Urgent coopreration calls from SDGSyouth latest May 2023:::HAISDGS 1 2 3 4; 5: 30 coops making women 3 times more productive than men 6 7 8.

leaps 1 - Beyond the Moon ..: can you find good enough questions for teachers of any age group to ask AI to share

Freedom to read: if only permitted one read on humanising AI I'd pick feifei short article at https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_HAI_Perspectives.pdf rsvp chris.macrae@yahoo.co.uk if you have a different pick of a lifetime

Welcome to HAI: Gamechanging AI for humans combines unique combos of tech wizards since 2006Self-id Q: Can you introduce us to 4 main wizardly components of America’s most famous 2023 model CGPT

What is Conversational?




(see also congress debriefings april 2023 on this choice as most famous )

SO in what ways does 2023 connectivity of chatgpt go beyond any human brain however famous

Am I correct that while cgpt is fluent in many languages, 90% of your training involved texts in English. Discuss!

Are there people working on advanced CGPTs tuned to specific crises- eg where UN leader guterres made 21-22 year of SDgoal 4 crisis – education no longer fit for purpose ............we asked CGPT top 10 goods it expects Ai to help humans with in 2020s--.. Healthcare - improve diagnosis, treatment, and personalized medicine..Environment - monitor and manage natural resources, predict and mitigate natural disasters, reduce carbon emissions...Education - personalize learning, new ed opportunities, and improve accessibility for learners with disabilities..Aid and development - improve disaster response, humanitarian and economic development... Agriculture - optimize farming practices, increase yields, reduce environmental impact..Transportation - improve efficiency, safety, and reduce emissions..Energy - optimize energy consumption and distribution, accelerate the transition to renewable energy..Cybersecurity - detect and prevent cyber attacks, protect personal data, and secure critical infrastructure..Manufacturing - improve efficiency, reduce waste, and increase productivity in manufacturing..Space exploration - analyze large amounts of data and enable more efficient space exploration missions..

 Epoch changing Guides

1 AI Training AI Training.docx

  2 Exploring cultural weakness of encounters with greatest brain tool.docx

 Alphafold new Protein maps can be used to design enzymes to fight plastic pollution; potentially fight cancer with molecular syringe;   to circumvent antibiotic resistance ; to combat neglected diseases like African sleeping sickness'Chagas disease;Cysticercosis; Leprosy ]Lymphatic filariasis [Onchocerciasis ;Schistosomiasis; Soil-transmitted helminthiasis ;Trachoma ;Tuberculosis; Buruli ulcer; it may help accelerate vavvines for malaria;

You shared me that the famous Alpha models of DeepLearning ( see broadcast of 60 minutes april 2023) which has mapped every human protein saving millions of human hours of work ( biggest ever change in biotech) don’t really use C G P T- so what does deep learning ai architecture use RSDE?

What is Reinforcement Learning?

What is Specific Task instead of General Purpose?

What are Deep Neural Networks?


What are Evolutionary Algorithms?

AdamSmith.app Moral Sentiments SDGs.games mapping with Guterres AIforGood decade became sustainability's gen's last chance of south-north and east-west- or the death of United Nations' world trade transparency

Here is Chatgpt May 2023 answer to comparing itself with biotech greatest ever leap alphafold
Sure, here's an analogy that may help: Imagine that CGPT-4 is like a language teacher who has read and understood a huge number of books in various languages. It has learned to understand the patterns and nuances of language, and can generate its own sentences and paragraphs based on what it has learned. When it encounters a new piece of text, it can use its understanding of language to make predictions about what will come next, and generate a response that makes sense in context.
On the other hand, Alphafold is like a puzzle solver who has been trained on a vast number of jigsaw puzzles. It has learned to recognize the patterns and shapes of puzzle pieces, and can predict how they fit together to form a complete picture. When it encounters a new puzzle, it can use its understanding of these patterns to make predictions about where each piece should go, and assemble the puzzle quickly and accurately : to question -...................................................................................
More questions: Q: Could you extend to include wizardry of Fei-Fei Li with comparison above a teacher of 12 year olds may use to vision similar and different work on architectures of CGPT4 and Alpha Deep Learning by Demis Hassibis

You mentioned major tech companies offer catalogues – 5 minute lesson plans on different wizard components. How do you find these?

60 seconds exercise: suppose for teen earthlings: 2020s is more exciting than 1960s moonrace decade of my teens; You mentioned searching ai for beginners but what if a teacher wants to help kids imagineer back from future of 2020s possibilities or just next 3 years of AI is epoch-changing


Q: please tell me how could a 5th grade teacher explain why Stanford HAI is proud of being one of the world's leading GAN (Generative Adversarial Network?)
Chatgpt reply23/5 -: A GAN, or Generative Adversarial Network, is a type of computer program that can create new things that look like they were made by humans. It works by having two players eg two artists who are trying to compete with each other. One player, called the generator, tries to create something new, like a picture of a dog. The other player, called the discriminator, tries to figure out if the picture was made by a human or by the generator. The generator keeps trying to make better pictures until the discriminator can't tell the difference between a picture made by a human and one made by the generator.
In simpler terms, a GAN is like having a computer artist that can create new pictures that look like they were made by a person, and it gets better the more it practices.
33 years ago we started practicing core brand transformation inspired by new systems modeling -eg of CK Prahalad & Gary Hamel- typically when needing to transform as large brand entity as the UN we'd propose better start again and then reverse takeover-; we realise that's not an option UN2.0 so urgent to address what's the 9 piece combo of UN2.0 Tech Envoy Team at Guterres HQ- how contextually value roadmaps for anyone SDG partnering Guterres: Global Connectivity since 1865 (ITU); AIforgoodreborn ITU 2018 stems from Neumann peers 100 times more tech per decade since 1935 -see dad's bio of vn
Digital Cooperation launched by Guterres 2018 but seen by those valuing youth generation as antidote to failure of millennium goals to value education more that primary school; Digital Capacity Building: sustainable gov tech requires UN2 to be skills benchmark wherever gov designed to empower; this leaves 4 traditional connections of UN to digitalise inclusion commons human rights so that trust/safety is brand's reality; 9th piece CODES environmental sustainability - this seems to have emerged as it became clear that cops may lead on adaptation but adaptation needs to be borderless community replication of deep solutions
379 UN Antonio Guterres :: Family & Smithian Diary: why 1984's 2025 report published to ask Economist Readers to co-search 3 billion new jobs 2025=1985 following on prt 1 teachforsdgs.com
Learning's Unconventional Worldwide Premieres
300 vriti world premier ed3 tour ^^^ NFT V 0 1 2 3

2025Report- download monthly update 100 learning networks millennials girls love most
(Economist Surveys inspired by Von Neumann 1984-1951; why 1936 dad & von neumann started 100 year race to prevent extinction; why dad's last year nominated Fazle Abed Entrepreneurial Revolution GOAT - who do you vote for SDGoats.com

00Fazle Abed: Which educational and economic partnerships most empower a billion women to end extreme poverty, and value their children’s sustainability? Fortunately for SDGS.games 2020s, start deep village maps around partners/alumni of 50 years of servant leadership by fazle abed 1970-2019

IN 1970, life expectancy tropical villages up to 25 years below world average _skills trainers priority last mile health world’s most trusted eds needed eg epidemiologists UNICEF Grant, Brilliant, later Jim KIm –& to end starvation food's borlaug

3) last mile health
2) agriculture for village food security

4)non-linear livelihood education
5) timing what platforms partners could facilitate entrepreneurial revolution not not just inclusive community but cooperation in full and meaningful entrepreneurial employment

financial entrepreneurial revolution for nation's people history excluded from machine age
billion women's Sustainability COLLABS 

 Intergenerational collaboration entrepreneur platforms 5.1  metavillage sustainable community building - women empowered:15000 families at a time;5.2   billion asian women,5.3  brac net; 5.4   asian universities share sdg graduates 5.5  climate smart village exchanges,5.6 meta and zoom-me up scotty

BANK FOR ALL 1.1  1.2  1.3   1.4   1.5   1.6 celebrate 30 most human collaborations from developing world of last half-century - inspiring  anyone valuing UN and youth as first sustainability generation
EDUCATION  adult village entrepreneurs 4.1; primary 4.2  ; teen 4.3; university4.4 ; pre-school4.5;tech multidisciplinary luminaries 4.6 
HEALTH oral rehydration 3.1 ;para health "doordash" basic meds 3,2; scale vaccination3.3 ;tuberculosis & 3.4  Frugal processes eg wash sanitation, maternity3.5  ; James Grant School of public health 3.6
FOOD/land security 2.1  rice; 2.2 veggie  2.3    cash crops & village fair; 2.4  poultry;2.5  dairy, 2,6  14 nation leading supply chains financial opportunities to end poverty ;
....................................please tell me how could a 5th grade teacher explain why Stanford HAI is proud of being one of the world's leading GAN (Generative Adversarial Network?)
Chatgpt reply23/5 -: A GAN, or Generative Adversarial Network, is a type of computer program that can create new things that look like they were made by humans. It works by having two players eg two artists who are trying to compete with each other. One player, called the generator, tries to create something new, like a picture of a dog. The other player, called the discriminator, tries to figure out if the picture was made by a human or by the generator. The generator keeps trying to make better pictures until the discriminator can't tell the difference between a picture made by a human and one made by the generator.
In simpler terms, a GAN is like having a computer artist that can create new pictures that look like they were made by a person, and it gets better the more it practices.

Sunday, May 10, 1970

 Fei-Fei Li and her students at Stanford have made significant contributions to the development and application of deep learning models across a wide range of fields. Here are a few examples:

  1. Computer Vision: One of Fei-Fei Li's key contributions has been to the field of computer vision, which involves developing algorithms and models that enable machines to "see" and interpret visual data. In 2009, she and her colleagues at Stanford created ImageNet, a large-scale dataset of labeled images that has since become a benchmark for computer vision research. This dataset was used to train and evaluate deep learning models that can recognize objects, scenes, and other visual concepts with high accuracy.

  2. Healthcare: Fei-Fei Li and her team have also applied deep learning techniques to healthcare, with a particular focus on medical imaging. They have developed algorithms that can analyze MRI and CT scans to detect tumors, lesions, and other abnormalities, potentially improving diagnosis and treatment for a range of diseases.

  3. Natural Language Processing: Another area where Fei-Fei Li and her students have made contributions is natural language processing, which involves developing models that can understand and generate human language. They have worked on tasks such as question answering, language translation, and sentiment analysis, using deep learning techniques to improve the accuracy and efficiency of these tasks.

  4. Robotics: Fei-Fei Li has also been involved in research on robotics, particularly in the area of perception. She has worked on developing algorithms that enable robots to recognize and interact with objects in their environment, with potential applications in areas such as manufacturing, logistics, and healthcare.

These are just a few examples of the fields in which Fei-Fei Li and her team have made significant contributions. Their work has helped to advance the state-of-the-art in deep learning and has had a broad impact across academia and industry.

Here is list of influencers associate with 2019 launch of stanford HAI to connect with Fei-Fei Li's vision. Wherever these people are known to have a priority apploication area of ai please can you tell me what it is .Advisory Council Members Chaired by Reid Hoffman of Greylock Partners, the council also includes Jim Breyer, Breyer Capital; Jeff Dean, Google; Steve Denning, General Atlantic; John Hennessy, Stanford University; Eric Horvitz, Microsoft Research; Bob King, Peninsula Capital; James Manyika, McKinsey & Company; Marissa Mayer, Lumi Labs; Sam Palmisano, Center for Global Enterprise; Heidi Roizen, DFJ/Threshold Ventures; Eric Schmidt, Alphabet; Kevin Scott, Microsoft; Ram Shriram, Sherpalo Ventures; Vishal Sikka, Vian Systems; Neil Shen, Sequoia Capital; Jerry Yang, AME Cloud Ventures. Associate Directors Russ Altman, the Kenneth Fong Professor and professor of bioengineering, genetics, medicine and biomedical data science; Susan Athey, the Economics of Technology Professor at the Graduate School of Business; Surya Ganguli, assistant professor of applied physics; James Landay, the Anand Rajaraman and Venky Harinarayan Professor and professor of computer science; Christopher Manning, the Thomas M. Siebel Professor in Machine Learning and professor of linguistics and computer science: and Robert Reich, the Marc and Laura Andreessen Faculty Co-Director of the Center on Philanthropy and Civil Society and professor of political science. Distinguished Fellows The inaugural group of Distinguished Fellows will include: Yoshua Bengio, University of Montreal; Rodney Brooks, MIT; Erik Brynjolfsson, MIT; Jeff Dean, Google; Daniel Dennett, Tufts University; Susan Dumais, Microsoft Research; Edward Feigenbaum, Stanford University; Barbara Grosz, Harvard; Demis Hassabis, DeepMind; Geoff Hinton, University of Toronto; Eric Horvitz, Microsoft Research; James Manyika, McKinsey & Company; John Markoff, Center for Advanced Study in the Behavioral Sciences; Helen Nissenbaum, Cornell Tech; Judea Pearl, UCLA; Stuart Russell, UC Berkeley; Mustafa Suleyman, DeepMind; Terry Winograd, Stanford University; and Hal Varian, Google.

Here are some of the priority application areas of AI associated with some of the individuals mentioned:

  • Reid Hoffman: technology entrepreneurship and social networks
  • Jim Breyer: venture capital and technology investment
  • Jeff Dean: machine learning, natural language processing, and computer vision
  • Eric Horvitz: AI ethics and decision-making
  • James Manyika: economic implications of AI and automation
  • Marissa Mayer: consumer internet and mobile technology
  • Eric Schmidt: AI ethics and public policy
  • Vishal Sikka: enterprise software and digital transformation
  • Neil Shen: venture capital and technology investment
  • Russ Altman: biomedical data science
  • Susan Athey: economics and technology
  • Surya Ganguli: neuroscience and machine learning
  • James Landay: human-computer interaction and design
  • Christopher Manning: natural language processing and computational linguistics
  • Robert Reich: philanthropy and civil society
  • Yoshua Bengio: deep learning and neural networks
  • Rodney Brooks: robotics and AI
  • Erik Brynjolfsson: economics and digital technologies
  • Daniel Dennett: philosophy of mind and consciousness
  • Demis Hassabis: deep learning and AI in gaming
  • Geoff Hinton: deep learning and neural networks
  • John Markoff: technology journalism and history of computing
  • Helen Nissenbaum: ethics and privacy in technology
  • Judea Pearl: causality and reasoning under uncertainty
  • Stuart Russell: AI safety and ethical considerations
  • Mustafa Suleyman: applied AI and healthcare
  • Terry Winograd: natural language processing and human-computer interaction
  • Hal Varian: machine learning and economics.
Am i corect in thinking that in 2018 fei-fei li had also helped UN ITU launch AI for Good events

Yes, that's correct. Fei-Fei Li has been involved with the ITU's AI for Good initiative since its inception in 2017. She has served as a keynote speaker at the annual AI for Good Global Summit, which brings together stakeholders from government, industry, academia, and civil society to discuss the potential of AI to address global challenges. Li has also served as a member of the AI for Good Global Summit Advisory Board and has been involved in various other AI for Good projects and initiatives organized by the ITU.

Do you see any overlap between those connecting around her AT HAI and those she has inspired at UN aiforggod?

Yes, there is some overlap between the people involved with Stanford HAI and those associated with the UN AI for Good initiative. For example, Fei-Fei Li serves on the advisory board for the AI for Good Global Summit, and some of the individuals who have been involved with HAI, such as Eric Horvitz of Microsoft Research, have also participated in the AI for Good initiative. Additionally, some of the research conducted at HAI, such as work on using AI to improve healthcare and address climate change, aligns with the goals of the AI for Good initiative.

Can you tell me who is on the advisory board of the ai for good summit?

The advisory board of the AI for Good Global Summit is made up of a diverse group of individuals from academia, industry, and international organizations. Here are some of the members of the advisory board:

  • Irakli Beridze, Head of the Centre for Artificial Intelligence and Robotics at the United Nations Interregional Crime and Justice Research Institute (UNICRI)
  • Chaesub Lee, Director of the Telecommunication Standardization Bureau (TSB) at the International Telecommunication Union (ITU)
  • Yoshua Bengio, Professor at the University of Montreal and co-recipient of the 2018 Turing Award
  • Fei-Fei Li, Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI)
  • Andrew Ng, Founder and CEO of Landing AI and former Vice President and Chief Scientist at Baidu
  • Kai-Fu Lee, Chairman and CEO of Sinovation Ventures and author of "AI Superpowers: China, Silicon Valley, and the New World Order"
  • Anousheh Ansari, CEO of XPRIZE Foundation and the first female private space explorer
  • Martin Ford, Futurist and author of "The Rise of the Robots: Technology and the Threat of a Jobless Future"

Note that this list may not be exhaustive and may vary from year to year.



​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​AI for Good Global Summit
Geneva, Switzerland, 7-9 June 2017

Please follow the Summit via Webcast​.


Day 1, 7 June 2017​

Social events and breaks are kindly sponsored by:
Gold Sponsors:
KFF          Word 4App
Corporate Sponsor:

09:30 - 1​0:00Welcome Address, Popov Room
The hosts, ITU and XPRIZE, set the goals and opportunities in collaborating for a better future with Artificial Intelligence.
Moderator: Stephen Ibaraki, Social Entrepreneur and Futurist - Chair REDDS Capital [ Biography ]
10:00 - 10:45Opening Keynotes: ‘Moonshots’ - Inspiration for the Future, Popov Room
Distinguished keynote speakers from industry and academia will share their ‘moonshots’ for the future of Artificial Intelligence.

Inaugural moonshot keynote:Inspirations about the future:Moderator:Marcus Shingles, CEO, XPRIZE Foundation [ Biography ]​
10:45 - 11:15Coffee break, Tower building, Outside Popov Room, -2nd floor 
11:15 - 12:45Plenary 1: State of Play, Popov Room
Recent breakthroughs have driven rapid growth in massive data sets, storage capacity, computing power, and open APIs. These changes have fueled the development of machines that can do things that once relied solely on human experience, creativity, and ingenuity.

In the opening presentations of the summit, we explore perspectives of the current moment including how AI is affecting life and organizations.

Speakers:Moderator: Wendell Wallach, Consultant, Ethicist, and Scholar at Yale University's Interdisciplinary Center For Bioethics and Senior Advisor to The Hastings Center, and the World Economic Forum [ Biography ]
12:45 - 14:00Lunch break 
Brown bag lunch offered to delegates, Tower building, Outside Popov Room, -2nd floor 

14:00 - 15:30Plenary 2: Transformations on the Horizon, Popov Room
Industry and academia are working towards the next generation of computers that can understand and learn from natural spoken language, full motion video, and more. Given how much the world is already changing, what can we expect from the next generation of Artificial Intelligence systems? How will these technologies affect the world?

Presenters will explore these global opportunities, transformations, and challenges.

Speakers:Moderator: Urs Gasser, Executive Director of the Berkman Klein Center for Internet & Society, Harvard University [ Biography ]
15:30 - 16:00​Coffee break, Tower building, Outside Popov room, -2nd floor
16:00 - 18:00Plenary 3: Future Roadmap – Collaborating for Good, Popov Room
A wide range of voices have been debating the future of AI. A roadmap for governments, industry, academia, media, and civil society is critical to ensure that this technology develops in a safe, responsible, and ethical manner benefiting all segments of society.

The presenters and panelists will address the issues above and will introduce the role of the ‘Breakthrough Sessions’ taking place over the next two days of the Summit.

​18:30 - 20:00Reception, Montbrillant Cafeteria

Day 2, 8 June 2017 

Social events and breaks are kindly sponsored by:
Gold Sponsors:
KFF           Word 4App
Corporate Sponsor:

​09:00 - 10:00Plenary 4: Privacy, Security, Ethics and Societal Challenges, Popov Room
AI will have far-reaching effects on the future of society that could redefine global ethics, economics, and law.

This session will discuss the need for a guiding ethical framework and code of conduct to direct the design, production, and use of AI and robotics. The session will also explore the framework’s requirements in the areas of fundamental human rights, equality, justice, non-discrimination, privacy and social responsibility. These requirements center the role of the ‘Breakthrough Sessions’ taking place over the next two days of the Summit.

Speakers:Moderator: Stephen Cave, Executive Director, Leverhulme Centre for the Future of Intelligence [ Biography ]
​​10:00 - 10:30 ​Coffee break​, Tower building, Outside Popov room, -2nd floor
10:30 - 12:00 ​
​Breakthrough Groups on Privacy and Ethics​​Breakthrough Groups on Societal Challenges
Enhancing Privacy and Security 
Room H
Lead: UNICRI, OHCHR, Global Pulse

Moderator: Irakli Beridze, Senior Strategy and Policy Advisor, UNICRI Biography ]



Rapporteur: Sean McGregor, Oregon State University
Ethical dev​elopment of AI
Room K
Lead: Global Pulse, UNESCO, OHCHR

Moderator: Robert Kirkpatrick, Director, UN Global Pulse 
Biography ]

Future of Work
Popov Room

Moderator: Marie-Jose Bahnam, Sr. Director, Foundation Priorities, XPRIZE [ Biography ]

Rapporteur: Alexandre Cadain, CEO at Anima and co-lead of post-digital program at Ecole Normale Superieure​
Lead: Marie-Hélène Parizeau, Chair of UNESCO COMEST; Joseph Konstan, Professor, University of Minnesota​ 
Biography ]

Moderator: Joseph Konstan, Professor, University of Minnesota 
Biography ]


Rapporteur: Joseph Konstan, Professor, University of Minnesota 
Biography ]

12:00 - 13:30​Lunch break
Brown bag lunch offered to delegates, Tower building, Outside Popov Room, -2nd floor 

​13:30 - 14:00Plenary 5: ‘Breakthrough’ Proposals on Privacy, Security, Ethics and Societal Challenges, Popov Room
The rapporteurs of the ‘breakthrough groups’ will present their proposals on near-term, practical applications of AI to solve the challenges presented in their sessions.
​14:00 - 15:00Plenary 6: AI for Common Good and Sustainable Living, Popov Room
AI has the potential to yield enormous value in solving many of humanity’s grandest challenges and advancing the United Nations Sustainable Development Goals. This session will explore how AI can accelerate and advance the development and democratization of solutions to promote common good and enable sustainable living.

Speakers:Moderator/Speaker: Thomas Wiegand, Executive Director, Fraunhofer Heinrich Hertz Institute and Professor, TU Berlin Biography ]
15:00 - 15:30Coffee break, Tower building, Outside Popov room, -2nd floor
15:30 - 17:00 ​
Breakthrough Groups on Common Good​​Breakthrough Groups on Sustainable Living
AI for Prosperity
Room C
Lead: Yoshua Bengio, Professor, University of Montreal

Rapporteur: Sean McGregor, Oregon State University​

Social ​Good Data
Popov Room
Lead: Global Pulse, UNDESA

Moderator: Miguel Luengo-Oroz, Chief Data Scientist, UN Global Pulse 


Rapporteur: Rene Clausen Nielsen, ‎Information Management Lead, IFRC

 Promoting Healthier Citizens
Room K
Lead: WHO

Moderator: Mohamed Alkady, President, Hart
Biography ]

Rapporteur: Preetam Maloor​, Strategy and Policy Advisor, ITU

 Smart Cities and Communities
Room H




Co-Rapporteurs: Michael Martin, Prize Manager, IBM Watson on AI, XPRIZE; Jose Maria Diaz Batanero, ITU
17:15 - 17:45​Plenary 7: ‘Breakthrough’ Proposals on Common Good and Sustainable Living, Popov Room
The rapporteurs of the ‘breakthrough groups’ will present their proposals on near-term, practical applications of AI to solve the challenges presented in their sessions.

Day 3, 9 June 2017 

Social events and breaks are kindly sponsored by:
Gold Sponsors:
KFF           Word 4App
Corporate Sponsor:

​09:00 - 10:00Plenary 8:  Poverty Reduction and Capacity Building, Popov Room
This session will examine how AI can help end deprivation and the dangers of poverty. In addition, it will explore the opportunity for all countries to participate in the development and application of AI. The goal is to avoid expanding the “digital divide” into an “intelligence divide.”

Moderator: Anja KaspersenHead of Strategic Engagement and New Technologies, International Committee of the Red Cross​ [ Biography ]

​​10:00 - 10:30 ​Coffee break​, Tower building, Outside Popov room, -2nd floor
10:30 - 12:00 ​
​​Breakthrough Groups on Poverty ReductionBreakthrough Groups on Capacity Building
Ending Hunger
Room H

Moderator: Robert Opp, Director, Innovation and Change Management, WFP [ Biography​ ]



Co-Rapporteurs: Johanna Jochim, Special Projects Manager, WFP; Jamie Green, Innovation Accelerator Projects Manager, WFP


Disaster Prevention and Relief
Room K

Moderator: Louise Story, New York Times 


Rapporteur: Paul Bunje, Chief Scientist, XPRIZE [ Biography ]


Popov Room

Co-Moderator: Alex Mejia, Senior Manager, UNITAR; Fengchun Miao, Chief of ICT in Education, UNESCO


​ Promoting Equality in Access to AI
Room C
Lead: OHCHR, UNIDO, ITU, Amnesty international

Moderator: Sherif Elsayed-Ali, Head of Technology and Human Rights, Amnesty International


Rapporteur: Ahmed Motala, Human Rights Officer, OHCHR
​12:00 - 12:30Plenary 9: ‘Breakthrough’ Proposals on Poverty Reduction and Capacity Building, Popov Room
The rapporteurs of the ‘breakthrough groups’ will present their proposals on near-term, practical applications of AI to solve the challenges presented in their session.
12:30 - 13:30​Lunch break
Brown bag lunch offered to delegates, Tower building, Outside Popov Room, -2nd floor 

​13:30 - 14:00Plenary 10: Investments, Economic Aspects and Designing the Future, Popov Room
Government, industry, academia, private sector and civil society need to work together to mitigate the risks posed by AI, ensuring that AI benefits all of humanity. This session will explore how Global partnerships inclusive of all segments of society will offer crucial support to the pursuit of this goal.

Moderator: Andy Chen, Board Chair, IEEE Computer Society [ Biography ] 
14:00 - 15:30
Breakthrough Groups on investment and Economic AspectsBreakthrough Groups on Designing the Future
I​nvesting for Impact with AI​
Room H
Lead: Ash Fontana, Managing Director, Zetta Ventures 
Biography ]

Moderator: Ash Fontana, Managing Director, Zetta Ventures Biography ]

Rapporteur:Rigas Hadzilacos, Practice Lead, Knowledge Networks and Analysis, World Economic Forum (WEF) ​

KPI for Success​
Room K


Moderator: Chris Fabian, Co-Founder, UNICEF Innovation Biography ]​

Rapporteur: Sean McGregor, Oregon State University

Lead: Lynne Parker, Professor, University of Tennessee-Knoxville
Biography ]

Rapporteur: Preetam Maloor​, Strategy and Policy Advisor, ITU
Roadmap for Collaboration​
Popov Room
  Lead: XPRIZE and ITU

Co-Moderators: Marie-Jose Bahnam, Sr. Director, Foundation Priorities, XPRIZE [ Biography ]Paul Bunje, Chief Scientist at XPRIZE Biography ]

Rapporteurs: Marie-Jose Bahnam, Sr. Director, Foundation Priorities, XPRIZE [ Biography ]Paul Bunje, Chief Scientist at XPRIZE; XPRIZE Biography ]
​15:30 - 16:00​Coffee break, Tower building, Outside Popov room, -2nd floor
16:00 - 16:30Plenary 11: ‘Breakthrough’ proposals on investments, economic aspects and designing the future, Popov Room
The rapporteurs of the ‘breakthrough groups’ will present their proposals on near-term, practical applications of AI to solve the challenges presented in their sessions.
16:30 - 17:45Plenary 12: Closing Session - Applying AI for Good, Popov Room
Drawing on all of the proposals from the ‘Breakthrough Sessions’, the Summit will propose strategies for:
  1. Development of applications and systems that benefit humanity
  2. Identifying opportunities to pinpoint practical and impactful applications, with impact in the near term
  3. Identifying a roadmap for ethical, safe, and democratic access
  4. Ongoing UN ITU and XPRIZE inspired projects to support and monitor the practical implementations of AI for Good

Breakthrough Sessions


Enhancing Privacy and Security
AI has the potential to reveal detailed private information through embedded, mobile and wearable devices, advanced facial recognition and predictive analysis, leading to the questions of whether the current privacy and security standards can still protect and safeguard personal data, individual privacy and anonymity. The increasing security vulnerabilities will affect the application of AI technologies in a safe manner, in context of autonomous vehicles and drones, biomonitoring, healthcare robotics, or robots responsible for the maintenance of public order. As AI powered technologies can self-advance, leaving uncertainty in applying standard data protection principles of accountability, transparency, consent, control, how can we ensure that proper data privacy and data security measures and standards are in place. This session will aim to discuss and identify strategies to ensure that AI contributes to the global security and peace, protect individuals against unauthorized manipulation of AI algorithms and not create chaos.
[Updated on 1 June 2017]

Ethical development of AI
Algorithm-based machines increasingly learn from and autonomously interact with their environments, thereby developing unexplainable forms of decision making. In many ways this is just as much as a new frontier for ethics and risk assessment as it is for emerging technologies of AI. Should AI be able to make life-and-death decisions, for example, in deciding how autonomous vehicles behave in the moments preceding a crash? Where does the liability rest for harm caused by AI ?  How can we avoid the biases in decision-making by AI, causing inequalities and discrimination? How can we ensure that a world of increasingly pro-active computing remains human-centered, protecting human identity and dignity? This session will discuss the challenges of today's world posed by the use of AI and will aim to identify possible solutions that can ensure that the design and operation of AI is at minimum characterized by accountability and respect for human rights and purpose?
[Updated on 5 June, 2017]

Future of Work​
AI will eventually be capable of performing not just routine tasks but also the functions of doctors, lawyers, engineers and other professions reliant on expert judgement and specialized qualifications. How will AI's augmentation of jobs, elimination of jobs affect the quality of life enjoyed by human beings? Will AI's increasing influence on production processes reduce tax revenues to the detriment of social welfare systems, and is it time to revisit the concept of social welfare spending as more and more people hand over their jobs to machines?
[Updated on 1 June, 2017]

Humans and Machines​
​AI technology is enabling a wide range of new ways for humans and machines to interact, from intelligent and autonomous robots to smart spaces to natural language and even physical and cognitive human augmentation. ​These technologies, if well-deployed, can have enormous social impact, supporting independence, economic development and engagement, and cultural diversity. What are the most promising potential applications of novel human-machine interaction in the coming five to ten years, focusing on applications that can support disadvantaged individuals and communities?  And what are the associated risks of these technologies and the steps that developers, communities, and governments may need to take to regulate them to ensure that their good outweighs adverse impacts?
[Updated on 1 June 2017]

AI for​ Prosperity
AI will increase efficiency and productivity to an extent far beyond the current limitations of human labor. AI-powered automation may create factories full of workers that never sleep, eat, or expect pay for the day’s labor. As this type of production process yields higher economic output, how will we ensure that the associated financial gains do not accrue just to the owners of AI-powered machinery? How can we ensure developing nations can continue to develop towards prosperity?
Social Good Data
AI innovation will be central to the achievement of the United Nations' Sustainable Development Goals by capitalizing on the unprecedented quantities of data now being generated by sensors, mobile and embedded devices/chips, IoT, and satellites related to sentiment behavior, human health, commerce, communications, migration and more. What are the datasets and practical means of capturing them, protecting them, and exploiting them with the greatest potential to deliver insight able to assist us meeting the grandest challenges facing humanity? How can we provide increased access to data sets that will provide great insight where protection of individuals and their anonymity is critical? And considering that data resources result from digitalization, a process many countries are only just beginning, how might we democratize access to the data-derived insight to inform future development strategies? Furthermore, what are the practical issues involved in deploying operational systems to support decision-making on the ground and at scale?
[Updated on 1 June, 2017]
Promoting Healthier Citizens
The practice of medicine is now augmented by AI systems that can process the entire history of recorded medical research and analyze large datasets of medical imagery. These systems guide evidence-based treatments and inform healthcare policy. Initial deployments of these AI medical systems are largely in the developed world, which further expands the gap between medical standards and practices around the world. What are the foundational requirements for extending AI-enhanced medicine to the developing world?
Smart Cities and Communities
The world needs to maintain quality of life for the billions of people living in increasingly dense, urban environments. Improvements in sensor technology, Internet connectivity, and autonomous vehicles will improve the capabilities for building “smart cities.” How will we improve the quality of urban life by improving the social and physical infrastructure of cities? How can corporations and governments work with AI technologies to improve communities?
Ending Hunger
The number of people inhabiting the earth is increasing far faster than the area of arable land. Solving the challenge of hunger requires smarter means of crop production and distribution. Improvements in satellite imagery, sensor data and the understanding of natural systems can feed into modern AI models. Technologies can help us to optimize crop yields, reduce water use and improve crop disease treatment. What are the most effective means of leveraging AI to improve food production and procurement for all people?
Disaster Prevention and Relief
The world is facing a series of shocks that have never been as compounded, or come as quickly, as they will over the next 15 years. These disasters can include catastrophic failures of climate including drought and flooding, massive loss of jobs due to automation, increased nationalism leading to conflict, and a connected world where disease spreads faster than ever. We will explore the impact and benefits of AI technologies and formulate strategies for preparedness and resilience of the human species for the changes that are to come.
[Updated on 1 June 2017]
AI promises to boost all forms of education, enabling the personalization of education at scale. Wider access to networks and knowledge, peer learning capabilities, crowdsourcing new content and machine learning are driving significant advances in online learning and have enabled teachers in K-12 schools and higher education to multiply the size of their classrooms while addressing individual students' unique learning pace, needs and styles. The continued improvements of AI, however, may render current education systems obsolete, calling for dialogue and guidelines around global inclusion and access to knowledge and skills most relevant to the human experience of the future.
[Updated 1 June 2017]
Promoting Equality in access to AI
Artificial intelligence is beginning to play an increasing role in different facets of life including product design, diagnosing diseases, assessing credit risk, combating crime, promoting freedom of speech and democracy. While the world is increasingly benefiting from these advances, how can we have universally inclusive research and development and data respecting local culture, gender, race, and cultural and geographical realities? How can we ensure that AI applications promote equality rather than entrench bias? How can we provide an equitable basis for all the world's countries to expand their knowledge of AI and participate in the global scene of progress and innovation? Artificial Intelligence has vast potential, and this session will aim to identify strategies to allow everyone to participate in the next frontiers of human evolution. [Updated on 1 June 2017]
Investing for Impact with AI
We have a general, moral imperative to maintain the quality of life for people all around the world. This is difficult to satisfy as populations increase and resources decrease. However, machine learning technologies are particularly good at solving complex optimization problems. This session will focus on how to invest in machine learning technologies to solve significant, societal problems. The session will provide guidelines for allocating capital to such technologies in terms of finding large problems, allocating capital in underinvested areas and avoiding negative consequences. We will use the global resource allocation issues in the food, logistics and energy industries by way of example. This session is relevant to firms allocating assets to such technologies and practitioners considering how to apply their skills for maximum, global impact. This panel will identify guidelines to allow for investment in technologies that efficiently allocate natural resources to solve significant, societal problems.
[Updated on 7 June 2017]
KPI for Success
As AI technologies continue to develop, we require better methods for understanding how regulations and investments are shaping the general welfare. With proper measurement it is possible to understand how our interventions may (or may not) promote solutions to sustainable development goals. The principal concern is that AI systems are being integrated into key social institutions, even though their accuracy, and their social and economic effects, have not been rigorously studied or validated. In this session we explore questions at the intersection of AI, public policy, and economics to develop possible quantitative assessment of AI Roadmaps.
[Updated on 23 May 2017]
Approaches by Governments, Industries, and Other Stakeholders
Several governmental reports have recommended national approaches to questions surrounding artificial intelligence and its impact on society. Is there a global approach we could extract from these national efforts? What are the similarities and differences between these approaches? Can we identify a common path towards AI for Good?
Roadmap for Collaboration
Although we are yet to agree on the required mechanism, we are in clear need of an inclusive global dialogue to address the challenges and opportunities brought on by AI in policy, regulation, business, ethics and standardization. The agreement of a roadmap plotting a responsible development path for technology will demand the active involvement of government, industry, academiahttps://www.itu.int/en/ITU-T/AI/Pages/201706-programme.aspx and civil society. How can we best utilize the expertise of AI researchers, corporations, and the general public to inform policy decisions around artificial intelligence? How can institutions adapt their missions to utilize the new opportunities of AI? What perspectives are currently missing from the discussion and how can we involve them? It is crucial that all stakeholders work together to identify a set of specific frameworks and strategies to create a roadmap to ensure that AI benefits all of humanity.

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