Intelligence's greatest data competitions

 Can you remind me who started the proyein folding data competition whiich Hassabis deep mind eventually won with alphafold?

The protein folding data competition that DeepMind won with AlphaFold was the Critical Assessment of Structure Prediction (CASP). This biennial competition was founded in 1994 by John Moult, a computational biologist at the University of Maryland.  

CASP has been a driving force in advancing protein structure prediction research, and DeepMind's breakthrough with AlphaFold has significantly accelerated progress in this field.  

Highly accurate protein structure prediction with AlphaFold - Nature

www.nature.com

A decade of CASP: Progress, bottlenecks and prognosis in protein structure prediction

www.researchgate.net

Are there other famous data competitions as far as AI is concerned in addition to CASP and Fei0Fei Li's imagenet? If so are any in the top league the way casp and imagenet seem to be?

Yes, there are several other famous data competitions in AI, in addition to CASP and ImageNet. Here are a few of the most notable ones:

Image Classification:

• CIFAR-10 and CIFAR-100: These are smaller datasets compared to ImageNet, but they are widely used for benchmarking image classification algorithms.
• Places365: A dataset of 365 scene categories, used for scene recognition and localization.

Natural Language Processing:

• GLUE (General Language Understanding Evaluation): A benchmark for evaluating the performance of natural language understanding systems on a variety of tasks, such as question answering, text classification, and sentiment analysis.
• SQuAD (Stanford Question Answering Dataset): A dataset for machine reading comprehension, where the goal is to answer questions based on a given passage of text.
• WMT (Workshop on Machine Translation): A workshop that includes a machine translation competition, where participants compete to build the best machine translation systems.

Speech Recognition:

• LibriSpeech: A large-scale speech recognition dataset, used for training and evaluating automatic speech recognition systems.
• TIMIT (Texas Instruments-MIT Spoken Sentence Corpus): A smaller dataset of phonetically balanced American English sentences, used for speech recognition research.

Other:

• Kaggle Competitions: Kaggle, a platform for data science and machine learning, hosts numerous competitions on a variety of topics, including image classification, natural language processing, and time series analysis.
• Netflix Prize: A famous competition that was held in 2006-2009, where participants competed to improve the accuracy of Netflix's movie recommendation algorithm.

These competitions have played a crucial role in driving progress in AI research and development. They provide a common benchmark for researchers to compare their methods and algorithms, and they often lead to significant breakthroughs in AI.

While CASP and ImageNet are undoubtedly among the most influential data competitions, the others listed above are also highly regarded and have made significant contributions to the field of AI.

Imagenet took a long time to set up (both years elapsed time and if one counts identification of 20 millionn images by amazon tutk) many years of haumn time- did casp take long time to set up?

Yes, CASP (Critical Assessment of Structure Prediction) also took a significant amount of time to set up.

While it doesn't involve the massive scale of image identification like ImageNet, CASP has involved:

• Decades of Protein Structure Research: The foundation for CASP lies in decades of research on protein structure prediction methods.
• Collaborative Efforts: The CASP initiative itself has been a collaborative effort involving researchers from around the world, coordinating experiments and evaluations.
• Data Collection and Preparation: Preparing the protein structures and associated data for CASP experiments requires significant time and computational resources.

While the exact timeline might vary depending on specific CASP iterations, it's safe to say that the setup and execution of CASP experiments have involved a substantial investment of time and effort from the scientific community.

 Jensen &

with cloud data providerrs service pklus snwflake ...

with big 5 amazin google facebook apple microst

24/9 c-span  Energy, Grid AI Games,  Gov as Ai Practitioners, Supercomputer revolution year 31 (deepst data fastest configuration); Digital Twin

with personal agent - eg classrrom smartspecs for all

24 Full tech map from 1993 -with coder interviewer

cuda from start; cuda world class unis from 2010

with pixar usd from 2016?

rtendering with eg adobe

with artist rights Getty - museum modernn art 3d design

with robotics metaverse - both ways in 2d-3d - eg medics planning 3 d operation from 2d data scan; get this safe before autonomous

with indutsry sectors - chose driverless cars earlyn as ultimately this sets standard for mobile roboit safety

with data supliers snowflake service plus databricks salesforce

with women ai and ed for all - fei-fei melinda gates; (king with salesfirce)

with bipartisan congrsss team c-span

with startups

with suppliers oc mobile worksation

with cloud data providerrs service pklus snwflake ...

with big 5 amazin google facebook apple microst

with earth2.0 - digital sovereignty of vital mations taiwan, king charles ai partners, barbados mia mottley, weatgher comoany and IBM

clara - life science ai , with inspirartion hassabis, with recursion - valley companies likely connectingh CZI opriscila chan, jennifer doudna noobell prize crisR

Koller latest biotech compana after NG' co-founder corsers

Gemini conversation AI @ Great Transnational Triad : Japan-Korea-Taiwan

 09/24 Gemini please tell me about the head of softbank? Would it be true to say that he has kept investments un AI going through AI winter as well as now from the visionary (even the data sovereignty) viewpoint of Japan?

Masayoshi Son, the visionary founder and CEO of SoftBank, has indeed been a key figure in the AI industry, particularly in Japan. He's often credited with persisting through the AI winters and maintaining significant investments in AI companies.

Key points about Masayoshi Son and his role in AI:

• Visionary Leadership: Son has a long-standing belief in the transformative power of technology, especially AI. He has consistently invested in AI companies, even during periods of skepticism and economic downturn.
• SoftBank Vision Fund: One of the most notable examples of Son's commitment to AI is the SoftBank Vision Fund. This massive investment fund has poured billions of dollars into AI startups worldwide, fueling innovation and growth in the sector. 
• Data Sovereignty: While Son hasn't explicitly emphasized data sovereignty in his public statements, his focus on Japanese technology and innovation aligns with a broader national desire to maintain control over data and technology within the country.

In conclusion, Masayoshi Son's unwavering belief in AI and his willingness to invest heavily in the field have played a crucial role in shaping the AI landscape, particularly in Japan. His visionary leadership and long-term perspective have helped to ensure that AI remains a priority for SoftBank and the nation.  

1. SoftBank Fund: All about Masayoshi Son's new $100 billion AI venture 'Izanagi' | Mint

www.livemint.com

2. “I don't look for companies. I look for Founders.” – Masayoshi Son - The VC Factory

thevcfactory.com

SoftBank Vision Fund - Wikipedia

en.wikipedia.org

SoftBank's Vision Fund 2 Pours Massive Investment Into Perplexity AI, Valuation Hits $3 Billion - Outlook Business

www.outlookbusiness.com

SoftBank Fund: All about Masayoshi Son's new $100 billion AI venture 'Izanagi' | Mint

www.livemint.com

“I don't look for companies. I look for Founders.” – Masayoshi Son - The VC Factory

thevcfactory.com

Is Masa Son (softbank leader) leading design of a Japanese LLM? If not which do you see as leading Japanese LLM and which AI genii are assembling it?

Gemini: Masa Son and Japan's LLM Landscape

While Masa Son and SoftBank have a significant influence on the Japanese tech landscape, they haven't been directly leading the design of a major Japanese LLM.

However, SoftBank has been actively involved in the AI space through investments and partnerships. They have invested in various AI startups and research institutions, potentially contributing to the development of Japanese LLMs indirectly.  

1. From Dot-Com Crash to Investment Renaissance: How SoftBank Reinvented Itself - EnvZone

envzone.com

Leading Japanese LLM Initiatives

Several Japanese companies and research institutions are at the forefront of LLM development:

1. NEC: NEC Corporation, a major Japanese technology company, has been actively researching and developing LLMs. They have focused on applications in various industries, including customer service and document summarization.  
   1. NEC Joins The Race To Develop Japan's Own Large Language Model (LLM)

   

   www.nec.com
2. NTT: Nippon Telegraph and Telephone Corporation, another major Japanese tech player, has been involved in LLM research. Their focus has been on developing LLMs for specific use cases, such as natural language processing in telecommunications.
3. RIKEN: The RIKEN Institute, a Japanese research organization, has been involved in AI research, including LLMs. Their efforts have often been focused on fundamental research and academic contributions.  
   1. AI Research in Japan: Challenges at the RIKEN Center for Advanced Intelligence Project

   

   www.japanhouselondon.uk
4. Tohoku University: This university has been involved in LLM research, with a focus on developing models for specific languages and applications.  
   1. Release of “Fugaku-LLM” – a large language model trained on the supercomputer “Fugaku” - Fujitsu

   

   www.fujitsu.com

Key AI Researchers and Teams

While specific individuals may not be as publicly recognized as their counterparts in the West, many Japanese researchers and teams are contributing to LLM development. These individuals often work within the aforementioned companies, research institutions, or universities.

Note: The landscape of LLM development is rapidly evolving, and new players may emerge. It's essential to stay updated with the latest developments in the Japanese AI ecosystem.

Would you like to know more about specific Japanese AI research institutions or LLM projects?

From Dot-Com Crash to Investment Renaissance: How SoftBank Reinvented Itself - EnvZone

envzone.com

NEC Joins The Race To Develop Japan's Own Large Language Model (LLM)

www.nec.com

AI Research in Japan: Challenges at the RIKEN Center for Advanced Intelligence Project

www.japanhouselondon.uk

Release of “Fugaku-LLM” – a large language model trained on the supercomputer “Fugaku” - Fujitsu

www.fujitsu.com

i would like to understand leading edge advancements of tech and AI in 3 countries -Japan, Korea S, Taiwan- both unique contributions and where their shared sme supply chains (and eg own language events with eg Nvidia including representation in cuda universities of excellence) may have advanced more than the sum of their parts. Because my father Norman Macrae surveyed consider Japan in The Economist 1962 (approved by both JFKennedy directly and advisers to Prince Charles and Japan Emperor family) we know quote a lot about 20th C development but can you help particularly with accelerating AI good in 21st C Japan, Korea, Taiwan? Dad's work was recognised in UK with CBE and Japan with Order of Rising sun with silk ribbon and gold bars

Gemini Accelerating AI Innovation in Japan, South Korea, and Taiwan

Unique Contributions and Synergies

These three East Asian nations have made significant strides in AI, each with its own unique strengths and contributions. Their close geographic proximity, shared cultural influences, and strong economic ties have fostered collaboration and accelerated innovation.

Japan

• Robotics and Automation: Japan has a long-standing reputation in robotics and automation, which has provided a solid foundation for AI development. This expertise has been applied to various fields, including manufacturing, healthcare, and service industries.
• Natural Language Processing (NLP): Given the complexity of the Japanese language, Japanese researchers have made significant advancements in NLP. This expertise is invaluable for applications such as machine translation, sentiment analysis, and chatbots.
• Hardware Development: Japan's strong electronics industry has contributed to the development of specialized hardware for AI, such as GPUs and AI accelerators.

South Korea

• Semiconductors and Electronics: South Korea's dominance in the semiconductor industry has provided a competitive advantage in AI development. The availability of high-performance computing resources has enabled researchers to train and deploy large-scale AI models.
• Internet Infrastructure: South Korea has a well-developed internet infrastructure, which has facilitated the growth of AI startups and research institutions.
• Government Support: The South Korean government has actively supported AI development through funding, policy initiatives, and talent cultivation programs.

Taiwan

• Manufacturing Excellence: Taiwan's world-class manufacturing capabilities have contributed to the development of AI hardware and components.
• Research and Development: Taiwanese universities and research institutions have made significant contributions to AI research, particularly in areas such as computer vision and machine learning.
• Collaboration with International Partners: Taiwan has actively collaborated with international partners, including the United States and Europe, to advance AI research and development.

Shared Strengths and Synergies

• Strong Supply Chains: The close economic ties between these countries have resulted in strong supply chains for AI components and hardware. This has enabled them to efficiently develop and deploy AI solutions.
• Language and Cultural Similarities: Shared language and cultural similarities have facilitated collaboration and knowledge exchange among researchers and developers in these countries.
• Talent Development: The educational systems in Japan, South Korea, and Taiwan have produced a large pool of talented AI researchers and engineers.

The Role of NVIDIA and Cuda Universities

NVIDIA's Cuda Universities program has played a crucial role in accelerating AI development in these countries. By providing access to advanced computing resources and training programs, NVIDIA has helped researchers and developers to train and deploy large-scale AI models.

Conclusion

The unique contributions and synergies among Japan, South Korea, and Taiwan have positioned them as global leaders in AI innovation. Their strong supply chains, talent development, and government support have enabled them to accelerate the development and deployment of AI solutions.

Would you like to explore any specific areas of AI development in these countries, such as healthcare, finance, or manufacturing?

I would like to explore any practice area where these countries have been good /deep partners in UN or world bank sustainability goals. When it comes to investment do they see any investment network eg Asian Development Bank as representing their synergetic inspiration? If also like to understand these countries connections with eg silicon valley as it seems the whole pacific and asean design of human intelligence has characteristics/vioson that are co-creatively (inter-generationally) different than Atlantic region

GEMINI:

Collaboration on Sustainability Goals and Investment Networks

UN and World Bank Partnerships

Japan, South Korea, and Taiwan have been active participants in the UN and World Bank's sustainability goals. Here are some notable areas of collaboration:

• Climate Change: All three countries have been involved in initiatives to reduce greenhouse gas emissions, promote renewable energy, and adapt to the impacts of climate change. They have contributed to international funds and participated in climate negotiations.
• Sustainable Development Goals (SDGs): These countries have implemented national strategies to achieve the SDGs, focusing on areas such as poverty reduction, education, healthcare, and gender equality. They have also supported international efforts to achieve the SDGs.
• Disaster Risk Reduction: Given their geographic location, these countries have experience in disaster risk management. They have shared their expertise and supported international efforts to build resilience to natural disasters.

Investment Networks

The Asian Development Bank (ADB) has been a key platform for investment and development in the region. It has supported projects in infrastructure, energy, agriculture, and social development. While not exclusively representing these three countries, the ADB has played a significant role in fostering regional cooperation and development.

Connections with Silicon Valley

The AI ecosystems in Japan, South Korea, and Taiwan have increasingly connected with Silicon Valley. This has led to collaborations, investments, and knowledge exchange. While there are differences in cultural and business practices, there is a shared interest in developing innovative AI technologies.

Unique Perspectives and Visions

The AI ecosystems in the Pacific and ASEAN regions do have distinct characteristics compared to the Atlantic region. Some key differences include:

• Cultural Values: Asian cultures often prioritize harmony, collectivism, and long-term perspectives, which can influence AI development and ethics.
• Government Involvement: Governments in the Pacific and ASEAN regions often play a more active role in AI development, shaping policies and providing support.
• Focus on Specific Applications: AI development in these regions is often driven by specific applications, such as agriculture, healthcare, and smart cities.

These differences can lead to unique perspectives and visions for AI development, potentially resulting in innovative solutions that address local challenges and opportunities.

Would you like to explore any of these topics in more detail, such as specific AI applications or the role of government in AI development in these regions?

Yes very interested un these topics- you suggestion of role of government in AU looks particularly exciting for millennials intelligence needs. I was hoping that the King Charles initiated AI world series Bletchley-Korea-Paris would build on this from Korea; for various reasons the pan-asia opportunity to contribute to AI world series 2 may was not easy to see; I am hopeful this can change after Paris hosts AI world series January 2025; Osaka Expo can be an idea opportunity; its a pity that Abe is no longer around- you could say that "Osaka Track" started conversation on translational deep data sharing and soc5.0 reminded world economic forum designer of 4th industrial revolution that peoples would need to be educationally linkedin to diversity of societal consequences (ie how local can deep data sovereignty get particularly if green world models necessarily reach out to root diversity -eg are culture, language truly/openly represented in biggest ai models as well as in governance )