Moonshots

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• Journey to Singularity
• Inside Out - Curious Optimistic Reasoning
• Metaverse
• Quantum

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?

• How to build your own AlphaZero AI using Python and Keras

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

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Can Conjure & Ask Questions

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• Inside Out - Curious Optimistic Reasoning
• Animals
• General Intelligence
• Consciousness

Able to Predict the Future

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Able to 'Learn' the Wide World Web

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Autonomous Vehicles

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• Transportation (Autonomous Vehicles)

Emergence from Analogies

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• Generative
• Framing Context
• Transfer Learning
• Analogy-Making as a Complex Adaptive System | Melanie Mitchell - Los Alamos National Laboratory
• Learning to Make Analogies by Contrasting Abstract Relational Structure | F. Hill, A. Santoro, D. Barrett, A. Morcos, and T. Lillicrap - DeepMind
• AI Is Transforming Google Search. The Rest of the Web Is Next | Craig G. Karl - Wired
• AI analyzed 3.3 million scientific abstracts and discovered possible new materials | Karen Hao - MIT Technology Review
• Learning by understanding analogies | Russell Greiner - ScienceDirect
• Emergence of analogy from relation learning | H. Lu, Y. Wu, and K. Holyoak - PNAS
• Learning to Make Analogies by Contrasting Abstract Relational Structure | F. Hill, A. Santoro, D. Barrett, A. Morcos, and T. Lillicrap - DeepMond
• To Spur Innovation, Teach A.I. To Find Analogies | Byron Spice - Futurity ...A method for teaching artificial intelligence analogies through crowdsourcing could allow a computer to search data for comparisons between disparate problems and solutions, highlighting important—but potentially unrecognized—underlying similarities.

Principles of analogical reasoning have recently been applied in the context of machine learning, for example to develop new methods for classification and preference learning. In this paper, we argue that, while analogical reasoning is certainly useful for constructing new learning algorithms with high predictive accuracy, is is arguably not less interesting from an interpretability and explainability point of view. More specifically, we take the view that an analogy-based approach is a viable alternative to existing approaches in the realm of explainable AI and interpretable machine learning, and that analogy-based explanations of the predictions produced by a machine learning algorithm can complement similarity-based explanations in a meaningful way. Towards Analogy-Based Explanations in Machine Learning | Eyke Hüllermeier

Analogies This video is part of the Udacity course "Deep Learning". Watch the full course at https://www.udacity.com/course/ud730

Complexity Concepts, Abstraction, & Analogy in Natural and Artificial Intelligence, Melanie Mitchell Complexity Concepts, Abstraction, & Analogy in Natural and Artificial Intelligence a talk by Melanie Mitchell at the GoodAI Meta-Learning & Multi-Agent Learning Workshop. See other talks from the workshop

Conceptual Abstraction and Analogy in Natural and Artificial Intelligence Melanie Mitchell, Santa Fe Institute; Portland State University While AI has made dramatic progress over the last decade in areas such as vision, natural language processing, and game-playing, current AI systems still wholly lack the abilities to create humanlike conceptual abstractions and analogies. It can be argued that the lack of humanlike concepts in AI systems is the cause of their brittleness—the inability to reliably transfer knowledge to new situations—as well as their vulnerability to adversarial attacks. Much AI research on conceptual abstraction and analogy has used visual-IQ-like tests or other idealized domains as arenas for developing and evaluating AI systems, and in several of these tasks AI systems have performed surprisingly well, in some cases outperforming humans. In this talk I will review some very recent (and some much older) work along these lines, and discuss the following questions: Do these domains actually require abilities that will transfer and scale to real-world tasks? And what are the systems that succeed on these idealized domains actually learning?

Melanie Mitchell: "Can Analogy Unlock AI’s Barrier of Meaning?" UCSB College of Engineering Speaker Bio: Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute and Professor of Computer Science (currently on leave) at Portland State University. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. She is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her latest book is Artificial Intelligence: A Guide for Thinking Humans. Abstract: In 1986, the mathematician and philosopher Gian-Carlo Rota wrote, “I wonder whether or when artificial intelligence will ever crash the barrier of meaning.” Here, the phrase “barrier of meaning” refers to a belief about humans versus machines: humans are able to “actually understand” the situations they encounter, whereas it can be argued that AI systems (at least current ones) do not possess such understanding. Some cognitive scientists have proposed that analogy-making is a central mechanism for concept formation and concept understanding in humans. Douglas Hofstadter called analogy-making “the core of cognition”, and Hofstadter and co-author Emmanuel Sander noted, “Without concepts there can be no thought, and without analogies there can be no concepts.” In this talk I will reflect on the role played by analogy-making at all levels of intelligence, and on how analogy-making abilities will be central in developing AI systems with humanlike intelligence.

Meeting the Winograd Schema Challenge (WSC)

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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"