Difference between revisions of "Moonshots"

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* [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 
* [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 +
* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 
* [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 
* [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 
* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Out-of-Distribution (OOD) Generalization]]
 
* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Out-of-Distribution (OOD) Generalization]]

Revision as of 06:55, 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.

Able to Predict the Future

Youtube search... ...Google search

Can Conjure & Ask Questions

Youtube search... ...Google search

Recursive Self Improvement

The topic of recursive self-improvement is a significant threshold in AI development. Currently, the process is largely driven by human software engineers who manually generate training data, run ablations to test data quality, and evaluate models against benchmarks.

However, many labs are now working to close this loop to automate the process:

  • Automated Judges: Different models will act as judges to evaluate the quality of outputs.
  • Generative Feedback: Models will generate new, high-quality training data autonomously.
  • Adversarial Reasoning: Advanced models will reason over which data to include, effectively filtering for quality.

By feeding this output back into the post-training process, development speed will likely increase significantly. While some speculate this could lead to an intelligence explosion, Mustafa Suleyman notes that achieving this requires substantial compute and, without proper human oversight or control, it introduces significant risks.


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

By September 2026, OpenAI hit a major milestone with its "automated research intern." Think of it as a tireless assistant that handles well-defined research tasks while a human steers the ship. For every eight hours a person puts in, the system completes about three days' worth of work. The ultimate goal is to have a fully autonomous AI researcher running by March 2028.

AI as Scientist

We have moved past seeing AI as just a tool; it is now a true collaborator. Models like Sakana AI's "AI Scientist" are actually producing fresh, peer-review-quality research. They are running experiments in areas like virtual cell modeling and designing new drugs from scratch. It is like having a digital post-doc working around the clock in the lab.

Unsolved Mathematics & Formal Verification

After hitting gold-medal performance in the International Mathematical Olympiad, AI is setting its sights on creating original proofs for open mathematical problems. To make sure the math is rock-solid, these systems use formal verification tools like Lean. Imagine a spell-checker, but instead of catching typos, it verifies complex logic step-by-step to guarantee accuracy.

Reliability & Alignment

Continual learning and long-term reliability are the unglamorous but necessary hurdles we still need to clear. Right now, current models tend to lose focus or degrade when they run on their own for too long. If we want AI agents to operate stably over weeks or months, fixing this drift is essential. Think of it like maintaining focus during a marathon rather than just running a quick sprint.

Embodied General Intelligence

Many people call this the "physical Turing test." Embodied General Intelligence is all about getting robots to navigate messy, unpredictable real-world spaces. Instead of repeating the same motion on an assembly line, these robots need to figure out how to fold laundry or do the dishes in a kitchen they have never seen before.

Autonomous Vehicles

Youtube search... ...Google search


Need to 'Learn' the Wide World Web

Youtube search... ...Google search

Discussions on the Future of AI

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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