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.

Multiple events hosted at a historic former hotel in Berkeley, California, have brought together people from intellectual movements popular at the highest levels in Silicon Valley while platforming prominent people linked to scientific racism, the Guardian reveals. But because of alleged financial ties between the non-profit that owns the building – Lightcone Infrastructure (Lightcone) – and jailed crypto mogul Sam Bankman-Fried, the administrators of FTX, Bankman-Fried's failed crypto exchange, are demanding the return of almost 5m that new court filings allege were used to bankroll the purchase of the property. During the last year, Lightcone and its director, Oliver Habryka, have made the 20m Lighthaven Campus available for conferences and workshops associated with the "longtermism", "rationalism" and "effective altruism" (EA) communities, all of which often see empowering the tech sector, its elites and its beliefs as crucial to human survival in the far future. At these events, movement influencers rub shoulders with startup founders and tech-funded San Francisco politicians – as well as people linked to eugenics and scientific racism. Since acquiring the Lighthaven property – formerly the Rose Garden Inn – in late 2022, Lightcone has transformed it into a walled, surveilled compound without attracting much notice outside the subculture it exists to promote.

While rationalism would say that the machine learns emotions from the training data, romanticism instead poses that they are innate.

The American Medical Association (AMA) recently released its first policy recommendations for augmented intelligence. It highlights some of the most serious challenges in artificial intelligence, including the need for transparency, bias avoidance, reproducibility, and privacy. Those working in medicine may find this list familiar. Medicine has long struggled with similar problems. The similarities are not a coincidence. There are deep philosophical and methodological intersections across AI and clinical medicine. Both professions recently experienced a pendulum swing in their prevailing approaches. And in the zeitgeist of big data, powerful interests in medicine and AI are presently aligned on the same side of a centuries-long ideological struggle. People are understandably excited about a digital convergence in health tech. But ideological alignment and entrenchment may reinforce these shared challenges in a perverse codependency. The philosophical intersections between AI and medicine are not well known within their respective communities, let alone across them. Yet a positive and productive collaboration may unfold. AI and medicine embody important differences that could elevate each side and catalyze innovation.

One morning in the summer of 2015, I sat in a featureless office in Berkeley as a young computer programmer walked me through how he intended to save the world. The world needed saving, he insisted, not from climate change -- or from the rise of the far right, or the treacherous instability of global capitalism -- but from the advent of artificial superintelligence, which would almost certainly wipe humanity from the face of the earth unless certain preventative measures were put in place by a very small number of dedicated specialists such as himself, who alone understood the scale of the danger and the course of action necessary to protect against it. This intense and deeply serious young programmer was Nate Soares, the executive director of MIRI (Machine Intelligence Research Institute), a nonprofit organization dedicated to the safe -- which is to say, non-humanity-obliterating -- development of artificial intelligence. As I listened to him speak, and as I struggled (and failed) to follow the algebraic abstractions he was scrawling on a whiteboard in illustration of his preferred doomsday scenario, I was suddenly hit by the full force of a paradox: The austere and inflexible rationalism of this man's worldview had led him into a grand and methodically reasoned absurdity. In researching and reporting my book, To Be a Machine, I had spent much of the previous 18 months among the adherents of the transhumanist movement, a broad church comprising life-extension advocates, cryonicists, would-be cyborgs, Silicon Valley tech entrepreneurs, neuroscientists looking to convert the human brain into code, and so forth -- all of whom were entirely convinced that science and technology would allow us to transcend the human condition.

Research effort, with its emphasis on evaluation and measurable progress, things began to change. Instead SHRDLU (WIN72) is perhaps the canonical example. of systems whose architecture and vocabulary were The rapid growth of efforts to found the next generation of based on linguistic theory (in this case acoustic phonetics), systems on general-purpose knowledge representation languages new approaches based on statistical modelling and Bayesian (I'm thinking of several varieties of semantic nets, probability emerged and quickly spread. "Every time I fire a from plain to partitioned, as well as KRL, KL-ONE and linguist my system's performance improves" (Fred Jellinek, their successors, ending (not yet, of course) with CYC (See head of speech recognition at IBM, c. 1980, latterly repudiated (BRA08) for all these) stumbled to a halt once their failure by Fred but widely attested). As advanced from resolution theorem provers through a number more and more problems are re-conceived as instances of of stages to the current proliferation of a range of Description the noisy channel model, the empiricist paradigm continually Logic'reasoners'; Whereas in the 1970s and 1980s there grew, so did the need to manage the impact of change and was real energy and optimism at the interface between computational conflict: enter'truth maintenance', subsequently renamed and theoretical linguistics, the overwhelming success'reason maintenance'. While still using some of But outflanking these'normal science' advances of AI, the terminology of linguistic theory, computational linguistics the paradigm shifters were coming up fast on the outside: practioners are increasingly detached from theory itself, over the last ten years machine learning has spread from which has suffered a, perhaps connected, loss of energy and small specialist niches such as speech recognition to become sense of progress.