Recurrent neural networks (RNNs) are a class of artificial neural networks for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series. https://en.wikipedia.org/wiki/Recurrent_neural_network

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.[4][5] TensorFlow was developed by the Google Brain team for internal Google use in research and production. https://en.wikipedia.org/wiki/TensorFlow

Simon Wardley: Maps as Code. Why the fuss about conversational programming? Part II. (2023-01-30) Wardley Why The Fuss About Conversational Programming (more)

Jon Udell: Elevating the Conversation with LLM Assistants. Here is some CSV data that created a problem for Steampipe’s CSV plugin. Can you see what’s wrong? Last week several of us failed to see, at first glance, what was wrong. Excel was happy with the data... ChatGPT-4 had a better idea. (more)

Gary Marcus on AI (GenAI). ...a new article on LLMs from six AI researchers at Apple: "we found no evidence of formal reasoning in language models .... Their behavior is better explained by sophisticated pattern matching--so fragile, in fact, that changing names can alter results by ~10%!" (more)

Gary Marcus: AGI by 2027? Apparently OpenAI’s internal roadmap alleged that AGI would be achieved by 2027, according to the suddenly ubiquitous Leopold Aschenbrenner (recently fired by OpenAI, previous employed by SBF), who yesterday published the math to allegedly justify it. ((2024-06-01) Leopold Aschenbrenner's AGI Situational Awareness Paper) (more)

The Algebraic Mind: Integrating Connectionism and Cognitive Science, by Gary Marcus; ISBN: 9780262632683 (more)

Gary Fred Marcus (born 8 February 1970) is an American psychologist, cognitive scientist, and author, known for his research on the intersection of cognitive psychology, neuroscience, and artificial intelligence (AI). Marcus is professor emeritus of psychology and neural science at New York University. In 2014 he founded Geometric Intelligence, a machine learning company later acquired by Uber.[3][4] His books include Guitar Zero[5] and Kluge. https://en.wikipedia.org/wiki/Gary_Marcus

Elite overproduction is a concept developed by Peter Turchin, which describes the condition of a society which is producing too many potential elite-members relative to its ability to absorb them into the power structure.[1][2][3] This, he hypothesizes, is a cause for social instability, as those left out of power feel aggrieved by their relatively low socioeconomic status. https://en.wikipedia.org/wiki/Elite_overproduction (more)

see Personal Data Server

component of BlueSky architecture (more)

Zvi Mowshowitz: AI #73: Openly Evil AI. What do you call a clause explicitly saying that you waive the right to whistleblower compensation, and that you need to get permission before sharing information with government regulators like the SEC? (more)

artificial super-intelligence - AGI but way smarter/faster than humans

Leopold Aschenbrenner's AGI Situational Awareness Paper. https://situational-awareness.ai/ The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace many college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence (ASI), in the true sense of the word. Along the way, national security forces not seen in half a century will be unleashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.

Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant[1] and others,[2][3] the effective construction of rich computational cognitive models demands the combination of symbolic reasoning and efficient machine learning. Gary Marcus argued, "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning."[4] Further, "To build a robust, knowledge-driven approach to AI we must have the machinery of symbol manipulation in our toolkit. Too much useful knowledge is abstract to proceed without tools that represent and manipulate abstraction, and to date, the only known machinery that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation.".. Henry Kautz,[6] Francesca Rossi,[7] and Bart Selman[8] also argued for a synthesis. Their arguments attempt to address the two kinds of thinking, as discussed in Daniel Kahneman's book Thinking Fast and Slow. It describes cognition as encompassing two components: System 1 is fast, reflexive, intuitive, and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is used for pattern recognition. System 2 handles planning, deduction, and deliberative thinking. In this view, deep learning best handles the first kind of cognition while symbolic reasoning best handles the second kind. Both are needed for a robust, reliable AI that can learn, reason, and interact with humans to accept advice and answer questions.... Implementations of neuro-symbolic approaches include: AllegroGraph: an integrated Knowledge Graph based platform for neuro-symbolic application development.[25][26][27]; Scallop: a language based on Datalog that supports differentiable logical and relational reasoning. Scallop can be integrated in Python and with a PyTorch learning module; .... https://en.wikipedia.org/wiki/Neuro-symbolic_AI

Barack Obama election-to-inauguration transition site. (more)

Zvi Mowshowitz: On Dwarkesh Patel's Podcast with Leopold Aschenbrenner. The central discussion was the theses of his paper, Situational Awareness, which I offered quotes from earlier, with a focus on the consequences of AGI rather than whether AGI will happen soon. There are also a variety of other topics. (2024-06-01) Leopold Aschenbrenner's AGI Situational Awareness Paper (more)

Zvi Mowshowitz: Quotes from Leopold Aschenbrenner's Situational Awareness Paper. This post is different. Usually I offer commentary and analysis. I share what others think, then respond. This is the second time I am importantly not doing that. The work speaks for itself. It offers a different perspective, a window and a worldview. It is self-consistent. This is what a highly intelligent, highly knowledgeable person actually believes after much thought. (2024-06-01) Leopold Aschenbrenner's AGI Situational Awareness Paper (more)

Serverless computing is a cloud-computing execution model in which the cloud provider runs the server, and dynamically manages the allocation of machine resources. Pricing is based on the actual amount of resources consumed by an application, rather than on pre-purchased units of capacity.[1] It can be a form of utility computing. https://en.wikipedia.org/wiki/Serverless_computing (more)

Simon Wardley: Why the fuss about conversational programming? *It’s not widely talked about yet, but it will be. To understand why, I’m going to build on my previous HackerNoon post on "Why the fuss about serverless?" (more)