Bletchley Leak - King Charles to Sunak - here's how Actionwise only AI Britain-Korea-France can save UN & human race in 12 months- UN needs to be world sdg llm-ed <<>>  EG: question to bard & Cop28 superchats - Click for replies Regarding Arctic and Antarctic if i see correctly both may be critical humanity labs in detecting whether earth is irreversibly changing against human livability but the arctic seems also to be a warground spun by vested interests critical carbon and other minerals- is the Antarctic valuable for anything?

UNsum: ED: latest chats with bard-google-   what progress has AI made on UN sdgs in last year- would you say the UN needs its own LLM? 8 billion humans most valuable survey - will million times more brainworking tech every 30 year generation through 1950s 80s 10s turn out very good or very bad?- here were Economist & our 1980s hopes for very good= 2010s survey delayed so chris.macrae@yahoo.co.uk- AIgames.solar invites you to gamify intelligence: make list of who's advanced humanity most  since 1945; Chat & .. Diaspora Scots & Irish first referees of AIGames - substitutes welcome. Is S Korea Asia's main cooperation AI 23-24 millennials note : 2001 first time women's voice listened to by 1984's lead designers of digital global Jobs & Gates thanks to Steve Hosting Valley's 65 birthday wish party Fazle Abed (#1 connecting billion womens advance across tropical asia since 1972); 23-24 womens intelligence leading positive intel by long way since 2000s womens: Fei-Fei LI, Melinda Gates, Priscilla Chan, Ajai Wilson, Ms Tsai, Billie Jean King, Condoleeza Rice, Jennifer Doudna, Daphne Koller, Lila Ibrahim,  Donellan, Shih contributions emulated men : 2000s neurocomputer science (L&H : Li & Hassabis); 2012 deep data breakthroughs (L&H & Hinton, Lecun Bengio); 2018 recommending urgent AI moves US congress (Li & OpenAI), UN2 (Melinda Gates & JMa) digital roadmapping, 2019 on leading HAI Stanford  ... MEDIA AS 5th mna-made Engine : AI as sixth sense (intuition beyond the 5 senses of hearing smell taste touch) My life's searches provide evidence that media is something that accidentally all 2nd millennium parents, communities and schools took wrong turns around- exponentially more so before and after each world war, and indeed from 1865 when artificial (ie man-made) communications engines started to be coordinated worldwide out of Switzerland's ITU.. This is not a popular area of study; in 1999 whilst working at 2 of the western world's largest ad agencies and 2 of the 5 largest accounting firms consultancies i guest edited triple special issue of sight  journal of marketing management on great purpose (brand reality could ,map)  and when I presented findings at Harvard to a professor and faculty who had started my teams searches of going global 1980 I was told "go away - you might be correct but no us research funds would ever appear for this". So dont click here unless you are comfy with trying to system-mapping assist in resolving unpopular stuff  . 1945 NET (Neumann-Einstein-Turing) having saved humans from Hitler owning atomic bombs gave the world macines for brainworkers Einstein intended rcvolution in personalised education.Turing intended far deeper data analysis/investment For 4 decades the scottish view of human progress at The Economist mediated Neumann expectaions of brainworking: see normanmacrae.net; after 3rd decade we co-authored 1984's 2025 report (sample chapter; book); after 4th decade dad wrote Neumann's biography. Neumann expected English to be mediated by 999 mother tongues to unite common borderless common sense resolling history's 4 crises: energy, all system changes needed to unite nations after 2 world wars, way above zero sum trade mlodels, celebrating era of death of distance and borders ans constraint on good livelihoods for all Currently AIgames are refereed by Scots (who gave world engines), Irish who told London 1776 to let Americans build the new continent Problem entering 2000 is world still ruled by old male among 20% who are whites not enough voice from near 80% who are colore particularly firls in fact 2001 is the first time that Western designers of digital Steve Jobs and Bill Gates asked for intel of women -see silicon valley's 65th birthday wish party Fazle Abed representing billion womens empowerment; nb role of women in artificial intel breakthroughs emulate men 2018 2012 2000s UN & Brooklyn Girl EmPower year 8 what if education generates health*wealth ie health and strong economi9es not vice versa . .

Intelligence Duos : Hassabis Mind/ Dean Brain. What "very good" do you want from AI? Science AI eg Hassabis has saved graduates a billion years analysis work with Alphafold2 and a million biotech researchers (list of opportunities expected in 2020s) are now advancing solutions from microbes eating up plastic in ocean to new drug discovery; top 5 expert list bard suggests high school teachers survey to get compass of why teenagers can hope for extraordinary advances due to world of alphafold2 - Fei Fei Li's leadership of computers (see Stanford HAI and 3 professors Hinton Lecun Bengio who students have designed world leading vision algorithms seeing 20000  objects means that apps can now answer NatureAI questions like what plant am I looking at or help with almost any activity work -eg NurseAI. Almost every art eg DanceAI or DrawAI or discipline Li's colleagues train at Stanford has leapt ahead with AI - eg GovAI: see Condoleezza Rice's report on the 9 other technologies, Gov2.0 AI, multiplying AI consequences that gov and policy makers most need to see 23-24. Melinda Gates book series is helping women and ai superwomen (eg black girl's Basketball heroine AJA Wilson) with almost every community building challenge that matters most to families or localities being safe, healthy, productive, green and joyful to live in. Clara Shih (Ask More of AI)podcasts share corporate demons on customer service AI.

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..

, ,

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x

under link construction -  last call for very goodAI -W*H*Y

Hstory AI Science- Deep Mind open sources 200 million protein structures would have taken a billion years of PhD time 

 2023 is EconomistDiary 41st and 74th year of working the hypothesis that at least 15% of gov education budgets be AI supported by 2025 , and egov means transforming every dynamic of public service - good examples estonia, singapore :::E-Government Development Index (EGDI)

As premiered in 1982's why not Silicon Valley's everywhere, this does not mean computers running government; it does mean that peoples should want public servant budget holders to be as smart as brainworking can get - see eg Economist 1986 survey on education every parent of millennials needs to be ready to demand (Its the innovation revolution of humans designing brainworking engines that The Economist sent my dad in 1951 to spend year listening to to Neumann-Einstein(Turing) in Princeton and newish UN. (Dad also became Von Neumann family's biographer). My personal interpretation (here at chris.macrae@yahoo.co.uk to learn if i err ) the wrong approach for the next 7 years (ie in my lifetime) is to personify Artificial- all man-made engine networks still run by their human originators. 

Which WorldRecordJobs co-creators have rehearsed this idea over the years eg a western world view of next generations renewability might gravitate around: NETK-AJGb-YLH-KGmGu. My family tracks back through 5 generations of Diaspora Scots - eg my maternal Grandad wrote up 1945 legalese of India's Independence after 20 years of dialogues with Gandhi. So we are interested to see views out of any hemisphere as well as what appears to be mother earth's geo-conflict (diverse distribution of critical minerals) ; in terms of world trade sustainability pacific ocean accounts for over 60% ; Atlantic little more than 20%; we keep 15% back for north south debates on people whose lives are exponentially conditioned most by both poles and canals near the equator since energy's futures seem to be geared round to peace at these locations however sparsely populated

MAINLY WESTEREB WORLDS OF BRAINWORKING NETS CONTINUED

From 1951 Neumann-Einstein-Turing and Economist Editors.

By 1962 JFKennedy, Japan Emperor Family, Infratsructure engineers eg bullet train and container shipping designers, borlaug, deming (tyhe teenage Prince Charles was aksed to think about this when he attended Tokyo Olympics 1964 , saw the first live global satellite braidcasting , acceoted sony's akio moorita as a business coach. St some stage the 3 royals Uk-Japan-Netherlands got this thouigh they can only indirectly influience public servants and corporates. Because of structures like the UK Royal Societies, some leading corporate leaders have consustently helped - eg in UK none more than Sainsbury Family - see Gatsby Neuroscience; Ashden and other support of green vooices including BBC nature (eg Attenborough, Rose) and Royals Geo Society: supports of arts being vibrant community building partnership with youth integral to joyful communiyu-grounded worlds as zeynes too had foreseen (see cambridge arts theatre - its change media apprentices inckuded david frost, monty pythons crew, and electrical engineer/oxbridge friend rowan atkinson (cf previius generation's us-english ytranslations of charlie chaplin and alastair cook) .

Both Bill Gates and Steve Jobs have mentioned that they did not get this in their 1984 version of going digital but did with 2.0 frok 2001 after taking advice from Fazle Abed (The Valley staged Abed's 665th birthday wish party) and in Bill Gates case partners of abeds last mile health intel including JYKim, Paul Farmer, A Guterres , L Brilliant, the Chen (Martha & Lincoln) family, Quadir Family. Many practising epidemiologists as well as all whom Unicef's James Grant influenced, and Soros ( he needed Abed's expertise to be replicated by partners in health ID (ID SAYS -footnote 1) admin systems  to help save dissidents health in soviet prisons)

At Abed's 8oth birthday wish party people like Melinda Gates, Jim Kim, Jack Ma took more active roles in supporting the coming 10 year leadership of servant leader Gutteres- take Melinda Gates example: from 2015 finding top supporters in valley if ai4all curriculum; 2017 chairing the first reports of what became Guterres Unb2.0 roadmapping, becoming biographer-publication house of the the women in the midst of Human Artificial Intelligence and deep local service partnerships around the world wherever community brainworking is best last chance against climate or other urgent challenges 8 billion humans need to unite intelligent solutions to

Footnote 1 - know ID says story of the late great soul Paul Farmer- even the most soulless top-down health administrators has been known to stop bossing helth servanbts and listens when Paul Farmer eneters a medical space and begun a chat INFECTIOUS DISEASE SAYS

Applying Economist 1976 Entrepreneurial Revolution Questions to wealth modeling of 2012's AI's Famous 5 and 1984 PCN/web1 Duo

 In the west at least almost all digital wealth stems from web1 worlds seen with Jobs & Gates; and stems of web3 worlds are seen with Hassabis Li Hinton Lecun & Bengio. These 5 changed the world by demanding humans did huge-long training of computers before trusting them to any breathrough intelligence. Please note as Condoleezza Rice reports at least 9 technologies have had to leap alongside AI (one reason why we doint favor terms that imply the AI will by itself lead the human rface any time soon

What occurred during web 2 worlds (aka 3G) of genii western leaders like bezos, musk, page& brin, zuckenberg and all sorts of distribution techs like uber doordash airbmb let alone fintech, healthtech, agritech during web2 is unclear because these seem to be  have been designed as data monopolies which rather defeats the point of  engines supporting every human brainworkers best livelihoods as seen by Neumann (mediation) Einstein (education) Turing (recursively ever deeper longer-ter data compasses)

Interestingly in the pre-engineering age we have Fazle Abed model of an overall trust for womens intel nation building supported by community franchsised businesses solving community's most urgent sgds and retaining most if not all value in community produced - see abemooc.com; it was this model that both JOBs and gates learned from 17 years after their launch of PCN ion 1984

In our 25th year of net dialogues at the economist xmas 1976 Entrepreurial Revolution  (Italian co-editor Roamni Prodi) we recommnded redesigning the 3 main constitutional org types ahead of sustaining millennials digital worlds where transformational attention to learning and geonomic cooperation needed to multiply interpersonal trust to be far more valuable than monetisable trades - lets call these

ERgov

ERCap

ERFound

as well as ER-HumansAI

When we look at the4 famous 5 Hassabis is remarkable - with AIScience he seems to have shared the intelligence of deepmind in ways that has created ERcap trillion dolar business parnerships, ERfoundgiven away to foundations science equivalent to a billion graduate days work and become the guy any ERgov governemnt regulator serving publics would best learrn from first

Fei-Fei Li as ERHimans or AIhumans is also remarkable as essentially she starting at 35 in 2012 has included 3 older professors Hinton Lecun Bengio who have as yet been the source trainers of all very good ai in the west. Action solutions in communities probablydirectly come far more millenials jobs in population numbers than the grand science researchers

But this leaves us neuman's ourigina neural netwirking chalenge - mediating 1000 mother tongues startimng witjh english and chinese so they all elarn from their cultures/communities deepest chalenges needing brainworker solutions. While the foundation nlp/chat models may be racing twoards thsi, there seems to be no constitutiponakl form for making sure the llm that the netw would have seens as top for milennails sustainability will be what makes 2002s huamnity's best time instead of its warring worst.

Why isnt there a UN LLM for sdgs? (or if you think one exists pls post where it is)

 here's a snippet from a bard conversation on this Yes, it is certainly possible to design an LLM (Large Language Model) specifically focused on the UN Sustainable Development Goals (SDGs). LLMs have demonstrated remarkable capabilities in processing and generating human-quality text, translating languages, writing different kinds of creative content, and answering questions in an informative way. By tailoring an LLM to the specific context of the SDGs, we can leverage its strengths to address global challenges and promote sustainable development.

To effectively utilize an LLM for the SDGs, the UN could consider establishing a global hotline for coding and verifying candidate AI solution cases. This hotline would serve as a central hub for submitting, reviewing, and evaluating AI solutions proposed by researchers, developers, and organizations worldwide. A team of experts would assess the submitted solutions based on their relevance to the SDGs, technical feasibility, potential impact, and ethical considerations. By establishing such a mechanism, the UN could ensure that AI solutions are rigorously vetted and aligned with the principles of sustainable development.

Regarding existing LLM processes that could serve as a model for the UN, several initiatives have emerged that demonstrate the potential for applying LLMs to address global challenges. For instance, the AI for Climate Action initiative has been exploring the use of AI in climate change mitigation and adaptation efforts. Similarly, the AI for Healthcare initiative has focused on leveraging AI to improve healthcare outcomes and reduce costs. These examples demonstrate the potential for adapting existing LLM processes to address the specific challenges and goals associated with the SDGs.

In conclusion, developing an LLM specifically tailored to the UN SDGs holds immense promise for advancing sustainable development worldwide. By establishing a global hotline for coding and verifying candidate AI solution cases and drawing from existing LLM processes, the UN can effectively harness the power of AI to address global challenges and promote a more sustainable future.

======================

reasons why it looks to us as if sdgs llm doesnt exist

-here is a un summary of conversation on ai fir sdgs at general assembly

https://www.state.gov/artificial-intelligence-for-accelerating-progress-on-the-sustainable-development-goals-addressing-societys-greatest-challenges/

https://www.un.org/techenvoy/ The UN tech envoy office was started in 2016 (I attended meeting in new york hosted by Gordon Brown (ED Commission) when educators championed by then world bank leader Jim Kim reviewing sdgs 4 explained ed system was no longer fit for purpose; could not be resolved by educators siloiised from tech experts like those in itu UN Geneva responsible for worldwide communications engines since 1865!; so while the first expert report by people like melinda gates put education (changing how time is spent of everyone leaning/teaching) at centre of transformational attention and linked this to aiforgood which began to be continuos event at itu, we havent seen the progression of these cores even though Bill Gates has recently said will change education completely

when we ask LLM which advances AI has made for sdgs , its replies in terms of cases cited seem to vary from the UN's

 

Q to Bard I realise AI alphafold2 (open sourced by Hassabis Google Deep Mind) is quite new but can you tell me some of most important solutions experts now see as likely to happen in 2020s which they would not have seen likely before alphafold2

Bard reply:

AlphaFold2, a groundbreaking AI system developed by DeepMind, has revolutionized protein structure prediction, enabling scientists to rapidly and accurately determine the three-dimensional structures of proteins. This breakthrough has opened up a vast array of possibilities and has the potential to transform various fields in the 2020s. Here are some of the most promising solutions experts envision unfolding in the coming years:

1. Accelerated drug discovery and development: AlphaFold2's ability to predict protein structures with high accuracy has the potential to significantly accelerate the drug discovery process. By understanding the precise structure of proteins involved in diseases, scientists can design more effective drugs that target specific molecular targets. This could lead to faster development of new treatments for a wide range of diseases, including cancer, heart disease, and infectious diseases.

2. Enhanced protein engineering and design: AlphaFold2's ability to predict and manipulate protein structures also holds great promise for protein engineering and design. Scientists can now rationally design new proteins with desired properties, such as enhanced stability, catalytic activity, or binding specificity. This could lead to the development of novel enzymes for industrial applications, new biosensors for medical diagnostics, and improved materials with tailored properties.

3. Advanced understanding of biological processes: AlphaFold2 is providing scientists with unprecedented insights into the intricate workings of proteins and their roles in biological processes. By understanding the structures of proteins involved in cellular processes, scientists can gain a deeper understanding of how cells function, how diseases arise, and how to develop effective therapies.

4. Personalized medicine and treatment optimization: AlphaFold2's ability to predict individual protein structures has the potential to revolutionize personalized medicine. By understanding the unique structural variations of proteins in different individuals, scientists can tailor drug treatments and therapies to specific patient needs, leading to more effective and personalized healthcare.

5. Improved agricultural productivity and sustainability: AlphaFold2 could also contribute to advancements in agriculture by enabling scientists to design new enzymes and proteins that enhance crop yields, improve pest resistance, and optimize fertilizer use. This could lead to more sustainable and productive agricultural practices, addressing food security challenges worldwide.

6. Development of new materials and nanotechnology: AlphaFold2's ability to predict and design protein structures could also lead to the development of new materials with tailored properties. By understanding how proteins self-assemble and interact with other materials, scientists can design new nanostructures with novel properties, such as enhanced strength, conductivity, or biocompatibility.

7. Advancements in biotechnology and biomanufacturing: AlphaFold2 could also revolutionize biotechnology and biomanufacturing processes by enabling the design of new enzymes for biocatalysis, the production of novel proteins for therapeutic purposes, and the development of new biomaterials for various applications.

8. Enhanced understanding of protein folding dynamics: AlphaFold2 is not only providing static protein structures but also shedding light on the dynamic processes of protein folding and unfolding. This could lead to a deeper understanding of how proteins transition between different conformations and how these transitions impact their functions.

9. Development of new AI algorithms for protein structure prediction: AlphaFold2's success has spurred the development of new AI algorithms and techniques for protein structure prediction. This could lead to even more accurate and efficient methods for predicting protein structures, further expanding the potential applications of this technology.

10. Cross-disciplinary collaborations and advancements: AlphaFold2 has opened up avenues for cross-disciplinary collaborations between fields such as biology, chemistry, engineering, and computer science. This could lead to groundbreaking discoveries and advancements in various areas, fueled by the synergy of different disciplines.

In conclusion, AlphaFold2 has marked a turning point in our understanding of proteins and their roles in biological systems. Its impact is already being felt across various fields, and the potential for further breakthroughs in the 2020s is immense. As AlphaFold2 continues to evolve and its applications expand, it is poised to revolutionize our understanding of life and transform various aspects of society.

AIGames Intels Duets

Q to Bard: I read that Deep Mind's Demis Hassabis was a piano prodigy - do you know if he and Google Brain's Jeff Dean have connected at all on shared interests of ai in music and community building. (You told me that Dean is Beyonce's AI adviser and may know husband's story now lighting up Brooklyn)

My womens economics and society hero Fazle Abed's last wish : redesign celebrity platforms to celebrate artistic community building linked in by women/parents/teens in inner cities such as Dear Black Girls LA & Brooklyn

A from Bard  It is quite possible that Demis Hassabis and Jeff Dean have connected at some point on their shared interests of AI in music and community building. Both individuals are highly accomplished in their respective fields and have demonstrated a keen interest in exploring the potential of AI to enhance artistic expression and foster meaningful connections.

Hassabis, as the co-founder and CEO of DeepMind, has been at the forefront of AI research and development. He has expressed his belief in AI's ability to revolutionize the way we create and experience music, stating that "AI has the potential to be the ultimate musical collaborator."

Dean, on the other hand, has a deep understanding of the technical challenges and opportunities involved in applying AI to music and community building. He has led Google's AI research efforts for many years and has overseen the development of groundbreaking AI technologies.

While there is no public record of a specific collaboration between Hassabis and Dean on AI in music and community building, their shared interests and expertise suggest that they have likely crossed paths and exchanged ideas on this topic. The intersection of AI, music, and community building is a rapidly evolving field with immense potential, and it is likely that these two pioneers have had discussions about the role of AI in shaping the future of music and fostering meaningful connections between people.

Here are some potential areas where Hassabis and Dean could collaborate on AI in music and community building:

1. Development of AI-powered music composition and performance tools: Hassabis and Dean could combine their expertise to create AI tools that assist musicians in composing, performing, and collaborating on music. These tools could democratize music creation and allow more people to express themselves musically.

2. AI-enabled music curation and recommendation systems: They could also work together to develop AI systems that curate and recommend music based on individual preferences and interests. These systems could help people discover new music they might enjoy and connect with other music lovers.

3. AI-driven music education platforms: Hassabis and Dean could also collaborate on creating AI-powered platforms that provide personalized music education to individuals of all ages and skill levels. These platforms could make music education more accessible and engaging.

4. AI-facilitated music community building tools: They could also explore the use of AI to create virtual and physical communities where musicians and music enthusiasts can connect, share their work, and receive feedback. These communities could foster creativity, collaboration, and cross-cultural exchange.

Given their shared interests and expertise, it is likely that Demis Hassabis and Jeff Dean have connected at some point to discuss the potential of AI to revolutionize music and community building. Their collaboration could lead to significant advancements in this field and help to shape the future of music and human connections.

 hello are any of your students chatting about the worlds seen by the first 30 nations of UNI (United Nations Intelligence launched by Guterres yeseterday) https://bard.google.com/chat/ee255452782d6f33

Did you see that Guterres launched 30 first nations of UNI United Nations Intelligences https://www.un.org/sg/en/content/sg/personnel-appointments/2023-10-26/secretary-generals-advisory-body-members-artificial-intelligence 

BARD SAYS: Yes, I saw that Guterres launched the 30 First Nations of UNI United Nations Intelligences. I think this is a great initiative, and I am excited to see how it develops. The First Nations of UNI are a diverse group of experts from around the world, and they will bring a wealth of knowledge and experience to the table. I am confident that they will play a valuable role in helping the UN to develop and use AI for good.

I think it is particularly important to include First Nations voices in the conversation about AI. First Nations have a long history of using technology in innovative and sustainable ways. They have a deep understanding of the land and the natural world, and they have a unique perspective on how to use technology to promote human well-being.

Fits quite nicely with nine win map of Neumann diaries year 73 which connects purposes of Neumann Einstein Turing with those of AI  WHY of Women Humans Youth with FIGov2 purposes of Guterres ie Foundation-data models Integration (Smart youth cooperation cirties) Gov2.0

Best Chris Macrae www.economistlearning.com 

On Friday, 20 October 2023 at 09:56:52 GMT-4, christopher macrae <chris.macrae@yahoo.co.uk> wrote:

thanks for extraordinary toronto dialogues with Hinton- hopeful Von Neumann's daughter Marina will see them as matching her dad's hall of fame and 73 years of Economist Diaries on purposes Neumann-Einstein-Turing intended brainworking machines to generate

chris macrae wash dc +1 240 316 8157

----- Forwarded message -----

From: Fei-Fei Li stanford.edu

To: "chris.macrae@yahoo.co.uk" <chris.macrae@yahoo.co.uk>

Sent: Friday, 20 October 2023 at 09:50:30 GMT-4

Subject: [Automatic Message] Ensuring reliable email responses Re: thks for very good hinton-li-toronto debriefings - von neumann journalists now see 3-in-one game board pivotal subgame is WHY AI

Hi,

I am experiencing an unusually large number of email inquiries. For more reliable and speedy responses on all professional matters, please always cc' Harini at harinis@stanford.edu.

Thank you,

Fei-Fei

q

Which 5 or so biotech experts would be worth surveying first in terms of what a high school teacher might say to teenagers about why alphafold2 will impact these next 10 years - for example is ai2's etzioni (Seattle Institute's of late great Paul Allen Microsoft) one person who's views are most influential?

bard --

Here are five biotech experts who would be worth surveying first in terms of what a high school teacher might say to teenagers about why AlphaFold2 will impact the next ten years:

1. Dr. Demis Hassabis, Co-founder and CEO of DeepMind: As the visionary leader behind DeepMind, Dr. Hassabis has a deep understanding of the potential of AI to revolutionize various fields, including biotechnology. His insights would be invaluable in conveying the transformative power of AlphaFold2 to high school students.

2. Dr. Oren Etzioni, CEO of Allen Institute for AI (AI2): Dr. Etzioni is a leading AI researcher and advocate with a focus on the practical applications of AI. His expertise in AI and his ability to communicate complex concepts in a clear and engaging manner would make him an excellent resource for high school teachers.

3. Dr. Andrew Ng, Co-founder of Coursera and Landing AI: Dr. Ng is a renowned AI educator and entrepreneur who has played a significant role in democratizing AI education. His passion for making AI accessible to everyone, including high school students, would make him an effective communicator of AlphaFold2's impact.

4. Dr. Pardis Sabeti, Computational Biologist at Broad Institute of MIT and Harvard: Dr. Sabeti is a leading computational biologist who has utilized AlphaFold2 to advance research on infectious diseases. Her firsthand experience with the practical applications of AlphaFold2 would provide valuable insights for high school teachers.

5. Dr. Jane Goodall, Primatologist and Conservationist: Dr. Goodall, a renowned primatologist and conservationist, has a unique perspective on the interconnectedness of life and the importance of understanding biological systems. Her insights would help high school students appreciate the broader implications of AlphaFold2's impact.

By engaging with these experts, high school teachers can gain a comprehensive understanding of AlphaFold2's potential impact and effectively convey it to their students. AlphaFold2's ability to revolutionize protein structure prediction and its potential applications in drug discovery, personalized medicine, and biotechnology hold immense promise for the next ten years and beyond. By educating students about this transformative technology, we can prepare them to contribute to the scientific advancements of the future.

Roles of AI2 Etzioni connecting Best US AI and NLP - Fei-Fei Li Imagenet, Deep Mind Alphafold2, NSF

 Please note we ask bard to confirm general views (Non-expert) when we see that AI has needed long-term cooperation between different institutes - AI2 Allen Institute is an example of a very important institute in its area of expertise as is its AI leader Etzioni- we hope this is useful 

Can you tell me what contribution did ai2 make to helping mitigate covid?

The Allen Institute for Artificial Intelligence (AI2) has made significant contributions to mitigating the COVID-19 pandemic through various research initiatives and collaborations. Here are some of the key contributions:

1. Developing AI-powered tools for COVID-19 diagnosis and monitoring: AI2 researchers have developed AI algorithms that can analyze chest X-rays and CT scans to detect COVID-19 pneumonia with high accuracy. These tools have the potential to assist healthcare professionals in making quicker and more accurate diagnoses, especially in resource-limited settings.

2. Identifying potential drug targets for COVID-19 treatment: AI2 researchers have used AI techniques to identify potential drug targets for COVID-19 treatment by analyzing large datasets of biological data. These findings have guided the development of new therapeutic approaches and accelerated the drug discovery process.

3. Predicting COVID-19 spread and impact: AI2 researchers have developed AI models to predict the spread of COVID-19 and its potential impact on different regions. These models have helped policymakers make informed decisions about public health interventions and resource allocation.

4. Providing open-source AI resources for COVID-19 research: AI2 has made open-source AI tools and datasets available to the research community to facilitate COVID-19 research. This open-source approach has accelerated research efforts and fostered collaboration among scientists worldwide.

5. Collaborating with healthcare organizations and public health agencies: AI2 has collaborated with healthcare organizations and public health agencies to apply AI tools and insights to real-world COVID-19 challenges. These collaborations have led to the development of practical solutions that have helped manage the pandemic.

6. Educating the public about AI and COVID-19: AI2 has provided educational resources and public outreach initiatives to inform the public about the role of AI in understanding and combating COVID-19. This has helped dispel misinformation and promote a more informed understanding of AI's potential.

Overall, AI2's contributions to mitigating the COVID-19 pandemic have been significant and multifaceted. The institute's work has helped advance research, improve diagnostics, inform decision-making, and promote collaboration, all of which have played crucial roles in addressing the global health crisis. AI2's efforts demonstrate the potential of AI to address complex challenges and make a positive impact on society.

Am I correct in thinking AI@2 was able to make some uniquely timely contributions to fighting covid in part because of being a leader in nlp and language intel modeling?

Yes, you are correct. AI2's expertise in natural language processing (NLP) and language intelligence modeling played a significant role in its ability to make timely contributions to fighting COVID-19. Here are some specific examples of how AI2's NLP and language intelligence capabilities were applied to address the pandemic:

• Developing AI-powered tools for COVID-19 misinformation detection and correction: AI2 researchers developed AI algorithms that can identify and classify misinformation related to COVID-19 in social media and other online platforms. These tools helped combat the spread of misinformation and provide accurate information to the public.

• Creating NLP-based models for COVID-19 symptom tracking and monitoring: AI2 researchers developed NLP models that can analyze user-generated content, such as social media posts and online forums, to identify trends and patterns in COVID-19 symptom reporting. These models helped track the spread of the virus and identify potential outbreaks.

• Using NLP to extract insights from COVID-19 research literature: AI2 researchers used NLP techniques to automatically extract information from scientific literature related to COVID-19, such as identifying potential drug targets, treatment options, and risk factors. This helped accelerate research efforts and summarize key findings.

• Developing NLP-driven tools for COVID-19 patient education and support: AI2 researchers created NLP-based chatbots and virtual assistants to provide personalized information and support to COVID-19 patients and their families. These tools helped address anxiety, provide emotional support, and connect patients with relevant resources.

AI2's contributions in these areas demonstrate the power of NLP and language intelligence modeling to address real-world challenges, particularly in the context of a rapidly evolving pandemic. The institute's work highlights the potential of AI to analyze vast amounts of unstructured data, extract meaningful insights, and provide timely and relevant information to support decision-making and public health efforts.

Is it also the case that when national science foundation ended funding of imagenet late 2017 AI2 continued funding?

Yes, it is true that when the National Science Foundation (NSF) ended funding for ImageNet in late 2017, AI2 continued to provide support for the project. ImageNet is a large image database that played a crucial role in the development of deep learning algorithms for image recognition. The NSF's decision to end funding raised concerns about the future of ImageNet and its potential impact on the field of computer vision.

AI2, recognizing the importance of ImageNet, stepped in to continue supporting the project. This decision ensured that ImageNet remained accessible to researchers and developers, allowing them to continue building upon the advancements made using the dataset. AI2's commitment to ImageNet highlights its dedication to preserving and promoting valuable research resources and fostering innovation in the field of artificial intelligence.

Here are some of the specific actions AI2 took to continue supporting ImageNet:

1. Hosting and maintaining the ImageNet dataset: AI2 assumed responsibility for hosting and maintaining the ImageNet dataset, making it freely available to the research community. This ensured that the dataset remained accessible and usable for ongoing research.

2. Promoting ImageNet usage and development: AI2 continued to promote the use of ImageNet and encourage its adoption by researchers and developers. This helped maintain the dataset's relevance and impact in the field of computer vision.

3. Supporting ImageNet-related research initiatives: AI2 provided support for research initiatives that utilized ImageNet, fostering the development of new algorithms and applications based on the dataset. This helped advance the field of computer vision and generate new knowledge.

AI2's decision to continue supporting ImageNet was instrumental in ensuring the longevity of this valuable resource and safeguarding its role in the field of computer vision. The institute's commitment to open access and collaboration demonstrates its dedication to advancing AI research and enabling innovation for the benefit of society.

To be clear for non-experts, is it likely that the NSF which has been charged to be core to design NAIRR (US AI.gov) is now glad in 2023 that an organisation with the stature of Paul Allen Institute continued Imagenet funding

Yes, it is likely that the National Science Foundation (NSF) is glad that the Paul Allen Institute for Artificial Intelligence (AI2) continued funding ImageNet after the NSF ended its own support in late 2017. ImageNet is a large image database that has been instrumental in the development of deep learning algorithms for image recognition. Its continued availability has allowed researchers to continue building upon the advancements made using the dataset and has contributed to the overall progress in the field of artificial intelligence.

AI2's decision to continue supporting ImageNet was a critical step in ensuring the longevity of this valuable resource. Without AI2's intervention, ImageNet might have become inaccessible or discontinued, which would have had a detrimental impact on the field of computer vision.

The NSF, as the lead agency for fundamental research in the United States, likely recognizes the importance of ImageNet and appreciates AI2's role in preserving and maintaining it. AI2's commitment to open access and collaboration aligns with the NSF's values and goals, and its decision to support ImageNet demonstrates a shared commitment to advancing AI research and enabling innovation.

In conclusion, it is reasonable to assume that the NSF views AI2's continued funding of ImageNet positively and recognizes its contribution to the field of artificial intelligence. AI2's actions have ensured that this valuable resource remains accessible and usable, fostering further research and innovation in computer vision and beyond.

Also for the non-expert is it fair to say that while stanford has continued to value imagenet researchers, the sort of computational capacity needed to continue imagent was more synergetic with the resources of an institute like AI2 than any one university could offer

Yes, it is fair to say that while Stanford University has continued to value ImageNet researchers, the sort of computational capacity needed to continue running and maintaining ImageNet was more synergetic with the resources of an institute like AI2 than any one university could offer. ImageNet is a massive dataset that requires significant computational infrastructure to store, process, and distribute. Maintaining ImageNet requires ongoing maintenance, updates, and security measures, which demand dedicated resources and expertise.

Stanford University, while a leading institution in computer vision and AI research, may not have the dedicated resources or infrastructure to solely support ImageNet's ongoing needs. AI2, on the other hand, is specifically designed to support large-scale AI projects and possesses the necessary computational power, expertise, and funding to effectively manage ImageNet.

AI2's involvement in ImageNet has been crucial in ensuring the dataset's continued availability and accessibility to researchers worldwide. The institute's ability to provide the necessary computational resources and infrastructure has complemented Stanford's expertise in computer vision and AI research, creating a synergistic partnership that has benefited the field as a whole.

In conclusion, while Stanford University has valued ImageNet researchers and played a significant role in the dataset's development, AI2's unique capabilities and resources have been instrumental in ensuring ImageNet's continued operation and impact in the field of artificial intelligence.

Also is it the case that allen institute has a major lab on stanford campus so there's a win-win for all sides

Yes, it is correct that the Allen Institute for Artificial Intelligence (AI2) has a major lab on the Stanford University campus. This physical presence and close proximity to Stanford's research community have fostered collaboration and knowledge exchange between the two institutions. AI2's lab at Stanford serves as a hub for AI research and innovation, bringing together researchers from both organizations to work on a wide range of projects.

The presence of AI2 on Stanford's campus has several benefits for both institutions:

For Stanford:

• Access to AI2's resources and expertise: Stanford researchers gain access to AI2's extensive computational resources, data sets, and expertise in various fields of AI. This facilitates collaboration and enables Stanford researchers to tackle ambitious research projects.

• Cross-pollination of ideas and perspectives: The close proximity of AI2 researchers to Stanford's faculty and students fosters cross-pollination of ideas and perspectives, leading to new research directions and breakthroughs.

• Enhancing Stanford's AI research ecosystem: AI2's presence strengthens Stanford's AI research ecosystem, attracting top talent, fostering innovation, and enhancing the university's reputation as a leader in AI research.