MagicSpex.com

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2023-4 is schools year 9 of UN2 transforming humans intelligence relationship around vision of millennials as first sustainability generation  magicspex.com (valuing peopels time and data as trust multipliers of advancing human race and moether earth's renewability Regarding system mapping: the 21st C initial millennial goals were under-specified- for example if you were building primary schools you could demand a lot of aid; intelligence for any form of livelihood education that wasn't a millennium goal. In 21st C, no region's human networks kept relentlessly questioning how to advance half-time (2015) millennials-sustaining goals as much as  Asia's 1billiongirls.com/girlsworldbank.com. What the west had since 1997 called microcreditsummit.com was actually microeducationsummit integration of sall deep community data if you travelleled to deep spaces like Bangladesh which our studennt mediatirs for good and ed3 fruends co-blog Abedmooc.com did 16 times. Their Entrepreneurial Revolution demanded  intelligence transformation of how public service and businesses worlds and communities map global village futures.(aka UN college yasr 23-24) 2023-4 marks the 8th college year of transformation empowerment wherever human architect intelligence blends with the UN. Here's how the magicspex system transformation  has evolved from talk to walk , last mile  systems

UN2 Guterres Kim Abed Steve Jobs Hassabis Fei-Fe Li. Larry Page & Jerry Yang Neumann- Einstein (Gandhi Freud) Turing Borlaug Freire Deming Jfk Korolev Moore Macarety Olsen - 52 players cards Architect Intel 23-4

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.year 1 review of sdgs UN H Q sept 2016- Finding on behalf of educators sdgs are impossible unless there is Digital Cooperation -  from this day on, if poverty-ending is the vision: , tech and every UN practice skills group must help each other and teachers end all silos, More than half of yout's time and livelihoods will be wasted unless we fix this Testimony source Jim Kim , Fazle Abed, Jack Ma. The next day presentations were being made to UNCTAD's leader based in Geneva but attending NY assembly. Within months the education demand was being valued by the ITU in Geneva which had since 1865! been the epicentre of how the world cooperates around transformations in (tele) communications system

2016's world bank leader and health intelligence mapmaker jim kim leadership unites 2016's springs 80th birthday intelligence update which  fazle abed and global villages' 1billiongirls had mapped since 2001 when they wer key practioner connectirs of global fund (with paul farmer gates health foiundation geore soros). Vilage tuberculosis networking showed how bottom-up solutions were needed to alelvaite both tb and aids. It had taken a lot of ID says(Infectious Disease) mapping to get either the WHO in Geneva or NIH in Dc to agree to this deeper modeling of health flows. 

By year 2 an expert pane;l to debate transformation to end wasting peopels time was set up; digital cooperation of how to end wasting youth's time was matched by digoita; capacity building ho0w to stop wasting teachers, parents and emders time. Simulataneouely the ITU launched AIforgod. Contextually this would be a space that transitioned from WSIS annual summit theITU had hosted since 2005 in peraparation for wi-fi and 4g and datata beaming up/down from every gps. Incredibly UN in NY had not looked at gps data maps from the bottom up until Guteres was apinted as un leader bring his decade of UNexpereince of serving refugees, and befire that Portuguese red cross partnerships with geneva's red cresecent, and before that mediating the EU's impact on Portuguese speaking peoples

By colege year 2018-9, the2nd lenses was being added - gov2.0 could achieve the Un foudational system oirpoeses rights, equality, trtut/safety, stewardship of common goods if it fl=ully valued women emowerpmet and youth-intergenerational inteligences; - the deep maths data scence piece CODES was added to the time spending lens.

2019-20 - covid hit - son the mabicspex becemae the most popular zooms instead of physdical leaderhsip summits

2022-23 is the year when teh general assemply hosts inquests of every sdgs, and chooses where intelligence cooperation technnolgy (aka Human AI) can be the transfrmation of all the magcspex

Government versions of MMagicSpex.com millennials-regeneration roadmping emerges in East/South from schools year 2008-9 but not as united empowerment of 8 billion people to first year UN review of the newly listed 17 sgds (schools year) 2015-16. Sadly the yes youth can biden/obama admin is competely blind to magic spex for 4 years; only the transformation of the valley of silicon to Human Ai offeres Americans any visions the world wants Architects of Intelligence to celebrate AIgames.solar. The emergemec og HimanAI valley from scho,s yera 2008-9 coincides with 58 yer of Von Neumann training Economist Journalsits that AI will need to be transparent and sysmic in ways paper-based acadndeic Economics throrists never saw as wither a possibility or responsibility to multiply goods oif trust, peoples time and data.It was only in 2012 that Obama asked the world's number 1 economic public health servant jim kim to lead teh worldf bank (nb a korea was also now leading the UN with Gutteres potentially the graetest health and public servant experimced leader now in year 7? of UN refugee services after 5 yeras leading purtuguesd red cross after years as portugal's Prime Minister.- see also global fund and 2004 tsunam as reasn why jim kim , gates family, guterres, fazle abed, soros all started connecting inteligence networks for communities to combat AIDs, TB and malaria

 because human development since 1970 in extremely por place sbegan with solutions incraesing life expectancy and maximising community capacity to build health, last mile food and apasulo freire type education for all: In E & S hemispheres it was common to hear amongst female community builders (1billiongirls AbedMooc girlsworldbank) from 2008 that education was broken in the sense that more than half of youth (whatever stage they left formal education) woiuld need to entrepreneur their own jobs or grow sme community networks because the hoistorical idea that was no lomger valiued that  big organsiations would have plenty of jobs to recruit best education certificated students. This chalenge had been anticipate in 1976 (25th year of The Economist surveys inspired by Von Neumann and launch year of Entrepreneurial Revolution). Although 2008-2009 saw the year itu launched 100 times more  data transfer from every gps G4 Cluds etc, subprime ws a downer; and in 2009 the new obab=ma/bdien admin beleived they had to save the old banking system not design ER. It was to take to the listing if sdgs for sany western gov2.0 mobvements to start to be mapped and curious;y it was multilatal leaders kim at world bank (who had stated experimenting with Ai) and Guterres whose eladership began start of 2017 to decalre gov2.0 roadmapping was needed at every place level but UN would provide a bechmark. Iromically year 2008-9 was also when Stanfird welcomed Fei-Fei LI start of rehgenerative ai/imagenet

diary 2016 first yera of sdgs - led by jim kim and jack ma educators say that goal 4 and all gaosls cannot be achieved unless the UN ends tech silos (ITU is in geneva; sdg leadership and kids unicef is in new york; goal 4 is cordinated by french UNCEF). First Genva connection made by Kituyi Uncatd who coning from Kenya is interested in Africa consdequence sof the question Digital Cooperation. So in terms of miagic spex year 1 od sdgs starts rioadmapping hod does E4 connect all SDGS and all technology- the Digital Cooperation inquiry

by yera 2 Guterres is making gov2.0 his signature roadmap; Houlin Zhao at Geneva ITU is asked to integrate AI fot Good. nad a Digital Coopertion expert panel from 15 countries led ny melindag gates and Jack Ma is led out of Geneva.

By year 4 the so-cateed tech envoy now has 8 transformation sub-pieces whoih add a 9th codes for envirinmental adaptation as roadmapping goes virtual during covid- see un tech envoy ungoing work

in termes of spend 8 billion epopels time:

Digital cooperation now meands revolution in edtech and sdgspractices for under 30s; over 30s and tecahers  need retraining too caled digit gopertaion; universal connectivity is the responsibility that the ITU is now asked to map to nit just design data beaming up/down from every gps but see that educational and other sdg concerned millennail movements connected from every gps

AIforgood is now in the middle of all UN institution - Geneva, Ny Head quarter , and about 50 operational branches with compoendium of ai pilots by every Un branch

Giv 2.0 (also league tabled as UN egov) is mapped as digita public goods, digital valuation/goverance trust/safety (ending compoud rissdks eg those caused by economists mot counting externalities like carbon)

digital inclusion with places

digita ights across borders of olaces and oransiationsl systems

codes deep maths places need to won so that what japang20 calls osaka data trach aond society 5.0 are mappable not just big corporates 4th industrial revolution

22-23 idecared year og education no longer fit for purpose for any age group

2023-2024 is decalerd as year of inquests - all 17 sdgs as subsystems are as yet collabping - what needs to be done with tech; in other words how can 2022-2023 as ai moment year also become yeras of turning round combos of sdg system ; 23-24 is decalred year of under 30s casking over 30s what global future transformations will they invest in and let AI LLM s medite 

Do humans have enough inteligence to end academics silos

 I liked the talk of the senator from michigan yesterday at axios DC showroom sponsored by world food program us. She is completing her 3rd 5 year synthesis of Farm USA. She says where I come from michigan we grow (eg food) and we make (eg cars) and we see the two's futures must win-win for our state, for america for the world. It led ne directly to obtaing this advice from and LLM.

Generative AI is experimental. Info quality may vary.

The 2023 Farm Bill is a significant piece of legislation that reauthorizes many agriculture and nutrition programs, including the Supplemental Nutrition Assistance Program (SNAP). The Farm Bill is considered critical for the food system, environment, and health and nutrition of communities across the nation. It also provides direct support to US farmers and safety net and nutrition assistance programs for US families.

Here are some details about the 2023 Farm Bill:

• The CBO's May baseline confirms that the 2023 Farm Bill could potentially be the first trillion-dollar farm bill in U.S. history
• Total spending is projected at $1.51 trillion, $31.5 billion higher than CBO's February baseline
• The Farm Bill gradually increases the acreage cap for Conservation Reserve Program (CRP) to 27 million by 2023
• The current Farm Bill is set to expire in September 2023
• If Congress does not pass a bill reauthorizing the programs in the Farm Bill by September 30, 2023, they must pass a short-term extension
• The baseline for all farm bill titles is estimated at $725 billion over 5 years and $1,463 billion over 10 years
• The Farm Bill determines federal policy in agriculture for the next five to 10 years following its passage

Then tiday i receieve this superb newsleter from rebecca fannin- frankly amerucans need ti see their problem is not woth china it is with the 1990s managemenyt consultancies that moved moist manufactiring jobs inckuding smart stuff eg semiconductirs and the experimenting of those semiconduvctirs on grow and make to all parts of Asia

Pergahsp you'll disagerree but in the hyperconnecetd woirld that my family and the economist first started future backing with von neuman n in 1951! www.economistdiary.com making ameic b=great again = making american millennails great again = making energy for humans  everywhere on the planet that families grow (and make)

put another way sdg 2 is about integration orrual (matures labs) and urban (ma's artificial labs- the word artic=ficial in engneering means man made); there is a conflict between feedoing 8 billion humans and feeding engines until or unless we app natures abundant clean sources eg solar, wind, waves. I understand america's greatness historically has been built on over 5 times more carbon than teh average human accessed; innovating beyond whatever sovieties are most addicted to (even stuff once best to use) is our species' chalenge

ai wont cause extinction as such - expoential errors of man-made engineering will (starting with what we feed the engines) 

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)

AI UN sdg2 ifad AIgoodmedia.com

 1. Description of Activities on AI Project: Athena: Leveraging Artificial Intelligence and Big Data for IFAD 2.0 • Project Description: The ATHENA project seeks to unlock the potential of artificial intelligence and machine learning to accelerate knowledge generation and strengthen data-driven decision-making in IFAD. The project developed an AI toolbox with three main objectives: (1) systemize IFAD’s portfolio to facilitate results measurement and institutional learning, (2) enhance knowledge management through deployment of AI/ML in IFAD ICT systems to make project results and lessons learned accessible and actionable for staff, and (3) predict performance and impact of IFAD projects to inform decisionmaking, optimize targeting, and maximize impact. The (AI) “tool box” contains: o AI-based Intervention Dashboard: a searchable dashboard that classifies IFAD’s investment portfolio by project features, including interventions, outcomes, animal and plant products, among others using natural language processing (NLP); o Lessons Learned Web App: an application to search for relevant “lessons learned” as reported in previous projects to inform new designs reports; o Trend analyses of strategic themes: historical evidence of activities related to strategic topics, such as SDGs, food systems and ICT4D, to understand IFAD’s support and allocation of resources to different activities historically; o Project performance prediction model: a framework for ex-ante prediction of project performance based on a set of project features to facilitate better design and early action throughout project implementation; o Project impact prediction model: a framework to predict the probability of a positive impact of IFAD-supported interventions using impact evaluation data; o Project targeting optimization model: framework and tool to identify beneficiary features to maximize project impact; and o Covid-19 impact prediction model: model to predict impact of the pandemic in IFAD’s beneficiary countries. The tools developed by the project fill a gap within IFAD and the field by aiding and simplifying IFAD reporting, especially for more complex and data hungry thematic areas (i.e. food systems); leveraging under-utilized data resources, namely textual data buried in project reports; and enabling ex-ante data driven design and decision-making by closing the gap between policymakers and project evaluation and by translating data and project insights into actionable metrics. Together, these tools not only enhance IFAD’s knowledge IFAD 22 United Nations Activities on Artificial Intelligence (AI) management but also embed learning and data-driven decision-making into existing project design and implementation processes • Department/Division: Programme Management Department (PMD) • Project Type/Output: Report, Academic paper, Dataset, Seminar/meeting, Software tool • Project Status: Complete • Project Start Year: 2019 • Project End Year: 2021 (Projected) • Project Domain: Agriculture, Poverty • Data Source: o AI-based Intervention Dashboard: Corporate IFAD data on the investment portfolio (financing, sectors, and project type) and textual data from project reports o Lessons Learned App: Textual data from project reports o Trend analyses of strategic themes: Corporate IFAD data on the investment portfolio (financing, sectors, and project type) and textual data from project reports o Project performance prediction model: Corporate data on project performance ratings at design, during implementation, and at completion, corporate financing data and project features, and external open-source data on country-specific risk factors and characteristics from World Bank (WDI), IMF (WEO), and other sources. o Project impact prediction model: Household survey data from IFAD impact assessments o Covid-19 impact prediction model: Google Mobility data, Google Trends data, John Hopkins Coronavirus Resource Center data, and INFORM Covid- 19 risk data, containing data on movement and search prevalence as well as actual reported Covid-19 incidence and risk factors. • Publicly Available Data: No • Technology/Platform: o AI-based Intervention Dashboard: Python, AWS Elasticsearch & Kibana o Lessons Learned App: R Shiny o Trend analyses of strategic themes: Python, R o Project performance prediction model: Stata, R o Project impact prediction model: Stata, Python o Covid-19 impact prediction model: Python, R • Reported as part of 2021 Compendium on UN AI Activities? Yes • Related Sustainable Development Goals (SDGs): All SDGs • Partnership(s)/Collaborator(s): o Academia: several consultants from various academic institutions contributed to the project over the two years of its implementation. Please refer to the acknowledgments sections in the final reports. • Links and Multimedia: o Phase 1 Report: https://www.ifad.org/en/web/knowledge/-/publication/accelerating -knowledge-generation-for-data-driven-decision-making 23 United Nations Activities on Artificial Intelligence (AI) o Phase 2 Report: https://www.ifad.org/en/web/knowledge/-/leveraging-artificial -intelligence-and-big-data-for-ifad-2.0-phase-2?p_l_back_url=%2Fen%2Fweb %2Fknowledge%2Fpublications • Lessons Learned: o Open-Source AI/ML: Open- Source AI/ML and code transparency are essential elements that ensure that dashboards and apps can be updated in a real time fashion, when new data comes in as well as integrated with the organization’s data ecosystem. o Human element to improve algorithmic performance: the human element is essential to improve the accuracy of algorithmic performance and overall quality of the models. In the case of the AI-based intervention dashboard, IFAD staff and domain experts have provided accurate taxonomies and training datasets that have fed the models, producing classifications that are “realistic”. o The complexity of IFAD project documentation is a key challenge for models that require standardized data. Not only are project reports written in four different languages, but they also vary in format and length. Data processing requires the development of multi-lingual algorithms and sensitive data filtration strategies to ensure relevant text is extracted for analysis. o Integration with existing ICT systems and business model: Sustainable and sustained AI/ML and “big data” use cases require appropriate data repositories, server space, and secure data storage within the business model. o Institutional buy-in and support for innovation: Support and buy-in from key actors and a willingness to experiment is crucial to the successful adoption and integration of new data-driven tools for decision-making. o The next steps would include the following activities: users’ validation and scaling-up of the algorithms and tools generated so that they can be integrated in existing IFAD systems (for automated reporting and briefs). Additionally, future phases of the project would also foresee additional work to explore and validate the prediction models by leveraging additional data sources and integrating additional cost data to predict return on investments. • Contact Information: Alessandra Garbero, Phd., Lead Regional Economist, Near East, North Africa, Europe and Central Asia Division (NEN) (a.garbero@ifad.org) 2. Related Sustainable Development Goals SDG 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 3. Relevant Links https://www.ifad.org/en/ Contact Information Alessandra Garbero, Phd., Lead Regional Economist, Near East, North Africa, Europe and Central Asia Division (NEN) (a.garbero@ifad.org) IFAD 24 United N