The Schrödinger Equation of the Hilbert Hyper Space

Quantum measurement requires an observer to prepare a specific macroscopic measuring device from various options. In previous papers we redefined this observer role through a new concept: the observer determination, that is, the observer’s unique selection between the various measurement-devices. Unlike the measurement itself that is rationalized as dictated by nature, we presented the observer determination as a selection that cannot be disputed since it can neither be measured nor proven to be true or false. In general, we suggest that every action or decision made by the observer is eventually an output of some measurement. The apparently contradiction between the observer free determination and the deterministic measurement output was solved by extending the Hilbert space into a Hyper Hilbert space that is a space with hierarchy. In that frame the so called free selection of the observer determination in a certain level turns out to be a deterministic measurement output in the next higher level of the hierarchy. An important role of the conventional Hilbert space is played by the Schr?dinger equation. It determines a basis of stationary states. In this paper we define the Schr?dinger equation that corresponds with the various levels and we show that each level can be characterized by a unique time scale. We also show how various levels can be synchronized. We believe that this hyperspace level represents a certain level in the physics of consciousness and therefore a level unique time scale can contribute to the time perception of the mind.

Homeostasis Processes Expressed as Flashes in a PoincaréSections

We describe a homeostasis system with a discrete map that is revealed by stroboscopic “flashes” (Poincaré sections) that are synchronized with the measurement events.

Bifurcation and Pattern Recognition

We propose a new approach in dealing with image recognition. We suggest that recognizing an image is related to the knowledge of a pure quantum state. Since most images are screened through incoherent photons, we introduce a method base on non-linear mapping iterations to regenerate coherence between the image photons.

Single Measurement of Figures

We introduce a new approach in dealing with pattern recognition issue. Recognizing a pattern is definitely not the exploration of a new discovery but rather the search for already known patterns. In reading for example the same text written in a hand writing, letters can appear in different shapes. Still, the text decoding corresponds with interpreting the large variety of hand writings shapes with fonts. Quantum mechanics also offer a kind of interpretation tool. Although, with the superposition principle it is possible to compose an infinite number of states, yet, an observer by conducting a measurement reduces the number of observed states into the predetermined basis states. Not only that any state collapses into one of the basis states, quantum mechanics also possesses a kind of correction mechanism in a sense that if the measured state is “close enough” to one of the basis states, it will collapse with high probability into this predetermined state. Thus, we can consider the collapse mechanism as a reliable way for the observer to interpret reality into his frame of concepts. Both interpretation ideas, pattern recognition and quantum measurement are integrated in this paper to formulate a quantum pattern recognition measuring procedure.

Rapid Quantum Search Algorithm

The collapse phenomenon, the parallelism principle and states correlation are used to define a type of a Grover rapid search engine. In our approach, the observer’s query and the Grover-unsorted-data are stored in different memories where the global state is represented by a tensor product of the associated states. In the proposed formalism, each query-state input activates an adjusted operator that implements the unsorted state in an appropriate 2-D Grover representation. It will be shown that once the representation is set, it takes mainly two operations to complete the whole query search. This seems to be a very efficient search algorithm.

Language-teaching method revisited (II)

High-stakes examinations The backwash from high-stakes examinations and other assessment procedures substantially influences the content and teaching method in courses.If,for example,an exam is mainly based on reading comprehension and writing—as most are,since the testing of oral proficiency is relatively expensive and time consuming—then classroom teaching is likely to focus on reading and writing at the expense of oral skills.If the marking of the exam involves substantial subtraction of points for grammatical and spelling mistakes,then obviously the teacher is going to make sure that he or she devotes lesson time to teaching and practising correct grammar and spelling.

Assessing prognosis in depression:comparing perspectives of AI models,mental health professionals and the general public

Background Artificial intelligence(AI)has rapidly permeated various sectors,including healthcare,highlighting its potential to facilitate mental health assessments.This study explores the underexplored domain of AI’s role in evaluating prognosis and long-term outcomes in depressive disorders,offering insights into how AI large language models(LLMs)compare with human perspectives.Methods Using case vignettes,we conducted a comparative analysis involving different LLMs(ChatGPT-3.5,ChatGPT-4,Claude and Bard),mental health professionals(general practitioners,psychiatrists,clinical psychologists and mental health nurses),and the general public that reported previously.We evaluate the LLMs ability to generate prognosis,anticipated outcomes with and without professional intervention,and envisioned long-term positive and negative consequences for individuals with depression.Results In most of the examined cases,the four LLMs consistently identified depression as the primary diagnosis and recommended a combined treatment of psychotherapy and antidepressant medication.ChatGPT-3.5 exhibited a significantly pessimistic prognosis distinct from other LLMs,professionals and the public.ChatGPT-4,Claude and Bard aligned closely with mental health professionals and the general public perspectives,all of whom anticipated no improvement or worsening without professional help.Regarding long-term outcomes,ChatGPT 3.5,Claude and Bard consistently projected significantly fewer negative long-term consequences of treatment than ChatGPT-4.Conclusions This study underscores the potential of AI to complement the expertise of mental health professionals and promote a collaborative paradigm in mental healthcare.The observation that three of the four LLMs closely mirrored the anticipations of mental health experts in scenarios involving treatment underscores the technology’s prospective value in offering professional clinical forecasts.The pessimistic outlook presented by ChatGPT 3.5 is concerning,as it could potentially diminish patients’drive to initiate or continue depression therapy.In summary,although LLMs show potential in enhancing healthcare services,their utilisation requires thorough verification and a seamless integration with human judgement and skills.

Identifying depression and its determinants upon initiating treatment: ChatGPT versus primary care physicians

Objective To compare evaluations of depressive episodes and suggested treatment protocols generated by Chat Generative Pretrained Transformer(ChatGPT)-3 and ChatGPT-4 with the recommendations of primary care physicians.Methods Vignettes were input to the ChatGPT interface.These vignettes focused primarily on hypothetical patients with symptoms of depression during initial consultations.The creators of these vignettes meticulously designed eight distinct versions in which they systematically varied patient attributes(sex,socioeconomic status(blue collar worker or white collar worker)and depression severity(mild or severe)).Each variant was subsequently introduced into ChatGPT-3.5 and ChatGPT-4.Each vignette was repeated 10 times to ensure consistency and reliability of the ChatGPT responses.Results For mild depression,ChatGPT-3.5 and ChatGPT-4 recommended psychotherapy in 95.0%and 97.5%of cases,respectively.Primary care physicians,however,recommended psychotherapy in only 4.3%of cases.For severe cases,ChatGPT favoured an approach that combined psychotherapy,while primary care physicians recommended a combined approach.The pharmacological recommendations of ChatGPT-3.5 and ChatGPT-4 showed a preference for exclusive use of antidepressants(74%and 68%,respectively),in contrast with primary care physicians,who typically recommended a mix of antidepressants and anxiolytics/hypnotics(67.4%).Unlike primary care physicians,ChatGPT showed no gender or socioeconomic biases in its recommendations.Conclusion ChatGPT-3.5 and ChatGPT-4 aligned well with accepted guidelines for managing mild and severe depression,without showing the gender or socioeconomic biases observed among primary care physicians.Despite the suggested potential benefit of using atificial intelligence(AI)chatbots like ChatGPT to enhance clinical decision making,further research is needed to refine AI recommendations for severe cases and to consider potential risks and ethical issues.