With the rise of artificial intelligence (A.I.) and large language models like ChatGPT, a new race for achieving artificial general intelligence (A.G.I) has started. While many speculate how and when A.I. will achieve A.G.I., there is no clear agreement on how A.G.I. can be detected in A.I. models, even when popular tools like the Turing test (and its modern variations) are used to measure their intelligence. In this work, we discuss why traditional methods like the Turing test do not suffice for measuring or detecting A.G.I. and provide a new, practical method that can be used to decide if a system (computer or any other) has reached or surpassed A.G.I. To achieve this, we make two new contributions. First, we present a clear definition for general intelligence (G.I.) and set a G.I. Threshold (G.I.T.) that can be used to distinguish between systems that achieve A.G.I. and systems that do not. Second, we present a new framework on how to construct tests that can detect if a system has achieved G.I. in a simple, comprehensive, and clear-cut fail/pass way. We call this novel framework the Turing test 2.0. We then demonstrate real-life examples of applying tests that follow our Turing test 2.0 framework on modern A.I. models.

" 'When we hear it said that wireless valves think,' [Sir Geoffrey] Jefferson said, 'we may despair of language.' But no cybernetician had said the valves thought, no more than anyone would say that the nerve-cells thought. It was the system as a whole that'thought', in Alan's [Turing] view…" -- Andrew Hodges (from his book Alan Turing: the Enigma). In his rewarding book, How to Create a Mind, Ray Kurzweil tackles John Searle's Chinese room argument. That said, I do find its philosophical sections somewhat naïve. Of course there's no reason why a "world-renowned inventor, thinker and futurist" should also be an accomplished philosopher.

Augusta Ada King, Countess of Lovelace, widely regarded as the world's first computer programmer, when talking about the Analytical Engine said, "The Analytical Engine has no pretensions whatever to originate anything" [1]. Hence, it is safe to say that the question "Can computers think?", in some form, not only predates the concept of Artificial Intelligence (AI) but is almost as old as the Analytical Engine. This question has stimulated the minds of pioneers and researchers from different domains including computer science, mathematics, psychology and philosophy. This essay delves into some of the important facets of this question. It is primarily driven by the thoughts and arguments of Alan M. Turing and John R. Searle, two pioneers who have extensively explored this question.

The Chinese room argument holds that a program cannot give a computer a "mind", "understanding" or "consciousness",[a] regardless of how intelligently or human-like the program may make the computer behave. The argument was first presented by philosopher John Searle in his paper, "Minds, Brains, and Programs", published in Behavioral and Brain Sciences in 1980. It has been widely discussed in the years since.[1] The centerpiece of the argument is a thought experiment known as the Chinese room.[2] The argument is directed against the philosophical positions of functionalism and computationalism,[3] which hold that the mind may be viewed as an information-processing system operating on formal symbols. The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.[b] Although it was originally presented in reaction to the statements of artificial intelligence (AI) researchers, it is not an argument against the goals of AI research, because it does not limit the amount of intelligence a machine can display.[4] The argument applies only to digital computers running programs and does not apply to machines in general.[5] Searle's thought experiment begins with this hypothetical premise: suppose that artificial intelligence research has succeeded in constructing a computer that behaves as if it understands Chinese. It takes Chinese characters as input and, by following the instructions of a computer program, produces other Chinese characters, which it presents as output. Suppose, says Searle, that this computer performs its task so convincingly that it comfortably passes the Turing test: it convinces a human Chinese speaker that the program is itself a live Chinese speaker. To all of the questions that the person asks, it makes appropriate responses, such that any Chinese speaker would be convinced that they are talking to another Chinese-speaking human being.

They thought that the Chinese Room argument showed that computationalism could never fully account for the first-person perspective, that the "computer metaphor for the mind" might lead to some vital social questions being ignored, that passing the Turing Test They conducted 20 interviews with a rather idiosyncratic collection of people, largely on the east and west coasts, to find out what the consensus was in the field. One of their happy discoveries was that connectionism (about which they initially knew little) was expected to overcome many of these obstacles. Each interview begins with a brief personal history of why the interviewee became involved with the subject and what they take it to be, and then moves into a discussion of contemporary issues which the editors find interesting. While the interviews do not conform to a set pattern, they return regularly to a few favorite themes: the Chinese Room, the importance of the Turing Test, why "symbolic AI" has failed (a claim that is made repeatedly throughout the book), and the significance of connectionism as a replacement for it Wilensky, and Winograd could possibly be said to be active in mainstream AI; on the other hand there are seven or eight philosophers, of whom only Dennett has a sympathetic interest in AI; all the others have rejected its premises, and Dreyfus, Searle and Weizenbaum are notorious for their passionate and sustained attacks on the subject. This would be less important but for the fact that AI is the main subject matter of several of the interviews.

The argument and thought-experiment now generally known as the Chinese Room Argument was first published in a paper in 1980 by American philosopher John Searle (1932-). It has become one of the best-known arguments in recent philosophy. Searle imagines himself alone in a room following a computer program for responding to Chinese characters slipped under the door. Searle understands nothing of Chinese, and yet, by following the program for manipulating symbols and numerals just as a computer does, he produces appropriate strings of Chinese characters that fool those outside into thinking there is a Chinese speaker in the room. The narrow conclusion of the argument is that programming a digital computer may make it appear to understand language but does not produce real understanding.

The Chinese room argument is a thought experiment of John Searle (1980a) and associated (1984) derivation. It is one of the best known and widely credited counters to claims of artificial intelligence (AI)---that is, to claims that computers do or at least can (someday might) think. According to Searle's original presentation, the argument is based on two key claims: brains cause minds and syntax doesn't suffice for semantics. Its target is what Searle dubs "strong AI." According to strong AI, Searle says, "the computer is not merely a tool in the study of the mind, rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states" (1980a, p. 417). Searle contrasts strong AI with "weak AI."

We have to haggle about what equally well means. We can get a 1-1 correspondence between Chinese dialogs and chess scores by enumerating Chinese dialogs and enumerating chess scores and putting the nth dialog correspond to the nth score. Both Chinese dialogs and chess scores have meaningful substructures, and the previously described correspondence does not make the substructures correspond. One structure is that of initial segments. The initial segment of a Chinese dialog is meaningful to a Chinese, and an initial segment of a chess score is meaningful to a chess player, and these meanings related to the meanings of the whole dialog and the whole score respectively.

Against "strong AI," Searle (1980a) asks you to imagine yourself a monolingual English speaker "locked in a room, and given a large batch of Chinese writing" plus "a second batch of Chinese script" and "a set of rules" in English "for correlating the second batch with the first batch." The rules "correlate one set of formal symbols with another set of formal symbols"; "formal" (or "syntactic") meaning you "can identify the symbols entirely by their shapes." A third batch of Chinese symbols and more instructions in English enable you "to correlate elements of this third batch with elements of the first two batches" and instruct you, thereby, "to give back certain sorts of Chinese symbols with certain sorts of shapes in response." Those giving you the symbols "call the first batch'a script' [a data structure with natural language processing applications], "they call the second batch'a story', and they call the third batch'questions'; the symbols you give back "they call . . . Nevertheless, you "get so good at following the instructions" that "from the point of view of someone outside the room" your responses are "absolutely indistinguishable from those of Chinese speakers." Just by looking at your answers, nobody can tell you "don't speak a word of Chinese." Producing answers "by manipulating uninterpreted formal symbols," it seems "[a]s far as the Chinese is concerned," you "simply behave like a computer"; specifically, like a computer running Schank and Abelson's (1977) "Script Applier Mechanism" story understanding program (SAM), which Searle's takes for his example. But in imagining himself to be the person in the room, Searle thinks it's "quite obvious . . .