Difference between revisions of "Moonshots"

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|keywords=ChatGPT, artificial, intelligence, machine, learning, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT  
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[https://www.youtube.com/results?search_query=moonshot+moon+shot+ai YouTube search...]
 
[https://www.youtube.com/results?search_query=moonshot+moon+shot+ai YouTube search...]
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* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 
* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 
* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 
* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
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* [https://www.nature.com/articles/s41586-024-07936-z The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery | Sakana AI Team - Nature] ...Breakthrough in AI-generated research papers achieving human-level acceptance at workshops.
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* [https://www.congress.gov/bill/119th-congress/senate-bill/AI-Grand-Challenges-Act AI Grand Challenges Act | U.S. Senate - 2026] ...Proposed bipartisan legislation to fund prize competitions for AI breakthroughs in health and security.
  
 
The “Sputnik” moment for [[Government Services#China|China]] came a year ago when a Google computer program, AlphaGo, beat the world’s top master of the ancient board game of Go. Now, [[Government Services#China|China]] is racing to become the world leader in artificial-intelligence. In [[context]], what do you think would be a "Moonshot" response?
 
The “Sputnik” moment for [[Government Services#China|China]] came a year ago when a Google computer program, AlphaGo, beat the world’s top master of the ancient board game of Go. Now, [[Government Services#China|China]] is racing to become the world leader in artificial-intelligence. In [[context]], what do you think would be a "Moonshot" response?
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In the [[context]] of AI, a "moonshot" refers to a project or goal that aims to achieve a major breakthrough in artificial intelligence that has the potential to transform society or address significant global challenges. The term "moonshot"  is derived from the Apollo program, which was a series of space missions undertaken by the United States in the 1960s and early 1970s with the goal of landing humans on the Moon. The Apollo program was considered a moonshot because it represented a major technological and engineering challenge that required significant innovation and investment.
  
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In the [[context]] of AI, a "moonshot" refers to a project or goal that aims to achieve a major breakthrough in artificial intelligence that has the potential to transform society or address significant global challenges. The term "moonshot"  is derived from the Apollo program, which was a series of space missions undertaken by the United States in the 1960s and early 1970s with the goal of landing humans on the Moon. The Apollo program was considered a moonshot because it represented a major technological and engineering challenge that required significant innovation and investment.
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== Agents & Agentic Workflows ==
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The frontier of AI has shifted from static chat interfaces to autonomous "Agentic Workflows." These systems are designed for recursive self-improvement and multi-agent orchestration, moving beyond simple prompt-response cycles.
  
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* '''The Automated Researcher:''' As of September 2026, OpenAI has reported a milestone in developing an "automated research intern." This system handles well-defined research tasks under human direction, logging approximately 3.1 agent-workdays per eight hours of human labor. The goal is a fully autonomous AI researcher by March 2028.
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* '''AI as Scientist:''' Moving from tool to collaborator, models like Sakana AI's "AI Scientist" are producing novel, peer-review-quality research. This includes experiments in virtual cell modeling and AI-designed drug discovery.
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* '''Unsolved Mathematics & Formal Verification:''' Following IMO-gold-level performance, the focus is shifting to original proofs of open conjectures, utilizing formal verification systems like Lean to ensure mathematical rigor.
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* '''Embodied General Intelligence:''' Often described as the "physical Turing test," this involves robots navigating unstructured environments (e.g., performing household chores in unfamiliar settings).
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* '''Reliability & Alignment:''' Continual learning and long-horizon reliability remain critical, unglamorous challenges. Current models often degrade over long autonomous runs; solving this is essential for stable, long-term agentic operation.
  
 
== Can Conjure & Ask Questions ==
 
== Can Conjure & Ask Questions ==
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= <span id="Meeting the Winograd Schema Challenge (WSC)"></span>Meeting the Winograd Schema Challenge (WSC) =
 
= <span id="Meeting the Winograd Schema Challenge (WSC)"></span>Meeting the Winograd Schema Challenge (WSC) =

Revision as of 06:07, 10 September 2026

YouTube search... ... Quora search ...Google search ...Google News ...Bing News

The “Sputnik” moment for China came a year ago when a Google computer program, AlphaGo, beat the world’s top master of the ancient board game of Go. Now, China is racing to become the world leader in artificial-intelligence. In context, what do you think would be a "Moonshot" response?

The 'moonshot' milestones along the road to Artificial General Intelligence (AGI)

In the context of AI, a "moonshot" refers to a project or goal that aims to achieve a major breakthrough in artificial intelligence that has the potential to transform society or address significant global challenges. The term "moonshot" is derived from the Apollo program, which was a series of space missions undertaken by the United States in the 1960s and early 1970s with the goal of landing humans on the Moon. The Apollo program was considered a moonshot because it represented a major technological and engineering challenge that required significant innovation and investment.

Agents & Agentic Workflows

The frontier of AI has shifted from static chat interfaces to autonomous "Agentic Workflows." These systems are designed for recursive self-improvement and multi-agent orchestration, moving beyond simple prompt-response cycles.

  • The Automated Researcher: As of September 2026, OpenAI has reported a milestone in developing an "automated research intern." This system handles well-defined research tasks under human direction, logging approximately 3.1 agent-workdays per eight hours of human labor. The goal is a fully autonomous AI researcher by March 2028.
  • AI as Scientist: Moving from tool to collaborator, models like Sakana AI's "AI Scientist" are producing novel, peer-review-quality research. This includes experiments in virtual cell modeling and AI-designed drug discovery.
  • Unsolved Mathematics & Formal Verification: Following IMO-gold-level performance, the focus is shifting to original proofs of open conjectures, utilizing formal verification systems like Lean to ensure mathematical rigor.
  • Embodied General Intelligence: Often described as the "physical Turing test," this involves robots navigating unstructured environments (e.g., performing household chores in unfamiliar settings).
  • Reliability & Alignment: Continual learning and long-horizon reliability remain critical, unglamorous challenges. Current models often degrade over long autonomous runs; solving this is essential for stable, long-term agentic operation.

Can Conjure & Ask Questions

Youtube search... ...Google search

Able to Predict the Future

Youtube search... ...Google search

Able to 'Learn' the Wide World Web

Youtube search... ...Google search

Autonomous Vehicles

Youtube search... ...Google search

Meeting the Winograd Schema Challenge (WSC)

Youtube search... ...Google search

The Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011. In this work we attempt to solve WSC problems by reasoning with additional knowledge. By using an approach built on top of graph-subgraph isomorphism encoded using Answer Set Programming (ASP) we were able to handle 240 out of 291 WSC problems. The ASP encoding allows us to add additional constraints in an elaboration tolerant manner. In the process we present a graph based representation of WSC problems as well as relevant commonsense knowledge. "Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge" | Arpit Sharma

The Sentences Computers Can't Understand, But Humans Can
The Winograd schema is a language test for intelligent computers. So far, they're not doing well.

The Winograd Schema Challenge - Models of Reasoning
This video corresponds to the online presentation assingment of the subject Models of Reasoning. Authors: Carla Fernández González and Teresa Grau Mateo. Taking the name from Terry Winograd, who first presented an example following the schema [13], Levesque, Davis and Morgenstern created Winograd Schemas as an alternative to the Turing Test and started a competition to encourage researchers to work in this area of commonsense reasoning.

Improving Winograd Schemas Using Ambiguous contexts
I created this presentation for a graduate class at NYU. It also serves as a gentle introduction to Winograd Schemas. There's one error: at 7:35 I say "she's the receiver of the thanks" when I mean "receiver of the help." Also, I mean no ill will to Tom Scott. He's a great computer enthusiast and content creator.

ICLP19 paper "Using ASP for Commonsense Reasoning in the Winograd Schema Challenge"
This video is a presentation which provides an overview of the ICLP 2019 conference paper titled "Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge"

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