MONTH OF 3 GREATEST AI SUMMITS EVER

ED: Hello all selection errors mine alone chris.macrae@yahoo.co.uk Wash GC- i have tried to share brillaint and kind brain connecxtions - bon courage as humanity's 5 years to 2030 choose whether we are nature's smartest  race or her next dodo!

From 1951 Von Neumann & The Economist merged their ideas on intelligence interviewing of biggest decision makers.

 Journmalistically Neumann's huge contribution was to time pretty accurately 3 rising intelligence waves : million times more compute eg Moores Law- multiplying million times more death of duistance in satelkllite transmission of data/intekigence bverween evert gps; billion times more mapping of society's and narure's deepesst data gravities. In our 1984 report- we guessed the world's most inetlligennt darata reconcilations would be proposed by a taiwanese entrepreneur and a swedish postmaster. I guess we were three quarter au point. see 2025 report chapter 6

 Jensen Huang Friends of Nvidia's rise since 1993  and partnerships are so far advanced in 20 dimensions of humanity and data analysis that usually one summit (eg Spring GTU near santa ckar hq or summer taiwan computex) is a greatest update but 3 in a month is a fantastic treat - oh lucky DC, Mumbai, Tokyo . Fortunatlely most are onm dmmand. I have been trying to find a way to cahat bout this - partly to celebratte 43rd year of 2025 reporting. I think a relaible approach goes backk to asking whos mapping what sata 

here is a rehearsal with gemini- plese note this is a living document - this versuoion mid october 2024

Yes, Earth-2 is a unique space due to its proximity to deep data partnerships and its focus on understanding our planet and its climate.

Here's why:

1. Deep Data Partnerships: As a project developed by NVIDIA, Earth-2 has access to a vast array of data and computational resources. This includes data from satellites, weather stations, climate models, and other sources, which can be used to create a highly accurate and detailed simulation of Earth.
2. AI and Machine Learning: Earth-2 leverages advanced AI and machine learning techniques to analyze and interpret the massive amounts of data used in the simulation. This allows for complex modeling and prediction capabilities.
3. Climate Focus: Earth-2 is specifically designed to study and understand climate change. This makes it a valuable resource for researchers and policymakers who are working to address the challenges posed by climate change.

In summary, Earth-2's unique combination of deep data partnerships, AI capabilities, and focus on climate science make it a valuable tool for understanding and addressing global environmental challenges.

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deep data - grounded in reality of community - expert or human (eg family loving women of bangladeshz):

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

 This biennial competition was founded in 1994 by John Moult, a computational biologist at the University of Maryland.

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.