could lead to widespread job losses in various sectors, requiring significant adaptation and reskilling efforts for the workforce.
- Bias and discrimination: AGI trained on biased data could perpetuate existing societal inequalities. Ethical considerations and unbiased data sourcing are crucial to prevent harmful impacts.
- Existential risk: Some experts express concerns about the potential for AGI to surpass human control and pose an existential threat. Robust safety measures and careful development are necessary to mitigate this risk.
- Privacy and security: AGI’s data-driven nature raises concerns about privacy violations and misuse of personal information. Strong data security measures and clear ethical guidelines are necessary.
- Dependence and loss of control: Overreliance on AGI could lead to a loss of human autonomy and decision-making skills. Promoting responsible use and maintaining human control over technology are crucial.
Overall, the impact of AGI on technology will depend on how it is developed and deployed. Responsible research, ethical considerations, and robust safety measures are essential to maximize the benefits while mitigating the risks. By actively shaping the development of AGI, we can ensure its positive impact on technology and society as a whole.
Projects in Artificial General Intelligence Field
While true AGI remains on the horizon, many exciting projects are pushing the boundaries of Artificial Intelligence towards its potential realization. Here are some noteworthy examples exploring different aspects of AGI:
Large-scale data and learning:
- Google AI’s Pathways system: Aims to train massive AI models on diverse datasets to learn generalizable skills and perform various tasks across different domains.
- OpenAI’s Anthropic model: Focuses on large-scale language models and safety research, exploring techniques to align AI with human values and goals.
- Meta AI’s Universal Language Model (UMLM): Aims to train a large-language model on billions of documents and code, enabling diverse capabilities like translation, programming, and reasoning.
Symbolic reasoning and knowledge representation:
- The OpenCog project: Strives to build an AGI framework based on interconnected modules representing different cognitive abilities like perception, memory, and reasoning.
- The GAI (Global Artificial Intelligence) project: Focuses on developing a formal, symbolic language for representing and reasoning about general knowledge and the world.
- The NuPIC project: Designs neuromorphic computing chips and software inspired by the human brain, aiming to achieve efficient and biologically plausible AI.
Robotics and embodiment:
- DeepMind’s AlphaStar project: Trained an AI agent to master the complex real-time strategy game StarCraft II, demonstrating mastery of perception, action, and planning in a dynamic environment.
- Boston Dynamics‘ humanoid robots: Showcase impressive motor skills and agility, pushing the boundaries of robot locomotion and adaptability in the real world.
- OpenAI Gym: Provides a platform for developing and testing reinforcement learning algorithms in various simulated environments, enabling research on embodied AI agents.
Safety and ethics:
- The Partnership on AI: A multi-stakeholder initiative promoting responsible development of AI, including ethics guidelines and research on safety aspects of powerful AI systems.
- The Future of Life Institute (FLI): Focuses on mitigating existential risks from advanced AI, advocating for research on safety measures and responsible development practices.
- The Center for Security and Emerging Technology (CSET): Conducts research and analysis on the societal impacts of AI, including potential risks and ethical considerations.
These are just a few examples of the diverse projects tackling different challenges on the path to AGI.
Each project contributes valuable insights and advancements, paving the way for a future where intelligent machines can collaborate with us to address some of humanity’s most pressing challenges.
The future of Artificial General Intelligence (AGI)
The future of Artificial General Intelligence (AGI) remains shrouded in both excitement and uncertainty. Let’s delve into some of the possible scenarios that may unfold:
Optimistic Future:
- Breakthroughs and acceleration: Significant advancements in AI research could lead to the realization of true AGI within the next few decades. This could usher in an era of unprecedented technological advancement and societal progress.
- Beneficial applications: AGI could be harnessed to solve some of humanity’s most pressing challenges, such as climate change, poverty, and disease. It could revolutionize industries like healthcare, education, and energy, improving the quality of life for all.
- Human-AGI collaboration: Humans and AGI could work together as partners, amplifying each other’s strengths and capabilities. AGI could handle complex tasks and calculations, while humans provide creativity, ethical guidance, and social intelligence.
Cautious Future:
- Gradual progress and challenges: The path to AGI may be more gradual than anticipated, with incremental advancements over a longer timeframe. Addressing challenges like data bias, explainability, and safety will be crucial for responsible development.
- Limited applications: Even if AGI is achieved, its capabilities may be specialized or have limitations, preventing a significant and universal impact on society. Careful consideration of how to integrate AGI into existing systems and address potential disruptions will be necessary.
- Ethical dilemmas: The development and deployment of AGI raise numerous ethical questions about bias, autonomy, and job displacement. Addressing these concerns through open dialogue, robust ethical frameworks, and responsible governance will be critical.
Pessimistic Future:
- Existential risks: Some experts warn of potential existential risks associated with AGI, such as loss of control or negative consequences of its actions. Ensuring AGI aligns with human values and remains under our control will be crucial for mitigating these risks.
- Widening inequality: Unequal access to and benefits from AGI could exacerbate existing societal inequalities. Ensuring equitable access and distribution of its benefits will be crucial for a just and sustainable future.
- Loss of agency and autonomy: Overreliance on AGI could lead to a loss of human agency and decision-making skills. Promoting responsible use and maintaining human control over technology will be essential.
Ultimately, the future of AGI lies in our hands. By taking a proactive approach, focusing on responsible research, addressing ethical concerns, and ensuring inclusive development and deployment, we can shape a future where AGI serves humanity in a positive and beneficial way.
The conclusion of Artificial General Intelligence (AGI) and the Future of Our Minds
The conclusion of Artificial General Intelligence (AGI) remains unwritten, an ever-evolving story shaped by ongoing research, ethical considerations, and the choices we make as a society.
Here are some key takeaways to consider:
Current State:
- AGI remains a theoretical concept, though significant progress in AI research brings us closer to its potential realization.
- Numerous challenges must be overcome, including data bias, explainability, safety, and ethical integration into society.
Potential Benefits:
- AGI holds immense potential to revolutionize various fields, from healthcare and education to scientific breakthroughs and addressing global challenges.
- Human-AGI collaboration could amplify our capabilities and tackle problems beyond our individual capacity.
Challenges and Risks:
- Job displacement, bias, and existential risks call for responsible development, ethical frameworks, and robust safety measures.
- Unequal access to AGI benefits could exacerbate existing societal inequalities, requiring inclusive development and distribution.
Moving Forward:
- Open dialogue, proactive governance, and continuous research are crucial for shaping a future where AGI serves humanity in a positive and beneficial way.
- Focusing on responsible development, prioritizing human values, and ensuring ethical use are key to unlocking the potential of AGI for good.
Ultimately, the conclusion of AGI lies in our hands. Through collaboration, foresight, and a commitment to responsible development, we can write a future where AGI empowers us to build a better world for all.
https://www.exaputra.com/2023/12/artificial-general-intelligence-and.html
Renewable Energy
Trump’s Destruction of Renewable Energy Benefits His Support Base, and That’s All that Matters
The death sentence that Trump has imposed on renewable energy in America is good for two groups: a) Big Oil and b) the MAGA crowd that rejects science and wants nothing more than to own the libs, aka “libtards.”
The unforeseen problem for the common American is that solar and wind are by far the least expensive sources of energy, so that the ratepayers in the U.S. are soon going to be shucking out huge amounts of extra cash each month.
Of course, this doesn’t account for the increases in the effects of climate change that, though they are devastating our planet, won’t be affecting the folks in Oklahoma too badly for the next few years while Trump does his best to profit by turning our Earth into a wasteland.
Trump’s Destruction of Renewable Energy Benefits His Support Base, and That’s All that Matters
Renewable Energy
WOMA 2026 Recap Live from Melbourne
Weather Guard Lightning Tech

WOMA 2026 Recap Live from Melbourne
Allen, Rosemary, and Yolanda, joined by Morten Handberg from Wind Power LAB, recap WOMA 2026 live from Melbourne. The crew discusses leading edge erosion challenges unique to Australia, the frustration operators face getting data from full service agreements, and the push for better documentation during project handovers. Plus the birds and bats management debate, why several operators said they’d choose smaller glass fiber blades over bigger carbon fiber ones, and what topics WOMA 2027 should tackle next year.
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 YouTube, Linkedin 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!
[00:00:00] The Uptime Wind Energy Podcast brought to you by Strike Tape protecting thousands of wind turbines from lightning damage worldwide. Visit strike tape.com and now your hosts. Welcome to the Uptime Winner Energy podcast. I’m your host, Alan Hall. I’m here with Yolanda Pone, Rosemary Barnes, and the Blade Whisperer, Morton Hamburg.
And we’re all in Melbourne at the Pullman on the park. We just finished up Woma 2026. Massive event. Over 200 people, two days, and a ton of knowledge. Rosemary, what did you think? Yeah, I mean it was a, a really good event. It was really nice ’cause we had event organization, um, taken care of by an external company this time.
So that saved us some headaches, I think. Um. But yeah, it was, it was really good. It was different than last year, and I think next year will be different again because yeah, we don’t need to talk about the same topics every single year. But, um, yeah, I got really great [00:01:00] feedback. So that’s shows we’re doing something right?
Yeah, a lot of the, the sessions were based upon feedback from Australian industry and, uh, so we did AI rotating bits, the, the drive train blades. Uh, we had a. Master class on lightning to start off. Uh, a number of discussions about BOP and electrical, BOP. All those were really good. Mm-hmm. Uh, the, the content was there, the expertise was there.
We had worldwide representation. Morton, you, you talked about blades a good bit and what the Danish and Worldwide experience was. You know, talked about the American experience on Blades. That opened up a lot of discussions because I’m never really sure where Australia is in the, uh, operations side, because a lot of it is full service agreements still.
But it does seem like from last year to this year. There’s more onboarding of the technical expertise internally at the operators. Martin, [00:02:00] you saw, uh, a good bit of it. This is your first time mm-hmm. At this conference. What were your impressions of the, the content and the approach, which is a little bit different than any other conference?
I see an industry that really wants to learn, uh, Australia, they really want to learn how to do this. Uh, and they’re willing to listen to us, uh, whether you live in Australia, in the US or in Europe. You know, they want to lean on our experiences, but they wanna, you know, they want to take it out to their wind farms and they ga then gain their own knowledge with it, which I think is really amicable.
You know, something that, you know, we should actually try and think about how we can copy that in Europe and the US. Because they, they are, they’re listening to us and they’re taking in our input, and then they try and go out. They go out and then they, they try and implement it. Um, so I think really that is something, uh, I’ve learned, you know, and, and really, um, yeah, really impressed by, from this conference.
Yeah. Yolanda, you were on several panels over the, the two days. What were your impressions of the conference and what were your thoughts [00:03:00] on the Australia marketplace? I think the conference itself is very refreshing or I think we all feel that way being on the, on the circuit sometimes going on a lot of different conferences.
It was really sweet to see everybody be very collaborative, as Morton was saying. Um, and it was, it was just really great about everybody. Yes, they were really willing to listen to us, but they were also really willing to share with each other, which is nice. Uh, I did hear about a few trials that we’re doing in other places.
From other people, just kind of, everybody wants to learn from each other and everybody wants to, to make sure they’re in as best a spot as they can. Yeah, and the, the, probably the noisiest part of the conferences were at the coffees and the lunch. Uh, the, the collaboration was really good. A lot of noise in the hallways.
Uh, just people getting together and then talking about problems, talking about solutions, trying to connect up with someone they may have seen [00:04:00]somewhere else in the part of the world that they were here. It’s a different kind of conference. And Rosemary, I know when, uh, you came up to with a suggestion like, Hey.
If there’s not gonna be any sales talks, we’re not gonna sit and watch a 30 minute presentation about what you do. We’re gonna talk about solutions. That did play a a different dynamic because. It allowed people to ingest at their own rate and, and not just sit through another presentation. Yeah. It was made it more engaging, I think.
Yeah, and I mean, anyway, the approach that I take for sales for my company that I think works best is not to do the hard sell. It’s to talk about smart things. Um, and if you are talking about describing a problem or a solution that somebody in the audience has that problem or solution, then they’re gonna seek you out afterwards.
And so. There’s plenty of sales happening in an event like this, but you’re just not like, you know, subjecting people to sales. It’s more presenting them with the information that they need. And then I, I think also the size of the conference really [00:05:00] helps ’cause yeah, about 200 people. Any, everybody is here for the same technical kind.
Content. So it’s like if you just randomly start talking to somebody while you’re waiting for a coffee or whatever, you have gonna have heaps to talk about with them, with ev every single other person there. And so I think that that’s why, yeah, there was so much talking happening and you know, we had social events, um, the first two evenings and so.
Mo like I was surprised actually. So many people stayed. Most people, maybe everybody stayed for those events and so just so much talking and yeah, we did try to have quite long breaks, um, and quite a lot of them and, you know, good enough food and coffee to keep people here. And I think that that’s as important as, you know, just sitting and listening.
Well, that was part of the trouble, some of the conference that you and I have been at, it’s just like six hours of sitting down listening to sort of a droning mm-hmm. Presenter trying to sell you something. Here we were. It was back and forth. A lot more panel talk with experts from around the world and then.[00:06:00]
Break because you just can’t absorb all that without having a little bit of a brain rest, some coffee and just trying to get to the next session. I, I think that made it, uh, a, a, a more of a takeaway than I would say a lot of other conferences are, where there’s spender booze, and. Brochures and samples being handed out and all that.
We didn’t have any of that. No vendor booze, no, uh, upfront sales going on and even into the workshop. So there was specific, uh, topics provided by people that. Provide services mostly, uh, speaking about what they do, but more on a case study, uh, side. And Rosie, you and I sat in on one that was about, uh, birds and bats, birds and bats in Australia.
That one was really good. Yeah, that was great. I learned, I learned a lot. Your mind was blown, but Totally. Yeah. It is crazy how much, how much you have to manage, um, bird and wildlife deaths related to wind farms in Australia. Like compared to, I mean, ’cause you see. Dead birds all the time, right? Cars hit [00:07:00] birds, birds hit buildings, power lines kill birds, and no one cares about those birds.
But if a bird is injured near a wind farm, then you know, everybody has to stop. We have to make sure that you can do a positive id. If you’re not sure, send it away for a DNA analysis. Keep the bird in a freezer for a year and make sure that it’s logged by the, you know, appropriate people. It’s, it’s really a lot.
And I mean, on the one hand, like I’m a real bird lover, so I am, I’m glad that birds are being taken seriously, but on the other hand, I. I think that it is maybe a little bit over the top, like I don’t see extra birds being saved because of that level of, of watching throughout the entire life of the wind farm.
It feels more like something for the pre-study and the first couple of years of operation, and then you can chill after that if everything’s under control. But I, I guess it’s quite a political issue because people do. Do worry about, about beds and bats? Mm-hmm. Yeah, I thought the output of that was more technology, a little or a little more technology.
Not a lot of technology in today’s world [00:08:00] because we could definitely monitor for where birds are and where bats are and, uh, you know. Slow down the turbines or whatever we’re gonna do. Yeah. And they are doing that in, in sites where there is a problem. But, um, yeah, the sites we’re talking about with that monitoring, that’s not sites that have a big, big problem at sites that are just Yeah, a few, a few birds dying every year.
Um, yeah. So it’s interesting. And some of the blade issues in Australia, or a little unique, I thought, uh, the leading edge erosion. Being a big one. Uh, I’ve seen a lot of leading edge erosion over the last couple of weeks from Australia. It is Texas Times two in some cases. And, uh, the discussion that was had about leading edge erosion, we had ETT junker from Stack Raft and, and video form all the way from Sweden, uh, talking to us live, which was really nice actually.
Uh, the, the amount of knowledge that the Global Blade group. Brought to the discussion and just [00:09:00] opening up some eyes about what matters in leading edge erosion. It’s not so much the leading edge erosion in terms of a EP, although there is some a EP loss. It’s more about structural damage and if you let the structure go too far.
And Martin, you’ve seen a lot of this, and I think we had a discussion about this on the podcast of, Hey, pay attention to the structural damage. Yeah, that’s where, that’s where your money is. I mean, if you go, if you get into structural damage, then your repair costs and your downtime will multiply. That is just a known fact.
So it’s really about keeping it, uh, coding related because then you can, you can, you can move really fast. You can get it the blade up to speed and you won’t have the same problems. You won’t have to spend so much time rebuilding the blade. So that’s really what you need to get to. I do think that one of the things that might stand out in Australia that we’re going to learn about.
Is the effect of hail, because we talked a lot about it in Europe, that, you know, what is the effect of, of hail on leading edge erosion? We’ve never really been able to nail it down, but down here I heard from an, [00:10:00] from an operator that they, they, uh, referenced mangoes this year in terms of hail size. It was, it was, it was incredible.
So if you think about that hitting a leading edge, then, uh, well maybe we don’t really need to, we don’t really get to the point where, so coding related, maybe we will be structural from the beginning, but. Then at least it can be less a structural. Um, but that also means that we need to think differently in terms of leading edge, uh, protection and what kinds of solutions that are there.
Maybe some of the traditional ones we have in Europe, maybe they just don’t work, want, they, they won’t work in some part of Australia. Australia is so big, so we can’t just say. Northern Territory is the same as as, uh, uh, um, yeah. Victoria or uh, or Queensland. Or Queensland or West Australia. I think that what we’re probably going to learn is that there will be different solutions fitting different parts of Australia, and that will be one of the key challenges.
Um, yeah. And Blades in Australia sometimes do. Arrive without leading edge protection from the OEMs. [00:11:00] Yeah, I’m sure some of the sites that I’ve been reviewing recently that the, the asset manager swears it’s got leading edge protection and even I saw some blades on the ground and. I don’t, I don’t see any leading edge protection.
I can’t feel any leading edge protection. Like maybe it’s a magical one that’s, you know, invisible and, um, yeah, it doesn’t even feel different, but I suspect that some people are getting blades that should have been protected that aren’t. Um, so why? Yeah, it’s interesting. I think before we, we rule it out.
Then there are some coatings that really look like the original coating. Mm. So we, we, I know that for some of the European base that what they come out of a factory, you can’t really see the difference, but they’re multilayer coating, uh, on the blades. What you can do is that you can check your, uh, your rotor certificate sometimes will be there.
You can check your, uh, your blade sheet, uh, that you get from manufacturer. If you get it. Um, if you get it, then it will, it will be there. But, um, yeah, I, I mean, it can be difficult to say, to see from the outset and there’s no [00:12:00]documentation then. Yeah, I mean. If I can’t see any leading edge erosion protection, and I don’t know if it’s there or not, I don’t think I will go so far and then start installing something on something that is essentially a new blade.
I would probably still put it into operation because most LEP products that can be installed up tower. So I don’t think that that necessarily is, is something we should, shouldn’t still start doing just because we suspect there isn’t the LEP. But one thing that I think is gonna be really good is, um, you know, after the sessions and you know, I’ve been talking a lot.
With my clients about, um, leading edge erosion. People are now aware that it’s coming. I think the most important thing is to plan for it. It’s not right to get to the point where you’ve got half a dozen blades with, you know, just the full leading edge, just fully missing holes through your laminate, and then your rest of your blades have all got laminate damage.
That’s not the time to start thinking about it because one, it’s a lot more expensive for each repair than it would’ve been, but also. No one’s got the budget to, to get through all of that in one season. So I do really [00:13:00] like that, you know, some of the sites that have been operating for five years or so are starting to see pitting.
They can start to plan that into their budget now and have a strategy for how they’re going to approach it. Um, yeah. And hopefully avoid getting over to the point where they’ve missing just the full leading edge of some of their blades. Yeah. But to Morton’s earlier point, I think it’s also important for people to stop the damage once it happens too.
If, if it’s something that. You get a site or for what, whatever reason, half of your site does look like terrible and there’s holes in the blade and stuff. You need to, you need to patch it up in some sort of way and not just wait for the perfect product to come along to, to help you with that. Some of the hot topics this week were the handover.
From, uh, development into production and the lack of documentation during the transfer. Uh, the discussion from Tilt was that you need to make sure it is all there, uh, because once you sign off. You probably can’t go back and get it. And [00:14:00] some of the frustration around that and the, the amount of data flow from the full service provider to the operator seemed to be a, a really hot topic.
And, and, uh, we did a little, uh, surveyed a about that. Just the amount of, um, I don’t know how to describe it. I mean, it was bordering on anger maybe is a way. Describe it. Uh, that they feel that operators feel like they don’t have enough insight to run the turbines and the operations as well as they can, and that they should have more insight into what they have operating and why it is not operat.
A certain way or where did the blades come from? Are there issues with those blades? Just the transparency WA was lacking. And we had Dan Meyer, who is from the States, he’s from Colorado, he was an xge person talking about contracts, uh, the turbine supply agreement and what should be in there, the full service [00:15:00] agreement, what should be in there.
Those are very interesting. I thought a lot of, uh, operators are very attentive to that, just to give themselves an advantage of what you can. Put on paper to help yourself out and what you should think about. And if you have a existing wind farm from a certain OEM and you’re gonna buy another wind farm from ’em, you ought to be taking the lessons learned.
And I, I thought that was a, a very important discussion. The second one was on repairs. And what you see from the field, and I know Yolanda’s been looking at a lot of repairs. Well, all of you have been looking at repairs in Australia. What’s your feeling on sort of the repairs and the quality of repairs and the amount of data that comes along with it?
Are we at a place that we should be, or do we need a little more detail as to what’s happening out there? It’s one of the big challenges with the full service agreements is that, you know, if everything’s running smoothly, then repairs are getting done, but the information isn’t. Usually getting passed on.
And so it’s seems fine and it seems like really good actually. Probably if you’re an [00:16:00] asset manager and everything’s just being repaired without you ever knowing about it, perfect. But then at some point when something does happen, you’ve got no history and especially like even before handover. You need to know all of the repairs that have happened for, you know, for or exchanges for any components because you know, you’re worried about, um, serial defects, for example.
You need every single one. ’cause the threshold is quite high to, you know, ever reach a serial defect. So you wanna know if there were five before there was a handover. Include that in your population. Um, yeah, so that’s probably the biggest problem with repairs is that they’re just not being. Um, the reports aren’t being handed over.
You know, one of the things that Jeremy Hanks from C-I-C-N-D-T, and he’s an NDT expert and has, has seen about everything was saying, is that you really need to understand what’s happening deep inside the blade, particularly for inserts or, uh, at the root, uh, even up in, with some, some Cory interactions happening or splicing that It’s hard to [00:17:00] see that hard to just take a drone inspection and go, okay, I know what’s happening.
You need a little more technology in there at times, especially if you have a serial defect. Why do you have a serial defect? Do you need to be, uh, uh, scanning the, the blade a little more deeply, which hasn’t really happened too much in Australia, and I think there’s some issues I’ve seen where it may come into use.
Yeah, I think it, it, it’ll be coming soon. I know some people are bringing stuff in. I’ve got emails sitting in my inbox I need to chase up, but I’m, I’m really going to, to get more into that. Yeah. And John Zalar brought up a very similar, uh, note during his presentation. Go visit your turbines. Yeah, several people said that.
Um, actually Liz said that too. Love it. And, um, let’s this, yeah, you just gotta go have a look. Oh, Barend, I think said bar said it too. Go on site. Have a look at the lunchroom. If the lunch room’s tidy, then you know, win turbine’s gonna be tidy too. And I don’t know about that ’cause I’ve seen some tidy lunchroom that were associated with some, you know, uh, less well performing assets, but it’s, you know, it’s [00:18:00] a good start.
What are we gonna hope for in 2027? What should we. Be talking about it. What do you think we’ll be talking about a year from now? Well, a few people, quite a few people mentioned to me that they were here, they’re new in the industry, and they heard this was the event to go to. Um, and so I, I was always asking them was it okay?
’cause we pitch it quite technical and I definitely don’t wanna reduce. How technical it is. One thing I thought of was maybe we start with a two to five minute introduction, maybe prerecorded about the, the topic, just to know, like for example, um, we had some sessions on rotating equipment. Um, I’m a Blades person.
I don’t know that much about rotating equipment, so maybe, you know, we just explain this is where the pitch bearings are. They do this and you know, there’s the main bearing and it, you know, it does this and just a few minutes like that to orient people. Think that could be good. Last, uh, this year we did a, a masterclass on lightning, a half day masterclass.
Maybe we change that topic every year. Maybe next year it’s blade design, [00:19:00] certification, manufacturing. Um, and then, you know, the next year, whatever, open to suggestions. I mean, in general, we’re open to suggestions, right? Like people write in and, and tell us what you’d wanna see. Um, absolutely. I think we could focus more on technologies might be an, an area like.
It’s a bit, it’s a bit hard ’cause it gets salesy, but Yeah. I think one thing that could actually be interesting and that, uh, there was one guy came up with an older turbine on the LPS system. Mm. Where he wanted to look for a solution and some of the wind farms are getting older and it’s older technology.
So maybe having some, uh, uh, some sessions on that. Because the older turbines, they are vastly different from what we, what we see in the majority with wind farms today. But the maintenance of those are just as important. And if you do that correctly, they’re much easier to lifetime extent than it will likely be for some of the nuance.
But, you know, let. Knock on wood. Um, but, but I think that’s something that could be really interesting and really relevant for the industry and something [00:20:00] that we don’t talk enough about. Yeah. Yeah, that’s true because I, I’m working on a lot of old wind turbines now, and that has been, um, quite a challenge for me because they’re design and built in a way that’s quite different to when, you know, I was poking, designing and building, uh, wind turbine components.
So that’s a good one. Other people mentioned end of life. Mm-hmm. Not just like end of life, like the life is over, but how do you decide when the life end of life is going to be? ’cause you know, like you have a planned life and then you might like to extend, but then you discover you’ve got a serial issue.
Are you gonna fix it? Or you know, how are you gonna fix it? Those are all very interesting questions that, um, can occur. And then also, yeah, what to do with the. The stuff at the end of the Wind Farm lifetime, we could make a half day around those kinds of sessions. I think recycling could actually be good to, to also touch upon and, and I think, yeah, Australia is more on the front of that because of, of your high focus on, on nature and sustainability.
So looking at, well, what do we do with these blades? Or what do we do with the towers of foundation once, uh, [00:21:00] once we do need to decommission them, you know, what is, what are we going to do in Australia about that? Or what is Australia going to do about that? But, you know, what can we bring to the, to the table that that can help drive that discussion?
I think maybe too, helping people sort of templates for their formats on, on how to successfully shadow, monitor, maybe showing them a bit mute, more of, uh. Like cases and stuff, so to get them going a bit more. ’cause we heard a lot of people too say, oh, we’re, we’re teetering on whether we should self operate or whether we continue our FSA, but we, we we’re kind of, we don’t know what we’re doing.
Yeah. In, in not those words. Right. But just providing a bit more of a guidance too. On that side, we say shadow monitoring and I think we all know what it means. If you’ve seen it done, if you haven’t seen it done before. It seems daunting. Mm-hmm. What do you mean shadow monitoring? You mean you got a crack into the SCADA system?
Does that mean I’ve gotta, uh, put CMS out there? Do I do, do I have to be out [00:22:00] on site all the time? The answer that is no to all of those. But there are some fundamental things you do need to do to get to the shadow monitoring that feels good. And the easy one is if there’s drone inspections happening because your FSA, you find out who’s doing the drone inspections and you pay ’em for a second set of drone inspections, just so you have a validation of it, you can see it.
Those are really inexpensive ways to shadow monitor. Uh, but I, I do think we say a lot of terms like that in Australia because we’ve seen it done elsewhere that. Doesn’t really translate. And I, if I, I’m always kind of looking at Rosemary, like, does it, this make sense? What I’m saying makes sense, Rosemary, because it’s hard to tell because so many operators are in sort of a building mode.
I, I see it as. When I talked to them a few years ago, they’re completely FSA, they had really small staffs. Now the staffs are growing much larger, which makes me feel like they’re gonna transition out an FSA. Do we need to provide a little more, uh, insight into how that is done deeper. [00:23:00] Like, these are the tools you, you will need.
This is the kind of people you need to have on staff. This is how you’re gonna organize it, and this is the re these are the resources that you should go after. Mm. Does that make a little si more sense? Yeah. That might be a good. Uh, idea for getting somebody who’s, you know, working for a company that is shadow monitoring overseas and bring them in and they can talk through what that, what that means exactly.
And that goes back to the discussion we were having earlier today by having operators talk about how they’re running their operations. Mm. And I know the last year we tried to have everybody do that and, and they were standoffish. I get it. Because you don’t want to disclose things that your company doesn’t want out in public.
And year two, it felt like there’s a little more. Openness about that. Yeah, there was a few people were quite open about, um, yeah, talking about challenges and some successes as well. I think we’ll have more successes next year ’cause we’ve got more, more things going on. But yeah, definitely would encourage any operators to think about what’s a you A case study that you could give about?
Yeah, it could just be a problem that’s unsolved and I bet you’ll find people that wanna help you [00:24:00] solve that problem. Or it could be something that you struggled with and then you’re doing a better job and Yeah, I mean the. Some operators think that they’re in competition with each other and some think that they’re not really, and the answer is somewhere, somewhere in the middle.
There are, you know, some at least small amounts of competition. But, you know, I just, I just really think that. We’re fighting against each other, trying to win within the wind industry. Then, you know, in 10, 20 years time, especially in Australia, there won’t be any new wind. It’ll just be wind and solar everywhere and, and the energy transition stalled because everyone knows that’s not gonna get us all the way to, you know, a hundred percent renewables.
So, um, I do think that we need to, first of all, fight for wind energy to improve. The status quo is not good enough to take us through the next 20 years. So we do need to collaborate to get better. And then, yeah, I don’t know, once we’re, once we’re one, wind has won, then we can go back to fighting amongst ourselves, I guess.
Is Australia that [00:25:00] laboratory? Yeah, I think I, I say it all the time. I think Australia is the perfect place because I, I do think we’re a little bit more naturally collaborative. For some reason, I don’t know why, it’s not really like a, a cultural thing, but seems to be the case in Australian wind. Um, and also our, our problems are harder than, uh, than what’s being faced elsewhere.
I mean, America has some specific problems right now that are, you know, worse, but in general, operating environment is very harsh Here. We’re so spread out. Everything is so expensive. Cranes are so expensive. Repairs are so expensive. Spares spare. Yeah, spares are crazy expensive. You know, I look every now and then and do reports for people about, you know, what, what’s the average cost for and times for repairs and you know, you get an American values and it’s like, okay, well at a minimum times by five Australia and you know, so.
It, there’s a lot more bang for buck. And the other thing is we just do not have enough, um, enough people, enough. Uh, we’ve got some really smart people. We need a lot more [00:26:00] people that are as smart as that. And you can’t just get that immediately. Like there has been a lot of good transfer over from related industries.
A lot of people that spoke so that, you know, they used to work for thermal power plants and, um, railway, a guy that spoke to a guy had come in from railway. Um. That’s, that’s really good. But it will take some years to get them up to speed. And so in the meantime, we just need to use technology as much as we can to be able to, you know, make the people that good people that we do have, you know, make them go a lot further, um, increase what they can do.
’cause yeah, I don’t think there’s a single, um, asset owner where they couldn’t, you know, double the number of asset managers they had and, you know, ev everyone could use twice as many I think. Yeah, I agree. Yeah. I think something that we really focused on this year is kind of removing the stones that are in people’s path or like helping at least like to, to say like, don’t trip over there.
Don’t trip over here. And I think part of that, like, like you mentioned, is that. [00:27:00] The, the collaborative manner that everyone seemed to have and just, I think 50% of our time that we were in those rooms was just people asking questions to experts, to anybody they really wanted to. Um, and it, it just, everybody getting the same answers, which is really just a really different way to, to do things, I think.
But more than, I mean, we, we we’re still. We’re still struggling with quality in Australia. That’s still a major issue on, on a lot of the components. So until we have that solved, we don’t really know how much of an influence the other factors they really have because it just overshadows everything. And yes, it will be accelerated by extreme weather conditions, but.
What will, how will it work if, if the components are actually fit, uh, fit for purpose in the sense that we don’t have wrinkles in the laminates, that we don’t have, uh, bond lines that are detaching. Mm-hmm. Maybe some of it is because of, uh, mango size hails hitting the blades. Maybe it’s because of extreme temperatures.
Maybe it’s [00:28:00] because of, uh, uh, yeah. At extreme topography, you know, creating, uh, wind conditions that the blades are not designed for. We don’t really know that. We don’t really know for sure. Uh, we just assume, um, Australia has some problems with, not problems, but some challenges with remoteness. We don’t, with, uh, with getting new, new spares that much is absolutely true.
We can’t do anything about that. We just have to, uh, find a way to, to mitigate that. Mm-hmm. But I think we should really be focused on getting quality, uh, getting the quality in, in order. You know, one thing that’s interesting about that, um, so yeah, Australia should be focused more on quality than anybody else, but in, in, in the industry, yeah.
Uh, entire world should be more focused on quality, but also Australia. Yeah. But Australia, probably more than anyone considering how hard it is to, you know, make up for poor quality here. Um. At the same time, Australia for some reason, loves to be the first one with a new technology, loves to have the biggest [00:29:00] turbine.
Um, and the, the latest thing and the newest thing, and I thought it was interesting. I mean, this was operations and maintenance, um, conference, so not really talking about new designs and manufacturing too much, but at least three or four people said, uh. Uh, I would be using less carbon fiber in blades. I would not be, not be going bigger and bigger and bigger.
If I was buying turbines for a new wind farm, I would have, you know, small glass blades and just more of them. So I think that that was really interesting to hear. So many people say it, and I wasn’t even one of them, even though, you know, I would definitely. Say that. I mean, you know, in terms of business, I guess it’s really good to get a lot of, a lot of big blades, but, um, because they just, people, I don’t think people understand that, that bigger blades just have dramatically more quality problems than the smaller ones.
Um, were really kind of exceeded the sweet spot for the current manufacturing methods and materials. I don’t know if you would agree, but it’s, it’s. Possible, but [00:30:00] it’s, it, you know, it’s not like a blade that’s twice as long, doesn’t have twice as many defects. It probably has a hundred times as many defects.
It’s just, uh, it’s really, really challenging to make those big blades, high quality, and no one is doing it all that well right now. I would, however, I got an interesting hypothetical and they’re. Congrats to her for, for putting out that out. But there was an operator that said to me at the conference, so what would you choose hypothetically?
A 70 meter glass fiber blade or a 50 meter carbon fiber blade, so a blade with carbon fiber reinforcement. And I did have to think quite a while about it because there was, it was she say, longer blades, more problems, but carbon blade. Also a lot of new problems. So, so what is it? So I, I ended up saying, well, glass fiber, I would probably go for a longer glass fiber blade, even though it will have some, some different challenges.
It’s easier to repair. Yeah, that’s true. So we can overcome some of the challenges that are, we can also repair carbon. We have done it in air, air, uh, aeronautics for many, many years. But wind is a different beast because we don’t have, uh, [00:31:00] perfect laboratory conditions to repair in. So that would just be a, a really extreme challenge.
So that’s, that’s why I, I would have gone for carbon if, for glass fiber, if, if I, if I could in that hypothe hypothetical. Also makes more energy, the 70 meter compared to it’s a win-win situation.
Well, it’s great to see all of you. Australia. I thought it was a really good conference. And thanks to all our sponsors, uh, til being the primary sponsor for this conference. Uh, we are starting to ramp up for 2027. Hopefully all of you can attend next year. And, uh, Rosie, it’s good to see you in person. Oh, it’s, uh, it’s, it’s exciting when we are actually on the same continent.
Uh, it doesn’t happen very often. And Morton, it’s great to see you too, Yolanda. I see you every day pretty much. So she’s part of our team, so I, it’s great to see you out. This is actually the first time, me and Rosie, we have seen each other. We’ve, we’ve known each other for years. Yeah. Yeah. The first time we actually, uh, been, been, yeah.
Within, uh, yeah. [00:32:00] Same room. Yep. And same continent. Yeah. Yeah. So that’s been awesome. And also it’s my first time meeting Yolanda in person too. So yeah, that’s our first time. And same. So thanks so much for everybody that attended, uh, woma 2026. We’ll see you at Woma 2027 and uh, check us out next week for the Uptime Wind Energy Podcast.
Renewable Energy
What Can Stop Climate Change?
I looked through a few of the many thousands of responses to the question above on social media and have concluded:
If you ask uneducated people who know essentially nothing about global warming, you’ll find that nothing can stop it, because it’s been going on since the origin of the planet. Others say that God controls the planet’s temperature.
If you ask climate scientists who work in laboratories around the globe who have been studying this subject for decades, you’ll find that there are two key answers: a) decarbonization of our transportation and energy sectors and b) halting the destruction of our rain forests.
As always, we have a choice to make: ignorance or science.
-
Greenhouse Gases7 months ago
Guest post: Why China is still building new coal – and when it might stop
-
Climate Change7 months ago
Guest post: Why China is still building new coal – and when it might stop
-
Greenhouse Gases2 years ago嘉宾来稿:满足中国增长的用电需求 光伏加储能“比新建煤电更实惠”
-
Climate Change2 years ago
Bill Discounting Climate Change in Florida’s Energy Policy Awaits DeSantis’ Approval
-
Climate Change2 years ago
Spanish-language misinformation on renewable energy spreads online, report shows
-
Climate Change2 years ago嘉宾来稿:满足中国增长的用电需求 光伏加储能“比新建煤电更实惠”
-
Climate Change Videos2 years ago
The toxic gas flares fuelling Nigeria’s climate change – BBC News
-
Carbon Footprint2 years agoUS SEC’s Climate Disclosure Rules Spur Renewed Interest in Carbon Credits















