ED: May The Games of Architect Intelligence (AI) be with you & Mother Earth's 8 billion beings & 1BnG & HAI .. breaking sept 2023 one of my fav 5 hours spent at university!!
.chris.macrae@yahoo.co.uk RIGHT OLD MESS
EE: Back in dad's teenage diary as navigator Allied Bomber Command Burma, a day headed ROM meant a friend's flight went missing. In 2023 ROM is politest term we can use for failure to help youth celebrate 73 years of research with von neumann on only good tech can save our species, and 36 years of world class brands architecture research round biggest decison makers started 1988 when dad restored from The Economist: on how bad media can destroy millennials futures. From the last articles we influenced in The Economist Trust has been the exponentially missing metric. Thanks to a chat with Von Neumann's daughter (who's advances for humanity would her dad have trusted most 2025-1950? EconomistDiary.com is launching a game Architect of Intelligence. Dare you play the most urgent cooperation game in sustainability goals hi-tech-trust-touch world?
ED dedication : To Architects Fazle Abed & Steve Jobs -who convened silicon valley's 65 birthday to Abed in 2001 giving 7 years of design foresight to why mobile digital network not seen in his 1984 launch of PC networking
INDUSTRIAL REV 260th GAMES-cards of sdg-gen
Uni2 :FFL*JOBS*DHgoog
welcome to AIsdgs.com where media designers help take down fake media wherever its wasting 8 billion peoples time

you may want to join economist dairy in 1951 when The Economist sub-ed  NM was seconded to NYPeinceton for year to listen to John Von Neumann design the number 1 journalism-for-humans quiz, Architecture of Intelligence (AI): it was agreed the most valuable scoop earthlings may e-vision = what goods can humans unite wherever celebrating early access to 100 times more tech per decade? - eg a billion times more 2015=1955

or back from future of 80 years of 2025report: join bard-solar express route 1843 to 2023-4-5: 1843 EconomistDiary.com under 30 queen victoria accepts Economist founder James Wilson help to start mapping commonwealth trading maps replacing britannia ruling all of asia waves round global market of englishmen's tea ; in 1859 victoria charters bank for Wilson to go design financial service for quarter of humans on india's subcontinent; after year 1 celebrations by most of the peopels, james dies of diarhea; it takes 112 years before former shell oil ceo educational intelligence empowers womens lesson plans round oral rehydration, 10 community business of goal 2 2 food, goal 3 health and 90% of the peoples trust in a regional bank for female generations to build nation

SDG 5  4  3   2  1  0 welcome to Asia and the top 5 sdgoals 50 years search scaling the most exciting collaborations women-led communities empower
ED soon after 2010 death of Von Neumann's first journalist of Architect Intelligence The Japan Ambassador to bangladesh hosted 2 brainstorming sessions- since 2001 Steve Jobs and Fazle Abed had united their support of net generations futures : would a moon of the top 30 cooperations visioned by 1billiongirls help bridge human intel until Steve Jobs gift of a university in phone (iphone 2007) might renew interest in man made engines blending human intelligence ... EconomistLearning.com from 2009 stanford's fei-fei li began the new entrereneurial revolution of pretraining computer visiosn (in about 10 different ways from science games deepmind, to 1000language games LLM , to object recognition of autonomous cars are ever needed, to nlp to literature veviews in real time of very covid publication to 2019 stanford hai inviting every human discipline grads spend time on to HAI ,,,,as pretraining of humans rose to 2015 hopes were that high that it was time to declare 17 cooperation dev goals and roadmapping of UN2 comprised of dynamic subystems of above zero-sum human networking. Bangladesh as deepest place branding of SDG5 celebrates being 52 years young in 2023 the 265th year of smithian moral sentiments at Abed's Alma mater Glasgow Universiity. Supporting hi-tech hi-trust Asian place winners include: singapore 2023; hong kong (22.1 Place winners 22.22022 ... Thailand2021 ..) . Abed was not just a world class civil engineer; he dedicated half a century until his death in December 2019 as servant leader. Aligned by HG Wells bon mots: civilisation is a race between education and catastrophe, Abed Bhai preferred to be seen as host of microeducationsummit not financiers summits: his gravitation purpose of 30 women empowered cooperations that of united refugees, villagers and civil societies in ENDING POVERTY. Fortunately for the worlds poorest new nation Bangaldesh 1971- Abed had networks like no other community leader. HIs friends' coop roadmapping reached out to intel vitalised by at least a billion village mothers in tropical inland asia where, a third of infants were dying of diarrhea before Abed's person to person networking became the best news ever chatted. Fro mid 1950s studies in Glasgow he spent nearly 13 years growing to be Royal Dutch Shell Oil Company's regional CEO. So his lifetime searches uniquely capitalised on what UK and Dutch Royal Societies (soon Japan Royals too) knew how to help end the poverty their colonial era had up to 1945 trapped the majority of humans world trade in. Simply put most Asian coastal belts link national borders defined by what these < a href="http://www.kc3.dev">3 kingdoms designed in to trading barriers over nearly half millennium. And which had made the English language that of world class engineering (digital age as well as pre-digital) So by 1970s these nations royal societies (including londons arts green-geographical, medicinie, science, architects ...) were happy that a grounded movement could link them into what they didnt fully know culturally or consciously. From 1970 on Abed linked in global village mapping like no one else - through these relationships and by designing business microfrachises not charity wherever possible for village women to own. To study with abed alumni is to join in the world's most cooperative empowering women movements for good as well as of childrens development.

Friday, July 14, 2023

UN sdg2 fao aigoodmedia.com

 last updated un compendium AI report up to end 2022

Project 1: Strengthening global access to agricultural information and knowledge (Hand-In-Hand Geospatial Platform) • Project Description: Earth observation and geospatial IT play a critical role in the agricultural and related sectors. FAO has created the Hand-in-Hand Geospatial Platform that hosts data sourced from FAO, FAO partners in the public and private sectors including from across the UN, NGOs, government institutions, academia and space agencies. The platform has significantly increased the interoperability of FAO geospatial data as well as the cost-effective maintenance and sustainability of different FAO geospatial applications. Machine learning and AI are used in cutting edge quantitative remote sensing in agriculture; world class cloud computing capabilities; enabling unprecedented cross-sectoral knowledge discovery by integrating data on Soil, Land, Water, Climate, Fisheries, Livestock, Crops, Forestry, Trade, Social and Economics and much more. • Project Type/Output: Dataset, Software tool • Project Status: Ongoing • Project Start Year: 2020 • Project End Year: 2025 • Project Domain: Agriculture • Data Source: FAO projects data and relevant data from external data providers, covering all sub-disciplines of agriculture from animal health to trade/markets. • Publicly Available Data: Yes • Technology/Platform: Google Cloud Platform; TerriaJS; GeoNetwork; CKAN • Reported as part of 2021 Compendium on UN AI Activities? Yes • Project Updates: more datasets added or updated, new functions such as zonal statistics and remote sensing data products integrated. • Related Sustainable Development Goals (SDGs): SDG 1 – No Poverty; SDG 2 – Zero Hunger; SDG 13 – Climate Action. • Links and Multimedia: o https://www.fao.org/hih-geospatial-platform/en/ o https://youtu.be/xKON7YWWXUI • Lessons Learned: Data federation supported by standardization make a huge difference in data sharing. Open data sharing enables various users to contribute and get good results to solve identified problems. • Contact information: Karl Morteo (karl.morteo@fao.org);Zhongxin Chen (zhongxin.chen@ fao.org) 5 United Nations Activities on Artificial Intelligence (AI) 

Project 2: Crop phenology and crop calendar with remote sensing and GEO-AI • Project Description: Crops phenology and crop calendars are essential to many agricultural applications. This project uses time-series satellite remote sensing data and auxiliary data to generate crop phenology data and crop calendar, with employing machine learning and GEO-AI. There are 2 phases of the project. First, algorithm development is committed in several pilot regions, and then global dataset will be produced. • Project Type/Output: Dataset • Project Status: Development • Project Start Year: 2022 • Project End Year: 2023 • Project Domain: Agriculture • Data Source: Satellite data, agricultural statistical data, landcover land use data and in-situ data • Publicly Available Data: Yes • Technology/Platform: Google Cloud Platform; Python • Reported as part of 2021 Compendium on UN AI Activities? Yes • Related Sustainable Development Goals (SDGs): SDG 1 – No Poverty, SDG 2 – Zero Hunger, SDG 13 – Climate Action • Contact information: Pengyu Hao (pengyu.hao@fao.org); Zhongxin Chen (zhongxin. chen@fao.org) 

Project 3: Global and Country ASIS (Agriculture Stress Index System) • Project Description: The Agricultural Stress Index System (ASIS) monitors agricultural areas with a high likelihood of water stress/drought at global, regional and country level, using satellite technology. ASIS uses satellite-based remote sensing data to detect agricultural areas (cropland or grassland) with a high likelihood of water stress (dry spells and drought). It simulates the analysis that an expert in remote sensing would undertake and simplifies the interpretation and use of the data for non-technical users (not remote sensing experts). • Project Type/Output: Dataset, Software tool • Project Status: Complete • Project Start Year: 2014 • Project End Year: 2016 • Project Domain: Agriculture • Data Source: o https://www.fao.org/giews/earthobservation/access.jsp?lang=en o https://www.fao.org/giews/earthobservation/reference.jsp?lang=en • Publicly Available Data: Yes • Technology/Platform: GLIMPSE, SPIRITS • Reported as part of 2021 Compendium on UN AI Activities? Yes • Related Sustainable Development Goals (SDGs): SDG 2 – Zero Hunger • Partnership(s)/Collaborator(s): o UN Partners: European Commission Joint Research Centre (JRC) o Private Sector: Flemish Institute for Technological Research (VITO) o Academia: The University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC) FAO 6 United Nations Activities on Artificial Intelligence (AI) • Links and Multimedia: o https://www.fao.org/giews/earthobservation/asis/index_2.jsp?lang=en o https://www.youtube.com/watch?v=QlW6qowJlU8 • Contact information: Yanyun Li (yanyun.li@fao.org), Mr. Oscar Rojas (oscar.rojas@fao.org) 

Project 4: FAO Data Lab • Project Description: The Data Lab developed and implemented a new set of tools in order to assist FAO and all stakeholders in analysing how exactly COVID-19 is affecting food value chains and food security around the world. The tools use open-access resources, are updated daily and are complemented with useful visual representations. This way, raw data are enriched with additional value, consisting in the possibility of sorting information by relevance and carrying out semantic searches, according to the users’ needs. • Project Type/Output: Dataset • Project Status: Ongoing Programme of work • Project Start Year: 2019 • Project Domain: Agriculture, Trade, Food and Agricultural Statistics • Data Source: Internet web scraping of prices’ data, sentiment analysis data, social media data, Earth Observation satellite imagery, Government policies • Publicly Available Data: Yes • Technology/Platform: R, Jupyter, Python, running on the Google Cloud Platform • Reported as part of 2021 Compendium on UN AI Activities? Yes • Related  Sustainable Development Goals (SDGs): SDG 2 – Zero Hunger; SDG 12 – Responsible Consumption and Production • Links and Multimedia: https://www.fao.org/datalab/website/web/home • Contact information: Carola Fabi (Carola.Fabi@fao.org) 

Project 5: Detecting Fall armyworm through user submitted photos (FAMEWS) • Project Description: Combines an online monitoring platform for mapping data collected by the FAMEWS mobile app whenever fields are scouted, or pheromone traps are checked for FAW. The platform provides a real-time situation overview with maps and analytics of FAW infestations at global, country and sub-country levels. The FAMEWS mobile app enables data collection of scouting data, which can be collected manually or through an image recognition model which provides immediate advice on FAW infestation. The global monitoring platform and the mobile app are designed to expand with the evolving needs of farmers, analysts and decision-makers. Both are accessible for free and are helping to reduce crop yield losses and minimize risk of further introduction and spread of FAW. • Department/Division: Plant Production and Protection Division (NSP) • Project Type/Output: Dataset • Project Status: Ongoing • Project Start Year: 2019 • Project End Year: 2022 • Project Domain: Agriculture • Data Sources: Field scouting geo-referenced data, Pheromone traps data, Picture of FAW damage from the field. • Link to Data: o https://app.powerbi.com/view?r=eyJrIjoiZDViYTBkMjctN2IyNi00NWM0LW JkOTUtNTQzN2NiY2NiZWM0IiwidCI6IjE2M2FjNDY4LWFiYjgtNDRkMC04MW 7 United Nations Activities on Artificial Intelligence (AI) ZkLWQ5ZGIx NWUzYWY5Ni IsImMiOjh9 & pageName = Rep ortSection 018d4484050280890bb1 o https://data.apps.fao.org/ • Publicly Available Data: Yes • Technology/Platform: Google AI, TensorFlow • Reported as part of 2021 Compendium on UN AI Activities? Yes • Related Sustainable Development Goals (SDGs): SDG 2 – Zero Hunger • Partnership(s)/Collaborator(s): o Academia: Penn state University, USA (PlantVillage platform) • Links and Multimedia: o www.fao.org/fall-armyworm/monitoring-tools/en/ o http://www.fao.org/3/CA1089EN/ca1089en.pdf. • Lessons Learned: o The most important challenge was promoting the adoption of the application and convincing FAO members to share their data. o The second challenge was the sustainability of the project as it is difficult to maintain and promote the system without any financial support. o The accuracy of the collected data was also another challenge as it is crowed sourced data. • Contact information: Maged Elkahky (maged.elkahky@fao.org)

 Project 6: FAO Digital Portfolio (FDP) • Project Description: The project covers internal efforts to apply machine-learning and natural language processing to transform FAO project related data into global digital trends and insights as well as an organization’ wide portfolio of products for reuse and reinvestment in future projects. Current effort is focused on taking FAO project descriptions as inputs and producing the related business and technology thematic areas the projects relate to as outputs. • Department/Division: Digitalization and Informatics Division (CSI) • Project Type/Output: Dataset • Project Status: Ongoing • Project Start Year: 2016 • Project End Year: 2025 • Project Domain: Agriculture, Business process improvement • Data Source: FAO Project data including project metadata. • Publicly Available Data: No • Technology/Platform: Microsoft Power Platform, Azure, Python, Google Cloud Platform • Reported as part of 2021 Compendium on UN AI Activities? Yes • Related Sustainable Development Goals (SDGs): SDG 2 – Zero Hunger • Contact information: Paul Whimpenny (paul.whimpenny@fao.org); Sergio Bogazzi (sergio.bogazzi@fao.org) FAO 8 United Nations Activities on Artificial Intelligence (AI) 

Project 7: iSharkFin (Identifying shark species from a picture of the fin) • Project Description: iSharkFin is an expert system that uses machine learning techniques to identify shark species from shark fin shapes. Aimed at port inspectors, custom agents, fish traders and other users without formal taxonomic training, iSharkFin allows the identification of shark species from a picture of the fin. The iSharkFin follows an interactive process. Users only need to take a standard photo, select some characteristics of a fin and choose a few points on the fin shape; iSharkFin will then automatically analyze the information and identify the shark species from which the fin comes. • Project Type/Output: Academic paper, Software tool • Project Status: Completed • Project Start Year: 2014 • Project End Year: 2019 • Project Domain: Agriculture • Data Source: Database of images of shark fins • Publicly Available Data: No • Technology/Platform: The software is a net based Windows-desktop application that ships with a small SQLite data base information of shark species. The iSharkFin algorithm is built on a decision tree, that despite being one of the oldest methods in machine learning, is accurate and recommended before trying any more complex learning algorithm. • Reported as part of 2021 Compendium on UN AI Activities? Yes • Related Sustainable Development Goals (SDGs): SDG 14 – Life Below Water • Partnership(s)/Collaborator(s): o UN Partners: Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) o Government: Government of Japan, National Oceanographic and Atmospheric Administration of the United States of America o Academia: University of Vigo (Spain) • Links and Multimedia: o https://www.fao.org/ipoa-sharks/tools/software/isharkfin/en/ • Lessons Learned: From our experience with the shark fin ID identification tools, the proper photo collection and the number of training images represented the main impediment to the use of more complex algorithms. • Contact information: Kim Friedman (Kim.Friedman@fao.org) Project 8: WaPOR (Water Productivity through Open access of Remotely sensed derived data) • Project Description: WaPOR is FAO’s portal to monitor Water Productivity through Open-access of Remotely sensed derived data. It assists countries in monitoring water productivity, identifying water productivity gaps, proposing solutions to reduce these gaps and contributing to a sustainable increase of agricultural production. At the same time, it considers ecosystems and the equitable use of water resources, which should lead eventually to an overall reduction of water stress. The WaPOR portal provides open access to key land and water variables (including reference and actual evapotranspiration, biomass, land cover, precipitation) in near – real time for the whole of Africa and the Near East, from 2009 to date, at a spatial resolution ranging between 30 and 250 meters. • Project Type/Output: Dataset, Software tool • Project Status: Ongoing 9 United Nations Activities on Artificial Intelligence (AI) • Project Start Year: 2016 • Project End Year: 2025 • Project Domain: Agriculture • Data Source: Geospatial Database with remote sensing data input. The database is publicly accessible, developed with open access data and open-source algorithms. It provides near real time information from 2009 to date. • Publicly Available Data: Yes • Technology/Platform: Google Cloud services, Python, Jupyter • Reported as part of 2021 Compendium on UN AI Activities? Yes • Related Sustainable Development Goals (SDGs): SDG 2 – Zero Hunger; SDG 6 – Clean Water and Sanitation; SDG 13 – Climate Action • Partnership(s)/Collaborator(s): o Government: Ministry of Foreign Affairs of the Netherlands o Private Sector: FRAME Consortium (eLEAF, VITO) o Civil Society: not for profit organizations in several project countries o Academia: IHE Delft, ITC Twente • Links and Multimedia: o https://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ o https://wapor.apps.fao.org/ o https://www.youtube.com/watch?v=ZX7SOhk97hA o https://www.youtube.com/watch?v=gA_t4HuFNhM • Contact information: Livia Peiser (Livia.Peiser@fao.org), Jippe Hoogeveen (Jippe. Hoogeveen@fao.org) 2. Related Sustainable Development Goals SDG 1, 2, 6, 12, 13, 14 3. Relevant Links https://www.fao.org/home/en Contact Information CSI-Director (CSI-Director@fao.org)

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