Podcasts about model based

Schneider Electric Announces Evolution of EcoStruxure IT with Model Based, Automated Sustainability Metric Reporting New features offer enhanced visibility of energy and resource consumption, historical data analysis and detailed metrics to help organisations meet imminent regulatory reporting requirements. Includes a fast, intuitive, and simple-to-use reporting engine with third-party integration and data export features, all at the touch of a button. Are the result of three years of strategic investment, and rigorous testing and development as part of Schneider Electric's CIO-led Green IT Program. Sustainability Metric Reporting from Schneider Electric Schneider Electric, the leader in digital transformation of energy management and automation, today announced the introduction of new model based, automated sustainability reporting features within its award-winning EcoStruxure IT data centre infrastructure management (DCIM) software. The release follows three years of strategic investment, and rigorous testing and development as part of Schneider Electric's Green IT Program, led by Schneider Electric's Chief Information Officer Elizabeth Hackenson. Available to all EcoStruxure IT users starting in April, the new and enhanced reporting features combine 20 years of sustainability, regulatory, data centre and software development expertise with advanced machine learning. Customers will have access to a new set of reporting capabilities, which traditionally had required a deep understanding of manual data calculation methods. Unlike anything available in the market, the new model offers customers a fast, intuitive, and simple-to-use reporting engine to help meet imminent regulatory requirements, including the European Energy Efficiency Directive (EED). In fact, the new capabilities go far-beyond the EED-required metrics, ensuring customers can measure their data centres' real-time and historical energy performance data against all of the advanced reporting metrics specified within Schneider Electric's White Paper 67. EcoStruxure IT software enables owners and operators to measure and report data centre performance based on historical data and trends analysis, combining it with artificial intelligence (AI) and real-time monitoring to turn it into actionable insights for improved sustainability. With the new download function, organisations can quickly quantify and report, at the click of a button - removing laborious manual tasks and making it faster and easier to harness the power of data to reduce the environmental impact of their data centres. Key benefits include: Calculate and track PUE per site/room over time with CEN/CENLEC 50600-4-2 methodology. Leverage data analytic models and cloud-based data lake to simplify reporting of PUE. Report current power consumption per site room and report against historical trends. Utilise "click of a button" reporting for regulations. Witness trending over time for various data centres and distributed IT environments. Empower customers to securely access and manipulate their data in their preferred tool via third-party integration and data export. "At Schneider Electric, we recognise that sustainability is a journey, and for the last three years, we've increased our investment to develop new software features that make it faster and simpler for our customers to operate resilient, secure and sustainable IT infrastructure," said Kevin Brown, Senior Vice President, EcoStruxure IT, Schneider Electric. "The new reporting capabilities included with EcoStruxure IT have been tested and adopted by our own organisation, and will allow customers to turn complex data into meaningful information, and report on key sustainability metrics." A new era for Green IT In 2021, Schneider Electric released its Schneider Sustainability Impact (SSIs), publicising the company's sustainability commitments. Aligning with the SSI purpose, Schneider Electric's CIO Elizabeth Hackenson kickstarted the company's Green ...

DTL S8A20 Toepassen van een Model-Based aanpak voor Predictive maintenanceIn deze aflevering van de De Dataloog | De Nederlandstalige podcast over data en AI Podcast, verkennen met deze keer de model based benadering van predictive maintenance binnen de railinfrastructuur. In deze boeiende sessie hebben we het genoegen om Dr. Ir. Annemieke Meghoe te verwelkomen, een expert in het veld van modelgebaseerde benaderingen voor het voorspellen van onderhoud.Dr. Meghoe's onderzoek belicht de rol van predictive maintenance in het realiseren van een duurzaam transportsysteem. Met een model based approach benadering combineert zij verminderde modellen, gebaseerd op eerste principes en omvangrijke datasets, om de falingskans of levensduur van spoorcomponenten te voorspellen. Deze aanpak integreert verschillende falingsmechanismen en verzamelt veldgegevens van treinen, het spoor, en de omgeving om een allesomvattende oplossing te bieden.In onze discussie duiken we dieper in hoe deze modellen en data bronnen ontwikkeld en op elkaar afgestemd worden om de uitdagingen in de spoorsector en daarbuiten aan te pakken. We verkennen de impact van haar werk op ProRail en de brede acceptatie van modelgebaseerde onderhoudsstrategieën. Bovendien gaan we in op de criteria voor data selectie, de combinatie van #faalmechanismen, het tijdrovende proces van het samenvoegen van deze elementen, en de rol van surrogaat- en meta-modellen in haar onderzoek.Wat een mooie verkenning van de technische en praktische aspecten van predictive maintenance, waarbij we de brug slaan tussen theorie en praktijk, en onderzoeken hoe Dr. Meghoe's baanbrekende aanpak bijdraagt aan het optimaliseren van onderhoudsstrategieën voor een van de meest duurzame transportnetwerken ter wereld.De Dataloog is de onafhankelijke Nederlandstalige podcast over data & kunstmatige intelligentie. Hier hoor je alles wat je moet weten over de zin en onzin van data, de nieuwste ontwikkelingen en echte verhalen uit de praktijk. Onze hosts houden het altijd begrijpelijk, maar schuwen de diepgang niet. Vind je De Dataloog leuk? Abonneer je op de podcast en laat een review achter.

Jason A. Churchill and Joe Doyle talk about Joe's recent mock draft, how the Dodgers land Tyler Glasnow, the state of the Yankees' farm system and how they add starting pitching, and what kind of arm the Orioles could land by using their top prospects.

Max Kolesnikov is a founder and CEO of MKS Technology, an embedded software and controls engineering firm. He has nearly 20 years of experience working in controls and software for real-time, safety-critical applications in automotive and industrial domains.Max offers embedded software and controls engineering consulting for automotive applications.Website: http://mks.technologyEmail: max.kolesnikov@mks.technologyLinkedIn: https://www.linkedin.com/in/max-kolesnikov-phd-9b41617/ You can find Jeff at https://jeffgable.com.You can find Luca at https://luca.engineer. 

IN THIS EPISODE...In today's digital age, safeguarding sensitive data is a non-negotiable requirement for every business owner. But navigating the labyrinth of cybersecurity can be daunting, and that's where our guest, Tracy Gregorio, comes in. As any business owner will tell you, protecting customer and employee data is paramount, and this is just the tip of the iceberg. For many industries, such as healthcare and finance, stringent requirements and regulatory demands make the cybersecurity landscape even more complex.Tracy Gregorio is the Chief Executive Officer of G2 Ops, Inc., an engineering firm specializing in model-based and cybersecurity systems engineering and strategic consulting. With a background in information technology, Tracy's strategic direction has enabled G2 Ops to provide tailored, cost-effective solutions in Model-Based and Cybersecurity Systems Engineering. These solutions are designed to address the ever-evolving threats posed by information warfare, catering to the unique needs of both government and commercial clients.------------Full show notes, links to resources mentioned, and other compelling episodes can be found at http://LeadYourGamePodcast.com. (Click the magnifying icon at the top right and type “Tracy”)Love the show? Subscribe, rate, review, and share! JUST FOR YOU: Increase your leadership acumen by identifying your personal Leadership Trigger. Take my free my free quiz and instantly receive your 5-page report. Need to up-level your workforce or execute strategic People initiatives? https://shockinglydifferent.com/contact or tweet @KaranRhodes.-------------ABOUT TRACY GREGORIO:Tracy Gregorio is CEO of G2 Ops, an IT engineering and cybersecurity company serving the U.S. Navy, government, and commercial enterprises. Her wide-ranging experiences include being a software engineer for the Navy, an analyst for a cable broadcast network, running enrollment management for an online university, and running a certified woman-owned firm recognized five years straight by Inc 5000 as one of our country's fastest-growing small businesses. She chairs the Cybersecurity Committee of the Virginia Ship Repair Association and served on the Executive Committee of the Virginia Commonwealth Cyber Initiative.WHAT TO LISTEN FOR:1. What is cybersecurity consulting for businesses?2. What is the role of gender in a male-dominated industry?3. What are the growth challenges in the cybersecurity landscape?4. Why is strategic decision-making significant?5. What are the key factors in defining success within a leadership role?FEATURED TIMESTAMPS:[04:35] Shattering Glass Ceilings in Cybersecurity: A Journey of Innovation[14:14] Leadership dynamics, growth challenges, and role of gender in a male-dominated industry[20:23] Drawing lessons from prior missteps[23:12] Signature Segment: Tracy's LATTOYG Tactics of Choice[26:37] Tracy's entry into the LATTOYG Playbook[28:06] How does Tracy prioritize self-care and manage personal well-being?[29:07] Tips and Encouragement for Aspiring Leaders and STEM Professionals[33:41] Signature Segment: Karan's TakeLINKS FOR TRACY:Website:

For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are artificial entities that sense their environment, make decisions, and take actions. Many efforts have been made to develop intelligent agents, but they mainly focus on advancement in algorithms or training strategies to enhance specific capabilities or performance on particular tasks. Actually, what the community lacks is a general and powerful model to serve as a starting point for designing AI agents that can adapt to diverse scenarios. Due to the versatile capabilities they demonstrate, large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI), offering hope for building general AI agents. Many researchers have leveraged LLMs as the foundation to build AI agents and have achieved significant progress. In this paper, we perform a comprehensive survey on LLM-based agents. We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. Building upon this, we present a general framework for LLM-based agents, comprising three main components: brain, perception, and action, and the framework can be tailored for different applications. Subsequently, we explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation. Following this, we delve into agent societies, exploring the behavior and personality of LLM-based agents, the social phenomena that emerge from an agent society, and the insights they offer for human society. Finally, we discuss several key topics and open problems within the field. A repository for the related papers at https://github.com/WooooDyy/LLM-Agent-Paper-List. 2023: Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Qin Liu, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, Wensen Cheng, Qi Zhang, Wenjuan Qin, Yongyan Zheng, Xipeng Qiu, Xuanjing Huan, Tao Gui https://arxiv.org/pdf/2309.07864v3.pdf

Autonomous agents have long been a prominent research topic in the academic community. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from the human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating autonomous agents based on LLMs. To harness the full potential of LLMs, researchers have devised diverse agent architectures tailored to different applications. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of autonomous agents from a holistic perspective. More specifically, our focus lies in the construction of LLM-based agents, for which we propose a unified framework that encompasses a majority of the previous work. Additionally, we provide a summary of the various applications of LLM-based AI agents in the domains of social science, natural science, and engineering. Lastly, we discuss the commonly employed evaluation strategies for LLM-based AI agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository for the related references at https://github.com/Paitesanshi/LLM-Agent-Survey. 2023: Lei Wang, Chengbang Ma, Xueyang Feng, Zeyu Zhang, Hao-ran Yang, Jingsen Zhang, Zhi-Yang Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-rong Wen https://arxiv.org/pdf/2308.11432v1.pdf

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: A Model-based Approach to AI Existential Risk, published by Samuel Dylan Martin on August 25, 2023 on The AI Alignment Forum. Introduction Polarisation hampers cooperation and progress towards understanding whether future AI poses an existential risk to humanity and how to reduce the risks of catastrophic outcomes. It is exceptionally challenging to pin down what these risks are and what decisions are best. We believe that a model-based approach offers many advantages for improving our understanding of risks from AI, estimating the value of mitigation policies, and fostering communication between people on different sides of AI risk arguments. We also believe that a large percentage of practitioners in the AI safety and alignment communities have appropriate skill sets to successfully use model-based approaches. In this article, we will lead you through an example application of a model-based approach for the risk of an existential catastrophe from unaligned AI: a probabilistic model based on Carlsmith's Is Power-seeking AI an Existential Risk? You will interact with our model, explore your own assumptions, and (we hope) develop your own ideas for how this type of approach might be relevant in your own work. You can find a link to the model here. In many poorly understood areas, people gravitate to advocacy positions. We see this with AI risk, where it is common to see writers dismissively call someone an "AI doomer", or "AI accelerationist". People on each side of this debate are unable to communicate their ideas to the other side, since advocacy often includes biases and evidence interpreted within a framework not shared by the other side. In other domains, we have witnessed first-hand that model-based approaches are a constructive way to cut through advocacy like this. For example, by leveraging a model-based approach, the Rigs-to-Reefs project reached near consensus among 22 diverse organisations on the contentious problem of how to decommission the huge oil platforms off the Santa Barbara coast. For decades, environmental groups, oil companies, marine biologists, commercial and recreational fishermen, shipping interests, legal defence funds, the State of California, and federal agencies were stuck in an impasse on this issue. The introduction of a model refocused the dialog on specific assumptions, objectives and options, and led to 20 out of the 22 organisations agreeing on the same plan. The California legislature encoded this plan into law with bill AB 2503, which passed almost unanimously. There is a lot of uncertainty around existential risks from AI, and the stakes are extremely high. In situations like this, we advocate quantifying uncertainty explicitly using probability distributions. Sadly, this is not as common as it should be, even in domains where such techniques would be most useful. A recent paper on the risks of unaligned AI by Joe Carlsmith (2022) is a powerful illustration of how probabilistic methods can help assess whether advanced AI poses an existential risk to humanity. In this article, we review Carlsmith's argument and incorporate his problem decomposition into our own Analytica model. We then expand on this starting point in several ways to demonstrate elementary ways to approach each of the distinctive challenges in the x-risk domain. We take you on a tour of the live model to learn about its elements and enable you to dive deeper on your own. Challenges Predicting the long-term future is always challenging. The difficulty is amplified when there is no historical precedent. But this challenge is not unique; we lack historical precedent in many other areas, for example when considering a novel government program or a fundamentally new business initiative. We also lack precedent when world conditions change due to changes in technology, ...

Model-Based Definition or MBD can fundamentally transform how we visualize and understand design intent, by adding new dimensions to 2D drawings and pushing the boundaries of 3D CAD models. Learn how to get your organization to realize the benefits of MBD to bring GD&T (Geometric Dimensioning and Tolerancing), tolerance analysis, annotations, and even the PMI (Product Manufacturing Information) into the 3D CAD space.

Business | Why Consistency & Daily Diligence Is Key to Building SUPER SUCCESS + How to Create Duplicable Business Model Based Upon the Foundations of a Duplicable Process, a Winning Team & Well-Defined Guardrails Clay Clark Testimonials | "Clay Clark Has Helped Us to Grow from 2 Locations to Now 6 Locations. Clay Has Done a Great Job Helping Us to Navigate Anything That Has to Do with Running the Business, Building the System, the Workflows, to Buy Property." - Charles Colaw (Learn More Charles Colaw and Colaw Fitness Today HERE: www.ColawFitness.com) See the Thousands of Success Stories and Millionaires That Clay Clark Has Coached to Success HERE: https://www.thrivetimeshow.com/testimonials/ Learn More About How Clay Has Taught Doctor Joe Lai And His Team Orthodontic Team How to Achieve Massive Success Today At: www.KLOrtho.com Learn How to Grow Your Business Full THROTTLE NOW!!! Learn How to Turn Your Ideas Into A REAL Successful Company + Learn How Clay Clark Coached Bob Healy Into the Success Of His www.GrillBlazer.com Products Learn More About the Grill Blazer Product Today At: www.GrillBlazer.com Learn More About the Actual Client Success Stories Referenced In Today's Video Including: www.ShawHomes.com www.SteveCurrington.com www.TheGarageBA.com www.TipTopK9.com Learn More About How Clay Clark Has Helped Roy Coggeshall to TRIPLE the Size of His Businesses for Less Money That It Costs to Even Hire One Full-Time Minimum Wage Employee Today At: www.ThrivetimeShow.com To Learn More About Roy Coggeshall And His Real Businesses Today Visit: https://TheGarageBA.com/ https://RCAutospecialists.com/ Clay Clark Testimonials | "Clay Clark Has Helped Us to Grow from 2 Locations to Now 6 Locations. Clay Has Done a Great Job Helping Us to Navigate Anything That Has to Do with Running the Business, Building the System, the Workflows, to Buy Property." - Charles Colaw (Learn More Charles Colaw and Colaw Fitness Today HERE: www.ColawFitness.com) See the Thousands of Success Stories and Millionaires That Clay Clark Has Coached to Success HERE: https://www.thrivetimeshow.com/testimonials/ Learn More About Attending the Highest Rated and Most Reviewed Business Workshops On the Planet Hosted by Clay Clark In Tulsa, Oklahoma HERE: https://www.thrivetimeshow.com/business-conferences/ Download A Millionaire's Guide to Become Sustainably Rich: A Step-by-Step Guide to Become a Successful Money-Generating and Time-Freedom Creating Business HERE: www.ThrivetimeShow.com/Millionaire See Thousands of Actual Client Success Stories from Real Clay Clark Clients Today HERE: https://www.thrivetimeshow.com/testimonials/

Daniel Campbell is VP of Model-Based Definition at Capvidia. He has more than 20 years of experience in the field of digital metrology, software design, and model-based definition. He is also currently the Chair of the ANSI Working Group, and a member of the Board of Directors of the Dimensional Metrology Standards Consortium.In this episode learn about Model Based Definition (MBD) and how large companies are using it to streamline workflows and increase efficiency in manufacturing and metrology.Aaron Moncur, hostAbout Being An Engineer The Being An Engineer podcast is a repository for industry knowledge and a tool through which engineers learn about and connect with relevant companies, technologies, people resources, and opportunities. We feature successful mechanical engineers and interview engineers who are passionate about their work and who made a great impact on the engineering community. The Being An Engineer podcast is brought to you by Pipeline Design & Engineering. Pipeline partners with medical & other device engineering teams who need turnkey equipment such as cycle test machines, custom test fixtures, automation equipment, assembly jigs, inspection stations and more. You can find us on the web at www.teampipeline.us

In this week's SlatorPod, Fireflies.ai CEO Krish Ramineni joins us to talk about scaling the AI meeting assistant and building on the latest advances in large language models.Krish starts with his journey to co-founding Fireflies, which began as a drone delivery service and as a result of conversations with customers and investors, evolved into an AI meeting assistant to solve their own pain point.The CEO shares how they found their product-market fit after focusing on automated transcripts over human-assisted note-taking. He discusses the early days of AI investment and how with the rise of APIs and large language models (LLMs), you no longer need multiple PhDs to attract investors. Krish explains how Fireflies leverages technologies like Whisper to improve their language transcription, allowing them to be more accessible to global companies. He talks about their decision to improve their Super Summaries feature through GPT technology.The CEO shares his excitement about the potential for LLMs and how Fireflies are building a Chrome extension that uses LLMs to summarize any article or video on the internet. He advises that simply building a wrapper on top of OpenAI is not a defensible moat for companies, but rather you should build a unique platform with a unique angle into the industry you're selling to.Kirsh talks about the current fundraising environment where there is a lot of money being thrown around for generative AI companies, but only a few will weather the storm. When it comes to hiring machine learning talent, Krish doesn't believe in prompt engineering and also holds the view that machine learning companies may no longer need to hire large cohorts of ML PhDs to scale.The pod rounds off with the company's roadmap for 2023, which includes creating an ecosystem of extensions on top of Fireflies. These extensions will offer powerful functionalities to users in different sectors like healthcare and recruiting. 

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.05.02.539144v1?rss=1 Authors: Shiu, P. K., Sterne, G. R., Spiller, N., Franconville, R., Sandoval, A., Zhou, J., Simha, N., Kang, C. H., Yu, S., Kim, J. S., Dorkenwald, S., Matsliah, A., Schlegel, P., Yu, S.-c., McKellar, C. E., Sterling, A., Costa, M., Eichler, K., Jefferis, G. S. X. E., Murthy, M., Bates, A. S., Eckstein, N., Funke, J., Bidaye, S. S., Hampel, S., Seeds, A. M., Scott, K. Abstract: Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Learn how to grow and preserve your wealth through unbiased decision-making from Michael Episcope in today's episode as we look into current and future market trends, how they could affect the real estate market, and why now is a good time to invest in real estate.WHAT YOU'LL LEARN FROM THIS EPISODE What business models can protect your business in a downturnOrigin Multilytics: What it is and what it doesThings to consider during asset acquisition in today's marketWhy it's important to communicate with your investorsThe unexpected effects of working from homeRESOURCE/LINK MENTIONEDThe Psychology of Money by Morgan Housel | Paperback: https://amzn.to/3Tk09xI and Kindle: https://amzn.to/3SnUZQ5ABOUT MICHAEL EPISCOPEMichael is a co-founder and co-CEO of Origin Investments. He co-chairs the investment committee and oversees investor relations and capital raising in the company and with Michael's leadership, Origin has acquired $1 billion in equity under management and has executed more than $2.6 billion in real estate transactions in fast-growing markets throughout the United States.CONNECT WITH MICHAELWebsite: Origin InvestmentsEmail: michael@origininvestments.com | investorrelations@origininvestments.com CONNECT WITH USWant a list of top-rated real estate conferences, virtual meetups, and mastermind groups? Send Tate an email at tate@glequitygroup.com to learn more about real estate using a relational approach.Looking for ways to make passive income? Greenlight Equity Group can help you invest in multifamily properties and create consistent cash flow without being a landlord. Book a consultation call and download Tate's free ebook, "F.I.R.E.-Financial Independence Retire Early via Apartment Investing," at www.investwithgreenlight.com to start your wealth-building journey today!

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.29.534672v1?rss=1 Authors: Li, W., Gao, J. Abstract: Sleep staging is crucial in assessing sleep quality and diagnosing sleep disorders. Recent advances in deep learning methods with electroencephalogram (EEG) signals have shown remarkable success in automatic sleep staging. However, the use of deeper neural networks may lead to the issues of gradient disappearance and explosion, while the non-stationary nature and low signal-to-noise ratio of EEG signals can negatively impact feature representation. To overcome these challenges, we proposed a novel lightweight sequence-to-sequence deep learning model, 1D-ResNet-SE-LSTM, to classify sleep stages into five classes using single-channel raw EEG signals. Our proposed model consists of two main components: a one-dimensional residual convolutional neural network with a squeeze-and-excitation module to extract and reweight features from EEG signals, and a long short-term memory network to capture the transition rules among sleep stages. In addition, we applied the weighted cross-entropy loss function to alleviate the class imbalance problem. We evaluated the performance of our model on two publicly available datasets, Sleep-EDF Expanded and ISRUC-Sleep, and obtained an overall accuracy rate of 86.39% and 81.97%, respectively, along with corresponding macro average F1-scores of 81.95% and 79.94%. Our model outperforms ex-isting sleep staging models, particularly for the N1 stage, where it achieves F1-scores of 59.00% and 55.53%. The kappa coefficient is 0.812 and 0.766 for the Sleep-EDF Expanded and ISRUC-Sleep datasets, respectively, indicating strong agreement with certified sleep experts. We also investigated the effect of different weight coefficient combinations and sequence lengths of EEG epochs used as input to the model on its performance. Furthermore, the ablation study was conducted to evaluate the contribution of each component to the model's performance. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Plan for mediocre alignment of brain-like [model-based RL] AGI, published by Steven Byrnes on March 13, 2023 on LessWrong. (This post is a more simple, self-contained, and pedagogical version of Post #14 of Intro to Brain-Like AGI Safety.) (Vaguely related to this Alex Turner post and this John Wentworth post.) I would like to have a technical plan for which there is a strong robust reason to believe that we'll get an aligned AGI and a good future. This post is not such a plan. However, I also don't have a strong reason to believe that this plan wouldn't work. Really, I want to throw up my hands and say “I don't know whether this would lead to a good future or not”. By “good future” here I don't mean optimally-good—whatever that means—but just “much better than the world today, and certainly much better than a universe full of paperclips”. I currently have no plan, not even a vague plan, with any prayer of getting to an optimally-good future. That would be a much narrower target to hit. Even so, that makes me more optimistic than at least some people. Or at least, more optimistic about this specific part of the story. In general I think many things can go wrong as we transition to the post-AGI world—see discussion by Dai & Soares—and overall I feel very doom-y, particularly for reasons here. This plan is specific to the possible future scenario (a.k.a. “threat model” if you're a doomer like me) that future AI researchers will develop “brain-like AGI”, i.e. learning algorithms that are similar to the brain's within-lifetime learning algorithms. (I am not talking about evolution-as-a-learning-algorithm.) These algorithms, I claim, are in the general category of model-based reinforcement learning. Model-based RL is a big and heterogeneous category, but I suspect that for any kind of model-based RL AGI, this plan would be at least somewhat applicable. For very different technological paths to AGI, this post is probably pretty irrelevant. But anyway, if someone published an algorithm for x-risk-capable brain-like AGI tomorrow, and we urgently needed to do something, this blog post is more-or-less what I would propose to try. It's the least-bad plan that I currently know. So I figure it's worth writing up this plan in a more approachable and self-contained format. 1. Intuition: Making a human into a moon-lover (“selenophile”) Try to think of who is the coolest / highest-status-to-you / biggest-halo-effect person in your world. (Real or fictional.) Now imagine that this person says: “You know what's friggin awesome? The moon. I just love it. The moon is the best.” You stand there with your mouth agape, muttering to yourself in hushed tones: “Wow, huh, the moon, yeah, I never thought about it that way.” (But 100× moreso. Maybe you're on some psychedelic at the time, or this is happening during your impressionable teenage years, or whatever.) You basically transform into a “moon fanboy” / “moon fangirl” / “moon nerd” / “selenophile”. How would that change your motivations and behaviors going forward? You're probably going to be much more enthusiastic about anything associated with the moon. You're probably going to spend a lot more time gazing at the moon when it's in the sky. If there are moon-themed trading cards, maybe you would collect them. If NASA is taking volunteers to train as astronauts for a trip to the moon, maybe you'd enthusiastically sign up. If a supervillain is planning to blow up the moon, you'll probably be extremely opposed to that, and motivated to stop them. Hopefully this is all intuitive so far. What's happening mechanistically in your brain? As background, I think we should say that one part of your brain (the cortex, more-or-less) has “thoughts”, and another part of your brain (the basal ganglia, more-or-less) assigns a “value” (in RL terminology) a....

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Plan for mediocre alignment of brain-like [model-based RL] AGI, published by Steven Byrnes on March 13, 2023 on LessWrong. (This post is a more simple, self-contained, and pedagogical version of Post #14 of Intro to Brain-Like AGI Safety.) (Vaguely related to this Alex Turner post and this John Wentworth post.) I would like to have a technical plan for which there is a strong robust reason to believe that we'll get an aligned AGI and a good future. This post is not such a plan. However, I also don't have a strong reason to believe that this plan wouldn't work. Really, I want to throw up my hands and say “I don't know whether this would lead to a good future or not”. By “good future” here I don't mean optimally-good—whatever that means—but just “much better than the world today, and certainly much better than a universe full of paperclips”. I currently have no plan, not even a vague plan, with any prayer of getting to an optimally-good future. That would be a much narrower target to hit. Even so, that makes me more optimistic than at least some people. Or at least, more optimistic about this specific part of the story. In general I think many things can go wrong as we transition to the post-AGI world—see discussion by Dai & Soares—and overall I feel very doom-y, particularly for reasons here. This plan is specific to the possible future scenario (a.k.a. “threat model” if you're a doomer like me) that future AI researchers will develop “brain-like AGI”, i.e. learning algorithms that are similar to the brain's within-lifetime learning algorithms. (I am not talking about evolution-as-a-learning-algorithm.) These algorithms, I claim, are in the general category of model-based reinforcement learning. Model-based RL is a big and heterogeneous category, but I suspect that for any kind of model-based RL AGI, this plan would be at least somewhat applicable. For very different technological paths to AGI, this post is probably pretty irrelevant. But anyway, if someone published an algorithm for x-risk-capable brain-like AGI tomorrow, and we urgently needed to do something, this blog post is more-or-less what I would propose to try. It's the least-bad plan that I currently know. So I figure it's worth writing up this plan in a more approachable and self-contained format. 1. Intuition: Making a human into a moon-lover (“selenophile”) Try to think of who is the coolest / highest-status-to-you / biggest-halo-effect person in your world. (Real or fictional.) Now imagine that this person says: “You know what's friggin awesome? The moon. I just love it. The moon is the best.” You stand there with your mouth agape, muttering to yourself in hushed tones: “Wow, huh, the moon, yeah, I never thought about it that way.” (But 100× moreso. Maybe you're on some psychedelic at the time, or this is happening during your impressionable teenage years, or whatever.) You basically transform into a “moon fanboy” / “moon fangirl” / “moon nerd” / “selenophile”. How would that change your motivations and behaviors going forward? You're probably going to be much more enthusiastic about anything associated with the moon. You're probably going to spend a lot more time gazing at the moon when it's in the sky. If there are moon-themed trading cards, maybe you would collect them. If NASA is taking volunteers to train as astronauts for a trip to the moon, maybe you'd enthusiastically sign up. If a supervillain is planning to blow up the moon, you'll probably be extremely opposed to that, and motivated to stop them. Hopefully this is all intuitive so far. What's happening mechanistically in your brain? As background, I think we should say that one part of your brain (the cortex, more-or-less) has “thoughts”, and another part of your brain (the basal ganglia, more-or-less) assigns a “value” (in RL terminology) a....

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Plan for mediocre alignment of brain-like [model-based RL] AGI, published by Steve Byrnes on March 13, 2023 on The AI Alignment Forum. (This post is a more simple, self-contained, and pedagogical version of Post #14 of Intro to Brain-Like AGI Safety.) (Vaguely related to this Alex Turner post and this John Wentworth post.) I would like to have a technical plan for which there is a strong robust reason to believe that we'll get an aligned AGI and a good future. This post is not such a plan. However, I also don't have a strong reason to believe that this plan wouldn't work. Really, I want to throw up my hands and say “I don't know whether this would lead to a good future or not”. By “good future” here I don't mean optimally-good—whatever that means—but just “much better than the world today, and certainly much better than a universe full of paperclips”. I currently have no plan, not even a vague plan, with any prayer of getting to an optimally-good future. That would be a much narrower target to hit. Even so, that makes me more optimistic than at least some people. Or at least, more optimistic about this specific part of the story. In general I think many things can go wrong as we transition to the post-AGI world—see discussion by Dai & Soares—and overall I feel very doom-y, particularly for reasons here. This plan is specific to the possible future scenario (a.k.a. “threat model” if you're a doomer like me) that future AI researchers will develop “brain-like AGI”, i.e. learning algorithms that are similar to the brain's within-lifetime learning algorithms. (I am not talking about evolution-as-a-learning-algorithm.) These algorithms, I claim, are in the general category of model-based reinforcement learning. Model-based RL is a big and heterogeneous category, but I suspect that for any kind of model-based RL AGI, this plan would be at least somewhat applicable. For very different technological paths to AGI, this post is probably pretty irrelevant. But anyway, if someone published an algorithm for x-risk-capable brain-like AGI tomorrow, and we urgently needed to do something, this blog post is more-or-less what I would propose to try. It's the least-bad plan that I currently know. So I figure it's worth writing up this plan in a more approachable and self-contained format. 1. Intuition: Making a human into a moon-lover (“selenophile”) Try to think of who is the coolest / highest-status-to-you / biggest-halo-effect person in your world. (Real or fictional.) Now imagine that this person says: “You know what's friggin awesome? The moon. I just love it. The moon is the best.” You stand there with your mouth agape, muttering to yourself in hushed tones: “Wow, huh, the moon, yeah, I never thought about it that way.” (But 100× moreso. Maybe you're on some psychedelic at the time, or this is happening during your impressionable teenage years, or whatever.) You basically transform into a “moon fanboy” / “moon fangirl” / “moon nerd” / “selenophile”. How would that change your motivations and behaviors going forward? You're probably going to be much more enthusiastic about anything associated with the moon. You're probably going to spend a lot more time gazing at the moon when it's in the sky. If there are moon-themed trading cards, maybe you would collect them. If NASA is taking volunteers to train as astronauts for a trip to the moon, maybe you'd enthusiastically sign up. If a supervillain is planning to blow up the moon, you'll probably be extremely opposed to that, and motivated to stop them. Hopefully this is all intuitive so far. What's happening mechanistically in your brain? As background, I think we should say that one part of your brain (the cortex, more-or-less) has “thoughts”, and another part of your brain (the basal ganglia, more-or-less) assigns a “value” (in RL ter...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Model-Based Policy Analysis under Deep Uncertainty, published by Max Reddel on March 6, 2023 on The Effective Altruism Forum. This post is based on a talk that I gave at EAGxBerlin 2022. It is intended for policy researchers who want to extend their tool kit with computational tools. I show how we can support decision-making with simulation models of socio-technical systems while embracing uncertainties in a systematic manner. The technical field of decision-making under deep uncertainty offers a wide range of methods to account for various parametric and structural uncertainties while identifying robust policies in a situation where we want to optimize for multiple objectives simultaneously. Summary Real-world political decision-making problems are complex, with disputed knowledge, differing problem perceptions, opposing stakeholders, and interactions between framing the problem and problem-solving. Modeling can help policy-makers to navigate these complexities. Traditional modeling is ill-suited for this purpose. Systems modeling is a better fit (e.g., agent-based models). Deep uncertainty is everywhere. Deep uncertainty makes expected-utility reasoning virtually useless. Decision-Making under Deep Uncertainty is a framework that can build upon systems modeling and overcome deep uncertainties. Explorative modeling > predictive modeling. Value diversity (aka multiple objectives) > single objectives. Focus on finding vulnerable scenarios and robust policy solutions. Good fit with the mitigation of GCRs, X-risks, and S-risks. Complexity Complexity science is an interdisciplinary field that seeks to understand complex systems and the emergent behaviors that arise from the interactions of their components. Complexity is often an obstacle to decision-making. So, we need to address it. Ant Colonies Ant colonies are a great example of how complex systems can emerge from simple individual behaviors. Ants follow very simplistic rules, such as depositing food, following pheromone trails, and communicating with each other through chemical signals. However, the collective behavior of the colony is highly sophisticated, with complex networks of pheromone trails guiding the movement of the entire colony toward food sources and the construction of intricate structures such as nests and tunnels. The behavior of the colony is also highly adaptive, with the ability to respond to changes in the environment, such as changes in the availability of food or the presence of predators. Examples of Economy and Technology Similarly, the world is also a highly complex system, with a vast array of interrelated factors and processes that interact with each other in intricate ways. These factors include the economy, technology, politics, culture, and the environment, among others. Each of these factors is highly complex in its own right, with multiple variables and feedback loops that contribute to the overall complexity of the system. For example, the economy is a highly complex system that involves the interactions between individuals, businesses, governments, and other entities. The behavior of each individual actor is highly variable and can be influenced by a range of factors, such as personal motivations, cultural norms, and environmental factors. These individual behaviors can then interact with each other in complex ways, leading to emergent phenomena such as market trends, economic growth, and financial crises. Similarly, technology is a highly complex system that involves interactions between multiple components, such as hardware, software, data, and networks. Each of these components is highly complex in its own right, with multiple feedback loops and interactions that contribute to the overall complexity of the system. The behavior of the system as a whole can then be highly unpredict...

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.02.530856v1?rss=1 Authors: Hoang, I. B., Munier, J. J., Verghese, A., Greer, Z., Millard, S. J., DiFazio, L. E., Sercander, C., Izquierdo, A., Sharpe, M. J. Abstract: Behavior is often dichotomized into model-free and model-based systems. Model-free behavior prioritizes associations that have high value, regardless of the specific consequence or circumstance. In contrast, model-based behavior involves considering all possible outcomes to produce behavior that best fits the current circumstance. We typically exhibit a mixture of these behaviors so we can trade-off efficiency and flexibility. However, substance use disorder shifts behavior more strongly towards model-free systems, which produces a difficulty abstaining from drug-seeking due to an inability to withhold making the model-free high-value response 3-10. The lateral hypothalamus (LH) is implicated in substance use disorder and we have demonstrated that this region is critical to Pavlovian cue-reward learning. However, it is unknown whether learning occurring in LH is model-free or model-based, where the necessary teaching signal comes from to facilitate learning in LH, and whether this is relevant for learning deficits that drive substance use disorder. Here, we reveal that learning occurring in the LH is model-based. Further, we confirm the existence of an understudied projection extending from dopamine neurons in the ventral tegmental area (VTA) to the LH and demonstrate that this input underlies model-based learning in LH. Finally, we examine the impact of methamphetamine self-administration on LH-dependent model-based processes. These experiments reveal that a history of methamphetamine administration enhances the model-based control that Pavlovian cues have over decision-making, which was accompanied by a bidirectional strengthening of the LH to VTA circuit. Together, this work reveals a novel bidirectional circuit that underlies model-based learning and is relevant to the behavioral and cognitive changes that arise with substance use disorders. This circuit represents a new addition to models of addiction, which focus on instrumental components of drug addiction and increases in model-free habits after drug exposure. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.28.530384v1?rss=1 Authors: Baspinar, E., Cecchini, G., DePass, M., Andujar, M., Pani, P., Ferraina, S., Moreno-Bote, R., Cos, I., Destexhe, A. Abstract: We present a new AdEx mean-field framework to model two networks of excitatory and inhibitory neurons, representing two cortical columns, and interconnected with excitatory connections contacting both Regularly Spiking (excitatory) and Fast Spiking (inhibitory) cells. This connection scheme is biophysically plausible since it is based on intercolumnar excitation and intracolumnar excitation-inhibition. This configuration introduces bicolumnar competition, sufficient for choosing between two alternatives. Each column represents a pool of neurons voting for one of two choices indicated by two stimuli presented on a monitor in human and macaque experiments. The task also requires maximizing the cumulative reward over each episode, which consists of a certain number of trials. The cumulative reward depends on the coherency between choices of the participant/model and preset strategy in the experiment. We endow the model with a reward-driven learning mechanism allowing to capture the implemented strategy, as well as to model individual exploratory behavior. We compare the simulation results to the behavioral data obtained from the human and macaque experiments in terms of performance and reaction time. This model provides a biophysical ground for simpler phenomenological models proposed for similar decision-making tasks and can be applied to neurophysiological data obtained from the macaque brain. Finally, it can be embedded in whole-brain simulators, such as The Virtual Brain (TVB), to study decision-making in terms of large scale brain dynamics. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.13.528425v1?rss=1 Authors: Ceravolo, L., Debracque, C., Pool, E., Gruber, T., Grandjean, D. Abstract: The ability to process verbal language seems unique to humans and relies not only on semantics but on other forms of communication such as affective vocalisations, that we share with other primate species--particularly great apes (Hominidae). To better understand these processes at the behavioural and brain level, we asked human participants to categorize vocalizations of four primate species including human, great apes (chimpanzee and bonobo), and monkey (rhesus macaque) during MRI acquisition. Classification was above chance level for all species but bonobo vocalizations. Imaging analyses were computed using a participant-specific, trial-by-trial fitted probability categorization value in a model-based style of data analysis. Model-based analyses revealed the implication of the bilateral orbitofrontal cortex and inferior frontal gyrus pars triangularis (IFGtri) respectively correlating and anti-correlating with the fitted probability of accurate species classification. Further conjunction analyses revealed enhanced activity in a sub-area of the left IFGtri specifically for the accurate classification of chimpanzee calls compared to human voices. Our data therefore reveal distinct frontal mechanisms that shed light on how the human brain evolved to process non-verbal language. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.12.528216v1?rss=1 Authors: Sobhan, R., Gkogkou, P., Johnson, G., Cameron, D. Abstract: Object: Dynamic susceptibility contrast MRI (DSC-MRI) is the current standard for cerebral perfusion estimation. Model-dependent approaches for DSC-MRI analysis involve assuming a parametric transit time distribution (TTD) to characterize the passage of contrast agent through tissue microvasculature. Here we compare the utility of four TTD models: namely, skewed-Gaussian, gamma, gamma-variate, and Weibull, to identify the optimal TTD for quantifying brain perfusion. Materials and Methods: DSC-MRI data were acquired in nine subjects at 1.5T, and normal-appearing white- and gray-matter signals were assessed. TTDs were compared in terms of: goodness-of-fit, evaluated using RMSE; noise sensitivity, assessed via Monte-Carlo-simulated noisy conditions; and fit stability, quantified as the proportion of total fits converging to the global minimum. Computation times for model-fitting were also calculated. Results: The gamma TTD showed higher fit stability, shorter computation times (p less than 0.008), and higher robustness against experimental noise as compared to other models. All functions showed similar RMSEs and the parameter estimates (p greater than 0.008) were congruent with literature values. Discussion: The gamma distribution represents the most suitable TTD for perfusion analysis. Moreover, due to its robustness against noise, the gamma TTD is expected to yield more reproducible estimates than the other models for establishing a standard, multi-center analysis pipeline. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Mel Andrews is an instructor and doctoral student in the department of philosophy at the University of Cincinnati. Their work focuses on the phenomena of cognition and life, comparing and contrasting the merits and explanatory scope of conceptual and formal models of life and mind, and exploring the implications of these considerations for science at large. Today Mel joined us to talk about the philosophy of math in science and mathematical models in scientific reasoning. How do models relate to the real world? When can models tell us something about ... anything other than their own mathematical substance? And perhaps most importantly, in the Q&A section, how can we build a formal mathematics for computer hacking

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.23.525256v1?rss=1 Authors: VonKaenel, E., Feidler, A., Lowery, R., Andersh, K., Love, T., Majewska, A., McCall, M. N. Abstract: Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still the gold standard for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. To fill this longstanding gap, we introduce a fully parametric model-based approach for analyzing Sholl data. We generalize our model to a hierarchical Bayesian framework so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to three real data examples and perform simulation studies comparing the proposed method with a popular alternative. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

In this week's episode Greg and Patrick revisit a topic they addressed in their 2nd-ever episode: statistical power. Here they continue their discussion by attempting to clarify the power of what, and they explore ways of obtaining meaningful power estimates using the structural equation modeling framework. Along the way they also discuss tearing arms off, German dentists, booby prizes, Dr. Strangelove, making it look like an accident, shrug emojis, the whale petting machine, baseball and war, where's Waldo, whale holes, the big R-squared, throwing reviewers against the wall, DIY power, in fairness to me, eggplants, and screw you guys, I'm going home. Stay in contact with Quantitude! Twitter: @quantitudepod Web page: quantitudepod.org Merch: redbubble.com

In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Dr. Sam Procter and Lutz Wrage, researchers with the SEI, discuss the Guided Architecture Trade Space Explorer (GATSE), a new SEI-developed model-based tool to help with the design of safety-critical systems. The GATSE tool allows engineers to evaluate more design options in less time than they can now. This prototype language extension and software tool partially automates the process of model-based systems engineering so that systems engineers can rapidly explore combinations of different design options.

Are model-based testing and record and configure-based testing mutually exclusive, or can they be used together to provide a comprehensive testing approach? In today's episode, Matthias Rapp, a test automation and Tricentis veteran, and Shawn Jaques, the Director of Product Marketing at Tricentis, discuss model-based testing and record and configure-based testing. We explore the differences between these two testing methods and when to use one over the other. We also discuss how they can work together and how AI and data-driven testing fit into these paradigms. Tune in to learn more about these testing techniques and how they can help ensure the quality and reliability of your systems. Check out Model-based testing in the cloud yourself: https://www.tricentis.com/products/tricentis-test-automation

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.04.515162v1?rss=1 Authors: Feher da Silva, C., Lombardi, G., Edelson, M., Hare, T. A. Abstract: A standard assumption in neuroscience is that low-effort model-free learning is automatic and continuously employed, while more complex model-based strategies are only used when the rewards they generate are worth the additional effort. We present evidence refuting this assumption. First, we demonstrate flaws in previous reports of combined model-free and model-based reward prediction errors in the ventral striatum that likely led to spurious results. More appropriate analyses yield no evidence of a model-free prediction errors in this region. Second, we find that task instructions generating more correct model-based behaviour reduce rather than increase mental effort. This is inconsistent with cost-benefit arbitration between model-based and model-free strategies. Together, our data suggest that model-free learning may not be automatic. Instead, humans can reduce mental effort by using a model-based strategy alone rather than arbitrating between multiple strategies. Our results call for re-evaluation of the assumptions in influential theories of learning and decision-making. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.26.509612v1?rss=1 Authors: Sanz Perl, Y., Fittipaldi, S., Gonzalez Campo, C., Moguilner, S., Cruzat, J., Herzog, R., Kringelbach, M. L., Deco, G., Prado, P., Ibanez, A., Tagliazucchi, E. Abstract: The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.14.507926v1?rss=1 Authors: Triplett, M. A., Gajowa, M., Antin, B., Sadahiro, M., Adesnik, H., Paninski, L. Abstract: Discovering how neural computations are implemented in the cortex at the level of monosynaptic connectivity requires probing for the existence of synapses from possibly thousands of presynaptic candidate neurons. Two-photon optogenetics has been shown to be a promising technology for mapping such monosynaptic connections via serial stimulation of neurons with single-cell resolution. However, this approach is limited in its ability to uncover connectivity at large scales because stimulating neurons one-by-one requires prohibitively long experiments. Here we developed novel computational tools that, when combined, enable learning of monosynaptic connectivity from high-speed holographic neural ensemble stimulation. First, we developed a model-based compressed sensing algorithm that identifies connections from postsynaptic responses evoked by stimulation of many neurons at once, considerably increasing the rate at which the existence and strength of synapses are screened. Second, we developed a deep learning method that isolates the postsynaptic response evoked by each stimulus, allowing stimulation to rapidly switch between ensembles without waiting for the postsynaptic response to return to baseline. Together, our system increases the throughput of monosynaptic connectivity mapping by an order of magnitude over existing approaches, enabling the acquisition of connectivity maps at speeds needed to discover the synaptic circuitry implementing neural computations. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Model-based cross-correlation search for gravitational waves from the low-mass X-ray binary Scorpius X-1 in LIGO O3 data by The LIGO Scientific Collaboration et al. on Wednesday 07 September We present the results of a model-based search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1 using LIGO detector data from the third observing run of Advanced LIGO, Advanced Virgo and KAGRA. This is a semicoherent search which uses details of the signal model to coherently combine data separated by less than a specified coherence time, which can be adjusted to balance sensitivity with computing cost. The search covered a range of gravitational-wave frequencies from 25Hz to 1600Hz, as well as ranges in orbital speed, frequency and phase determined from observational constraints. No significant detection candidates were found, and upper limits were set as a function of frequency. The most stringent limits, between 100Hz and 200Hz, correspond to an amplitude h0 of about 1e-25 when marginalized isotropically over the unknown inclination angle of the neutron star's rotation axis, or less than 4e-26 assuming the optimal orientation. The sensitivity of this search is now probing amplitudes predicted by models of torque balance equilibrium. For the usual conservative model assuming accretion at the surface of the neutron star, our isotropically-marginalized upper limits are close to the predicted amplitude from about 70Hz to 100Hz; the limits assuming the neutron star spin is aligned with the most likely orbital angular momentum are below the conservative torque balance predictions from 40Hz to 200Hz. Assuming a broader range of accretion models, our direct limits on gravitational-wave amplitude delve into the relevant parameter space over a wide range of frequencies, to 500Hz or more. arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2209.02863v1

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.04.506507v1?rss=1 Authors: Mathar, D., Wiebe, A., Tuzsus, D., Peters, J. Abstract: Computational psychiatry focuses on identifying core cognitive processes that appear altered across a broad range of psychiatric disorders. Temporal discounting of future rewards and model-based control during reinforcement learning have proven as two promising candidates. Despite its trait-like stability, temporal discounting has been suggested to be at least partly under contextual control. For example, highly arousing cues such as erotic pictures were shown to increase discounting, although overall evidence to date remains somewhat mixed. Whether model-based reinforcement learning is similarly affected by arousing cues is unclear. Here we tested cue-reactivity effects (erotic pictures) on subsequent temporal discounting and model-based reinforcement learning in a within-subjects design in n=39 healthy male participants. Self-reported and physiological arousal (cardiac activity and pupil dilation) were assessed before and during cue exposure. Arousal was increased during exposure of erotic vs. neutral cues both on the subjective and autonomic level. Erotic cue exposure nominally increased discounting as reflected by reduced choices of delayed options. Hierarchical drift diffusion modeling (DDM) linked increased discounting to a shift in the starting point bias of evidence accumulation towards immediate options. Model-based control during reinforcement learning was reduced following erotic cues according to model-agnostic analysis. Notably, DDM linked this effect to attenuated forgetting rates of unchosen options, leaving the model-based control parameter unchanged. Our findings replicate previous work on cue-reactivity effects in temporal discounting and for the first time show similar effects in model-based reinforcement learning. Our results highlight how environmental cues can impact core human decision processes and reveal that comprehensive drift diffusion modeling approaches can yield novel insights in reward-based decision processes. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

There's a lot of talk about Model-Based Definition (MBD), which means there's also a lot of different opinions out there. It's one thing to paint a picture of some future vision for a Model-Based Enterprise—but how can you start taking steps today to make MBD practical for your team? That's exactly what the Action Engineering team does. Jennifer Herron, Founder and CEO, and Rhiannon Gallagher, Chief Business Psychologist, join Adam Keating to talk all about MBD and what they've seen working with organizations to achieve MBD and MBE goals. In this episode, we discuss: Why bother with MBD at all The challenges of shifting toward MBD How psychological safety impacts manufacturing organizations MBD and supply chain relationships More information about Jennifer Herron and Rhiannon Gallagher and today's topics: Jennifer Herron: https://www.linkedin.com/in/jennifer-herron-cad/ Rhiannon Gallagher: https://www.linkedin.com/in/rhiannongallagher/ Action Engineering: https://www.action-engineering.com/ Peer Check Homepage: https://www.colabsoftware.com/podcast/peer-check To hear this interview and more like it, subscribe to Peer Check! Find us on Apple Podcasts, Spotify, or our website—or just search for Peer Check in your favourite podcast player.

This week's construction tech news with Jeff Sample (@IronmanofIT), and Lonnie Cumpton (@LonnieCumpton) Featuring: - Interview with John Theis from Bidlight - Construction Tech News of the Week Follow @TheConTechCrew on social media for more updates and to join the conversation! Listen to the show at http://thecontechcrew.com Powered by JBKnowledge Learn more at http://thecontechcrew.com or follow @JBKnowledge & @TheConTechCrew on Twitter.

To subscribe: Critical Point Podcast $27.99 per month recurring. Billing begins two weeks from signup (a form of 2-week free trial). Cancel anytime. Primary focus on the US stock market and the major grains. Short-term to super-long-term forecasts from the business cycle model, including signals. Additional model-based opinion/signals include global stock market indexes, interest rates, dollar, bitcoin, gold, oil, the boom/bust cycle of the economy, and the cyclical climate events that can cause crop problems. For information, education, explanation see criticalpointpod.com. Email: rich@ag-financial.com Twitter: @rich_posson

Learn how team leaders should choose the best real estate team compensation model based on a variety of different real estate team organizational structures.

QuantSpeak host, Dan Tudball, is joined by Samit Ahlawat, Senior Vice President in Quantitative Research, Capital Modeling at J.P. Morgan Chase, to discuss what researchers should prioritize when using artificial intelligence and machine learning in building automated trading strategies, his career path in quantitative finance and machine learning, and where his research will take him next. 

This episode is part of our MBSE Methodology series. Dealing with product lines and variants keeps many projects busy. And now we are making the whole thing model-based as well: Model-Based Product Line Engineering (MBPLE). Our guest can tell us how MBPLE works. Marco Forlingieri is responsible at Airbus for introducing MBPLE into the Airbus-wide product development methodology. Der Beitrag Episode 21 – Model-Based Product Line Engineering with Marco Forlingieri from Airbus erschien zuerst auf The MBSE Podcast.

This episode explores a share farming model which is radically different.Food system change-makers Kirsten Larsen and Serenity Hill reveal their ground-breakingnew collaborative farming model and succession plan designed to improve ecologicalfunction, support a diverse range of small-scale businesses, and ensure security of tenurefor emerging farmers. These inspirational farmers in North Eastern Victoria have establishedput the family farm in a trust and created an agreement which over 80 years shifts theequity of their family's farm into a not-for-profit – conditional on improving ecologicalconditions of the land.Show notes:Open Food Network (OFN): Started as an online marketplace to match farmers with eaters which supports collaborative distribution OFN as an open-source platform: Working with people in 29 countries to develop platform. Model keeps on improving Regenerative farming something positive for climate and improving ecosystem but it is so labour intensive. We need solution to this problem Trust the collaborative process to bring along people in to solve problems on the farm Leasing 400 acres North East Victoria from family (Pukawidgee) and marketing lamb on OFN Core issue of young farmers is they need security of tenure and longstanding connection Set up a Non-profit with a long-term lease arrangement with a trust over 80 years Shift from Land custodianship rather than ownership: improving health should be tied to land custodianship Building a succession plan around the condition of improving ecological conditions. Further resources:Full story about https://farmingtogether.com.au/new-collaborative-farming-model/?cid=1 (Kirsten Larsen and Serenity Hill share farming model)https://openfoodnetwork.org.au/ (Open Food Network website)Order from https://openfoodnetwork.org.au/warrenbayne-farm-collective/shop (Pukawidgee (Warrenbayne Farm Collective))https://www.facebook.com/openfoodnetworkaus (Open Food Network Facebook Page)

The first episode of 2022 is part of our MBSE tool series and starts right away with a hot topic: "Connected engineering information for a connected world" - that's the claim of the OpenMBEE platform. Their promise sounds like a dream for every (system) engineer: "It enables engineers to work in the language of their choice and easily share and document their work across other tools.". So, let's have a look at how to make the engineers dream true. Der Beitrag Episode 20 – The Open Model-Based Engineering Environment (OpenMBEE) with Chris Delp from Nasa JPL erschien zuerst auf The MBSE Podcast.

Stephen is the go-to person for creative design work at the Mayo Clinic and has been working at Mayo for the past 20 years. He's also the Chair of documentations standards workgroup, he figures out how to document SolidWorks designs. Stephen is a mechanical engineer with a P.E. license. In this episode we discuss the advantages of Model-Based Definition (MBD), and specializing vs. being a jack of all trades.  ABOUT BEING AN ENGINEERThe Being an Engineer podcast is a repository for industry knowledge and a tool through which engineers learn about and connect with relevant companies, technologies, people resources, and opportunities. We feature successful mechanical engineers and interview engineers who are passionate about their work and who made a great impact on the engineering community.The Being An Engineer podcast is brought to you by Pipeline Design & Engineering. Pipeline partners with medical & other device engineering teams who need turnkey equipment such as cycle test machines, custom test fixtures, automation equipment, assembly jigs, inspection stations and more. You can find us on the web at www.teampipeline.us***Valued listener, we need your help getting to 100 podcast reviews. Win a $50 Amazon Gift card if you leave us a review on the Apple Podcasts. Simply email a screenshot of your 5-star review to Podcast@teampipeline.us , the email will be in the show notes. We will announce 5 lucky winners at the end of the first quarter in 2022.LINKS:SolidWorks luggage handle mechanism by Rafael Testai (video sponsored by Pipeline)Rafael Testai (co-host) on Linkedin

As your life changes, the way you run your business will also have to change. It's one thing to run your gym when you're single. It's another to do it when you're in a serious relationship. And it's a completely different game when you're starting a family. Each of life will require different things from you. They will all require that you give less time to the gym and more time to your life outside the gym. In this episode, we dive into the business model you need to have in mind as your stage in life change. If you're interested in working with us, head to www.factoryforged.com/call

Increased electronics complexity has directly impacted all stages of the design and manufacturing processes. This comes at a time when companies are being pushed by market forces to cut the time to market and still make quality products at affordable prices. One of the possible ways to meet these expectations is through the automation of some of the resource-consuming tasks within the design stage.Today's hosts are Nicholas Finberg of Siemens Global Marketing, and Tim Kinman, Vice President of Trending Solutions and Global Program Lead for Systems Digitalization at Siemens Digital Industries Software. They are joined by Mark Malinoski and Matt Bromley from the EDA space to talk about vertical connected development.In this episode, you'll learn about the challenges that can be solved by automating some tasks within the design process. You'll also learn about the benefits of continuous verification and how it impacts product design adaptability. Lastly, you'll hear about what the future holds for MBSE and the role that increased complexity will play in the world of electronics.What You'll Learn in this Episode:How automation can play an important role in MBSE (00:43)The challenges that stem from a lack of continuous verification in electronics design (05:11)How the supply chain is evolving and the cause of the changes being experienced (08:06)What the future holds for MBSE (12:47)Connect with Matt Bromley: LinkedInConnect with Mark Malinoski: LinkedInConnect with Tim Kinman: LinkedInConnect with Nicholas Finberg: LinkedIn See acast.com/privacy for privacy and opt-out information.

Is it really your strokes that are holding you back from your best tennis? In this episode, Tennis Canada Level 4 coach Wayne Elderton joins the show and dives deep into ‘model' vs ‘game-based' coaching - and why a game-based or 'tactical' approach to coaching is more effective & efficient when it comes to learning. Wayne and I also discuss the shot cycle, the various tennis 'situations', scaling courts + equipment for young/beginner players and his take on practices that emphasize 0-4 shots.

In episode 13 of The Gradient Podcast, we interview Stanford Professor Chelsea FinnSubscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSChelsea is an Assistant Professor at Stanford University. Her lab, IRIS, studies intelligence through robotic interaction at scale, and is affiliated with SAIL and the Statistical ML Group. I also spend time at Google as a part of the Google Brain team. Her research deals with the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction.Links:Learning to Learn with GradientsVisual Model-Based Reinforcement Learning as a Path towards Generalist RobotsRoboNet: A Dataset for Large-Scale Multi-Robot LearningGreedy Hierarchical Variational Autoencoders for Large-Scale VideoExample-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks   Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music". Get full access to The Gradient at thegradientpub.substack.com/subscribe

In Part 2 of this podcast series, IpX Director of Model Based Enterprise, Max Gravel, and Vertex Product Marketing Manager, John Heller, continue tearing down misconceptions and myths to starting a digital twin journey with further exploration into:- How open access to 3D data encourages collaboration and reward that collaboration to improve company culture - Creating a customer-centric organization with direct product feedback and improvements to engineering - A focus on downstream users and the ability for faster decision making without internal conflict- How and why to jumpstart model based definition initiatives to move to a model based enterprise- How to jumpstart model based definition inside an organization by leveraging data across the enterprise and looking for solutions that free that data in a secure way- Finding solutions that allow you to free 3D data and augment innovative business applications that accelerate digital transformationRead the referenced blog, “Connect Your Enterprise: How To Overcome Challenges in Change Management & 3D Visualization” coauthored by Max and John from November 2020

In this 2-part podcast series, IpX Director of Model Based Enterprise, Max Gravel, and Vertex Product Marketing Manager, John Heller, dive into the model based world to tear down misconceptions and myths to starting a digital twin journey. They illustrate how data visibility throughout an enterprise accelerates business goals and unifies departments. Throughout the series, hear John and Max discuss:How digital twins go beyond engineering and support the overall user experience when representing 3D data with full traceabilityHow 3D data acts as a company's universal language spanning departments, company culture and locationsThe immediate value from breaking down silos in change and increasing cross-functional collaboration Ways for your organization to begin breaking down silos to share 3D data and adopt a digital mindsetHow and why to jumpstart model based definition initiatives to move to a model based enterpriseRead the referenced blog, “Connect Your Enterprise: How To Overcome Challenges in Change Management & 3D Visualization” coauthored by Max and John from November 2020. 

Don't throw away those owl pellets just yet.

Todd Campbell shares with us successful strategies and resources to use when implementing model based inquiry in your NGSS classroom. He describes ways to guide students through the sense making process. Check out the show notes for links to unit templates with this framework as well as other useful resources mentioned in the show: www.ngsnavigators.com/blog/031