To many Chomsky's debates with Quine and Skinner are an updated version of the Rationalist Empiricist debates of the 17th century. The consensus being that Chomsky's Rationalism was victorious. This dispute has reemerged with the advent of Large Language Models. With some arguing that LLMs vindicate rationalism because of the necessity of building in innate biases to make them work. The necessity of building in innate biases is taken to prove that empiricism hasn't got the conceptual resources to explain linguistic competence. Such claims depend on the nature of the empiricism one is endorsing. Externalized Empiricism has no difficulties with innate apparatus once they are determined empirically (Quine 1969). Thus, externalized empiricism is not refuted because of the need to build in innate biases in LLMs. Furthermore, the relevance of LLMs to the rationalist empiricist debate in relation to humans is dubious. For any claim about whether LLMs learn in an empiricist manner to be relevant to humans it needs to be shown that LLMs and humans learn in the same way. Two key features distinguish humans and LLMs. Humans learn despite a poverty of stimulus and LLMs learn because of an incredibly rich stimulus. Human linguistic outputs are grounded in sensory experience and LLMs are not. These differences in how the two learn indicates that they both use different underlying competencies to produce their output. Therefore, any claims about whether LLMs learn in an empiricist manner are not relevant to whether humans learn in an empiricist manner.

Discussion of AI alignment (alignment between humans and AI systems) has focused on value alignment, broadly referring to creating AI systems that share human values. We argue that before we can even attempt to align values, it is imperative that AI systems and humans align the concepts they use to understand the world. We integrate ideas from philosophy, cognitive science, and deep learning to explain the need for concept alignment, not just value alignment, between humans and machines. We summarize existing accounts of how humans and machines currently learn concepts, and we outline opportunities and challenges in the path towards shared concepts. Finally, we explain how we can leverage the tools already being developed in cognitive science and AI research to accelerate progress towards concept alignment.

Let the OSS Enterprise newsletter guide your open source journey! Oregon headquartered ThatDot, a startup that offers a complex event processing (CEP) platform to capture the full value of streaming data for advanced AI and ML applications, has released an open-source software to help developers and data pipeline engineers build high volume, real-time event processing workflows at scale. Officially dubbed Quine, the solution combines event streaming and graph data technologies to connect to existing data streams and build data into a stateful graph. Then, it analyzes this graph for user-specified "standing queries" and streams results out to trigger real-time event-driven workflows. The offering comes as the answer to event processing frameworks such as Flink.

We provide a framework for experimentation at https://mbuliga.github.io/quinegraphs/ic-vs-chem.html#icvschem with two artificial chemistries: directed interaction combinators (dirIC, defined in section 2) and chemlambda. We are interested if these chemistries allow for artificial life behaviour: replication, metabolism and death. The main conclusion of these experiments is that graph rewrites systems which allow conflicting rewrites are better than those which don't, as concerns their artificial life properties. This is in contradiction with the search for good graph rewrite systems for decentralized computing, where non-conflicting graph rewrite systems are historically preferred. This continues the artificial chemistry experiments with chemlambda, lambda calculus or interaction combinators, available from the entry page at https://chemlambda.github.io/index.html and described in arXiv:2003.14332.

The following correction was printed in the Guardian's Corrections and Clarifications column, Thursday April 22 2004 The seminar at which Stephen Jay Gould was rigorously questioned by Dennett's students was Dennett's seminar at Tufts, not Gould's at Harvard. Dennett wrote Darwin's Dangerous Idea before, not after, Gould called him a "Darwinian fundamentalist". Dan Dennett is a sailor, with a billowing white beard and moustaches that he twiddles when thinking. He uses "salty" as a term of praise and has just bought a 42ft boat that sleeps five and could, if he wished, cross the Atlantic. His passion for sailing may be the best way to approach his philosophy. In both, un-charted and dangerous areas are to be navigated by explorers ingeniously equipped. Like all sailors, he has stories. One concerns a French couple he met when sailing off Greenland. They were on their honeymoon, sailing from France to Iceland, then Greenland, and finally, in one long reach, from Greenland to the Falklands.

A robot, in order to act intelligently, must be able to reason from facts which its sensors detect to conclusions which govern its actions. This reasoning process is so central to human intelligence that it seems immediately relevant to the problems of robot design to consider its properties, how it might be analysed and imitated.