selected library stanford 1 Generative_AI_HAI_Perspectives.pdf Global-Futures-Forum-2023-draft-3.6-4un youth do you have a future ... .. common agenda aka 20-21 5th annual review Un Tech Envoy & egov rankings- month 1 of 15 journey to UNsummitfuture.com waterbot.docx humanspring.pptx
ED: What exponentially determines future human possibilities- in 1930s, keynes argued a very few economists more than most; since my dad met von neumann in 1951 it has been mathematically likely that at very least economists will need share their seat with ai or become a disaster area to any nation that still lets them bowl alone
x DC has just experienced a year 1 round up of national taskforce of ai.gov - while many things are going on there's a relatively small stanford circle that i personally find consistent in advancing human values AI - for that reason I aim to keep a library track referencing their work separate from other links email@example.com (just a way O have of knowing who's ai is playing with my mind and networks I spend my time linking!)
Our March dialogues with chatgpt are themed around race to sdg6 water/sanitation - un ny summit march 22 to 24 - Economistwater.com
year 1 review of what washington dc understands provided starting lone march 1 DChati.com- key files include https://hai.stanford.edu/sites/default/files/2023-03/Generative_AI_...
x02march.docx In this chat , unsuccessfully I sought to discover if those tooling ai can be ethical without a conscious motivation for above zero sum. I am not recommending it as a conversation of general interest but because its one of my deepest concerns I log it up as i need to think could i have done a better interview?
how far will chatgpt go outside its conventional wisdom - test with 1 something usa got wrong intangibles accounting ; 2 who on other side of world is scaling sdg solutions- next chat shows that chatgpt view of how ai evolved and gets massive practised has biases if only by omission of detailed diary-access
firstname.lastname@example.org - my first month of chats at https://chat.openai.com/chat
.There are some innovations stories that can unite our species morals and actions. O believe they should be known by every teacher, every child before they leave school, every public broadcaster-- one of these concerns the agriculture research of borlaug. He discovered that the staple food of almost every country could be farmed with up to 10 times more local productivity - this is certainly the case fir rice on which over 60% of humans depend to prevent starvation. Actually father published in 1977 Economist what he called the happiest chart in the world.; it indexed rice production taking japan as a benchmark- some asian nations were still only achieving 10% of with its small farrners as japan did. The good news was borlaug had always demanded his work was open sourced and certainly Japan had always honored that. Until poor countries both shared and with nature adapted borlaug crop science with their poorest farmers millions of village mothers would die of starvation every year.
COOPERATION THE ABEDIAN WAY
Its foundational natural application/intelligence like Borlaugs AI can double check every human community has access to...
wow 3/8 of world trade ai does not exist, is all macroeconomics fakery?
xThe world's happiest chart as The Economist reported it - this Agriculture Intel chart and the open source crop science knowledge of Borlaug's that maps it - evidence ending starvation (end hunger sdg 2) an achievable goal except where man declared war on one another or on Nature herself.
Un 1951 The Economist's end poverty sub-editor had been seconded to New York for a year. There he met Von Neumann who gave him the most exciting intelligence story human development economics has experienced. Ask what good can be united by peoples who have first access to 100 times more (computational and intelligence) tech per decade. By year 10 of the survey The Economist had started monitoring 5 corridors- the 5th celebrated by President Kennedy was Japan and the coastal corridor to its south; the fir=st had been the Swiss corridor that had emanated from 1865! ; Switzerland had been given the duty of the ITU to share cooperation standards across nations developing tele sectors - be that telegrams , telephone standards, radio and so forth . The 20th c greats of maths - von neumann , einsten had been schooled in that corridor stretching from einstein's munich to Neumann's Budapest. Fleeing hitker and stalin they had transferred cooperation intel corridors to princeton NY (UN) NE to boston MIT(later moon landing coding was done there). Neumann had open sourced his hardware intel to ibm and his studies on brain, software and artificial intel to Yale. Meanwhile the corridor south of new york had thanks to kennedy's 10 year vision become the space corridor; san francisco whose opera house had been where the Un was born (twinned with MIT) but already had a valley corridor south to santa clara via stanford (driving minutarisation and micro) soon to become world famous as silicon valley thanks to Intel (Moore's Law), However the huger order that had caused intel to invent the programable chip had come frim a japanese calculator manufacturers ,,, Game on Note transfers across corridors happened frequently - US xerox parc conceived at NE hq was transferred to the West Coast valley.
conversation with chatgpt - lesson plan of einstein/von neumann wish that their legacy would be human-centris AI not arms races
Yes, I can provide some references related to John von Neumann's contributions to the development of computing and AI. Here are some resources that you may find helpful:
In addition, you may want to refer to von Neumann's own writings, such as his book "The Computer and the Brain," which explores the relationship between computing and human intelligence, and his lectures on the theory of automata and self-reproducing machines. You can also encourage students to explore the history and development of computing and AI more broadly, including the role of other pioneers such as Alan Turing and Claude Shannon.
I apologize for the confusion. It seems that there was an error in my response earlier. I do not have a specific reference for a MIT Technology Review article titled "John von Neumann: The Scientific Genius Who Pioneered the Modern Computer, Game Theory, Nuclear Deterrence, and Much More" from August 2020, as it appears to be a misattributed title.
However, there is a possibility that the article you are referring to may have been an updated version or a republished excerpt from your father's biography of von Neumann. In any case, Norman Macrae's book "John von Neumann: The Scientific Genius Who Pioneered the Modern Computer, Game Theory, Nuclear Deterrence, and Much More" could be a valuable resource for understanding von Neumann's contributions to computing, AI, and related fields.
Once again, I apologize for any confusion caused by my earlier response.
Sure, I can help you create a lesson plan for this topic. Here's a suggested outline:
Topic: Einstein and peer greats of maths, and their hope for human-centric AI and nature's deep coding
Target Audience: College students in their 23-24th year of study, inspired by the Guterres convening Summit Future summer 2024 UN.
Duration: 1-2 hours
I. Introduction (15 mins)
II. Main Content (60 mins)
III. Reflection and Conclusion (30 mins)
IV. Follow-up Activities (Optional)
Sure, here are some references you can use to support the lesson plan on Einstein and his peers' legacy of human-centric AI and nature's deep coding:
You may also want to refer to the official website of the Guterres convening Summit Future summer 2024 UN and the Sustainable Development Goals website for more information and resources related to the topic.
Additionally, you can encourage students to explore academic databases such as JSTOR, Google Scholar, and IEEE Xplore to find relevant articles, research papers, and other resources to support their learning and research.
Yes, the MIT Technology Review article titled "How the Pandemic Defeated America" highlights some of the technological and societal failures that have contributed to the impact of the COVID-19 pandemic in the United States, such as inadequate public health infrastructure, insufficient data collection and analysis, and the spread of misinformation through social media.
As you mentioned, the failure to prioritize virus prevention as a topmost AI priority despite warnings from experts is one such example of a failure in allocating resources and attention towards critical issues. Additionally, the article points out the lack of investment in public health infrastructure, including disease surveillance systems and contact tracing, as another contributing factor.
In terms of addressing other extinction risks, such as those related to climate change and biodiversity loss, AI for SDGs can certainly play a role in empowering millennials and others to take action towards sustainability. By leveraging AI and other emerging technologies, we can better understand the complexities of these issues and develop effective solutions. However, it is crucial to ensure that the development and deployment of AI is done in an ethical, transparent, and inclusive manner that considers the diverse needs and perspectives of different communities
While the MIT Technology Review article focuses on the specific failures in the United States, the issue of inadequate prioritization of virus prevention as a top AI priority is not unique to the US. Many countries around the world have faced similar challenges in responding to the COVID-19 pandemic, such as lack of preparedness, insufficient public health infrastructure, and spread of misinformation.
In terms of AI prioritization more broadly, there has been growing awareness and attention towards the potential of AI to address global challenges such as climate change, healthcare, and education. However, the allocation of resources and attention towards specific AI priorities can vary greatly depending on factors such as government policies, industry trends, and societal values. In some cases, certain AI priorities may be overemphasized while others are overlooked.
There is certainly a lesson to be learned from the COVID-19 pandemic in terms of the importance of prioritizing critical issues and allocating resources towards them. This applies not only to funding decisions made by elders, but also to the freedom and agency of younger generations to shape their own priorities and contribute to the collective effort towards achieving the SDGs. By ensuring that AI prioritization is aligned with the needs and values of diverse communities, we can create a more equitable and sustainable future for all." /"> ---
There are many networks and associations focused on advancing AI for social good and sustainable development. Here are a few examples:
The Partnership on AI: This is a multi-stakeholder initiative that brings together technology companies, civil society organizations, academics, and others to collaborate on the development of AI in a way that is ethical, transparent, and inclusive.
The United Nations Global Pulse: This is an innovation initiative of the United Nations that uses data science and AI to support sustainable development and humanitarian action.
The AI for Good Foundation: This is a nonprofit organization that focuses on using AI to address global challenges in areas such as healthcare, education, and climate change.
The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems: This is a program of the Institute of Electrical and Electronics Engineers (IEEE) that focuses on developing standards and guidelines for ethical AI.
The AI Alliance: This is a network of European organizations and individuals working on AI ethics and policy issues.
These are just a few examples, and there are many other networks and associations around the world that are working on advancing AI for social good and sustainable development.--
Fei-Fei Li is certainly a prominent and respected researcher in the field of AI for social good, particularly in the areas of computer vision and machine learning. She has been involved in a number of initiatives focused on using AI to address social and environmental challenges, such as the AI for Earth program and the Stanford AI Lab's work on AI and healthcare.
There are many other researchers who are also doing important work in this area. Some other names that come to mind include:
Yoshua Bengio: A pioneer in deep learning and a vocal advocate for ethical AI.
Timnit Gebru: A researcher and activist who has been a leading voice on issues of bias and discrimination in AI.
Cynthia Breazeal: A roboticist who has focused on developing socially and emotionally intelligent robots.
Joy Buolamwini: A researcher and activist who has brought attention to issues of bias in facial recognition technology.
Andrew Ng: A prominent figure in AI and machine learning who has been involved in a number of initiatives focused on using AI for social good, such as the AI for Everyone program.
These are just a few examples, and there are many other researchers who are doing important work in this area. If Marina von Neumann Whitman does reach out to a first 5 or 10 people to discuss the idea of an AI Hall of Fame, I'm sure that there will be many excellent candidates to consider.
You are correct that the list I provided is dominated by researchers based in North America and Europe, and it is important to acknowledge and address the biases and limitations of this perspective. There are certainly many researchers and organizations in other regions of the world who are doing important work in the field of AI for social good.
Here are a few examples:
Dr. Fei-Fei Li, whom we previously mentioned, is originally from China and has been involved in a number of initiatives focused on using AI to address challenges in developing countries.
Dr. Tshilidzi Marwala is a South African researcher who has written extensively on the intersection of AI and development, particularly in the context of Africa.
Dr. Yoshua Bengio, whom we previously mentioned, has been involved in a number of collaborations with researchers in Africa, and has advocated for more investment in AI research and education in developing countries.
The AI for Good Foundation, based in Hong Kong, is a non-profit organization focused on using AI to address social and environmental challenges in Asia.
The African Institute for Mathematical Sciences (AIMS), based in Rwanda, has launched a number of initiatives focused on using AI to address challenges in the region, including the AIMS-Next Einstein Initiative.
These are just a few examples, and there are many other researchers and organizations around the world who are doing important work in this area. It is important to seek out and learn from these diverse perspectives in order to ensure that the development and deployment of AI is equitable and inclusive.
Yes, there are several other youth-inspiring references related to the broad interpretation of AI for Good. Here are a few:
Timnit Gebru: She is a computer scientist and the former co-lead of the Ethical AI team at Google. She is known for her research on algorithmic bias and advocating for more inclusive and diverse AI systems.
Joy Buolamwini: She is a computer scientist and founder of the Algorithmic Justice League, an organization that aims to highlight and mitigate the negative impacts of AI on marginalized communities. She is known for her work on facial recognition technology and bias in AI.
Mustafa Suleyman: He is a co-founder of DeepMind, an AI research company that was acquired by Google. He is known for his work on developing AI systems for healthcare and other social good applications.
Olga Russakovsky: She is a computer scientist and co-founder of the AI4ALL organization, which aims to increase diversity and inclusion in AI research and development. She is known for her research on computer vision and for advocating for more diverse and inclusive AI systems.
Rediet Abebe: She is a computer scientist and assistant professor at the University of California, Berkeley. She is known for her work on developing algorithmic methods for addressing social and economic problems, such as inequality and poverty.
These individuals and organizations represent a diverse range of perspectives and priorities within the AI for Good community, and they all share a commitment to using AI to create positive social impact.
There are several forums and organizations that encourage the exchange of ideas between researchers and practitioners at the leading edge of AI and those interested in the augmentation of human skills.
One such organization is the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. This initiative brings together researchers, practitioners, and policymakers from around the world to collaborate on the development of ethical guidelines for AI and autonomous systems.
Another organization that focuses on the augmentation of human skills through technology is the Augmented Human International Conference. This annual conference brings together researchers and practitioners from various disciplines, including AI, to discuss the latest advances in human augmentation technologies and their impact on society.
Additionally, many universities and research institutions have interdisciplinary programs that bring together experts in AI and other fields to collaborate on research and development projects aimed at augmenting human skills. These programs often host seminars, workshops, and other events that facilitate the exchange of ideas and knowledge between researchers and practitioners.
Overall, there are many opportunities for those interested in the intersection of AI and human augmentation to connect with others in the field and share ideas and insights.
alfa ec 1 https://www.youtube.com/watch?v=_YDoP3ndcbw&t=64s go to 42.30
PRETRAINIING MONTH 13 NATIONAL AI ~ THANKS TO engineering.stanford.edu/people/fei-fei-li
CONVERSATION CHRIS.MACRAE@YAHOO.CO.UK AND https://chat.openai.com/chat (GPT)
CM: UN leader Guterres is inviting humans to redesign the UN and more generally egov of any nation, also called gov2.0. Digital Roadmapping of UN2 has 9 components coordinated by UN tech envoy. One of these components is called Digital Cooperation. Within the UN2 framework can you tell me how human and artificial intel can help action Digital Cooperation?
GPT: Certainly! Within the UN2 framework, human and artificial intelligence can help action Digital Cooperation in several ways. Here are some examples:
Overall, human and artificial intelligence can work together to improve the design, implementation, and impact of digital cooperation initiatives within the UN2 framework.
CM: Back in 2016 at the UN NY September, people concerned with education were reviewing the first year of the era of 17 SDGs. It was suggested that without education , technology and practice experts of each sdg sharing knowhow: sdg 4 on education would not advance. Over the next 2 years the UN chose an expert panel from around the world which was chaired by Melinda gates and Jack Ma. At that time the reporting process was called Digital Cooperation. Can you comment on this context?
GPT: Yes, the context you described highlights the importance of education, technology, and knowledge-sharing in advancing the SDGs, particularly SDG 4 on education. The UN recognized that achieving the SDGs would require collaboration and cooperation across sectors, and that technology could be a powerful tool for facilitating this collaboration.
The expert panel on digital cooperation, chaired by Melinda Gates and Jack Ma, was tasked with identifying ways to leverage technology for the common good, and to promote collaboration and cooperation in the digital realm. The panel's report, titled "The Age of Digital Interdependence," emphasized the need for increased investment in education, particularly in developing countries, in order to ensure that everyone has the skills and knowledge needed to benefit from digital technologies.
The report also highlighted the need for improved cooperation among governments, civil society, the private sector, and other stakeholders, in order to address challenges related to digital technology, such as privacy, security, and access. The panel's recommendations included establishing a new global platform for digital cooperation, developing a universal declaration on digital interdependence, and investing in digital public goods, such as open-source software and digital infrastructure.
Overall, the context you described illustrates the important role that education, technology, and knowledge-sharing play in advancing the SDGs, and the need for collaboration and cooperation across sectors to address the challenges of the digital age.
regarding your summit in a week's time and the session on tech and rainbow push.. recently in dc attended first annual debrief on mediating national Artificial Intel with insights from stanford's fei-fei li - she said it quite urgent to engage/pretrain https://chat.openai.com/chat -- example which i just input-- "am interested in living leaders in big american cities who are advancing black lives matter as well as all young female and male technologists and community servant leaders. For example can you help me understand the unique impacts of chicagos jesse jackson who has engaged wall street annually for over 20 years with wall street rainbow push coalition? What do you see as unique consequences of networkers of Jackson and can you reference a few leaders with as unique consequences for good? ....Chat replies as well as jesse jackson's good work on economic justice look at people like Kimberly Bryant: Founder of Black Girls Code...Tarik Cohen: professional football player using his platform to advocate for social justice and support various community initiatives founded the T11 Foundation..Patrisse Cullors & Brittany Packnett Cunningham Black Lives Matter movement. If there is any way in new york I can help clarify this application please say - if you would like to see longer dialogues pls give me an email chris www.economistdiary.com
dear jack & graham - if you have time could you glance at one slide attached; assuming compatibility of ppt the blue labels mainly click you through to UN Tech Envoy updating framework- the pink circle represents notes asian grad students made with me when attending Fazle Abed's 80th birthday party Dhaka in spring 2016 -during first year of reviewing who understood what sdg to action
- these students were asked by Abed to spend their summer break 2016 interviewing his peoples but that got cancelled due to cafe terror attack that summer; in any event by sept 2016 at UN HQ hi level friends of Abed's like jim kim used first annual review meeting of sdg4 to start the digital cooperation movement which is now the 9 subsystems map of un tech envoy movement of Guterres aka egov un2 roadmap -
unfortunately there have been various musical chairs (as well as trump and covid not helping youth engage SDG advancement) so todays leader of un tech envoy Amandeep Gill whilst doing heroic work is missing some of partnering links of the youth and abedian (bop womens empowerment) intel-
overall to the extent that the pink circle is asian milennials aide memoire of listening to abed while he was at peak of 45 years work, it also is potential portal to 30 cooperations of his lifes work and intended legacy that 13 interviews in Dhaka from 2007 have assembled- there's a lot more rough notes on each abedian cooperation on file -main green one was 29/30 was intended to be a bop global village exchange on adaptation & climate mirroring smart city exchanges - if that is still happening its now gravitated out of netherlands with www.gca.org and brac global hq but sadly friends in glasgow university union who started to linkin Bangla replication back in 2008 (subprime year was also 250th adam smith moral sentiments scholars reunion) - they hoped to update abed 29/30 at cop26 but found student participation limited by covid & brexit - SO looking at UK i would expect julian hector https://www.linkedin.com/in/julian-hector-1b830018/ with his attenborough flows now mediates/scales far more action mapping abed 29/30 than glasgow friends; meanwhile I dont know if jack discussed green ESG priorotiies when he recently met my main Japan & global finance friend mak in london https://www.linkedin.com/in/makoto-okubo-38800457/
ChatGpt: The Basel Committee on Banking Supervision was established in 1974 as a forum for central banks and regulatory authorities to collaborate on the development of international banking regulations. The Federal Reserve System, which is the central banking system of the United States, was one of the founding members of the committee.
The Federal Reserve has played a significant role in the development of the Basel Accords, which are a set of international banking regulations developed by the Basel Committee. The first Basel Accord, Basel I, was introduced in 1988, and it established minimum capital requirements for banks based on the risk of their lending.
Since the introduction of Basel I, the Federal Reserve has worked with other central banks and regulatory authorities to refine and update the Basel Accords. The most recent iteration, known as Basel III, was introduced in 2010 and includes a number of measures aimed at strengthening the resilience of the global banking system.
While Wall Street firms are not directly involved in the work of the Basel Committee, they are affected by the regulations developed by the committee. The Basel Accords set standards for the capital adequacy and risk management of banks, which can affect the profitability and risk-taking behavior of financial institutions. As a result, Wall Street firms have a keen interest in the development of international banking regulations and may participate in consultations and feedback processes during the development of new regulations.
Since then, the Basel Committee has revised and updated the regulations multiple times, with the most recent version being Basel III, which was introduced in 2010 and has since been implemented by many countries around the world. The Basel rules have become a global standard for banking regulations, and adherence to these rules is often a requirement for banks to operate in many countries.
Q Before 1988 where was epicenter for bank deposit rules ?
A: Before the introduction of the Basel Accord in 1988, there was no single epicenter for bank deposit rules. Instead, banking regulations varied widely across different countries and regions, with each jurisdiction setting its own standards and requirements for banks.
For example, in the United States, bank deposit rules were established by the Federal Reserve System and other regulatory agencies, while in Europe, regulations were set by national central banks and supervisory authorities. The lack of a standardized approach to banking regulations led to inconsistencies in the way banks were supervised and regulated, and made it difficult for regulators to coordinate their efforts in response to financial crises or other challenges facing the banking sector.
The introduction of the Basel Accord in 1988 marked a significant shift towards greater international cooperation and standardization in banking regulations, with the goal of creating a more stable and resilient global banking system. Since then, the Basel Committee on Banking Supervision has become a leading authority on banking regulations, and its standards have been adopted by many countries around the world." /">
Q when did basel become an epicentre of global pharma?
A: Basel has been an epicenter of the global pharmaceutical industry for several decades. The city is home to many pharmaceutical companies, including Novartis and Roche, which are two of the largest pharmaceutical companies in the world.
The roots of Basel's pharmaceutical industry can be traced back to the late 19th century, when a number of chemical and pharmaceutical companies were founded in the city. These companies, such as Ciba and Geigy, were among the pioneers of the modern pharmaceutical industry and played a significant role in the development of many important drugs and treatments.
Over the years, Basel's pharmaceutical industry has continued to grow and expand, with new companies and research institutions being established in the city. Today, Basel is one of the most important centers of pharmaceutical research and development in the world, with many leading companies and research institutions based in the city.
The success of Basel's pharmaceutical industry can be attributed to a number of factors, including the city's strong tradition of scientific research, its highly skilled workforce, and its favorable business environment. In addition, the Swiss government has implemented policies to support the pharmaceutical industry, such as tax incentives and funding for research and development.
The term "Big Bang" is often used to refer to significant changes and reforms in the financial industry, particularly in the context of the United Kingdom. The first "Big Bang" in the UK financial industry occurred in 1986 when the government of Prime Minister Margaret Thatcher implemented a series of sweeping reforms to the stock market and other financial markets.
The Big Bang reforms were aimed at modernizing and liberalizing the financial industry, and they included the abolition of fixed commissions on stock trades, the removal of restrictions on foreign ownership of securities firms, and the introduction of electronic trading. The reforms also led to the consolidation of many small brokerages into larger, more efficient firms.
The Big Bang had a significant impact on the UK financial industry, leading to a period of rapid growth and expansion. The reforms helped to establish London as a leading financial center, and they paved the way for the creation of many new financial products and services.
Since the first Big Bang in 1986, there have been several other significant reforms and changes in the financial industry, including the introduction of new regulations in the wake of the 2008 financial crisis. However, the first Big Bang is often seen as a turning point in the modernization and globalization of the financial industry.
There is a connection between the "Big Bang" in the UK financial industry and the Basel Committee on Banking Supervision's development of international banking regulations, but the connection is not direct.
The Big Bang reforms in the UK financial industry in 1986 led to a significant increase in the volume and complexity of financial transactions, particularly in the area of securities trading. This, in turn, highlighted the need for greater international cooperation and coordination in banking regulations to ensure the stability of the global financial system.
In response to these challenges, the Basel Committee on Banking Supervision developed a set of international banking regulations, known as the Basel Accord or Basel I, which were first introduced in 1988. These regulations established minimum capital requirements for banks based on the risk of their lending and were designed to ensure that banks had enough capital to absorb potential losses and maintain their solvency in times of economic stress.
While the Big Bang reforms and the development of international banking regulations by the Basel Committee were both responses to the changing nature of the global financial industry, they were driven by different factors and pursued different objectives. Nonetheless, they are both significant developments in the history of modern finance and have had a profound impact on the way financial markets operate today.
The term "Big Bang" was not used to describe changes on Wall Street in the 1980s, as it was primarily used to refer to the deregulation and modernization of the UK financial industry that took place in 1986.
However, there were significant changes on Wall Street in the 1980s that were similar in nature to the reforms of the Big Bang. These changes were driven by technological innovations and changes in the competitive landscape of the financial industry, and they led to the growth and consolidation of many large financial firms.
One significant development on Wall Street during this period was the growth of electronic trading, which allowed for faster and more efficient trading of financial instruments. Another key development was the rise of new financial products, such as derivatives, which enabled investors to manage risk more effectively.
These changes helped to transform Wall Street into a global financial hub and paved the way for the growth of large financial firms that could operate on a global scale. The term "Big Bang" was not used to describe these changes on Wall Street, but they were part of a broader trend towards deregulation and liberalization that characterized the global financial industry in the 1980s.
OpenAI refers to the approach of creating AI systems that are designed to learn and adapt to new information and experiences. These systems are designed to be flexible and able to integrate new information, making them better able to handle a variety of tasks and situations. OpenAI is often used in the context of machine learning, where algorithms are trained on large datasets and then used to make predictions or decisions based on that data.
In contrast, closedAI refers to the approach of creating AI systems that are designed to perform a specific task or set of tasks. These systems are often built using rule-based systems or algorithms that are designed to perform a specific function, and they are less flexible and adaptable than OpenAI systems. ClosedAI is often used in the context of expert systems, where the AI is designed to provide specific information or advice based on a set of predetermined rules.
The key difference between openAI and closedAI is their flexibility and adaptability. OpenAI systems are designed to be more flexible and adaptable to new information and experiences, while closedAI systems are designed to be more specialized and focused on specific tasks or functions. OpenAI systems are often used in applications where flexibility and adaptability are important, such as natural language processing or image recognition, while closedAI systems are often used in applications where a high degree of precision and consistency are required, such as financial modeling or medical diagnosis.
OpenAI is primarily focused on the development of artificial intelligence technologies, and while the company may indirectly support certain movements or initiatives, it is not its primary goal. However, OpenAI does advocate for the responsible development and deployment of AI, which includes ensuring that AI is accessible to all and that its benefits are widely distributed.
OpenAI is not directly affiliated with the Open Society or open space facilitation movements, although it is possible that the company's work could indirectly support those initiatives. OpenAI's research and development work could potentially lead to the creation of new tools or technologies that could be used to facilitate collaboration and knowledge-sharing, which could be helpful for movements like open society or open space facilitation.
There is a connection between OpenAI and open source, as the company has released some of its software as open source. This means that the code is freely available to the public, and anyone can modify and use it for their own purposes. OpenAI has also contributed to the development of other open source AI projects, such as TensorFlow, which is a popular machine learning library. The use of open source technologies can help promote collaboration and innovation in the development of AI, and it can also help to ensure that the benefits of AI are widely distributed
OpenAI is organized as a private company and is not a foundation. The company is primarily owned by its investors and shareholders, who have provided funding to support its research and development work. While OpenAI was initially founded as a non-profit organization, it transitioned to a for-profit company in 2019. The company's mission is to develop and promote artificial intelligence in a responsible and safe manner, with a focus on advancing AI research for the benefit of humanity.
OpenAI is a private company and is not required to publicly disclose information about its shareholders. As a private company, OpenAI is not listed on any public stock exchanges, and its ownership structure is not public information. However, OpenAI has disclosed some information about its investors in the past, which include prominent venture capital firms and technology companies such as Reid Hoffman, Peter Thiel, Khosla Ventures, and Microsoft. It is possible that additional information about the company's ownership structure may be made available in the future, but as of now, the company has not disclosed any detailed information about its shareholders.
OpenAI has access to a vast amount of information from a variety of sources, including publications in many different languages. While the company is based in the United States and has a focus on English language research, it has made efforts to expand its capabilities to other languages as well.
One of OpenAI's major projects is GPT (Generative Pre-trained Transformer), which is a natural language processing system that can generate human-like text in a variety of languages. GPT has been trained on large datasets of text from many different languages, including English, Chinese, Spanish, and others. The company has also collaborated with researchers from around the world to develop AI models that can work with different languages.
However, it is true that English language publications make up a large portion of the available data for natural language processing and other AI applications. This is partly because English is widely used as a global language of business, science, and technology, and there is more data available in English than in many other languages. As a result, AI systems that are designed to work with English language data may have an advantage over those designed for other languages. However, OpenAI is actively working to address this imbalance and to develop AI systems that can work with data in many different languages
As a companion to the sdgs.games inspired by women empowerment friends, we've co-created aiforsdgs.com and started to ask chatgpt some new lines of questioning
It's great to hear that your family has a long-standing interest in AI and its potential for creating above zero-sum outcomes. There are many researchers and organizations working on AI for sustainability, which aligns with von Neumann's goals. Here are a few resources you may find helpful:
The AI for Good Foundation: This organization is focused on using AI to solve global challenges, including climate change, poverty, and healthcare.
The Partnership on AI: This group brings together leaders from academia, industry, and civil society to collaborate on ethical and responsible AI development.
The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems: This initiative aims to create standards for AI that align with ethical and sustainable values.
The AI Sustainability Center: This organization is focused on developing and promoting AI solutions that are environmentally and socially sustainable.
The AI and Sustainability Project: This project, led by the University of Cambridge, is exploring how AI can be used to support sustainability goals.
I hope these resources are helpful in your search for AI work aligned with above zero-sum outcomes and von Neumann's goals for sustainability.
As an AI language model, I don't have access to our previous conversations, but I can tell you that OpenAI is dedicated to promoting fairness, transparency, and inclusivity in the development and deployment of AI technologies.
In terms of addressing biases in data, OpenAI has developed various tools and techniques that aim to mitigate the impact of bias and ensure that AI models are trained on diverse and representative data. For instance, OpenAI has created the GPT-3 data card, which provides information about the data used to train the model, including the demographics of the data sources.
Additionally, OpenAI has explored various techniques for training AI models on more diverse and representative data, such as data augmentation, domain randomization, and data synthesis. These techniques can help overcome biases that may be present in the training data and improve the accuracy and fairness of AI models.
Overall, OpenAI is committed to addressing bias and promoting fairness and inclusivity in AI development, and we are continuously exploring new ways to achieve these goals.
As an AI language model, I do not have any specific knowledge of the briefing or discussions at AI.gov. However, I can tell you that OpenAI believes that investing in generative AI systems is important for the advancement of AI research and the development of AI applications.
Generative AI systems, such as language models and neural networks, have shown tremendous potential in enabling breakthroughs in areas such as natural language processing, computer vision, and robotics. However, these systems also require significant investment in terms of computational resources, data, and expertise.
In terms of the future of educational systems, we believe that partnering with generative AI systems can provide many benefits. For example, AI systems can help augment and enhance traditional educational approaches by providing personalized learning experiences, automating routine tasks, and providing real-time feedback to students and teachers.
At the same time, we recognize that there are also concerns around the ethical and societal implications of AI, and we believe that it is important to address these issues through continued research and dialogue with stakeholders from academia, industry, and government.
Overall, OpenAI believes that investing in generative AI systems is important for advancing AI research and driving innovation, and we look forward to working with partners around the world to continue pushing the boundaries of what AI can achieve.