HAPPY 2024: in this 74th year since The Economist started mediating futures of brainworking machines clued by the 3 maths greats NET (Neumann, Einstein, Turing) people seem to be chatting about 5 wholly different sorts of AI. 1BAD: The worst tech system designers don't deserve inclusion in human intel at all, and as Hoover's Condoleezza Rice . 2 reports their work is result of 10 compound techs of which Ai is but one. Those worst for world system designs may use media to lie or multiply hate or hack, and to perpetuate tribal wars and increase trade in arms. Sadly bad versions of tv media began in USA early 1960s when it turned out what had been the nation's first major export crop, tobacco, was a killer. Please note for a long time farmers did not know bac was bad: western HIStory is full of ignorances which lawyer-dominated societies then cover up once inconvenient system truths are seen. A second AI ecommerce type (now 25 years exponential development strong) ; this involves ever more powerful algorithms applied to a company's data platform that can be app'd to hollow out community making relatively few people richer and richer, or the reverse. You can test a nation's use of this ai by seeing if efinance has invested in the poorest or historically most disconnected - see eg bangladesh's bklash, one of the most populous digital cash systems . Digital money is far cheaper to distribute let alone to manually account for so power AI offers lots of lessons but whether its good or not depends in part on whether there are enough engineers in gov & public service to see ahead of what needs regulating. There are 2 very good ai's which have only scaled in recent years that certainly dont need regulating by non engineers and one curious ai which was presented to congress in 2018 but which was left to multiply at least 100 variants today the so-called chats or LLMs. Lets look at the 2 very good ai's first because frankly if your community is concerned about any extinction risks these AI may most likely save you, One I call science AI and frankly in the west one team is so far ahead that we should count ourselves lucky that its originator Hassabis has mixed wealth and societal growth. His deep mind merged with google to make wealth but open sourced the 200 million protein databank equivalent to a billion hours of doctorate time- so now's the time for biotech to save humanity if it ever does. Alongside this the second very good AI graviates around Fei-Fei Li) in developing 20 million imagenet database so that annual competitions training computers to see 20000 of the most everyday sights we humans view around the world including things and life-forms such as nature's plants and animals. Today, students no longer need to go back to 0.1 programming to ask computer about any of these objects; nor do robots or and autonomous vehicles - see fei-fei li's book worlds i see which is published in melinda gates Entrepreneurial Revolution of girl empowerment
ED , VN Hypothesis: in 21st C brainworking worlds how people's times & data are spent is foundational to place's community health, energy and so natural capacity to grow/destroy wealth -thus species will depend on whether 1000 mother tongue language model mediates intelligence/maths so all communities cooperatively celebrate lifetimes and diversity's deep data ) . Check out "Moore exponential patterns" at year 73 of celebrating Game : Architect Intelligence (Ai) - players welcome .. some jargon

Friday, May 22, 1970

 

Who's redesigning cooperation networks ahead of what Stanford Human AI leaders are descirbing the great innovation inflection point ever seen in recent memory.

Google ceo pichai announced these as major accomplishments of google brain and deep mind when he asked them to merge lastApril 2023. Their collective accomplishments in AI over the last decade span AlphaGo, Transformers, word2vec, WaveNet, AlphaFold, sequence to sequence models, distillation, deep reinforcement learning, and distributed systems and software frameworks like TensorFlow and JAX for expressing, training and deploying large scale ML models Please can you give me for each of these innovation areas some references including an overall description of each that a 5th grade teacher could adopt to make sure that teens are monitoring these opportunities in making 2020s more and more human

Sure, here is a brief overview of each of the innovation areas that you mentioned, along with some references that you can use to learn more. I have also included some ideas for how a 5th grade teacher could adopt these technologies to help their students learn and grow.

AlphaGo

AlphaGo is a computer program that was developed by Google DeepMind to play the game of Go. In 2016, AlphaGo defeated a professional Go player, Lee Sedol, in a series of matches. This was a major breakthrough for AI, as Go is a very complex game that was previously thought to be too difficult for computers to master.

Transformers

Transformers are a type of neural network that are used for natural language processing tasks. They are particularly good at tasks such as machine translation, text summarization, and question answering.

Word2vec

Word2vec is a method for learning the vector representations of words. These vector representations can be used for a variety of tasks, such as text classification, sentiment analysis, and natural language generation.

WaveNet

WaveNet is a neural network that can be used to generate realistic speech. It is used in Google Assistant and other voice-activated products.

AlphaFold

AlphaFold is a protein folding prediction program that was developed by Google DeepMind. It is able to predict the structure of proteins with unprecedented accuracy. This could have a major impact on the fields of medicine and biology, as it could help scientists to better understand how proteins work and how they can be used to treat diseases.

Sequence to sequence models

Sequence to sequence models are a type of neural network that can be used to translate text from one language to another. They are also used for tasks such as machine translation, text summarization, and question answering.

Distillation

Distillation is a technique that can be used to improve the performance of a machine learning model. It works by transferring the knowledge from a large, complex model to a smaller, simpler model.

Deep reinforcement learning

Deep reinforcement learning is a type of machine learning that can be used to train agents to learn how to behave in complex environments. It is used in a variety of applications, such as robotics, game playing, and finance.

Distributed systems and software frameworks

Distributed systems and software frameworks are used to develop and deploy large-scale machine learning models. They provide a way to manage the resources required to train and run these models, and they make it easier to develop and deploy models across multiple machines.

How to use these technologies in the classroom

There are many ways that teachers can use these technologies in the classroom. For example, they can use AlphaGo to teach students about the game of Go, or they can use Transformers to help students learn about natural language processing. They can also use Word2vec to help students learn about the vector representations of words, or they can use WaveNet to help students learn about how to generate realistic speech.

In addition to these specific technologies, there are many other ways that teachers can use AI in the classroom. For example, they can use AI to personalize instruction, to provide feedback, or to grade assignments. They can also use AI to create interactive learning experiences, or to help students learn about complex topics.

The possibilities are endless, and it is up to each teacher to decide how they want to use AI in their classroom. However, one thing is for sure: AI has the potential to revolutionize education, and it is something that all teachers should be aware of.

profile picture

Sure, here are some of the practitioners who could benefit from following the latest AI models from Google DeepMind:

  • Biotechnologists

As you mentioned, AlphaFold has the potential to revolutionize biotechnology. It could help scientists to better understand how proteins work and how they can be used to treat diseases.

  • Pharmacologists

Pharmacologists could use AI to design new drugs and to better understand how existing drugs work.

  • Chemical engineers

Chemical engineers could use AI to design new materials and to improve the efficiency of chemical processes.

  • Materials scientists

Materials scientists could use AI to design new materials with specific properties, such as strength, lightness, or conductivity.

  • Engineers

Engineers could use AI to design new machines and to improve the efficiency of existing machines.

  • Scientists

Scientists in a variety of fields could use AI to help them to collect and analyze data, to make predictions, and to develop new theories.

  • Businesses

Businesses could use AI to improve their operations, to make better decisions, and to develop new products and services.

  • Governments

Governments could use AI to improve their services, to make better decisions, and to protect their citizens.

These are just a few of the many practitioners who could benefit from following the latest AI models from Google DeepMind. AI is a rapidly developing field, and it is important for practitioners in all fields to stay up-to-date on the latest developments.

Tuesday, May 19, 1970

 www.youthmarkets.com fathers work entrepreneurial revoulition at the economist,  co-author 1984's 2025 report,  youth maps;   linkin glasgow's smith scholars/ asian women 21st c moral sentiments journal 2010- can we help talloires cop26 sdgs communities?

Grants for Engaged Universities

Information Session: Grants for Engaged Universities
To expand access and participation of university heads, administrators, faculty, staff, students and community partners in under-resourced countries and/or institutions in the Talloires Network Leaders Conference, the Network is providing Grants for Engaged Universities.

The Talloires Network Secretariat will be holding this information session to provide an opportunity to address any questions regarding the process of applying for a Grant for Engaged Universities. Please submit your questions through the registration form below or email talloiresnetwork@tufts.edu.

You will receive the Zoom meeting information by email after registering below.

Learn more about the process and apply on our website here: https://talloiresnetwork.tufts.edu/programs/grants-for-engaged-universities
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To Join the Meeting

 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;

Tuesday, May 12, 1970

 


Digital identity is a key enabler for advancing human-centric AI. It provides a way to securely and uniquely identify individuals, which is essential for many AI applications. For example, digital identity can be used to:

  • Authorize access to AI-powered services and applications. This ensures that only authorized users can access these services, which helps to protect privacy and security.
  • Personalize AI experiences. AI can be used to deliver personalized experiences to users, but this requires a way to identify and track users. Digital identity can provide this information.
  • Measure the impact of AI. It is important to be able to measure the impact of AI, so that we can understand how it is affecting people and society. Digital identity can be used to collect data on how people are interacting with AI, which can be used to measure its impact.

Overall, digital identity is a critical enabler for advancing human-centric AI. It provides a way to securely and uniquely identify individuals, which is essential for many AI applications. As AI continues to develop, digital identity will become even more important.

Here are some additional benefits of digital identity in the context of advancing human-centric AI:

  • Increased trust and transparency: Digital identity can help to increase trust and transparency in AI systems. When users know who they are interacting with, they are more likely to trust the system and be transparent about their own data.
  • Improved security: Digital identity can help to improve the security of AI systems. By securely identifying users, it can help to prevent fraud and unauthorized access to systems.
  • Enhanced privacy: Digital identity can help to enhance privacy by giving users more control over their data. Users can choose to share their data with AI systems, or they can choose to keep it private.

Overall, digital identity is a powerful tool that can be used to advance human-centric AI. By providing a way to securely and uniquely identify individuals, digital identity can help to improve trust, security, and privacy in AI systems

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.

User
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.
ChatGPT

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.
User
Am i corect in thinking that in 2018 fei-fei li had also helped UN ITU launch AI for Good events
ChatGPT

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.

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

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.

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

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.

x

Programme

​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​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.

Speakers:Panel:Co-Moderators:
​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 ]

Speakers:

​​Pane​lists:

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 ]

Speakers:
Panelists:
Future of Work
Popov Room
Lead: ILO, UNIDO, UNDESA

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

Speakers:
Panelists:
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 ]

Panels:

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

Panelists:
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 

Speaker:

Panelists: 
Rapporteur: Rene Clausen Nielsen, ‎Information Management Lead, IFRC
 


 Promoting Healthier Citizens
Room K
Lead: WHO

Moderator: Mohamed Alkady, President, Hart
Biography ]

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

 Smart Cities and Communities
Room H
Lead: ITU, UNIDO

Co-Moderators:

Speaker:

Panelists:

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.”

Speakers:
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
Lead: WFP, IFAD

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

Speaker:

Panelists:

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

 

Disaster Prevention and Relief
Room K
Lead: UNICEF

Moderator: Louise Story, New York Times 

Panelists:

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

 

Education
Popov Room
Lead: UNESCO, UNITAR, WIPO, UNICRI

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

Speakers:

Panelists:
​ 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

Speakers:

​Panelists:
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.

Speaker:
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 ]

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

KPI for Success​
Room K

Lead: XPRIZE 

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

Panelists: 
Rapporteur: Sean McGregor, Oregon State University

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

Panelists:
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 ]

 
Panelists: 
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
Speakers:

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]
Education
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 
upd July 2023 - from Hai's 2022 annual report
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.

HAI Events

HAI brings its extended community together by hosting a variety of events throughout the year, including workshops, conferences, and seminars. The institute held 81 events during AY 2021-22, reaching a total 19,475 attendees between in-person and virtual platforms.

September
Hoffman-Yee Symposium

Hoffman-Yee Symposium 

Inaugural recipients of Hoffman-Yee Research Grants presented results from their research to date and plans for the future.

November
HAI Fall Conference on Policy & AI
HAI Fall Conference on Policy & AI

Four Radical Proposals for a Better Society 

Proposals were presented to a panel of experts from multiple disciplines and backgrounds, who vetted, debated, and judged the merits of each one.

Data-Centric AI Virtual Workshop

Data-Centric AI Virtual Workshop 

This workshop explored challenges and opportunities across the data-for-AI pipeline.

December
AI+HEALTH

AI+HEALTH 

This recorded online conference convened experts and leaders from academia, government, and clinical practice to explore critical and emerging issues related to AI’s impact across the spectrum of health, healthcare, and related arenas.

January
Strengthening the Technical Foundations of U.S. Security

Strengthening the Technical Foundations of U.S. Security 

HAI Managing Director for Policy and Society Russell Wald, CSET Senior Fellow Andrew Lohn, and Stanford HAI Postdoctoral Fellow Jeff Ding discussed how a National AI Research Resource (NAIRR) will impact U.S. national security.

February
Creating a National AI Research Resource

Creating a National AI Research Resource 

HAI Managing Director for Policy and Society Russell Wald, Center for Data Innovation Policy Analyst Hodan Omaar, and Founder & President of SeedAI Austin Carson held a discussion on how to design, implement and maintain a National Artificial Intelligence Research Resource (NAIRR).

April
HAI Spring Conference
HAI Spring Conference

Key Advances in Artificial Intelligence 

The HAI Spring Conference explored three key advances in artificial intelligence—accountable AI, foundation models, and embodied AI in virtual and real worlds—as well as what the future of this technology might hold.

Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI

Artificial Intelligence and the Economy: Charting a Path for Responsible and Inclusive AI 

This symposium brought together leaders from government, industry, civil society, and academia to explore potential opportunities and challenges posed by artificial intelligence and machine learning deployment across different economic sectors.

Avoiding the Turing Trap: Workshop on AI & the Future of Work
S-DEL Spring Workshop

Avoiding the Turing Trap: Workshop on AI & the Future of Work 

This workshop brought together DEL’s community of faculty, students, fellows, industry leaders, and supporters to share research and discuss groundbreaking ideas related to the future of work and the digital economy.

July
Advancing Technology for a Sustainable Planet

Advancing Technology for a Sustainable Planet 

Hosted together with IEEE and the Stanford Woods Institute for the Environment, this event explored regulatory, policy, and financial frameworks critical to advancing technology which prioritizes people, planet, and purpose-driven progress.

August
Stanford HAI Congressional Boot Camp on Artificial Intelligence

Stanford HAI Congressional Boot Camp on Artificial Intelligence 

This multi-day, bi-partisan educational event for a diverse cohort of 26 congressional staffers unpacked what AI means for international security, the future of work, and healthcare, and included field trips to Stanford labs for interactive experiences.

Explore Our Work

Research

HAI’s research focuses on projects that will advance human intelligence, augmenting human capabilities, and human impact.

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Policy

HAI brings AI leaders and policymakers together to guide human-centered AI.

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Education

HAI offers educational programs that explore the societal and ethical implications of AI.

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Hai's report