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

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

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

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

What is Conversational?

Generative?

Pretraining

Transformer

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

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

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

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

 Epoch changing Guides

1 AI Training AI Training.docx

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

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

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

What is Reinforcement Learning?

What is Specific Task instead of General Purpose?

What are Deep Neural Networks?




 

What are Evolutionary Algorithms?


Neumann.ning.com
Today's tech is brilliant beyond words - so WHAT IS THE WORLDS BIGGEST PROBLEM? 2021 -1761 -CAN HUMANS FOR FIRST TIME IN 250 YEARS CELEBRATE VALUING MATHS INTEL A DECADE AHEAD OF TIME?
AdamSmith.app Moral Sentiments SDGs.games mapping with Guterres AIforGood decade became sustainability's gen's last chance of south-north and east-west- or the death of United Nations' world trade transparency

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

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

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

 

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

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

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

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

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


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

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

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

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

Tuesday, January 31, 2023

 stanfird HAI issued it latest report on climate ai in january 2023  https://hai.stanford.edu/sites/default/files/2023-01/HAI_IndustryBrief6_v8.pdf

These are definitions used in the climate report

Intelligence might be defined as the ability to learn and perform a range of techniques to solve problems and achieve goals—techniques that are appropriate to the context in an uncertain, ever-varying world. A fully pre-programmed factory robot is flexible, accurate, and consistent, but not intelligent. 

Artificial Intelligence (AI) (seee language alteady popularuised by Vine Neumann and peers - eg computer & the brain -, is a term coined in 1955 by John McCarthy, Stanford’s first faculty member in AI, who defined it as “the science and engineering of making intelligent machines.” Much research has human program software agents with the knowledge to behave in a particular way, like playing chess, but today, we emphasize agents that can learn, just as human beings navigating our changing world. Autonomous systems can independently plan and decide sequences of steps to achieve a specified goal without being micromanaged. A hospital delivery robot must autonomously navigate busy corridors to succeed in its task. In AI, autonomy doesn’t have the sense of being self-governing common in politics or biology. Machine Learning (ML) is the part of AI that studies how computer systems can improve their perception, knowledge, decisions, or actions based on experience or data. For this, ML draws from computer science, statistics, psychology, neuroscience, economics, and control theory. In supervised learning, a computer learns to predict human-given labels, such as particular dog breeds based on labeled dog pictures. Unsupervised learning does not require labels, but sometimes adopts selfsupervised learning, constructing its own prediction tasks such as trying to predict each successive word in a sentence. Reinforcement learning enables autonomy by allowing an agent to learn action sequences that optimize its total rewards, such as winning games, without explicit examples of good techniques. Deep Learning is the use of large multi-layer (artificial) neural networks that compute with continuous (real number) representations, similar to the hierarchically organized neurons in human brains. It is successfully employed for all types of ML, with better generalization from small data and better scaling to big data and compute budgets. A recent breakthrough is the transformer, a neural net architecture which flexibly incorporates context via an attention mechanism, allowing powerful and computationally efficient analysis and generation of sequences, such as words in a paragraph. Foundation models are an emerging class of models, often transformers trained by self-supervision on large-scale broad data, that can be easily adapted to perform a wide range of downstream tasks. The best-known examples are large pretrained language models like GPT-3, but the term extends to models for all modalities of data and knowledge. An algorithm is a precise list of steps to take, such as a computer program. AI systems contain algorithms, but typically just for a few parts like a learning or reward calculation method. Much of their behavior emerges via learning from data or experience, a fundamental shift in system design that Stanford alumnus Andrej Karpathy dubbed Software 2.0. Narrow AI is intelligent systems for particular tasks, e.g., speech or facial recognition. Human-level AI, or artificial general intelligence (AGI), seeks broadly intelligent, context-aware machines. It is needed for effective, adaptable social chatbots or human-robot interaction. Human-Centered Artificial Intelligence is AI that seeks to augment the abilities of, address the societal needs of, and draw inspiration from human beings. It researches and builds effective partners and tools for people, such as a robot helper and companion for the elderly. Text by Professor Christopher Manning, v 1.2, April 2022


=================bard on computer and the brain

what can you tell me about computer and the brain - lectures given at Yale soon after death of John Von Neumann

Sure. The Computer and the Brain is a series of lectures given at Yale University in 1958 by John von Neumann, a Hungarian-American mathematician and polymath. The lectures were given shortly after von Neumann's death, and they were published posthumously in 1958.

In the lectures, von Neumann discusses the similarities and differences between computers and brains. He argues that computers and brains are both information-processing systems, but that they use different methods to process information. Computers use digital logic, while brains use analog logic.

Von Neumann also discusses the potential for computers to surpass human intelligence. He argues that computers have the potential to be more intelligent than humans because they can be programmed to learn and adapt.

The Computer and the Brain is a seminal work in the field of artificial intelligence. It has been influential in the development of computer science and neuroscience. The lectures are still relevant today, and they continue to be studied by researchers and scholars.

Here are some of the key points that von Neumann made in the lectures:

  • Computers and brains are both information-processing systems.
  • Computers use digital logic, while brains use analog logic.
  • Computers have the potential to surpass human intelligence.
  • Computers can be programmed to learn and adapt.

The Computer and the Brain is a valuable resource for anyone interested in artificial intelligence, computer science, or neuroscience. It is a thought-provoking and insightful work that continues to be relevant today.

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