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High Water Ahead: The New Normal of American Flood Risks
According to a map created by the National Oceanic and Atmospheric Administration (NOAA) that highlights ‘hazard zones’ in the U.S. for various flooding risks, including rising sea levels and tsunamis. Here’s a summary and analysis: Summary: The NOAA map identifies areas at risk of flooding from storm surges, tsunamis, high tide flooding, and sea level rise. Red areas on the map indicate more…
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#AI News#climate forecasts#data driven modeling#ethical AI#flood risk management#geospatial big data#News#noaa#sea level rise#uncertainty quantification
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How DeepSeek AI Revolutionizes Data Analysis
1. Introduction: The Data Analysis Crisis and AI’s Role2. What Is DeepSeek AI?3. Key Features of DeepSeek AI for Data Analysis4. How DeepSeek AI Outperforms Traditional Tools5. Real-World Applications Across Industries6. Step-by-Step: Implementing DeepSeek AI in Your Workflow7. FAQs About DeepSeek AI8. Conclusion 1. Introduction: The Data Analysis Crisis and AI’s Role Businesses today generate…
#AI automation trends#AI data analysis#AI for finance#AI in healthcare#AI-driven business intelligence#big data solutions#business intelligence trends#data-driven decisions#DeepSeek AI#ethical AI#ethical AI compliance#Future of AI#generative AI tools#machine learning applications#predictive modeling 2024#real-time analytics#retail AI optimization
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Scientists use generative AI to answer complex questions in physics
New Post has been published on https://thedigitalinsider.com/scientists-use-generative-ai-to-answer-complex-questions-in-physics/
Scientists use generative AI to answer complex questions in physics


When water freezes, it transitions from a liquid phase to a solid phase, resulting in a drastic change in properties like density and volume. Phase transitions in water are so common most of us probably don’t even think about them, but phase transitions in novel materials or complex physical systems are an important area of study.
To fully understand these systems, scientists must be able to recognize phases and detect the transitions between. But how to quantify phase changes in an unknown system is often unclear, especially when data are scarce.
Researchers from MIT and the University of Basel in Switzerland applied generative artificial intelligence models to this problem, developing a new machine-learning framework that can automatically map out phase diagrams for novel physical systems.
Their physics-informed machine-learning approach is more efficient than laborious, manual techniques which rely on theoretical expertise. Importantly, because their approach leverages generative models, it does not require huge, labeled training datasets used in other machine-learning techniques.
Such a framework could help scientists investigate the thermodynamic properties of novel materials or detect entanglement in quantum systems, for instance. Ultimately, this technique could make it possible for scientists to discover unknown phases of matter autonomously.
“If you have a new system with fully unknown properties, how would you choose which observable quantity to study? The hope, at least with data-driven tools, is that you could scan large new systems in an automated way, and it will point you to important changes in the system. This might be a tool in the pipeline of automated scientific discovery of new, exotic properties of phases,” says Frank Schäfer, a postdoc in the Julia Lab in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-author of a paper on this approach.
Joining Schäfer on the paper are first author Julian Arnold, a graduate student at the University of Basel; Alan Edelman, applied mathematics professor in the Department of Mathematics and leader of the Julia Lab; and senior author Christoph Bruder, professor in the Department of Physics at the University of Basel. The research is published today in Physical Review Letters.
Detecting phase transitions using AI
While water transitioning to ice might be among the most obvious examples of a phase change, more exotic phase changes, like when a material transitions from being a normal conductor to a superconductor, are of keen interest to scientists.
These transitions can be detected by identifying an “order parameter,” a quantity that is important and expected to change. For instance, water freezes and transitions to a solid phase (ice) when its temperature drops below 0 degrees Celsius. In this case, an appropriate order parameter could be defined in terms of the proportion of water molecules that are part of the crystalline lattice versus those that remain in a disordered state.
In the past, researchers have relied on physics expertise to build phase diagrams manually, drawing on theoretical understanding to know which order parameters are important. Not only is this tedious for complex systems, and perhaps impossible for unknown systems with new behaviors, but it also introduces human bias into the solution.
More recently, researchers have begun using machine learning to build discriminative classifiers that can solve this task by learning to classify a measurement statistic as coming from a particular phase of the physical system, the same way such models classify an image as a cat or dog.
The MIT researchers demonstrated how generative models can be used to solve this classification task much more efficiently, and in a physics-informed manner.
The Julia Programming Language, a popular language for scientific computing that is also used in MIT’s introductory linear algebra classes, offers many tools that make it invaluable for constructing such generative models, Schäfer adds.
Generative models, like those that underlie ChatGPT and Dall-E, typically work by estimating the probability distribution of some data, which they use to generate new data points that fit the distribution (such as new cat images that are similar to existing cat images).
However, when simulations of a physical system using tried-and-true scientific techniques are available, researchers get a model of its probability distribution for free. This distribution describes the measurement statistics of the physical system.
A more knowledgeable model
The MIT team’s insight is that this probability distribution also defines a generative model upon which a classifier can be constructed. They plug the generative model into standard statistical formulas to directly construct a classifier instead of learning it from samples, as was done with discriminative approaches.
“This is a really nice way of incorporating something you know about your physical system deep inside your machine-learning scheme. It goes far beyond just performing feature engineering on your data samples or simple inductive biases,” Schäfer says.
This generative classifier can determine what phase the system is in given some parameter, like temperature or pressure. And because the researchers directly approximate the probability distributions underlying measurements from the physical system, the classifier has system knowledge.
This enables their method to perform better than other machine-learning techniques. And because it can work automatically without the need for extensive training, their approach significantly enhances the computational efficiency of identifying phase transitions.
At the end of the day, similar to how one might ask ChatGPT to solve a math problem, the researchers can ask the generative classifier questions like “does this sample belong to phase I or phase II?” or “was this sample generated at high temperature or low temperature?”
Scientists could also use this approach to solve different binary classification tasks in physical systems, possibly to detect entanglement in quantum systems (Is the state entangled or not?) or determine whether theory A or B is best suited to solve a particular problem. They could also use this approach to better understand and improve large language models like ChatGPT by identifying how certain parameters should be tuned so the chatbot gives the best outputs.
In the future, the researchers also want to study theoretical guarantees regarding how many measurements they would need to effectively detect phase transitions and estimate the amount of computation that would require.
This work was funded, in part, by the Swiss National Science Foundation, the MIT-Switzerland Lockheed Martin Seed Fund, and MIT International Science and Technology Initiatives.
#ai#approach#artificial#Artificial Intelligence#Bias#binary#change#chatbot#chatGPT#classes#computation#computer#Computer modeling#Computer Science#Computer Science and Artificial Intelligence Laboratory (CSAIL)#Computer science and technology#computing#crystalline#dall-e#data#data-driven#datasets#dog#efficiency#Electrical Engineering&Computer Science (eecs)#engineering#Foundation#framework#Future#generative
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As someone who works deep in the weeds of corporate hellscape, the thing is... are you complaining?
Because if you're not complaining - ie, actually calling the phone number in the package and leaving a polite but strongly worded objection in their "feedback" channel - then... yeah, companies do think that you don't notice shrinkflation. It's not that the impacts it has are not real, it's that they're not reportable data.
One of the stupidest things about the corporate hell we live in today is that decisions are judged not by naturally observable things or even rational thought, but by data and metrics.
If someone says "we can make more money if we water down the soup", and someone tries to argue back against it by saying "customers will be mad and it will damage the brand", but after testing people don't complain or don't complain enough... yeah, as far as the corporation is concerned, people don't care.
And this is not me defending it, btw. I'm just trying to shine a light on a fucking horrid side effect of turbo capitalism: you're not allowed to exist, you have to spend time and energy constantly defending your right to receive the goods you're actually paying for, because someone else is trying to squeeze out a liiitle bit more profit out of it.
Like that's the ideal, as far as corporate goes: squeeze people and try to scam them as much as possible, until the complaining is unbearable. This is why you see stories blow up of people getting real fucking pissed and as you go down the facts, you realize they've been exploited and taken advantage of for years. Because that's what corporate considers sustainable.
"If people don't like it, they will complain."
We live in a system that expects you to complain about every aspect of your life, but if you don't do it in the correct channel or with the correct frequency, you will be assumed to be complicit in whatever new way they're looking to fuck you over. It's insane.
companies are delusional if they think consumers don't notice shrinkflation. less food in the package, less medicine in the jar, less whatever in the wherever, it doesn't matter where and it's almost always noticeable. like i just finished one box of medicine and we opened another allegedly identical one that we just bought and lo and behold, the four middle medicine segments were gone from the package. they took out four pills from the same sized box and sold it at the same price without any indication on the box other than the small number in the corner. ridiculous
#our business model revolves around pushing until we find the line our customers won't let us cross and in the meantime#profits!#shut up rie#“our business is data driven” is corpo speak for
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Why ILRI's Kapiti Ranch in Kenya is the Ultimate Test-Bed for Digital Innovation in Livestock Research
“Learn how ILRI’s digital twin project at Kapiti Ranch is transforming livestock management in Africa, offering data-driven insights into cattle health, pasture availability, and climate adaptation.” “Explore ILRI’s innovative use of digital twin technology in Kenya, enhancing livestock research with real-time health monitoring, climate-resilient breeding, and sustainable rangeland…
#3D ranch modeling#African livestock management#Agricultural Innovation#agriculture digital tools#animal breeding research#animal phenotyping#Bodit Bluetooth collars#cattle health monitoring#CGIAR digital innovation#climate-resistant cattle#dairy farming technology#data-driven farming#digital twins in livestock#ILRI Kenya#Kapiti ranch Kenya#livestock research advancements#livestock technology#pasture monitoring#Smaxtech bolus sensors#sustainable rangeland management
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The importance of predictive analytics in healthcare using big data can enhance patient care and address chronic diseases efficiently.
As someone deeply immersed in the healthcare industry, I’ve witnessed a profound transformation driven by the integration of predictive analytics and big data. The importance of predictive analytics in healthcare using big data cannot be overstated, as it offers unprecedented opportunities to improve patient care, optimize operations, and advance medical research. The vast amounts of data generated daily in healthcare settings provide the foundation for predictive analytics, enabling us to forecast future events based on historical and current data. In this blog, I’ll explore the significance of predictive analytics in healthcare, its benefits, practical applications, and the future of this technology.
#Predictive Analytics in Healthcare#Big Data in Healthcare#Healthcare Predictive Analytics#Predictive Analytics for Chronic Diseases#Patient Care Analytics#Big Data Analytics in Healthcare#Predictive Healthcare Analytics#Healthcare Data Analytics#Predictive Modeling in Healthcare#Data-Driven Healthcare Solutions
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Unlocking the Impact & Potential of AI.
Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in A Forward-Thinking Exploration for Senior IT Leaders on Harnessing the Transformative Power of AI Discover expert insights and actionable strategies to unlock AI’s potential. A forward-thinking guide for IT leaders ready to innovate. Executive Summary – Charting a Course for AI-Driven Success
In today’s fast-changing…
#AI Impact#AI Integration#AI Potential#Business Innovation#CIO priorities#Data-driven decision-making in IT#digital transformation leadership#emerging technology strategy#IT operating model evolution#News#Sanjay Kumar Mohindroo#Technology Leadership
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AI-Powered Decision-Making: How to Execute with Precision and Confidence
AI-Powered Decision-Making How to Execute with Precision and Confidence Scaling a business is one thing, but making the right decisions at the right time? That’s the real challenge. We’ve already explored AI-powered leadership, customer experience, innovation, and strategic planning. Now, it’s time to connect the dots and focus on something that determines whether all of those efforts succeed…
#AI-driven AI-human hybrid decision-making#AI-driven AI-powered automated financial decision-making#AI-driven AI-powered business adaptability#AI-driven AI-powered leadership optimization#AI-driven AI-powered omnichannel business insights#AI-driven AI-powered risk mitigation#AI-driven AI-powered scenario planning#AI-driven algorithmic decision-making#AI-driven business intelligence dashboards#AI-driven cognitive decision augmentation#AI-driven competitive intelligence#AI-driven data-backed business strategies#AI-driven digital transformation intelligence#AI-driven executive workflow automation#AI-driven goal-driven AI-powered AI-powered strategy adaptation#AI-driven high-performance decision-making#AI-driven integrated financial intelligence#AI-driven intelligent business decision networks#AI-driven machine learning-based strategic execution#AI-driven next-gen predictive business modeling#AI-driven next-level business automation#AI-driven performance tracking#AI-driven predictive performance optimization#AI-driven real-time financial modeling#AI-driven risk assessment#AI-driven smart automation for decision-making#AI-driven smart executive decision dashboards#AI-driven strategic execution#AI-driven sustainable growth decision-making#AI-driven transformational business intelligence
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Generative AI for Startups: 5 Essential Boosts to Boost Your Business

The future of business growth lies in the ability to innovate rapidly, deliver personalized customer experiences, and operate efficiently. Generative AI is at the forefront of this transformation, offering startups unparalleled opportunities for growth in 2024.
Generative AI is a game-changer for startups, significantly accelerating product development by quickly generating prototypes and innovative ideas. This enables startups to innovate faster, stay ahead of the competition, and bring new products to market more efficiently. The technology also allows for a high level of customization, helping startups create highly personalized products and solutions that meet specific customer needs. This enhances customer satisfaction and loyalty, giving startups a competitive edge in their respective industries.
By automating repetitive tasks and optimizing workflows, Generative AI improves operational efficiency, saving time and resources while minimizing human errors. This allows startups to focus on strategic initiatives that drive growth and profitability. Additionally, Generative AI’s ability to analyze large datasets provides startups with valuable insights for data-driven decision-making, ensuring that their actions are informed and impactful. This data-driven approach enhances marketing strategies, making them more effective and personalized.
Intelisync offers comprehensive AI/ML services that support startups in leveraging Generative AI for growth and innovation. With Intelisync’s expertise, startups can enhance product development, improve operational efficiency, and develop effective marketing strategies. Transform your business with the power of Generative AI—Contact Intelisync today and unlock your Learn more...
#5 Powerful Ways Generative AI Boosts Your Startup#advanced AI tools support startups#Driving Innovation and Growth#Enhancing Customer Experience#Forecasting Data Analysis and Decision-Making#Generative AI#Generative AI improves operational efficiency#How can a startup get started with Generative AI?#Is Generative AI suitable for all types of startups?#marketing strategies for startups#Streamlining Operations#Strengthen Product Development#Transform your business with AI-driven innovation#What is Generative AI#Customized AI Solutions#AI Development Services#Custom Generative AI Model Development.
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How Big Data Analytics is Changing Scientific Discoveries
Introduction
In the contemporary world of the prevailing sciences and technologies, big data analytics becomes a powerful agent in such a way that scientific discoveries are being orchestrated. At Techtovio, we explore this renewed approach to reshaping research methodologies for better data interpretation and new insights into its hastening process. Read to continue
#CategoriesScience Explained#Tagsastronomy data analytics#big data analytics#big data automation#big data challenges#big data in healthcare#big data in science#big data privacy#climate data analysis#computational data processing#data analysis in research#data-driven science#environmental research#genomics big data#personalized medicine#predictive modeling in research#real-time scientific insights#scientific data integration#scientific discoveries#Technology#Science#business tech#Adobe cloud#Trends#Nvidia Drive#Analysis#Tech news#Science updates#Digital advancements#Tech trends
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Creating an Effective Power BI Dashboard: A Comprehensive Guide
Introduction to Power BI Power BI is a suite of business analytics tools that allows you to connect to multiple data sources, transform data into actionable insights, and share those insights across your organization. With Power BI, you can create interactive dashboards and reports that provide a 360-degree view of your business.
Step-by-Step Guide to Creating a Power BI Dashboard
1. Data Import and Transformation The first step in creating a Power BI dashboard is importing your data. Power BI supports various data sources, including Excel, SQL Server, Azure, and more.
Steps to Import Data:
Open Power BI Desktop.
Click on Get Data in the Home ribbon.
Select your data source (e.g., Excel, SQL Server, etc.).
Load the data into Power BI.
Once the data is loaded, you may need to transform it to suit your reporting needs. Power BI provides Power Query Editor for data transformation.
Data Transformation:
Open Power Query Editor.
Apply necessary transformations such as filtering rows, adding columns, merging tables, etc.
Close and apply the changes.
2. Designing the Dashboard After preparing your data, the next step is to design your dashboard. Start by adding a new report and selecting the type of visualization you want to use.
Types of Visualizations:
Charts: Bar, Line, Pie, Area, etc.
Tables and Matrices: For detailed data representation.
Maps: Geographic data visualization.
Cards and Gauges: For key metrics and KPIs.
Slicers: For interactive data filtering.
Adding Visualizations:
Drag and drop fields from the Fields pane to the canvas.
Choose the appropriate visualization type from the Visualizations pane.
Customize the visual by adjusting properties such as colors, labels, and titles.
3. Enhancing the Dashboard with Interactivity Interactivity is one of the key features of Power BI dashboards. You can add slicers, drill-throughs, and bookmarks to make your dashboard more interactive and user-friendly.
Using Slicers:
Add a slicer visual to the canvas.
Drag a field to the slicer to allow users to filter data dynamically.
Drill-throughs:
Enable drill-through on visuals to allow users to navigate to detailed reports.
Set up drill-through pages by defining the fields that will trigger the drill-through.
Bookmarks:
Create bookmarks to capture the state of a report page.
Use bookmarks to toggle between different views of the data.

Different Styles of Power BI Dashboards Power BI dashboards can be styled to meet various business needs. Here are a few examples:
1. Executive Dashboard An executive dashboard provides a high-level overview of key business metrics. It typically includes:
KPI visuals for critical metrics.
Line charts for trend analysis.
Bar charts for categorical comparison.
Maps for geographic insights.
Example:
KPI cards for revenue, profit margin, and customer satisfaction.
A line chart showing monthly sales trends.
A bar chart comparing sales by region.
A map highlighting sales distribution across different states.
2. Sales Performance Dashboard A sales performance dashboard focuses on sales data, providing insights into sales trends, product performance, and sales team effectiveness.
Example:
A funnel chart showing the sales pipeline stages.
A bar chart displaying sales by product category.
A scatter plot highlighting the performance of sales representatives.
A table showing detailed sales transactions.
3. Financial Dashboard A financial dashboard offers a comprehensive view of the financial health of an organization. It includes:
Financial KPIs such as revenue, expenses, and profit.
Financial statements like income statement and balance sheet.
Trend charts for revenue and expenses.
Pie charts for expense distribution.
Example:
KPI cards for net income, operating expenses, and gross margin.
A line chart showing monthly revenue and expense trends.
A pie chart illustrating the breakdown of expenses.
A matrix displaying the income statement.
Best Practices for Designing Power BI Dashboards To ensure your Power BI dashboard is effective and user-friendly, follow these best practices:
Keep it Simple:
Avoid cluttering the dashboard with too many visuals.
Focus on the most important metrics and insights.
2. Use Consistent Design:
Maintain a consistent color scheme and font style.
Align visuals properly for a clean layout.
3. Ensure Data Accuracy:
Validate your data to ensure accuracy.
Regularly update the data to reflect the latest information.
4. Enhance Interactivity:
Use slicers and drill-throughs to provide a dynamic user experience.
Add tooltips to provide additional context.
5. Optimize Performance:
Use aggregations and data reduction techniques to improve performance.
Avoid using too many complex calculations.
Conclusion Creating a Power BI dashboard involves importing and transforming data, designing interactive visuals, and applying best practices to ensure clarity and effectiveness. By following the steps outlined in this guide, you can build dashboards that provide valuable insights and support data-driven decision-making in your organization. Power BI’s flexibility and range of visualizations make it an essential tool for any business looking to leverage its data effectively.
#Dynamic Data Visualization#Business Analytics#Interactive Dashboards#Data Insights#Data Transformation#KPI Metrics#Real-time Reporting#Data Connectivity#Trend Analysis#Visual Analytics#Performance Metrics#Data Modeling#Executive Dashboards#Sales Performance#Financial Reporting#Data Interactivity#Data-driven Decisions#Power Query#Custom Visuals#Data Integration
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The State of AI: Navigating the Future of Enterprise Intelligence.
Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in Explore expert insights and actionable strategies on the state of AI to drive innovation and cut costs in your enterprise. Executive Summary – A Bold Overview for Global IT Leaders In 2025, AI stands at a crossroads of innovation and integration. The rise of generative AI has pushed enterprises into a new era of digital…
#AI Integration#AI tax#centralized AI platforms#CIO priorities#Data-driven decision-making in IT#digital transformation leadership#emerging technology strategy#enterprise AI#Generative AI#IT operating model evolution#News#Sanjay Kumar Mohindroo
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AI and Business Strategy: The Secret to Sustainable, Scalable Success
AI and Business Strategy The Secret to Sustainable, Scalable Success Scaling is one thing. Sustaining it? That’s the real challenge. If you’ve been following this series, you know we’ve talked about AI-driven leadership, customer experience, and innovation—all crucial pieces of the puzzle. But today, we’re tackling something even more foundational: how AI transforms business strategy…
#AI-driven AI-enhanced executive workflows#AI-driven AI-first business frameworks#AI-driven AI-first executive decision-making#AI-driven AI-human hybrid strategy#AI-driven AI-powered workflow automation#AI-driven automated corporate vision execution#AI-driven business intelligence automation#AI-driven business model reinvention#AI-driven competitive intelligence#AI-driven cost optimization strategies#AI-driven cross-functional strategic execution#AI-driven customer behavior analysis#AI-driven data-backed competitive analysis#AI-driven digital transformation strategy#AI-driven executive decision support#AI-driven executive performance insights#AI-driven financial forecasting#AI-driven frictionless decision-making#AI-driven high-impact decision-making#AI-driven innovation acceleration#AI-driven intelligent automation for business success#AI-driven KPI tracking#AI-driven market intelligence tools#AI-driven next-gen business intelligence#AI-driven precision-driven corporate strategy#AI-driven predictive analytics#AI-driven real-time financial modeling#AI-driven risk assessment#AI-driven sales and marketing alignment#AI-driven smart decision automation
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I've heard this called a "scream test" - turn it off/make it worse and see who screams about it.
companies are delusional if they think consumers don't notice shrinkflation. less food in the package, less medicine in the jar, less whatever in the wherever, it doesn't matter where and it's almost always noticeable. like i just finished one box of medicine and we opened another allegedly identical one that we just bought and lo and behold, the four middle medicine segments were gone from the package. they took out four pills from the same sized box and sold it at the same price without any indication on the box other than the small number in the corner. ridiculous
#fuck capitalism#Scream test#“our business is data driven” is corpo speak for#our business model revolves around pushing until we find the line our customers won't let us cross and in the meantime#profits!
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