Alberto González Martínez Ph. D.

My research paper, "Evolutions of Planetary Systems and the Role of Planetary Systems to Determine the Metallicity and Habitability of Exoplanets," has been accepted at PhysicsRN (Physics Research Network) under SSRN! Using machine learning techniques, astrophysical calculations, and a dataset of over 13,000 confirmed/unconfirmed exoplanets from NASA Exoplanet Archive and TESS, my study uncovers: ✅ An innovative method to differentiate between confirmed and unconfirmed exoplanets based on planetary attributes. ✅ A novel equation to calculate stellar metallicity levels. ✅ Predictive insights into stellar metallicity levels that determine the likelihood of habitability in exoplanets. This research provides fresh perspectives on what it takes for a planet to potentially harbor life. I invite you to read the full paper attached below:

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Anthropic releases Fascinating paper diving deeper into model inner workings. The paper studies how neurons in language models respond to specific features and how this changes as the models get bigger. Key Terms: Monosemantic Neurons:** Neurons that respond to one specific feature (i.e. a specific word). Polysemantic Neurons:** Neurons that respond to multiple features. Main Points: 1. Detection: Create methods to identify neurons that only respond to one feature. 2. Inhibition: Develop ways to stop neurons from only responding to one feature during training. Why It Matters: Reducing monosemantic neurons can make AI models more flexible and understandable, leading to better overall performance. Paper terms Neuron: A basic unit in a neural network that processes information. Language Models: AI models that understand and generate human language. Feature: A specific characteristic or element that a neuron responds to. This approach can lead to more reliable and easier-to-understand AI models. Kelly Cohen Josette Riep #artificialintelligence #llm

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Leaving this here for reference because I wanna watch this with great attention, and also so you can comment. What do you make of these ideas re> Bioregional Weaving? Always impressed by the great minds and comprehensive thinking of Bill Baue, Joe Brewer, Daniel Christian Wahl and Melina Angel

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📰 News: 📰 --- Unlocking Quantum Potential: A Game-Changer for SMBs 🚀 The intersection of science and technology celebrates yet another milestone with the introduction of a Senate companion bill that aims to establish a nonprofit foundation for the National Institute of Standards and Technology -NIST-. If you are an SMB owner, now is the moment to consider how quantum computing could revolutionize your business! The Senate version of the Expanding Partnerships for Innovation and Competitiveness -EPIC- Act, filed by a bipartisan group of senators, promises to create the Foundation for Standards and Metrology. This could be a leap forward for private and philanthropic funding, especially for emerging technologies like quantum computing. Here’s why your small or medium business should start exploring quantum computing solutions now: - 🌟 NIST's new foundation will mobilize resources to support U.S. leadership on technologies such as AI, cybersecurity, and quantum computing. - 📈 Studies show that quantum computing can solve problems 1000 times faster than traditional systems. - 💡 An IBM report estimates that quantum computing could bring about over $17 billion in economic gains by 2025. This newly introduced legislation will empower NIST to forge closer ties with the private sector, nonprofits, and academia. This will not only aid in talent retention but also focus on training a technology workforce for the future. If you're an SMB, this is a substantial opportunity to integrate cutting-edge technology and become more efficient. Join the conversation! Share your thoughts below. How do you think quantum computing could transform your business? Follow Spin Quantum Tech for more updates and insights. #Future #Futurism #Innovation #Business #ArtificialIntelligence #QuantumComputing #SQT https://lnkd.in/emdY5nWA

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The idea of #AIDO (AI-driven Digital Organism) is put under close cross-examination and discussion in an intriguing article by Matteo Wong in The Atlantic today. As for any disruptive idea, there are both concurrence and skepticism of the AIDO vision, but I hereby want to share my optimism: no one would have imagined 10 years ago what we now see from #GPTs or #Geminis in terms of simulating human intelligence; and likewise, what we envision for AIDO to simulate biology at all levels is not a fantasy either thanks to even greater advancements in AI architecture, infrastructure, and data for the years to come. Coming along with #AIDO is a concrete and operationalizable engineering roadmap as outlined in https://lnkd.in/d8eZ7mJp, built on a multi-scale foundation model system which we will be implementing in our venture GenBio AI. I look forward to updating soon our next release of the AIDO models. Stay tuned! https://lnkd.in/dr8kxgVH

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How do people (and how can robots) engage in efficient Referring Expression Generation in realistic, large-scale environments? Mark Higger explores in his 2024 #CogSci paper, "GAIA: A Givenness Hierarchy Theoretic Model of Situated Referring Expression Generation"! Traditional models of Referring Expression Generation enable a speaker to select a set of properties to use to refer to an entity, in a way that disambiguates the target with respect to a set of distractors. But clearly we don't typically bother to rule out ALL distractors. In a classroom you can say "the blackboard" without worrying about all the other irrelevant blackboards you know about in the world. How do human speakers do this (and how could robots)? In this work, Mark answers this question by leveraging our the Givenness Hierarchy, identifying the insight that perhaps it is sufficient to only rule out entities with the same (or higher) /cognitive status/ as your target. If your target is activated, perhaps you only need to rule out other activated entities. If your target is familiar, you only need to rule out other familiar entities! Based on this intuition, Mark presents GAIA, a Givenness Hierarchy theoretic REG model. He then preliminarily validates it through a set of case studies. Look out in the future for full statistical validation. :) To learn more, read the paper! https://lnkd.in/g_fU89Fu And you can learn more about our lab's work on computational modeling of the Givenness Hierarchy at http://mirrorlab.mines.edu. #robotics #nlproc #hri #cogsci #nlg

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New study challenges long-held assumption: Small-scale turbulence is anisotropic! 😱 Using a novel framework, researchers from UC Irvine found that anisotropy persists even at the smallest scales. 🔬 Implications for understanding and modeling turbulence, a ubiquitous phenomenon in nature and engineering. Source (s) : Article [Ref. in comment section] 🚀 #turbulence #physics #research #UCirvine #anisotropy

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The Reality of #AI and Biorisk "We find that existing studies around AI-related biorisk are nascent, often speculative in nature, or limited in terms of their methodological maturity and transparency." ~ Sara Hooker et al. https://lnkd.in/guBHFEEt #GenerativeAI

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Hey everyone - pyOpenSci is looking for two reviewers for a #python package that handles disrometer data: please see here: https://lnkd.in/g8S4qBYX We ideally want one reviewer who understands atmospheric data and the data the package handles. The other reviewer can focus more on #Python packaging documentation and usability. We offer mentorship if you are new to software review. Please share! #openscience #science

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When tackling decision-making tasks, humans rely on two crucial information sources: historical policy data and analytical insights in natural language form. Yet, existing research often focuses on just one of these sources. In a recent paper, the authors advocate for autonomous agents to leverage both. ChessGPT is a groundbreaking model that integrates policy learning and language modeling by merging data from these two sources into chess games. The work showcases ChessCLIP and ChessGPT models, demonstrating the fusion of policy learning and language modeling. Additionally, They present a comprehensive evaluation framework to assess language models' chess proficiency. The experimental results confirm the effectiveness of the model and dataset. Access to the open-source code, model, and dataset here: https://lnkd.in/dsMN6pcB

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Gravity might be a universal decoherence mechanism, underlying quantum-to-classical transitions and the emergence of classical behaviors from a fundamentally quantum universe. https://lnkd.in/dijVRFzV

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I talked about the Journal of Open Source Software (JOSS) today at #force2024, including some comments on how JOSS works with the Principles of Open Scholarly Infrastructure (POSI) as well as issues we've had with scholarly gatekeeping organizations (e.g. Scopus, Web of Science, PubMed Central) slides: https://lnkd.in/guWm5zDj

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Here is an interesting article on the "mess" of the concepts and misnomers "open science", "open access", and open access with "paying for publishing". What is the meaning of "open"?

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Curious how AI actually affects the #climate? Want solid data and analysis rather than sound bites? Download this fascinating, valuable, data-rich resource about AI #climate impact. I was especially intrigued by the case study that analyzed five problems with Microsoft's claims about AI impact. I'm downloading a copy of this report for myself and for my favorite climate researcher. If I were leading #IT or AI, I'd also want a copy. Disclosure: Kerry O'Donnell and I were good friends in elementary school, and she was a whiz at mathematics and operations back then too.

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Chapters 8 and 9 of "Building Quantum Software" are in MEAP now. Applications of Quantum Fourier Transforms, and Quantum Phase Estimation. All 12 chapters are going into the final review at the end of the week, we are getting closer to the finish. https://mng.bz/x2jY Manning Publications Co. #quantumcomputing #fourier

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Yesterday Anthropic published a paper on AI model interpretability that I think represents a milestone in the evolution of AI explanaiability. Here is why this paper is super important to the AI weather and climate community: We now have a method for identifying millions of features in a cutting edge model. We could use a similar method for interpreting weather forecasting models like FourCastNet and GraphCast. While gridded data is obviously totally different than text, the same general method of using a variational auto-encoder to find the model input that activates a particular feature can be used. In our case, the feature can be a particular pattern in pixel space or it can be a pattern across gridded variables. For hurricanes this means that a feature as general as “rain band” can be isolated and activated as needed. We can use the “clamping” method introduced in the paper to then ramp up or down the activation on “rain band”. In this way we can adjust the expected output of the model without actually changing the model. You can imagine a case where you have high level features that correspond to radar signatures indicative of tornados or satellite image patterns along the lines of the “Dvorak” method that indicate the strength of a hurricane. If our feature space is complex enough we can ramp up activations that are analogous to Dvorak intensity levels so that we can interpret the result of a hurricane intensity model using proven meteorological frameworks. Finally, we can identify the features that are most likely to produce a certain model result. For example if we have features such as eye wall symmetry, radius of maximum winds, and rain band formation we could actually increase and decrease the importance of each of these features and see how the model result changes. In this way we can create a mapping between patterns in the physical world and AI model results. This was not something that was possible using previous interpetability methods. We were limited to using tools like SHAP to understand the relative importance of scalar features on model results, but we were never able to understand feature clusters that correspond to the dynamics of the atmosphere. In a best case scenario, we could create a direct mapping between the conservation equations that define atmospheric dynamics and the millions of activations that determine an AI model output - although this is probably many years away.

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California is addressing AI disinformation, but are new measures sufficient to safeguard the public? It's encouraging to see legislative action, yet more might be needed. Perhaps broader national and global regulations be the next step. Read The Associated Press https://lnkd.in/ghbCEhF5 #California #AIEthics #Deepfakes #Disinformation #AIRegulation #2024elections

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> - Sharing resources - <  Interesting read today: "Comparing Algorithms for Loading Classical Datasets into Quantum Memory" by Andriy Miranskyy, Mushahid Khan & Udson Cabral Mendes Abstract: —Quantum computers are gaining importance in various applications like quantum machine learning and quantum signal processing. These applications face significant challenges in loading classical datasets into quantum memory. With numerous algorithms available and multiple quality attributes to consider, comparing data loading methods is complex. Our objective is to compare (in a structured manner) various algorithms for loading classical datasets into quantum memory (by converting statevectors to circuits). We evaluate state preparation algorithms based on five key attributes: circuit depth, qubit count, classical runtime, statevector representation (dense or sparse), and circuit alterability. We use the Pareto set as a multi-objective optimization tool to identify algorithms with the best combination of properties. To improve comprehension and speed up comparisons, we also visually compare three metrics (namely, circuit depth, qubit count, and classical runtime). We compare seven algorithms for dense statevector conversion and six for sparse statevector conversion. Our analysis reduces the initial set of algorithms to two dense and two sparse groups, highlighting inherent trade-offs. This comparison methodology offers a structured approach for selecting algorithms based on specific needs. Researchers and practitioners can use it to help select data-loading algorithms for various quantum computing tasks. Link: https://lnkd.in/eQBZfHRq #quantummachinelearning #quantumcomputing

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