upset by kissinger & Economist using UN day of families to vision wwar3
Chris Macrae MA DAMTP Cantab
ai20s.com 2025reportersclub thanks Kamala for most decency White House ever seen,,,
EL: intel quiz - who do you bard with first ?
It was Economist founder Wilson and Queen Victoria's charter from 1843 that the economist was to mediate the good of peoples -not this blah https://www.economist.com/kissinger-transcrip t
ironically when this dismal [iece flashed my screens i was just publishing my family's 73rd annual update futureofintel-intro3pager.docx on when The Economist 1951 met von neumann for a year in NYP (New York Princeton) and agreed the greatest journalist scoop pf all times - what goods can peoples unite wherever they get first access to more than 100 times more tech per decade?
how about we the billion beings on mother earth take back how our time is spent and how are money is dealt out with a little help from HumansAI - always ready to zoom if you see some of us 8 billion who dare to dream that olders as parents must unite to advance humanity round our children -wherever their brains or the ARTS may GPS
upd 5/15/23 UN day of families: 3 opposite visions -cf Fantasy H-AI Dao membership
X Kissinger & The Economist envision world war 3??https://www.economist.com/kissinger-transcript
Y MIT's new president Sally Kornbluth cheerleads schools day of AI?Day of AI 2023
Z My family's 73rd annual update on purpose V Neumann expected UN and Human AI attached
sincerely chris macrae if anyone has conversations for?https://www.bard.solar ?please tell me
breaking apparently fed gov has 400?& rising uses of AI its happy to spread with the peoples https://docs.google.com/spreadsheets/u/0/d/1FH-fzqwOsifhG-rp-MB7me6W9_XZIbRFkwfQRMObfRs/htmlview#gid=0
more at www.bard.solar
what happened next is perhaps stranger than fiction - yes abed did start his university in 2001 and came up with another 10 legacy ideas for university and other advanced cooperation platforms to connect
and the wi-fi ngo took its new standard to the UN's ITU but instead of web 2 (aka WSIS) becoming tech for UN scaling millennials goals we westerners spread mainly the MAGAF microsoft-apple-google-amazon-facebook-- with one entrepreneurial revolution exception a lady who has spent her teen as a chiense laudromat entrepreneur asked what if stanford uses the new supercomputers to train ai to vision the way a baby does- with such pretriaining done all sorts of deeply kind intel apps may generate
no doubt if you connect stanford HAI and the community intel of women at Bottom of Pyramid you can help scale solutions to goals 1 through 8 - quite whats going to happen counting down goal 16 peace - land- life on water- climate - well you know there notr so much wrong (to need wars) with families intel or the ai that helps us - its the games played by people who are too powerful to be seen to fail that scares the dickens (let alone the bard) out of me
could you give me top 10 intelligence quotes from will shakespeare on lasting human values?
Sure, here are 10 intelligence quotes from William Shakespeare on lasting human values:
These quotes offer insights into the human condition and the importance of lasting values such as love, mercy, courage, and self-knowledge. They are as relevant today as they were when Shakespeare wrote them centuries ago.
Dear Bard --Can you help me summarise the 400 ai projects listed at https://docs.google.com/spreadsheets/u/0/d/1FH-fzqwOsifhG-rp-MB7me6W9_XZIbRFkwfQRMObfRs/htmlview#gid=0 I would like to make a few lesson plans for different age grades on what to be optimistic abour regarding egov or HAI and gov
Sure, I can help you summarize the 400 AI projects listed in the spreadsheet. Here are some of the most optimistic things about AI and government:
Here are some specific examples of AI projects that are being used to improve government:
These are just a few examples of the many ways that AI is being used to improve government. As AI technology continues to develop, we can expect to see even more innovative and effective ways to use AI to improve the lives of citizens.
Here are some lesson plans that you can use to teach students about the optimistic potential of AI and government:
In this lesson, students will learn about the basics of AI and how it can be used to improve government. Students will also learn about some specific examples of AI projects that are being used to improve government.
In this lesson, students will learn more about the potential benefits of AI for government. Students will also learn about some of the challenges that need to be addressed in order to realize the full potential of AI for government.
In this lesson, students will learn about the ethical implications of using AI in government. Students will also discuss some of the potential risks of AI for government.
I hope this information is helpful. Please let me know if you have any other questions.
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ABCDEFGHIJKLMNOPQRSTUVWX https://docs.google.com/spreadsheets/u/0/d/1FH-fzqwOsifhG-rp-MB7me6W9_XZIbRFkwfQRMObfRs/htmlview#gid=0
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Operational water supply forecasting for western US riversUSDA Natural Resources Conservation Service (NRCS) Snow Survey and Water Supply Forecast (SSWSF) program
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B2B MatchmakingInternational Trade Administration (ITA)The system's algorithms and AI technology qualifies data and makes B2B matches with event participants according to their specific needs and available opportunities. The systems inputs are data related to event participants and the outputs are suggested B2B matches between participants and a match strength scorecard.
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Chatbot PilotInternational Trade Administration (ITA)Chatbot embedded into trade.gov to assist ITA clients with FAQs, locating information and content, suggesting events and services. ITA clients would enter input into the chatbot in the form of questions or responses to prompts. The chatbot would scan ITA content libraries and input from ITA staff and return answers and suggestions based on client persona (exporter, foreign buyer, investor).
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Consolidated Screening ListInternational Trade Administration (ITA)The Consolidated Screening List (CSL) is a list of parties for which the United States Government maintains restrictions on certain exports, reexports, or transfers of items. It consists of the consolidation of 13 export screening lists of the Departments ofCommerce, State, and Treasury. The CSL search engine has “Fuzzy Name Search”capabilities, allowing a search without knowing the exact spelling of an entity’s name. In Fuzzy Name mode, the CSL returns a “score” for results that exactly or nearly match the searched name. This is particularly helpful when searching on CSL for names that have been translated into English from non-Latin alphabet languages.
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AD/CVD Self InitiationInternational Trade Administration (ITA)The ADCVD program investigates allegations of dumping and/or countervailing of duties. Investigations are initiated when a harmed US entity files a petition identifying the alleged offence and the specific harm inflicted. Self-Initiation will allow ITA to monitor trade patterns for this activity and preemptively initiate investigations by identifying harmed US entities, often before these entities are aware of the harm.
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Market Diversification ToolkitInternational Trade Administration (ITA)The Market Diversification Tool identifies potential new export markets using current trade patterns. A user enters what products they make and the markets they currently export to. The Market Diversification Tool applies a ML algorithm to identify and compare markets that should be considered. The tool brings together product-specific trade and tariff data and economy-level macroeconomic and governance data to provide a picture of which markets make sense for further market research. Users can limit the markets in the results to only the ones they want to consider and modify how each of the eleven indicators in the tool contributes to a country’s overall score. Users can export all the data to a spreadsheet for further analysis.
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Fisheries Electronic Monitoring Image LibraryNational Oceanic and Atmospheric Administration (NOAA)The Fisheries Electronic Monitoring Library (FEML) will be the central repository for electronic monitoring (EM) data related to marine life.
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Passive acoustic analysis using ML in Cook Inlet, AKNational Oceanic and Atmospheric Administration (NOAA)Passive acoustic data is analyzed for detection of beluga whales and classification of the different signals emitted by these species. Detection and classification are done with an ensemble of 4 CNN models and weighted scoring developed in collaboration with Microsoft. Results are being used to inform seasonal distribution, habitat use, and impact from anthropogenic disturbance within Cook Inlet beluga critical habitat. The project is aimed to expand to other cetacean species as well as anthropogenic noise.
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AI-based automation of acoustic detection of marine mammalsNational Oceanic and Atmospheric Administration (NOAA)Timely processing of these data is critical for adapting mitigation measures as climate change continues to impact Arctic marine mammals. Infrastructure for Noise and Soundscape Tolerant Investigation of Nonspecific Call Types (INSTINCT) is command line software which was developed in-house for model training, evaluation, and deployment of machine learning models for the purpose of marine mammal detection in passive acoustic data. It also includes annotation workflows for labeling and validation. INSTINCT has been successfully deployed in several analyses, and further development of detectors within INSTINCT is desired for future novel studies and automation. Continued integration of AI methods into existing processes of the CAEP acoustics group requires a skilled operator familiar with INSTINCT, machine learning, and acoustic repertoire of Alaska region marine mammals.
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Developing automation to determine species and count using optical survey data in the Gulf of Mexico
National Oceanic and Atmospheric Administration (NOAA)VIAME - This project focuses on optical survey collected in the Gulf of Mexico: 1) develop an image library of landed catch, 2) develop of automated image processing (ML/DL) to identify and enumerate species from underwater imagery and 3) develop automated algorithms to process imagery in near real time and download information to central database.
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Fast tracking the use of VIAME for automated identification of reef fishNational Oceanic and Atmospheric Administration (NOAA)We've been compiling image libraries for use in creating automated detection and classification models for use in automating the annotation process for the SEAMAP Reef Fish Video survey of the Gulf of Mexico. This work is being conducted in VIAME but we're looking at several other paths forward in the project to identify best performing models. Current status is that models are performing well enough that we will incorporate automated analysis in video reads this spring as part of a supervised annotation-qa/qc process.
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A Hybrid Statistical-Dynamical System for the Seamless Prediction of Daily Extremes and Subseasonal to Seasonal Climate Variability
National Oceanic and Atmospheric Administration (NOAA)Demonstrate the skill and suitability for operations of a statistical- dynamical prediction system that yields seamless probabilistic forecasts of daily extremes and sub seasonal-to- seasonal temperature and precipitation. We recently demonstrated a Bayesian statistical method for post-processing seasonal forecasts of mean temperature and precipitation from the North American Multi-Model Ensemble (NMME). We now seek to test the utility of an updated hybrid statistical-dynamical prediction system that facilitates seamless sub seasonal and seasonal forecasting. Importantly, this method allows for the representation of daily extremes consistent with climate conditions. This project explores the use of machine learning.
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FathomNetNational Oceanic and Atmospheric Administration (NOAA)FathomNet provides much-needed training data (e.g., annotated, and localized imagery) for developing machine learning algorithms that will enable fast, sophisticated analysis of visual data. We've utilized interns and college class curriculums to localize annotations on NOAA video data for inclusion in FathomNet and to begin training our own algorithms.
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ANN to improve CFS T and P outlooksNational Oceanic and Atmospheric Administration (NOAA)Fan Y., Krasnopolsky, V., van den Dool H., Wu, C. , and Gottschalck J. (2021). Using Artificial Neural Networks to Improve CFS Week 3-4 Precipitation and Temperature Forecasts.
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Drought outlooks by using ML techniquesNational Oceanic and Atmospheric Administration (NOAA)Drought outlooks by using ML techniques with NCEP models. Simple NN and Deep Learning techniques used for GEFSv12 to predict Week 1-5 Prcp & T2m over CONUS
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EcoCast: A dynamic ocean management tool to reduce bycatch and support sustainable fisheries
National Oceanic and Atmospheric Administration (NOAA)Operational tool that uses boosted regression trees to model the distribution of swordfish and bycatch species in the California Current
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Coastal Change Analysis Program (C-CAP)National Oceanic and Atmospheric Administration (NOAA)Beginning in 2015, C-CAP embarked on operational high resolution land cover development effort that utilized geographic object-based image analysis and ML algorithms such as Random Forest to classify coastal land cover from 1m multispectral imagery. More recently, C-CAP has been relying on a CNN approach for the deriving the impervious surface component of their land cover products. The majority of the work is accomplished through external contracts. Prior to the high-res effort, C-CAP focused on developing Landsat based moderate resolution multi-date land cover for the coastal U.S. In 2002, C-CAP adopted a methodology that employed Classification and Regression Trees for land cover data development.
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Deep learning algorithms to automate right whale photo idNational Oceanic and Atmospheric Administration (NOAA)AI for right whale photo id began with a Kaggle competition and has since expanded to include several algorithms to match right whales from different viewpoints (aerial, lateral) and body part (head, fluke, peduncle). The system is now live and operational on the Flukebook platform for both North Atlantic and southern right whales. We have a paper in review at Mammalian Biology.
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NN RadiationNational Oceanic and Atmospheric Administration (NOAA)Developing fast and accurate NN LW- and SW radiations for GFS and GEFS. NN LW- and SW radiations have been successfully developed for previous version of GFS, see: doi: 10.1175/2009MWR3149.1 and the stability and robustness of the approach used was demonstrated, see: https://arxiv.org/ftp/arxiv/papers/2103/2103.07024.pdf NN LW- and SW radiations will be developed for the current versions of for GFS and GEFS.
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NN training software for the new generation of NCEP modelsNational Oceanic and Atmospheric Administration (NOAA)Optimize NCEP EMC Training and Validation System for efficient handling of high spatial resolution model data produced by the new generation of NCEP's operational models
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Coral Reef WatchNational Oceanic and Atmospheric Administration (NOAA)For more than 20 years, NOAA Coral Reef Watch (CRW) has been using remote sensing, modeled, and in situ data to operate a Decision Support System (DSS) to help resource managers (our target audience), researchers, decision makers, and other stakeholders around the world prepare for and respond to coral reef ecosystem stressors, predominantly resulting from climate change and warming of the Earth's oceans. Offering the world's only global early-warning system of coral reef ecosystem physical environmental changes, CRW remotely monitors conditions that can cause coral bleaching, disease, and death; delivers information and early warnings in near real-time to our user community; and uses operational climate forecasts to provide outlooks of stressful environmental conditions at targeted reef locations worldwide. CRW products are primarily sea surface temperature (SST)-based but also incorporate light and ocean color, among other variables.
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Robotic microscopes and machine learning algorithms remotely and autonomously track lower trophic levels for improved ecosystem monitoring and assessment
National Oceanic and Atmospheric Administration (NOAA)Phytoplankton are the foundation of marine food webs supporting fisheries and coastal communities. They respond rapidly to physical and chemical oceanography, and changes in phytoplankton communities can impact the structure and functioning of food webs. We use a robotic microscope called an Imaging Flow Cytobot (IFCB) to continuously collect images of phytoplankton from seawater. Automated taxonomic identification of imaged phytoplankton uses a supervised machine learning approach (random forest algorithm). We deploy the IFCB on fixed (docks) and roving (aboard survey ships) platforms to autonomously monitor phytoplankton communities in aquaculture areas in Puget Sound and in the California Current System. We map the distribution and abundance of phytoplankton functional groups and their relative food value to support fisheries and aquaculture and describe their changes in relation to ocean and climate variability and change.
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Edge AI survey payload developmentNational Oceanic and Atmospheric Administration (NOAA)Continued support of multispectral aerial imaging payload running detection model pipelines in real-time. This is a nine camera (color, infrared, ultraviolet) payload controlled by dedicated on-board computers with GPUs. YOLO detection models run at a rate faster than image collection, allowing real-time processing of imagery as it comes off the cameras. Goals of effort are to reduce overall data burden (by TBs) and reduce the data processing timeline, expediting analysis and population assessment for arctic mammals.
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Ice seal detection and species classification in multispectral aerial imageryNational Oceanic and Atmospheric Administration (NOAA)Refine and improve detection and classification pipelines with the goal of reducing false positive rates (to < 50%) while maintaining > 90% accuracy and significantly reducing or eliminating the labor intensive, post survey review process.
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First Guess Excessive Rainfall OutlookNational Oceanic and Atmospheric Administration (NOAA)Machine Learning Product that is a first guess for the WPC Excessive Rainfall Outlook - It is learned from the ERO with atmospheric variables. It is for the Day 4-7 products
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cam you tell me what is meant by "what the dickens"
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Sure. The phrase "what the dickens" is an exclamation that is used to express surprise, anger, or frustration. It is thought to have originated in the 16th century as a euphemism for the word "devil." The name "Dickens" was often used as a nickname for the devil, and so the phrase "what the dickens" came to be used as a way of avoiding saying the word "devil" directly.
The phrase "what the dickens" is still used today, although it is considered to be somewhat dated. It is most often used by older people, and it is not as common in everyday speech as it once was.
Here are some examples of how the phrase "what the dickens" can be used: