#AI in materials engineering

[amrin25]

AI Revolution: 350,000 Protein Structures and Beyond

The Evolution of AI in Scientific Research

Historical Context: Early Uses of AI in Research

The journey of Artificial Intelligence in scientific research began with simple computational models and algorithms designed to solve specific problems. In the 1950s and 1960s, AI was primarily used for basic data analysis and pattern recognition. Early AI applications in research were limited by the time's computational power and data availability. However, these foundational efforts laid the groundwork for more sophisticated AI developments.

AI in Medicine

AI in Drug Discovery and Development

AI is transforming the pharmaceutical industry by accelerating drug discovery and development. Traditional drug discovery is a time-consuming and expensive endeavor, often taking over a decade and billions of dollars to bring a new drug to market. AI algorithms, however, can analyze vast datasets to identify potential drug candidates much faster and at a fraction of the cost.

Explanation of AI Algorithms Used in Identifying Potential Drug Candidates

AI drug discovery algorithms typically employ machine learning, deep Learning, and natural language processing techniques. These algorithms can analyze chemical structures, biological data, and scientific literature to predict which compounds will likely be effective against specific diseases. By modeling complex biochemical interactions, AI can identify promising drug candidates that might have been overlooked through traditional methods.

Case Studies

BenevolentAI

This company uses AI to mine scientific literature and biomedical data to discover new drug candidates.BenevolentAI's platform has identified several potential treatments for diseases such as ALS and COVID-19, demonstrating the efficiency of AI in accelerating drug discovery.

Atomwise

Atomwise utilizes deep learning algorithms to predict the binding affinity of small molecules to protein targets. Their AI-driven approach has led to the discovery of promising drug candidates for diseases like Ebola and multiple sclerosis.

Impact on Reducing Time and Costs in Drug Development

AI significantly reduces the time and cost associated with drug development. By automating the analysis of vast datasets, AI can identify potential drug candidates in months rather than years. Additionally, AI can optimize the design of clinical trials, improving their efficiency and success rates. As a result, AI-driven drug discovery is poised to revolutionize the pharmaceutical industry, bringing new treatments to market faster and more cost-effectively than ever before.

AI in Personalized Medicine

AI Applications in Interpreting Medical Images

AI is revolutionizing medical imaging by providing tools to analyze medical images with high accuracy and speed. Deep learning algorithms, particularly convolutional neural networks (CNNs), detect abnormalities in medical images, such as tumors in MRI scans or fractures in X-rays.

How AI Helps Tailor Treatments to Individual Patients

Personalized medicine aims to tailor medical treatments to each patient's individual characteristics. AI plays a crucial role in this field by analyzing genetic, clinical, and lifestyle data to develop personalized treatment plans. Machine learning algorithms can identify patterns and correlations in patient data, enabling healthcare providers to predict how patients will respond to different treatments.

Examples of AI-driven personalized Treatment Plans (e.g., IBM Watson for Oncology)

IBM Watson for Oncology: This AI system analyzes patient data and medical literature to provide oncologists with evidence-based treatment recommendations. By considering the genetic profile and medical history of each patient,Watson helps oncologists develop personalized cancer treatment plans.

Benefits and Challenges of Implementing AI in Personalized Medicine:The benefits of AI in personalized medicine include improved treatment outcomes, reduced side effects, and more efficient use of healthcare resources. However, challenges remain, such as ensuring data privacy, managing the complexity of AI models, and addressing potential biases in AI algorithms. Overcoming these challenges is essential to fully realizing the potential of AI in personalized medicine.

Benefits and Challenges of Implementing AI in Personalized Medicine

The benefits of AI in personalized medicine include improved treatment outcomes, reduced side effects, and more efficient use of healthcare resources. However, challenges remain, such as ensuring data privacy, managing the complexity of AI models, and addressing potential biases in AI algorithms. Overcoming these challenges is essential to fully realizing the potential of AI in personalized medicine.

AI in Medical Imaging and Diagnostics

AI Applications in Interpreting Medical Images

AI is revolutionizing medical imaging by providing tools to analyze medical images with high accuracy and speed. Deep learning algorithms, particularly convolutional neural networks (CNNs), detect abnormalities in medical images, such as tumors in MRI scans or fractures in X-rays

Examples of AI Tools in Diagnostics (e.g., Google's DeepMind, Zebra Medical Vision)

Google's DeepMind: DeepMind's AI systems have been used to accurately interpret retinal scans and diagnose eye diseases. Their algorithms can detect conditions like diabetic retinopathy and age-related macular degeneration early, improving patient outcomes.

Zebra Medical Vision: This company offers AI-powered solutions for interpreting medical images across various modalities, including CT, MRI, and X-ray. Their algorithms can detect various conditions, from liver disease to cardiovascular abnormalities.

The Future of AI in Improving Diagnostic Accuracy and Speed

AI has the potential to significantly improve diagnostic accuracy and speed, leading to earlier detection of diseases and better patient outcomes. As AI technology advances, it will become an integral part of medical diagnostics, assisting healthcare professionals in making more accurate and timely decisions.

AI in Climate Science

AI for Climate Modeling and Prediction

Artificial Intelligence (AI) has significantly enhanced the precision and reliability of climate models. Traditional climate models rely on complex mathematical equations to simulate the interactions between the atmosphere, oceans, land surface, and ice. However, these models often need help with climate systems' sheer complexity and scale.

AI-driven models can process data from numerous sources, including satellite imagery, weather stations, and historical climate data, to improve short-term weather forecasts and long-term climate projections. For instance, AI algorithms can detect subtle patterns in climate data that might be overlooked by conventional models, leading to more accurate predictions of extreme weather events and climate change impacts.

Examples of AI Projects in Climate Science

Climate Change AI: This initiative brings together researchers and practitioners from AI and climate science to harness AI for climate action. They work on projects that apply AI to improve climate models, optimize renewable energy systems, and develop climate mitigation strategies. For example, AI has been used to enhance the resolution of climate models, providing more detailed and accurate forecasts.

IBM's Green Horizon Project: IBM uses AI to predict air pollution levels and track greenhouse gas emissions. The system employs machine learning algorithms to analyze environmental data and forecast pollution patterns, helping cities manage air quality more effectively.

Impact of AI on Understanding and Mitigating Climate Change

AI's ability to analyze large datasets and identify trends has profound implications for understanding and mitigating climate change. By providing more accurate climate models, AI helps scientists better understand the potential impacts of climate change, including sea level rise, temperature increases, and changes in precipitation patterns. This knowledge is crucial for developing effective mitigation and adaptation strategies. AI also plays a critical role in optimizing renewable energy systems. For instance, AI algorithms can predict solar and wind power output based on weather forecasts, helping to integrate these renewable sources into the power grid more efficiently. This optimization reduces reliance on fossil fuels and helps lower greenhouse gas emissions.

Use of AI in Tracking Environmental Changes

AI technologies are increasingly used to monitor environmental changes, such as deforestation, pollution, and wildlife populations. These applications involve analyzing data from satellites, drones, and sensors to track changes in the environment in real time.

Wildbook

Wildbook uses AI and computer vision to track and monitor wildlife populations. By analyzing photos and videos uploaded by researchers and the public, Wildbook identifies individual animals and tracks their movements and behaviors.This data is invaluable for conservation efforts, helping to protect endangered species and their habitats.

Global Forest Watch

This platform uses AI to monitor deforestation and forest degradation worldwide. AI algorithms process satellite imagery to detect changes in forest cover, providing timely alerts to conservationists and policymakers. This real-time monitoring helps prevent illegal logging and supports reforestation efforts .

The Role of AI in Promoting Sustainability and Conservation Efforts

AI promotes sustainability by enabling more efficient resource management and supporting conservation initiatives. For example, AI can optimize water usage in agriculture by analyzing soil moisture data and weather forecasts to recommend precise irrigation schedules. This reduces water waste and enhances crop yields. In conservation, AI helps monitor ecosystems and detect threats to biodiversity. AI-powered drones and camera traps can automatically identify and count species, providing valuable data for conservationists. These technologies enable more effective management of protected areas and support efforts to restore endangered species populations.

AI in Materials Engineering

Explanation of How AI Accelerates the Discovery of New Materials

The discovery of new materials traditionally involves trial and error, which can be time-consuming and expensive. AI accelerates this process by predicting the properties of potential materials before they are synthesized. Machine learning models are trained on vast datasets of known materials and their properties, allowing them to predict the characteristics of new, hypothetical materials.

Materials Project

This initiative uses AI to predict the properties of thousands of materials. Researchers can use the platform to explore new materials for energy storage, electronics, and other applications. The Materials Project has led to the discovery of new battery materials and catalysts, significantly speeding up the research process.

Citrine Informatics

Citrine uses AI to analyze data on materials and predict optimal compositions for specific applications. Their platform has been used to develop new alloys, polymers, and ceramics with enhanced properties, such as increased strength or conductivity.

Potential Breakthroughs Enabled by AI in Materials Science

AI-driven materials research has the potential to revolutionize various industries. For instance, AI could lead to the discovery of new materials for more efficient solar panels, lightweight and durable materials for aerospace, and high-capacity batteries for electric vehicles. These breakthroughs would have significant economic and environmental benefits, driving innovation and sustainability.

AI in Predicting Material Properties

How AI Models Predict Properties and Behaviors of Materials

AI models use data from existing materials to predict the properties and behaviors of new materials. These models can simulate how a material will respond to different conditions, such as temperature, pressure, and chemical environment. This predictive capability allows researchers to identify promising materials without extensive laboratory testing.

Polymers and Alloys

AI models have been used to predict the mechanical properties of polymers and alloys, such as tensile strength, elasticity, and thermal stability. This helps design materials that meet specific performance criteria for industrial applications

Impact on Developing Advanced Materials for Various Industries

AI's predictive capabilities accelerate the development of advanced materials, reducing the time and cost associated with traditional experimental methods. In electronics, aerospace, and energy industries, AI-driven materials discovery leads to the development of components with superior performance and durability. This innovation drives progress in technology and manufacturing, supporting economic growth and environmental sustainability.

Tools and Technologies Driving AI in Research

Detailed Overview of AlphaFold and Its Significance

AlphaFold developed by DeepMind, is an AI system with remarkable breakthroughs in predicting protein structures. Accurately predicting protein structures is vital because the shape of a protein determines its function, and misfolded proteins can lead to diseases such as Alzheimer's and Parkinson's. Defining a protein's structure traditionally required techniques like X-ray crystallography and cryo-electron microscopy, which are both time-consuming and expensive.

How AlphaFold Has Revolutionized Protein Structure Prediction

In 2020, AlphaFold achieved a significant milestone by outperforming other methods for the Critical Assessment of Protein Structure Prediction (CASP) competition. AlphaFold's predictions were comparable to experimental results, achieving a median Global Distance Test (GDT) score of 92.4 out of 100 for the hardest targets in CASP14. This level of accuracy had never been achieved before by computational methods.

The AI system uses neural networks trained on a vast dataset of known protein structures and sequences. It can predict the 3D shapes of proteins based solely on their amino acid sequences, which traditionally took months or years but are now reduced to days.

AlphaFold's success has had a profound impact on various fields:

Drug Discovery

With accurate protein structures, drug developers can design more effective drugs targeting specific proteins. This could significantly reduce the time and cost of bringing new medicines to market.

Biology and Medicine

Understanding protein structures helps researchers decipher their functions, interactions, and roles in diseases. This knowledge is crucial for developing new treatments and understanding biological processes.

Biotechnology

Industries relying on enzymes and other proteins can use AlphaFold to optimize and engineer proteins for specific applications, enhancing efficiency and innovation.

AI Platforms and Frameworks

Several AI platforms and frameworks are widely used in scientific research to facilitate the development and deployment of AI models. Key platforms include:

TensorFlow

Google developed this open-source machine learning framework for various AI applications, including research.

PyTorch

Developed by Facebook's AI Research lab, PyTorch is known for its flexibility and ease of use. It has gained immense popularity among researchers, with over 100,000 stars on GitHub as of 2023.

Keras

A high-level neural networks API running on top of TensorFlow, Keras provides a simplified interface for building and training models. It is used extensively in academic research and industry .

Examples of How These Platforms Facilitate Scientific Discovery

TensorFlow

TensorFlow has been used in projects ranging from image recognition to natural language processing. For instance, it has been used to develop AI models for detecting diabetic retinopathy from retinal images with an accuracy comparable to that of human specialists.

PyTorch

PyTorch's dynamic computational graph makes it ideal for research. Researchers have used PyTorch to create models for climate prediction and medical image analysis, leading to significant advancements in these fields.

Keras

Keras simplifies the process of designing and testing deep learning models, making them accessible to both beginners and experts. It has been used in applications such as genomics and neuroscience, where rapid prototyping and iteration are crucial (Harvard)

The Role of Open-Source AI Tools in Accelerating Innovation

Open-source AI tools democratize access to advanced technologies, enabling researchers worldwide to collaborate and innovate. These tools provide a shared foundation for developing new algorithms, sharing datasets, and building upon each other's work. The collaborative nature of open-source projects accelerates innovation, leading to rapid advancements in AI research and its applications across various scientific disciplines.

Real-Life Examples of AI in Scientific Discovery

AlphaFold's Breakthrough in Protein Folding

In 2020, DeepMind's AlphaFold made a groundbreaking advancement by accurately predicting protein structures. This achievement has far-reaching implications for drug discovery and understanding of diseases. The system has been used to indicate the structure of over 350,000 proteins across 20 different organisms, helping researchers understand protein functions and interactions at an unprecedented scale.

AI in COVID-19 Research

During the COVID-19 pandemic, AI played a crucial role in accelerating vaccine development and drug repurposing. Companies like Moderna used AI to speed up the design of mRNA sequences for their vaccines, significantly reducing development time from years to months. AI algorithms also helped identify existing drugs that could be repurposed to treat COVID-19, leading to faster clinical trials and treatments. For example, AI identified Baricitinib as a potential treatment that was later approved by the FDA.

IBM Watson in Oncology

IBM Watson for Oncology uses AI to analyze large medical literature and patient data to provide personalized cancer treatment recommendations. This tool has been deployed in various hospitals worldwide, improving treatment accuracy and outcomes.

AI in Climate Science: Project Climate Change AI

The Climate Change AI initiative leverages AI to enhance climate modeling, predict extreme weather events, and optimize renewable energy systems. AI models have been used to indicate the impact of climate change on agricultural yields, helping farmers adapt to changing conditions. For instance, AI-driven models have improved the accuracy of weather forecasts, aiding in disaster preparedness and response. These advancements help mitigate the impacts of climate change and promote sustainability.

Citrine Informatics in Materials Science

Citrine Informatics uses AI to accelerate the discovery and development of new materials. Their platform combines machine learning with materials science data to predict material properties and optimize formulations, leading to faster innovation in industries such as aerospace and electronics. The company's AI-driven approach has resulted in new materials with enhanced performance characteristics, reducing the time and cost of traditional materials research. For example, Citrine's platform has helped develop new alloys with improved strength and durability for aerospace applications.

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At Coditude, we are committed to driving innovation and helping businesses leverage the power of Artificial Intelligence to achieve extraordinary results. Our expertise spans various fields, from accelerating drug discovery to optimizing renewable energy systems and revolutionizing materials science.

Are you ready to harness AI's transformative potential for your organization? Whether you're looking to enhance your research capabilities, streamline operations, or develop groundbreaking solutions, Coditude guides you every step of the way.

[jadynwaymire1997blog]

[materiallugy]

What can AI do in materials science and engineering?

AI (Artificial Intelligence) plays a transformative role in materials science and engineering, offering a wide range of capabilities that enhance research, development, and application of materials.

AI intervenes in materials discovery and design, high-throughput screening and experimentation, materials characterization and analysis, materials modeling and simulation, materials performance optimization, materials lifecycle and sustainability, and collaborative platforms and knowledge sharing.

Read more:

[nuadox]

Researchers leverage AI to unveil key insights into 2D halide perovskites for stable, efficient solar cells

- By Nuadox Crew -

Researchers at Chalmers University of Technology in Sweden have used computer simulations and machine learning to better understand how 2D halide perovskite materials function, paving the way for more efficient and stable solar cells.

These perovskites, seen as promising alternatives to traditional silicon in solar energy and optical applications, are cost-effective and efficient in absorbing and emitting light. However, their tendency to degrade quickly has been a challenge.

By simulating the atomic behavior of 2D perovskites under different conditions, the research team gained detailed insights into how the materials respond to heat, light, and other factors.

Their findings highlight the role of organic molecules in controlling atomic movements within the material layers, which affect the material's optical properties and stability.

Header image: The illustration depicts the 2D perovskite material studied by the researchers, with the yellow sections representing the linker molecules and the purple and pink areas indicating the perovskite layer. Credit: Julia Wiktor, Chalmers University of Technology.

Read more at Chalmers University of Technology

Scientific paper: Impact of Organic Spacers and Dimensionality on Templating of Halide Perovskites Erik Fransson, Julia Wiktor, and Paul Erhart ACS Energy Letters 2024 9 (8), 3947-3954 DOI: 10.1021/acsenergylett.4c01283

Read Also

Off the roof: The quest to harness energy from facades

[claptraprights]

more nisha that I think turned out nice

[jcmarchi]

Need a research hypothesis? Ask AI.

New Post has been published on https://thedigitalinsider.com/need-a-research-hypothesis-ask-ai/

Need a research hypothesis? Ask AI.

Crafting a unique and promising research hypothesis is a fundamental skill for any scientist. It can also be time consuming: New PhD candidates might spend the first year of their program trying to decide exactly what to explore in their experiments. What if artificial intelligence could help?

MIT researchers have created a way to autonomously generate and evaluate promising research hypotheses across fields, through human-AI collaboration. In a new paper, they describe how they used this framework to create evidence-driven hypotheses that align with unmet research needs in the field of biologically inspired materials.

Published Wednesday in Advanced Materials, the study was co-authored by Alireza Ghafarollahi, a postdoc in the Laboratory for Atomistic and Molecular Mechanics (LAMM), and Markus Buehler, the Jerry McAfee Professor in Engineering in MIT’s departments of Civil and Environmental Engineering and of Mechanical Engineering and director of LAMM.

The framework, which the researchers call SciAgents, consists of multiple AI agents, each with specific capabilities and access to data, that leverage “graph reasoning” methods, where AI models utilize a knowledge graph that organizes and defines relationships between diverse scientific concepts. The multi-agent approach mimics the way biological systems organize themselves as groups of elementary building blocks. Buehler notes that this “divide and conquer” principle is a prominent paradigm in biology at many levels, from materials to swarms of insects to civilizations — all examples where the total intelligence is much greater than the sum of individuals’ abilities.

“By using multiple AI agents, we’re trying to simulate the process by which communities of scientists make discoveries,” says Buehler. “At MIT, we do that by having a bunch of people with different backgrounds working together and bumping into each other at coffee shops or in MIT’s Infinite Corridor. But that’s very coincidental and slow. Our quest is to simulate the process of discovery by exploring whether AI systems can be creative and make discoveries.”

Automating good ideas

As recent developments have demonstrated, large language models (LLMs) have shown an impressive ability to answer questions, summarize information, and execute simple tasks. But they are quite limited when it comes to generating new ideas from scratch. The MIT researchers wanted to design a system that enabled AI models to perform a more sophisticated, multistep process that goes beyond recalling information learned during training, to extrapolate and create new knowledge.

The foundation of their approach is an ontological knowledge graph, which organizes and makes connections between diverse scientific concepts. To make the graphs, the researchers feed a set of scientific papers into a generative AI model. In previous work, Buehler used a field of math known as category theory to help the AI model develop abstractions of scientific concepts as graphs, rooted in defining relationships between components, in a way that could be analyzed by other models through a process called graph reasoning. This focuses AI models on developing a more principled way to understand concepts; it also allows them to generalize better across domains.

“This is really important for us to create science-focused AI models, as scientific theories are typically rooted in generalizable principles rather than just knowledge recall,” Buehler says. “By focusing AI models on ‘thinking’ in such a manner, we can leapfrog beyond conventional methods and explore more creative uses of AI.”

For the most recent paper, the researchers used about 1,000 scientific studies on biological materials, but Buehler says the knowledge graphs could be generated using far more or fewer research papers from any field.

With the graph established, the researchers developed an AI system for scientific discovery, with multiple models specialized to play specific roles in the system. Most of the components were built off of OpenAI’s ChatGPT-4 series models and made use of a technique known as in-context learning, in which prompts provide contextual information about the model’s role in the system while allowing it to learn from data provided.

The individual agents in the framework interact with each other to collectively solve a complex problem that none of them would be able to do alone. The first task they are given is to generate the research hypothesis. The LLM interactions start after a subgraph has been defined from the knowledge graph, which can happen randomly or by manually entering a pair of keywords discussed in the papers.

In the framework, a language model the researchers named the “Ontologist” is tasked with defining scientific terms in the papers and examining the connections between them, fleshing out the knowledge graph. A model named “Scientist 1” then crafts a research proposal based on factors like its ability to uncover unexpected properties and novelty. The proposal includes a discussion of potential findings, the impact of the research, and a guess at the underlying mechanisms of action. A “Scientist 2” model expands on the idea, suggesting specific experimental and simulation approaches and making other improvements. Finally, a “Critic” model highlights its strengths and weaknesses and suggests further improvements.

“It’s about building a team of experts that are not all thinking the same way,” Buehler says. “They have to think differently and have different capabilities. The Critic agent is deliberately programmed to critique the others, so you don’t have everybody agreeing and saying it’s a great idea. You have an agent saying, ‘There’s a weakness here, can you explain it better?’ That makes the output much different from single models.”

Other agents in the system are able to search existing literature, which provides the system with a way to not only assess feasibility but also create and assess the novelty of each idea.

Making the system stronger

To validate their approach, Buehler and Ghafarollahi built a knowledge graph based on the words “silk” and “energy intensive.” Using the framework, the “Scientist 1” model proposed integrating silk with dandelion-based pigments to create biomaterials with enhanced optical and mechanical properties. The model predicted the material would be significantly stronger than traditional silk materials and require less energy to process.

Scientist 2 then made suggestions, such as using specific molecular dynamic simulation tools to explore how the proposed materials would interact, adding that a good application for the material would be a bioinspired adhesive. The Critic model then highlighted several strengths of the proposed material and areas for improvement, such as its scalability, long-term stability, and the environmental impacts of solvent use. To address those concerns, the Critic suggested conducting pilot studies for process validation and performing rigorous analyses of material durability.

The researchers also conducted other experiments with randomly chosen keywords, which produced various original hypotheses about more efficient biomimetic microfluidic chips, enhancing the mechanical properties of collagen-based scaffolds, and the interaction between graphene and amyloid fibrils to create bioelectronic devices.

“The system was able to come up with these new, rigorous ideas based on the path from the knowledge graph,” Ghafarollahi says. “In terms of novelty and applicability, the materials seemed robust and novel. In future work, we’re going to generate thousands, or tens of thousands, of new research ideas, and then we can categorize them, try to understand better how these materials are generated and how they could be improved further.”

Going forward, the researchers hope to incorporate new tools for retrieving information and running simulations into their frameworks. They can also easily swap out the foundation models in their frameworks for more advanced models, allowing the system to adapt with the latest innovations in AI.

“Because of the way these agents interact, an improvement in one model, even if it’s slight, has a huge impact on the overall behaviors and output of the system,” Buehler says.

Since releasing a preprint with open-source details of their approach, the researchers have been contacted by hundreds of people interested in using the frameworks in diverse scientific fields and even areas like finance and cybersecurity.

“There’s a lot of stuff you can do without having to go to the lab,” Buehler says. “You want to basically go to the lab at the very end of the process. The lab is expensive and takes a long time, so you want a system that can drill very deep into the best ideas, formulating the best hypotheses and accurately predicting emergent behaviors. Our vision is to make this easy to use, so you can use an app to bring in other ideas or drag in datasets to really challenge the model to make new discoveries.”

[aippals]

Artificial Intelligence for Developers in pune | India

Artificial intelligence developers are concentrating on developing algorithms that allow robots to emulate human intelligence. These people create artificial intelligence systems that are capable of sorting through massive amounts of data, identifying patterns, projecting results, and resolving challenging problems.

[laicatone1994blog]

[roborevolutionaryrenditionsblog]

[7artsysoulroamblog]

[joyandella-123]

The Impact of AI-Generated Content

In recent years, the rapid advancement of artificial intelligence (AI) has transformed various industries, including content creation. AI algorithms are increasingly being used to generate written content for websites, blogs, and marketing materials. As AI-generated content becomes more prevalent, questions about transparency and ethics arise, leading us to ponder whether it should be labeled as such. In this article, we explore how Google treats AI content and the debate surrounding the labeling of AI-generated content.

Google's Stance on AI-Generated Content

Google, as the dominant search engine, plays a crucial role in shaping how online content is discovered and ranked. To understand its approach to AI-generated content, we must delve into its search algorithms and policies.

Google's algorithms are designed to assess the quality, relevance, and usefulness of web content. In doing so, they evaluate factors such as keywords, backlinks, user engagement, and more. The goal is to provide users with the most valuable and accurate information in response to their queries.

As AI-generated content continues to proliferate, Google's algorithms adapt to assess the quality of such content. Google has stated that it doesn't specifically penalize or favor AI-generated content. Instead, it evaluates it based on the same criteria as any other content. This means that AI-generated content can rank well in search results if it meets Google's quality guidelines.

Examine Google's official statements regarding content generated by artificial intelligence.

From Google Search Liaison Danny Sullivan’s November 2022 tweets:

“We haven’t said AI content is bad. We’ve said, pretty clearly, content written primarily for search engines rather than humans is the issue. That’s what we’re focused on.”

Gary Illyes’ statement on labeling AI-generated content, June 16, 2023:

“We do not label it as AI-generated content. Again, it’s not whether the AI ​​wrote it, but whether it’s high quality.”

The Need for Transparency

While Google may not penalize AI-generated content, the question of labeling remains contentious. Many argue that transparency is essential, as it ensures that users are aware of the content's origin and can make informed judgments.

Labeling AI-generated content can serve several purposes:

User Awareness: When content is generated by AI, labeling it as such informs users that they are consuming machine-generated material. This transparency can help users assess the reliability and trustworthiness of the information.

Ethical Considerations: Disclosing AI-generated content promotes ethical practices in content creation. It ensures that creators give credit to AI tools while also taking responsibility for the content they publish.

Content Originality: Labeling AI content helps distinguish it from human-generated content. This distinction can be crucial when it comes to issues of copyright and intellectual property.

Algorithmic Fairness: Transparency in labeling AI content contributes to the fairness of search engine results. Users and content creators should understand how algorithms work and how AI influences what they see in search results.

Balancing Act: Benefits and Concerns

The debate on labeling AI-generated content is not without its complexities. On one hand, transparency is critical to building trust and ensuring ethical content creation. On the other hand, labeling AI content might inadvertently stigmatize it or lead users to dismiss it as less credible.

Additionally, labeling raises questions about what constitutes AI-generated content. Is it content entirely generated by machines, or does it include content that is edited or curated by humans using AI assistance? Striking the right balance is crucial to avoid stifling innovation while upholding transparency.

The rise of AI-generated content presents both opportunities and challenges for content creators, consumers, and search engines like Google. While Google does not discriminate against AI-generated content, the labeling debate underscores the importance of transparency, ethics, and user awareness.

As AI continues to shape the content landscape, it is essential for stakeholders to engage in a thoughtful dialogue about the role of AI-generated content and the best practices for its integration into the digital ecosystem. Ultimately, finding a balance that promotes innovation, preserves trust, and upholds ethical content creation is key to navigating this evolving landscape successfully.

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[mit]

Video: Melanie Gonick/MIT

A team of engineers has developed a new 3D inkjet printing system that utilizes computer vision for contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.

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[fuck-customers]

(half rant half story)

I'm a physicist. I work for a company that helps develop car parts. Essentially, car companies come to us with ideas on what they want from a part or material, and we make/test the idea or help them make/test it. Usually this means talking to other scientists and engineers and experts and it's all fine. Sometimes this means talking to businesspeople and board execs and I hate them

A bit ago when AI was really taking off in the zeitgeist I went to a meeting to talk about some tweaks Car Company A wanted to make to their hydraulics- specifically the master cylinder, but it doesn't super matter. I thought I'd be talking to their engineers - it ends up being just me, their head supervisor (who was not a scientist/engineer) and one of their executives from a different area (also not a scientist/engineer). I'm the only one in the room who actually knows how a car works, and also the lowest-level employee, and also aware that these people will give feedback to my boss based on how I 'represent the company ' whilst I'm here.

I start to explain my way through how I can make some of the changes they want - trying to do so in a way they'll understand - when Head Supervisor cuts me off and starts talking about AI. I'm like "oh well AI is often integrated into the software for a car but we're talking hardware right now, so that's not something we really ca-"

"Can you add artificial intelligence to the hydraulics?"

"..sorry, what was that?"

"Can you add AI to the hydraulics system?"

can i fucking what mate "Sir, I'm sorry, I'm a little confused - what do you mean by adding AI to the hydraulics?"

"I just thought this stuff could run smoother if you added AI to it. Most things do"

The part of the car that moves when you push the acceleration pedal is metal and liquid my dude what are you talking about "You want me to .add AI...to the pistons? To the master cylinder?"

"Yeah exactly, if you add AI to the bit that makes the pistons work, it should work better, right?"

IT'S METAL PIPES it's metal pipes it's metal pipes "Sir, there isn't any software in that part of the car"

"I know, but it's artificial intelligence, I'm sure there's a way to add it"

im exploding you with my mind you cannot seriously be asking me to add AI to a section of car that has as much fucking code attached to it as a SOCK what do you MEAN. The most complicated part of this thing is a SPRING you can't be serious

He was seriously asking. I've met my fair share of idiots but I was sure he wasn't genuinely seriously asking that I add AI directly to a piston system, but he was. And not even in the like "oh if we implement a way for AI to control that part" kind of way, he just vaguely thought that AI would "make it better" WHAT THE FUCK DO YOU MEANNNNN I HAD TO SPEND 20 MINUTES OF MY HARD EARNED LIFE EXPLAINING THAT NEITHER I NOR ANYONE ELSE CAN ADD AI TO A GOD DAMNED FUCKING PISTON. "CAN YOU ADD AI TO THE HYDRAULICS" NO BUT EVEN WITHOUT IT THAT METAL PIPE IS MORE INTELLIGENT THAN YOU

Posted by admin Rodney.

[mcmansionhell]

the motel room, or: on datedness

I.

Often I find myself nostalgic for things that haven't disappeared yet. This feeling is enhanced by the strange conviction that once I stop looking at these things, I will never see them again, that I am living in the last moment of looking. This is sense is strongest for me in the interiors of buildings perhaps because, like items of clothing, they are of a fashionable nature, in other words, more impermanent than they probably should be.

As I get older, to stumble on something truly dated, once a drag, is now a gift. After over a decade of real estate aggregation and the havoc it's wreaked on how we as a society perceive and decorate houses, if you're going to Zillow to search for the dated (which used to be like shooting fish in a barrel), you'll be searching aimlessly, for hours, to increasingly no avail, even with all the filters engaged. (The only way to get around this is locational knowledge of datedness gleaned from the real world.) If you try to find images of the dated elsewhere on the internet, you will find that the search is not intuitive. In this day and age, you cannot simply Google "80s hotel room" anymore, what with the disintegration of the search engine ecosystem and the AI generated nonsense and the algorithmic preference for something popular (the same specific images collected over and over again on social media), recent, and usually a derivative of the original search query (in this case, finding material along the lines of r/nostalgia or the Backrooms.)

To find what one is looking for online, one must game the search engine with filters that only show content predating 2021, or, even better, use existing resources (or those previously discovered) both online and in print. In the physical world of interiors, to find what one is looking for one must also now lurk around obscure places, and often outside the realm of the domestic which is so beholden to and cursed by the churn of fashion and the logic of speculation. Our open world is rapidly closing, while, paradoxically, remaining ostensibly open. It's true, I can open Zillow. I can still search. In the curated, aggregated realm, it is becoming harder and harder to find, and ultimately, to look.

But what if, despite all these changes, datedness was never really searchable? This is a strange symmetry, one could say an obscurity, between interiors and online. It is perhaps unintentional, and it lurks in the places where searching doesn't work, one because no one is searching there, or two, because an aesthetic, for all our cataloguing, curation, aggregation, hoarding, is not inherently indexable and even if it was, there are vasts swaths of the internet and the world that are not categorized via certain - or any - parameters. The internet curator's job is to find them and aggregate them, but it becomes harder and harder to do. They can only be stumbled upon or known in an outside, offline, historical or situational way. If to index, to aggregate, is, or at least was for the last 30 years, to profit (whether monetarily or in likes), then to be dated, in many respects, is the aesthetic manifestation of barely breaking even. Of not starting, preserving, or reinventing but just doing a job.

We see this online as well. While the old-web Geocities look and later Blingee MySpace-era swag have become aestheticized and fetishized, a kind of naive art for a naive time, a great many old websites have not received the same treatment. These are no less naive but they are harder to repackage or commodify because they are simple and boring. They are not "core" enough.

As with interiors, web datedness can be found in part or as a whole. For example, sites like Imgur or Reddit are not in and of themselves dated but they are full of remnants, of 15-year old posts and their "you, sir, have won the internet" vernacular that certainly are. Other websites are dated because they were made a long time ago by and for a clientele that doesn't have a need or the skill to update (we see this often with Web 2.0 e-commerce sites that figured out how to do a basic mobile page and reckoned it was enough). The next language of datedness, like the all-white landlord-special interior, is the default, clean Squarespace restaurant page, a landing space that's the digital equivalent of a flyer, rarely gleaned unless someone needs a menu, has a food allergy or if information about the place is not available immediately from Google Maps. I say this only to maintain that there is a continuity in practices between the on- and off-line world beyond what we would immediately assume, and that we cannot blame everything on algorithms.

But now you may ask, what is, exactly, datedness? Having spent two days in a distinctly dated hotel room, I've decided to sit in utter boredom with the numinous past and try and pin it down.

II.

I am in an obscure place. I am in Saint-Georges, Quebec, Canada, on assignment. I am staying at a specific motel, the Voyageur. By my estimation the hotel was originally built in the late seventies and I'd be shocked if it was older than 1989. The hotel exterior was remodeled sometime in the 2000s with EIFS cladding and beige paint. Above is a picture of my room, which, forgive me, is in the process of being inhabited. American (and to a lesser extent Canadian) hotel rooms are some of the most churned through, renovated spaces in the world, and it's pretty rare, unless you're staying in either very small towns or are forced by economic necessity to stay at real holes in the wall, to find ones from this era. The last real hitter for me was a 90s Day's Inn in the meme-famous Breezewood, PA during the pandemic.

At first my reaction to seeing the room was cautionary. It was the last room in town, and certainly compared to other options, probably not the world's first choice. However, after staying in real, genuine European shitholes covering professional cycling I've become a class-A connoisseur of bad rooms. This one was definitively three stars. A mutter of "okay time to do a quick look through." But upon further inspection (post-bedbug paranoia) I came to the realization that maybe the always-new brainrot I'd been so critical of had seeped a teeny bit into my own subconscious and here I was snubbing my nose at a blessing in disguise. The room is not a bad room, nor is it unclean. It's just old. It's dated. We are sentimental about interiors like this now because they are disappearing, but they are for my parents what 2005 beige-core is for me and what 2010s greige will become for the generation after. When I'm writing about datedness, I'm writing in general using a previous era's examples because datedness, by its very nature, is a transitional status. Its end state is the mixed emotion of seeing things for what they are yet still appreciating them, expressed here.

Datedness is the period between vintage and contemporary. It is the sentiment between quotidian and subpar. It is uncurated and preserved only by way of inertia, not initiative. It gives us a specific feeling we don't necessarily like, one that is deliberately evoked in the media subcultures surrounding so-called "liminal" spaces: the fuguelike feeling of being spatially trapped in a time while our real time is passing. Datedness in the real world is not a curated experience, it is only what was. It is different from nostalgia because it is not deliberately remembered, yearned for or attached to sweetness. Instead, it is somehow annoying. It is like stumbling into the world of adults as a child, but now you're the adult and the child in you is disappointed. (The real child-you forgot a dull hotel room the moment something more interesting came along.) An image of my father puts his car keys on the table, looks around and says, "It'll do." We have an intolerance for datedness because it is the realization of what sufficed. Sufficiency in many ways implies lack.

However, for all its datedness, many, if not all, of the things in this room will never be seen again if the room is renovated. They will become unpurchaseable and extinct. Things like the bizarrely-patterned linoleum tile in the shower, the hose connecting to the specific faucet of the once-luxurious (or at least middling) jacuzzi tub whose jets haven't been exercised since the fall of the Berlin Wall. The wide berth of the tank on the toilet. There is nothing, really, worth saving about these things. Even the most sentimental among us wouldn't dare argue that the items and finishes in this room are particularly important from a design or historical standpoint. Not everything old has a patina. They're too cheaply made to salvage. Plastic tile. Bowed plywood. The image-artifacts of these rooms, gussied up for Booking dot com, will also, inevitably disappear, relegated to the dustheap of web caches and comments that say "it was ok kinda expensive but close to twon (sic)." You wouldn't be able to find them anyway unless you were looking for a room.

One does, of course, recognize a little bit of design in what's here. Signifiers of an era. The wood-veneer of the late 70s giving way to the pastel overtones of the 80s. Perhaps even a slow 90s. The all-in-one vanity floating above the floor, a modernist basement bathroom hallmark. White walls as a sign of cleanliness. Gestures, in the curved lines of the nightstands, towards postmodernity. Metallic lamp bases with wide-brimmed shades, a whisper of glamor. A kind of scalloped aura to the club chairs. The color teal mediated through hundreds if not thousands of shoes. Yellowing plastic, including the strips of "molding" that visually tie floor to wall. These are remnants (or are they intuitions?) of so many movements and micromovements, none of them definite enough to point to the influence of a single designer, hell, even of a single decade, just strands of past-ness accumulated into one thread, which is cheapness. Continuity exists in the materials only because everything was purchased as a set from a wholesale catalog.

In some way a hotel is supposed to be placeless. Anonymous. Everything tries to be that way now, even houses. Perhaps because we don't like the way we spy on ourselves and lease our images out to the world so we crave the specificity of hotel anonymity, of someplace we move through on our way to bigger, better or at least different things. The hotel was designed to be frictionless but because it is in a little town, it sees little use and because it sees little use, there are elements that can last far longer than they were intended and which inadvertently cause friction. (The janky door unlocks with a key. The shower hose keeps coming out of the faucet. It's deeply annoying.)

Lack of wear and lack of funds only keep them that way. Not even the paper goods of the eighties have been exhausted yet. Datedness is not a choice but an inevitability. Because it is not a choice, it is not advertised except in a utilitarian sense. It is kept subtle on the hotel websites, out of shame. Because it does not subscribe to an advertiser's economy of the now, of the curated type rather than the "here is my service" type, it disappears into the folds of the earth and cannot be searched for in the way "design" can. It can only be discovered by accident.

When I look at all of these objects and things, I do so knowing I will never see them again, at least not all here together like this, as a cohesive whole assembled for a specific purpose. I don't think I'll ever have reason to come back to this town or this place, which has given me an unexpected experience of being peevish in my father's time. Whenever I end up in a place like this, where all is as it was, I get the sense that it will take a very long time for others to experience this sensation again with the things my generation has made. The machinations of fashion work rapaciously to make sure that nothing is ever old, not people, not rooms, not items, not furniture, not fabrics, not even design, that old matron who loves to wax poetic about futurity and timelessness. The plastic-veneered particleboard used here is now the bedrock of countless landfills. Eventually it will become the chemical-laced soil upon which we build our condos. It is possible that we are standing now at the very last frontier of our prior datedness. The next one has not yet elided. It's a special place. Spend a night. Take pictures.

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[nuadox]

A novel AI model could aid in the production of clean water

- By Nuadox Crew -

Around 2.2 billion people lack access to safe drinking water, and half of the global population faces severe water scarcity at some point each year.

To address these challenges, expensive solutions like sewer irrigation, rainwater reuse, and seawater desalination are used. However, centralized water systems struggle to quickly adapt to changes in demand, prompting interest in decentralized, electrochemical water production technologies.

Dr. Son Moon's team at the Korea Institute of Science and Technology (KIST), in collaboration with Professor Baek Sang-Soo’s team, developed an AI-based model using random forest techniques to accurately predict ion concentrations during electrochemical water treatment.

This model can monitor individual ions like Na+, K+, Ca2+, and Cl- and requires minimal computing resources compared to deep learning models.

The research offers potential improvements in national water quality management systems, helping to better monitor and manage water resources.

Image: Summary of ion concentration prediction in water using machine learning (random forest) based on conductivity. Credit: Korea Institute of Science and Technology.

Read more at National Research Council of Science and Technology/Tech Xplore

Scientific paper: Hoo Hugo Kim et al, Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model, Water Research (2024). DOI: 10.1016/j.watres.2024.122092

Other recent news

Magnetic Prosthetic Hands: Researchers at the BioRobotics Institute have developed a new magnetic prosthetic hand that allows for natural, precise control of robotic fingers.

Revolutionary OLEDs: A new device, thinner than a human hair, has been developed to replace bulky night vision goggles with lightweight glasses. This could significantly enhance the usability and comfort of night vision technology.

Energy Harvesting at Room Temperature: Scientists have created an organic thermoelectric device that can harvest energy at room temperature without needing a temperature gradient. This breakthrough could lead to new ways of generating energy sustainably.

Battery Breakthrough: Columbia Engineers have developed a new, more powerful electrolyte for batteries, potentially solving one of the biggest challenges in renewable energy storage.

Silk and Graphene Electronics: Researchers have found a way to create a two-dimensional silk protein layer on graphene, which could lead to advancements in flexible and wearable electronics.

[reasonsforhope]

Green energy is in its heyday. 

Renewable energy sources now account for 22% of the nation’s electricity, and solar has skyrocketed eight times over in the last decade. This spring in California, wind, water, and solar power energy sources exceeded expectations, accounting for an average of 61.5 percent of the state's electricity demand across 52 days. 

But green energy has a lithium problem. Lithium batteries control more than 90% of the global grid battery storage market. 

That’s not just cell phones, laptops, electric toothbrushes, and tools. Scooters, e-bikes, hybrids, and electric vehicles all rely on rechargeable lithium batteries to get going. 

Fortunately, this past week, Natron Energy launched its first-ever commercial-scale production of sodium-ion batteries in the U.S. 

“Sodium-ion batteries offer a unique alternative to lithium-ion, with higher power, faster recharge, longer lifecycle and a completely safe and stable chemistry,” said Colin Wessells — Natron Founder and Co-CEO — at the kick-off event in Michigan. 

The new sodium-ion batteries charge and discharge at rates 10 times faster than lithium-ion, with an estimated lifespan of 50,000 cycles.

Wessells said that using sodium as a primary mineral alternative eliminates industry-wide issues of worker negligence, geopolitical disruption, and the “questionable environmental impacts” inextricably linked to lithium mining. 

“The electrification of our economy is dependent on the development and production of new, innovative energy storage solutions,” Wessells said. 

Why are sodium batteries a better alternative to lithium?

The birth and death cycle of lithium is shadowed in environmental destruction. The process of extracting lithium pollutes the water, air, and soil, and when it’s eventually discarded, the flammable batteries are prone to bursting into flames and burning out in landfills. 

There’s also a human cost. Lithium-ion materials like cobalt and nickel are not only harder to source and procure, but their supply chains are also overwhelmingly attributed to hazardous working conditions and child labor law violations. 

Sodium, on the other hand, is estimated to be 1,000 times more abundant in the earth’s crust than lithium. 

“Unlike lithium, sodium can be produced from an abundant material: salt,” engineer Casey Crownhart wrote ​​in the MIT Technology Review. “Because the raw ingredients are cheap and widely available, there’s potential for sodium-ion batteries to be significantly less expensive than their lithium-ion counterparts if more companies start making more of them.”

What will these batteries be used for?

Right now, Natron has its focus set on AI models and data storage centers, which consume hefty amounts of energy. In 2023, the MIT Technology Review reported that one AI model can emit more than 626,00 pounds of carbon dioxide equivalent. 

“We expect our battery solutions will be used to power the explosive growth in data centers used for Artificial Intelligence,” said Wendell Brooks, co-CEO of Natron. 

“With the start of commercial-scale production here in Michigan, we are well-positioned to capitalize on the growing demand for efficient, safe, and reliable battery energy storage.”

The fast-charging energy alternative also has limitless potential on a consumer level, and Natron is eying telecommunications and EV fast-charging once it begins servicing AI data storage centers in June. 

On a larger scale, sodium-ion batteries could radically change the manufacturing and production sectors — from housing energy to lower electricity costs in warehouses, to charging backup stations and powering electric vehicles, trucks, forklifts, and so on. 

“I founded Natron because we saw climate change as the defining problem of our time,” Wessells said. “We believe batteries have a role to play.”

-via GoodGoodGood, May 3, 2024

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Note: I wanted to make sure this was legit (scientifically and in general), and I'm happy to report that it really is! x, x, x, x