Language Is the Manifestation of Thought – Why Language Models Can Speak Without Thinking
Anyone who wants to know whether artificial intelligence thinks must first know what thinking actually is. That is precisely what is currently missing—in media reporting, in large parts of science, and among the makers of language models themselves. AI is credited with intelligence, understanding, learning, decision-making, or even empathy without first clarifying what these terms actually designate. A system that appears linguistically assured is credited with understanding; one that solves problems is deemed intelligent; and one that says “I” already seems to possess consciousness. Instead of determining the matter conceptually, people are dazzled by the impressive performance and infer an equally impressive machine mind.
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As long as the majority of the population is not immune to SARS-COV2, the health care system must be protected from collapse due to overload with COVID patients. Therefore, for the past year, we have been testing measures ranging from increased hand washing to total lockdown with some success. Measures are introduced, tightened, relaxed, or abolished, only to be reintroduced, … and so it goes. Politicians justify their actions with incidence values, utilization of hospitals, model calculations and the advice of experts (see also here in my blog). Undeniably, many of these (anti)Corona measures have enormous plausibility. It is also trivial to realize that a total lockdown can severely limit the spread of a virus. However, this cannot be sustained forever. Therefore, the question of which of the measures from the black box lockdown have an effect, and for which the harm outweighs the benefit, is immensely relevant. With this knowledge, one might put together an evidence-based package of Corona measures that is less drastic than a lockdown, but just as effective. And perhaps in this way persuade some skeptics to participate. This is why the question of which evidence is available for the effectiveness of individual measures is so important. But beware. 
Science gobbles up massive amounts of societal resources, not just financial ones. For academic research in particular, which is self-governing and likes to invoke the freedom of research (which in Germany is even enshrined in the constitution), this raises the question of how it allocates the resources made available to it by society. There is no natural limit to how much research can be done – but there is certainly a limit to the resources that society can and will allocate to research. So which research should be funded, which scientists should be supported?
In this post I’ll be looking at the question of why scientific careers today depend so much on the Journal Impact Factor (JIF). And the acquisition of as much third-party funding as possible. Or, more generally, why the content, originality and reliability of research results are often a secondary matter when commissions talk their heads off about who to include in their own ranks. And who not. Or which grant applications deserve to be funded. In short, follow me on a brief and incomplete history of how and why we ended up judging the quality of science through proxies such as JIF and amount of third party funding. Perhaps a historical perspective will also yield clues as to how we can overcome this mess. But I am getting ahead of myself. Let’s start where it all began, with the founding fathers of modern science. 
