Tandym

tandym

Recently I came across the issue of using the module in Nuxt.js and invoking a $ in subsequent line of code in the same method. The conundrum began when the lines after the method did not execute as intended since the page was redirected to the redirect URI. auth router.push auth.loginWith It has been only a week in the Vue.js land, so I suppose this issue is something faced by many newbies. So, here goes where it all started: I have a function, whose body looks like: authenticate() { .$auth.loginWith( , { : .user }) .$router.push( .localePath({ : }))
} (e) { } try await this 'local' data this // some other line of code that shows loading msgs // ... this this path 'dashboard' catch // handling error Now, notice that once the gets invoked, the execution is handed over to the middleware of nuxt.js. Thus, using a in is redundant. line: 2 auth $router.push line:5 Before we proceed any further, let’s take a look where the auth’s configs are defined: Go to: nuxt.config.js Find the key auth auth: { : { : , : , : , : 
    }, redirect login '/login' logout '/' callback '/login' home Notice, the key. home Bingo! This is exactly where we want to tweak. Before we do any tweaking, let’s make it clear what we are trying to do and : why We want to use all the good things offered by the module, other than the thing. auth redirect The lines after the are necessary to be executed before the view can be updated. In this case it is a loading and success notification that needs to be shown before the user is redirected to the dashboard upon successful login. $auth.loginWith Now, the only thing left to do is disabling the redirect case in option auth will do the job! home: false The object in would look like this now: auth nuxt.config.js auth: { : { : , : , : , : }, redirect login '/login' logout '/' callback '/login' home false Bam! We are done. Previously published at https://medium.com/consol/using-auth-modules-redirect-in-tandem-with-router-push-in-nuxt-js-d6d703e0a85a

Using $auth Module’s Redirect in Tandem With $router.push in Nuxt.js

Explanation of ERC-6900: Modular Smart Contract Accounts and Plugins

Primer on Large Language Model (LLM) Inference Optimizations: 2. Introduction to Artificial Intelligence (AI) Accelerators

Primer on Large Language Model (LLM) Inference Optimizations: 2. Úvod do akcelerátorů umělé inteligence (AI)

Introducción a las optimizaciones de inferencia de modelos de lenguaje grandes (LLM): 2. Introducción a los aceleradores de inteligencia artificial (IA)

Manual sobre as optimizacións de inferencia do modelo de linguaxe grande (LLM): 2. Introdución aos aceleradores de intelixencia artificial (IA)

वृहद भाषा मॉडल (एलएलएम) अनुमान अनुकूलन पर प्राइमर: 2. कृत्रिम बुद्धिमत्ता (एआई) त्वरक का परिचय

Introduzione alle ottimizzazioni dell'inferenza del Large Language Model (LLM): 2. Introduzione agli acceleratori dell'intelligenza artificiale (IA)

大規模言語モデル (LLM) 推論最適化入門: 2. 人工知能 (AI) アクセラレータの概要

Primer on Large Language Model (LLM) დასკვნის ოპტიმიზაცია: 2. შესავალი ხელოვნური ინტელექტის (AI) ამაჩქარებლებში

Үлкен тілдік үлгідегі (LLM) қорытындыны оңтайландырудағы праймер: 2. Жасанды интеллект (AI) үдеткіштеріне кіріспе

Том хэлний загвар (LLM) дүгнэлтийг оновчтой болгох праймер: 2. Хиймэл оюун ухаан (AI) хурдасгуурын танилцуулга

Horudhac ku saabsan Qaabka Luqadda Weyn (LLM) Kordhinta Farshaxanka: 2. Horudhaca Sirdoonka Artificial (AI) Dardariyeyaasha

Primer on Large Language Model (LLM) Inference Optimizations: 2. Panimula sa Artificial Intelligence (AI) Accelerators

Sách nhập môn về Tối ưu hóa suy luận mô hình ngôn ngữ lớn (LLM): 2. Giới thiệu về bộ tăng tốc trí tuệ nhân tạo (AI)

The WEF continues to be a propaganda arm for the Chinese Communist Party and its social credit system based on digital ID & CBDC: perspective. Communist China’s digital yuan serves as a model Central Bank Digital Currency (CBDC) to the world because it reduces reliance on physical cash while “democratizing access to banking services,” according to a World Economic Forum (WEF) report. Published on October 8, the report “Global Financial Inclusion Practices: Case Studies from China, India and the USA” heaps heavy praise on the communist regime for having implemented a programmable digital yuan that fosters what the authors call “financial inclusion” through the democratization of banking services. “By reducing reliance on physical cash and democratizing access to banking services, the digital yuan serves as a model for CBDC use to boost financial inclusion”
WEF, “Global Financial Inclusion Practices,” October 2024 If you’re wondering why the WEF is praising communist China for “democratizing” anything, it’s worth noting that the report was produced in collaboration with Beijing’s Tsinghua University Peoples Bank of China School of Finance, which was founded on March 29, 2012, as a joint venture between the University and the People’s Bank of China (PBC). In the report, there is no mention of the Chinese Communist Party (CCP) nor its social credit system and how it leverages digital ID and CBDC to incentivize, coerce, or otherwise manipulate human behavior. Instead, the report says: “The introduction of China’s digital yuan has played an important role in promoting financial inclusion by making financial services more accessible and affordable to underserved populations. “By reducing the reliance on physical cash and integrating the digital yuan into everyday transactions, even in areas with poor network connectivity, the e-CNY has helped to bring unbanked or underbanked individuals into the formal financial system.” Try telling that to the million or so Uighurs that have been sent to “re-education” camps, or try telling that to the millions of Chinese “netizens” who have been blacklisted by the social credit system. Under China’s social credit system, citizens are given a credit score based on their online and offline behavior. It’s a system that rewards “good” behavior like spending time with the elderly while punishing “bad” behavior like criticizing the government or spending too much time playing videogames. When “trust” is broken in one area, restrictions are placed everywhere — meaning citizens who commit even minor infractions can be blacklisted from traveling, going to restaurants, renting a home, or even having insurance. This has happened to over 30 million citizens, according to Chinese State-run propaganda channel CGTN. “The digital yuan has revolutionized Suzhou’s financial landscape, enhancing financial inclusion by providing broader and more convenient access to affordable services […] With thorough attention to infrastructure, regulation, technology and education, Suzhou’s approach offers a proven model for cities worldwide”
WEF, “Global Financial Inclusion Practices,” October 2024 Speaking of CCP propaganda, the WEF report highlights how China’s CBDC revolutionized the city of Suzhou’s financial landscape, stating that Suzhou is a proven model for cities worldwide. According to the report, “Suzhou, prominently featured in the [CBDC] pilot phases, stands out as a leader in the adoption of the digital yuan in China.” “The city has not only trialed traditional uses but also explored innovative applications by integrating the digital yuan with local transport and government services, enhancing its utility and acceptance among residents. “For example, users with a digital yuan wallet can touch their phone screen to the turnstile at metro stations, providing easy access to the station.” What the authors fail to mention in their example of a model city is that CBDCs cannot function without being linked to a person’s digital identity, and that both CBDC and digital ID are two of the three key components of Digital Public Infrastructure (DPI) that also includes massive data exchanges. In communist China, users with a low social credit score cannot use their digital yuan wallets on their phones to pass the turnstiles at the metro. And the CCP propagandists at the WEF have the gall to say that it’s a model system for the world! “The digital yuan enhances transaction efficiency and security, promoting public trust and providing a model for other CBDCs aimed at expanding financial inclusion globally”
WEF, “Global Financial Inclusion Practices,” October 2024 Three years ago the Center for a New American Security (CNAS) published a report detailing China’s ambitions for an authoritarian Digital Currency / Electronic Payment (DCEP) system in which the State aimed to process and support over 300,000 financial transactions per second, with the data feeding the Communist Party’s social credit system in real-time. According to the CNAS report, “This digital currency push is a major step in expanding the party’s digital authoritarianism, adding real-time financial data into the CCP’s strategy for technology-driven governance.” But it’s not just the financial transactions that the CCP is interested in — it’s about combining that financial data with data collected from the Internet of Things, smart homes, CCTV footage, and of course mobile phone usage, including citizens’ geolocation and browsing histories. According to CNAS, “The system will also enable the CCP to exercise greater control over private transactions, as well as to wield punitive power over Chinese citizens in tandem with the social credit system.” “In addition to basic information about users and transactions, it is possible that various metadata associated with users’ movements and devices could also be infused with such big data,” the CNAS reports states, adding, “The PBOC [People’s Bank of China] will become a possessor of a significant data trove to combine with its tools for censuring and surveilling individuals.” The WEF continues to be a propaganda arm of the Chinese Communist Party, calling its digital ID-based CBDC a model for the world despite it being used as a totalitarian tool for social credit. Tim Hinchliffe, Editor, The Sociable The WEF continues to be a propaganda arm for the Chinese Communist Party and its social credit system based on digital ID & CBDC: perspective. The WEF continues to be a propaganda arm for the Chinese Communist Party and its social credit system based on digital ID & CBDC: perspective. Communist China’s digital yuan serves as a model Central Bank Digital Currency ( CBDC ) to the world because it reduces reliance on physical cash while “democratizing access to banking services,” according to a World Economic Forum (WEF) report. CBDC Published on October 8, the report “ Global Financial Inclusion Practices: Case Studies from China, India and the USA ” heaps heavy praise on the communist regime for having implemented a programmable digital yuan that fosters what the authors call “financial inclusion” through the democratization of banking services. the report Global Financial Inclusion Practices: Case Studies from China, India and the USA “By reducing reliance on physical cash and democratizing access to banking services, the digital yuan serves as a model for CBDC use to boost financial inclusion” WEF, “Global Financial Inclusion Practices,” October 2024 “By reducing reliance on physical cash and democratizing access to banking services, the digital yuan serves as a model for CBDC use to boost financial inclusion” “By reducing reliance on physical cash and democratizing access to banking services, the digital yuan serves as a model for CBDC use to boost financial inclusion” “By reducing reliance on physical cash and democratizing access to banking services, the digital yuan serves as a model for CBDC use to boost financial inclusion” WEF, “ Global Financial Inclusion Practices ,” October 2024 Global Financial Inclusion Practices If you’re wondering why the WEF is praising communist China for “democratizing” anything, it’s worth noting that the report was produced in collaboration with Beijing’s Tsinghua University Peoples Bank of China School of Finance , which was founded on March 29, 2012, as a joint venture between the University and the People’s Bank of China (PBC). Tsinghua University Peoples Bank of China School of Finance In the report, there is no mention of the Chinese Communist Party (CCP) nor its social credit system and how it leverages digital ID and CBDC to incentivize, coerce, or otherwise manipulate human behavior. Instead, the report says: “The introduction of China’s digital yuan has played an important role in promoting financial inclusion by making financial services more accessible and affordable to underserved populations. “The introduction of China’s digital yuan has played an important role in promoting financial inclusion by making financial services more accessible and affordable to underserved populations. “The introduction of China’s digital yuan has played an important role in promoting financial inclusion by making financial services more accessible and affordable to underserved populations. “By reducing the reliance on physical cash and integrating the digital yuan into everyday transactions, even in areas with poor network connectivity, the e-CNY has helped to bring unbanked or underbanked individuals into the formal financial system.” “By reducing the reliance on physical cash and integrating the digital yuan into everyday transactions, even in areas with poor network connectivity, the e-CNY has helped to bring unbanked or underbanked individuals into the formal financial system.” “By reducing the reliance on physical cash and integrating the digital yuan into everyday transactions, even in areas with poor network connectivity, the e-CNY has helped to bring unbanked or underbanked individuals into the formal financial system.” Try telling that to the million or so Uighurs that have been sent to “re-education” camps, or try telling that to the millions of Chinese “netizens” who have been blacklisted by the social credit system. Uighurs Under China’s social credit system, citizens are given a credit score based on their online and offline behavior. It’s a system that rewards “good” behavior like spending time with the elderly while punishing “bad” behavior like criticizing the government or spending too much time playing videogames. When “trust” is broken in one area, restrictions are placed everywhere — meaning citizens who commit even minor infractions can be blacklisted from traveling, going to restaurants, renting a home, or even having insurance. This has happened to over 30 million citizens, according to Chinese State-run propaganda channel CGTN. “The digital yuan has revolutionized Suzhou’s financial landscape, enhancing financial inclusion by providing broader and more convenient access to affordable services […] With thorough attention to infrastructure, regulation, technology and education, Suzhou’s approach offers a proven model for cities worldwide” WEF, “Global Financial Inclusion Practices,” October 2024 “The digital yuan has revolutionized Suzhou’s financial landscape, enhancing financial inclusion by providing broader and more convenient access to affordable services […] With thorough attention to infrastructure, regulation, technology and education, Suzhou’s approach offers a proven model for cities worldwide” “The digital yuan has revolutionized Suzhou’s financial landscape, enhancing financial inclusion by providing broader and more convenient access to affordable services […] With thorough attention to infrastructure, regulation, technology and education, Suzhou’s approach offers a proven model for cities worldwide” “The digital yuan has revolutionized Suzhou’s financial landscape, enhancing financial inclusion by providing broader and more convenient access to affordable services […] With thorough attention to infrastructure, regulation, technology and education, Suzhou’s approach offers a proven model for cities worldwide” WEF, “ Global Financial Inclusion Practices ,” October 2024 Global Financial Inclusion Practices Speaking of CCP propaganda, the WEF report highlights how China’s CBDC revolutionized the city of Suzhou’s financial landscape, stating that Suzhou is a proven model for cities worldwide. According to the report, “ Suzhou, prominently featured in the [CBDC] pilot phases, stands out as a leader in the adoption of the digital yuan in China .” Suzhou, prominently featured in the [CBDC] pilot phases, stands out as a leader in the adoption of the digital yuan in China Suzhou, prominently featured in the [CBDC] pilot phases, stands out as a leader in the adoption of the digital yuan in China “ The city has not only trialed traditional uses but also explored innovative applications by integrating the digital yuan with local transport and government services, enhancing its utility and acceptance among residents . The city has not only trialed traditional uses but also explored innovative applications by integrating the digital yuan with local transport and government services, enhancing its utility and acceptance among residents The city has not only trialed traditional uses but also explored innovative applications by integrating the digital yuan with local transport and government services, enhancing its utility and acceptance among residents “ For example, users with a digital yuan wallet can touch their phone screen to the turnstile at metro stations, providing easy access to the station .” For example, users with a digital yuan wallet can touch their phone screen to the turnstile at metro stations, providing easy access to the station For example, users with a digital yuan wallet can touch their phone screen to the turnstile at metro stations, providing easy access to the station What the authors fail to mention in their example of a model city is that CBDCs cannot function without being linked to a person’s digital identity, and that both CBDC and digital ID are two of the three key components of Digital Public Infrastructure (DPI) that also includes massive data exchanges. Digital Public Infrastructure In communist China, users with a low social credit score cannot use their digital yuan wallets on their phones to pass the turnstiles at the metro. And the CCP propagandists at the WEF have the gall to say that it’s a model system for the world! “The digital yuan enhances transaction efficiency and security, promoting public trust and providing a model for other CBDCs aimed at expanding financial inclusion globally” WEF, “Global Financial Inclusion Practices,” October 2024 “The digital yuan enhances transaction efficiency and security, promoting public trust and providing a model for other CBDCs aimed at expanding financial inclusion globally” “The digital yuan enhances transaction efficiency and security, promoting public trust and providing a model for other CBDCs aimed at expanding financial inclusion globally” “The digital yuan enhances transaction efficiency and security, promoting public trust and providing a model for other CBDCs aimed at expanding financial inclusion globally” WEF, “ Global Financial Inclusion Practices ,” October 2024 Global Financial Inclusion Practices Three years ago the Center for a New American Security (CNAS) published a report detailing China’s ambitions for an authoritarian Digital Currency / Electronic Payment (DCEP) system in which the State aimed to process and support over 300,000 financial transactions per second, with the data feeding the Communist Party’s social credit system in real-time. published a report According to the CNAS report, “ This digital currency push is a major step in expanding the party’s digital authoritarianism, adding real-time financial data into the CCP’s strategy for technology-driven governance .” This digital currency push is a major step in expanding the party’s digital authoritarianism, adding real-time financial data into the CCP’s strategy for technology-driven governance This digital currency push is a major step in expanding the party’s digital authoritarianism, adding real-time financial data into the CCP’s strategy for technology-driven governance But it’s not just the financial transactions that the CCP is interested in — it’s about combining that financial data with data collected from the Internet of Things, smart homes, CCTV footage, and of course mobile phone usage, including citizens’ geolocation and browsing histories. According to CNAS, “ The system will also enable the CCP to exercise greater control over private transactions, as well as to wield punitive power over Chinese citizens in tandem with the social credit system .” The system will also enable the CCP to exercise greater control over private transactions, as well as to wield punitive power over Chinese citizens in tandem with the social credit system The system will also enable the CCP to exercise greater control over private transactions, as well as to wield punitive power over Chinese citizens in tandem with the social credit system “ In addition to basic information about users and transactions, it is possible that various metadata associated with users’ movements and devices could also be infused with such big data ,” the CNAS reports states, adding, “ The PBOC [People’s Bank of China] will become a possessor of a significant data trove to combine with its tools for censuring and surveilling individuals .” In addition to basic information about users and transactions, it is possible that various metadata associated with users’ movements and devices could also be infused with such big data In addition to basic information about users and transactions, it is possible that various metadata associated with users’ movements and devices could also be infused with such big data The PBOC [People’s Bank of China] will become a possessor of a significant data trove to combine with its tools for censuring and surveilling individuals The PBOC [People’s Bank of China] will become a possessor of a significant data trove to combine with its tools for censuring and surveilling individuals The WEF continues to be a propaganda arm of the Chinese Communist Party, calling its digital ID-based CBDC a model for the world despite it being used as a totalitarian tool for social credit. Tim Hinchliffe, Editor, The Sociable Tim Hinchliffe, Editor, The Sociable Tim Hinchliffe, Editor, The Sociable

China’s Digital Yuan: A CBDC Model Reducing Cash Dependency and Expanding Financial Access, Says WEF

China se digitale yuan: 'n CBDC-model wat kontantafhanklikheid verminder en finansiële toegang uitbrei, sê WEF

Кітайскі лічбавы юань: мадэль CBDC, якая зніжае залежнасць ад грашовых сродкаў і пашырае фінансавы доступ, кажа WEF

চীনের ডিজিটাল ইউয়ান: একটি CBDC মডেল নগদ নির্ভরতা হ্রাস করে এবং আর্থিক অ্যাক্সেস সম্প্রসারণ করে, WEF বলে

Kineski digitalni juan: CBDC model koji smanjuje ovisnost o gotovini i proširuje pristup finansijama, kaže WEF

El yuan digital chino: un modelo de CBDC que reduce la dependencia del efectivo y amplía el acceso financiero, afirma el WEF

चीन का डिजिटल युआन: नकदी निर्भरता को कम करने और वित्तीय पहुंच का विस्तार करने वाला एक सीबीडीसी मॉडल, WEF का कहना है

Yuan Digital Tiongkok: Model CBDC yang Mengurangi Ketergantungan Uang Tunai dan Memperluas Akses Keuangan, Kata WEF

中国のデジタル人民元：WEFによると、現金依存を減らし金融アクセスを拡大するCBDCモデル

चीनको डिजिटल युआन: नगद निर्भरता घटाउने र वित्तीय पहुँच विस्तार गर्ने CBDC मोडेल, WEF भन्छ

China's Digital Yuan: A CBDC Model Kuderedza Kutsamira Kwemari uye Kuwedzera Kuwanikwa Kwemari, inodaro WEF.

Кинески дигитални јуан: ЦБДЦ модел који смањује зависност од готовине и проширује финансијски приступ, каже ВЕФ

WEF'e göre Çin'in Dijital Yuanı: Nakit Bağımlılığını Azaltan ve Finansal Erişimi Genişleten Bir CBDC Modeli

Authors:
(1) Timothy R. McIntosh;
(2) Teo Susnjak;
(3) Tong Liu;
(4) Paul Watters;
(5) Malka N. Halgamuge. Table of Links Abstract and Introduction Background: Evolution of Generative AI The Current Generative AI Research Taxonomy Innovative Horizon of MOE Speculated Capabilities of Q* Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References V. SPECULATED CAPABILITIES OF Q* In the burgeoning realm of AI, the anticipated Q* project stands as a beacon of potential breakthroughs, heralding advancements that could redefine the landscape of AI capabilities (Fig. 5). A. Enhanced General Intelligence Q*’s development in the arena of general intelligence represents a paradigm shift from specialized to holistic AI, indicating a broadening of the model’s cognitive abilities akin to human intelligence. This advanced form of general intelligence involves integrating diverse neural network architectures and machine learning techniques, enabling the AI to process and synthesize multifaceted information seamlessly. The universal adapter approach, mirroring models like T0, could endow Q* with the capability to rapidly assimilate knowledge from various domains. This method allows Q* to learn adaptable module plugins, enhancing its ability to tackle new data types while preserving existing skills, leading to an AI model that combines narrow specializations into a comprehensive, adaptive, and versatile reasoning system. The corresponding quasi-mathematical formulation can be expressed as: Such advancements in AI suggest the emergence of an intelligence that not only parallels but potentially exceeds human cognitive flexibility, with far-reaching implications in facilitating cross-disciplinary innovations and complex problem-solving. The speculated capabilities of Q* bring forth complex ethical implications and governance challenges. As AI systems approach higher levels of autonomy and decision-making, it is crucial to establish robust ethical frameworks and governance structures to ensure responsible and transparent AI development. This involves mitigating potential risks associated with advanced AI capabilities, emphasizing the need for comprehensive and dynamic ethical guidelines that evolve in tandem with AI advancements. B. Advanced Self-Learning and Exploration In the realm of advanced AI development, Q* is anticipated to represent a significant evolution in self-learning and exploration capabilities. It is speculated to utilize sophisticated Policy Neural Networks (NNs), similar to those in AlphaGo, but with substantial enhancements to handle the complexities of language and reasoning tasks. These networks are expected to employ advanced reinforcement learning techniques like Proximal Policy Optimization (PPO), which stabilizes policy updates and improves sample efficiency, a crucial factor in autonomous learning. The integration of these NNs with cutting-edge search algorithms, potentially including novel iterations of Tree or Graph of Thought, is predicted to enable Q* to autonomously navigate and assimilate complex information. This approach might be augmented with graph neural networks to bolster meta-learning capacities, allowing Q* to rapidly adapt to new tasks and environments while retaining previously acquired knowledge. The corresponding quasi-mathematical formulation can be represented as: ASLE(Q∗) = RL(P NN, SA) × GNN Where: • ASLE: “Advanced Self-Learning and Exploration” • RL: to reinforcement learning algorithms, particularly Proximal Policy Optimization (PPO). • PNN: Policy Neural Networks, adapted for language and reasoning tasks. • SA: sophisticated search algorithms, like Tree or Graph of Thought. • GNN: the incorporation of Graph Neural Networks for meta-learning. • ×: the cross-functional enhancement of RL with GNN. Such capabilities indicate a model not limited to understanding existing data but equipped to actively seek and synthesize new knowledge, effectively adapting to evolving scenarios without the need for frequent retraining. This signifies a leap beyond current AI models, embedding a level of autonomy and efficiency previously unattained. C. Superior Human-Level Understanding Q*’s aspiration to achieve superior human-level understanding is speculated to hinge on an advanced integration of multiple neural networks, including a Value Neural Network (VNN), paralleling the evaluative components found in systems like AlphaGo. This network would extend beyond assessing accuracy and relevance in language and reasoning processes, delving into the subtleties of human communication. The model’s deep comprehension capabilities may be enhanced by advanced natural language processing algorithms and techniques, such as those found in transformer architectures like DeBERTa. These algorithms would empower Q* to interpret not just the text but also the nuanced socio-emotional aspects such as intent, emotion, and underlying meanings. Incorporating sentiment analysis and natural language inference, Q* could navigate layers of socio-emotional insights, including empathy, sarcasm, and attitude. The corresponding quasi-mathematical formulation can be expressed as: Where: • SHLU: “Superior Human-Level Understanding”. • VNN: the Value Neural Network, similar to evaluative components in systems like AlphaGo. • NLP: a set of advanced NLP algorithms. • ⊕: the combination of VNN evaluation with NLP algorithms. • alg: individual algorithms within the NLP set. This level of understanding, surpassing current language models, would position Q* to excel in empathetic, context aware interactions, thus enabling a new echelon of personalization and user engagement in AI applications. D. Advanced Common Sense Reasoning Q*’s anticipated development in advanced common sense reasoning is predicted to integrate sophisticated logic and decision-making algorithms, potentially combining elements of symbolic AI and probabilistic reasoning. This integration aims to endow Q* with an intuitive grasp of everyday logic and an understanding akin to human common sense, thus bridging a significant gap between artificial and natural intelligence. Enhancements in Q*’s reasoning abilities might involve graphstructured world knowledge, incorporating physics and social engines similar to those in models like CogSKR. This approach, grounded in physical reality, is expected to capture and interpret the everyday logic often absent in contemporary AI systems. By leveraging large-scale knowledge bases and semantic networks, Q* could effectively navigate and respond to complex social and practical scenarios, aligning its inferences and decisions more closely with human experiences and expectations. The corresponding quasi-mathematical formulation can be represented as: Where: • ACSR: “Advanced Common Sense Reasoning”. • LogicAI and ProbAI: symbolic AI and probabilistic reasoning components, respectively. • WorldK: the integration of graph-structured world knowledge. • ⊙: the integrated operation of these elements for common sense reasoning. E. Extensive Real-World Knowledge Integration edge is speculated to involve the use of advanced formal verification systems, which would provide a robust basis for validating its logical and factual reasoning. This method, when coupled with sophisticated neural network architectures and dynamic learning algorithms, would enable Q* to engage deeply with the complexities of the real world, transcending conventional AI limitations. Additionally, Q* might employ mathematical theorem proving techniques for validation, ensuring that its reasoning and outputs are not only accurate but also ethically grounded. The incorporation of Ethics classifiers in this process further strengthens its capacity to deliver reliable and responsible understanding and interaction with real-world scenarios. The corresponding quasi-mathematical formulation can be represented as: Where: • ERWKI: “Extensive Real-World Knowledge Integration”. • F V S: Formal Verification Systems. • NN: neural network architectures. • LT P: mathematical theorem proving for logical and factual validation. • EC: the incorporation of Ethics classifiers. • ⊗: the comprehensive integration for knowledge synthesis and ethical alignment. Furthermore, the speculated capabilities of Q* have the potential to significantly reshape the job market and labor dynamics. With its advanced functionalities, Q* could automate complex tasks, leading to a shift in job requirements and the emergence of new skill demands. This necessitates a reevaluation of workforce strategies and educational paradigms, aligning them with the evolving technological landscape and ensuring that the workforce is equipped to interact with and complement these advanced AI systems. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Authors: Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Table of Links Abstract and Introduction Abstract and Introduction Background: Evolution of Generative AI Background: Evolution of Generative AI The Current Generative AI Research Taxonomy The Current Generative AI Research Taxonomy Innovative Horizon of MOE Innovative Horizon of MOE Speculated Capabilities of Q* Speculated Capabilities of Q* Projected Capabilities of AGI Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References Conclusions, Disclaimer, and References V. SPECULATED CAPABILITIES OF Q* In the burgeoning realm of AI, the anticipated Q* project stands as a beacon of potential breakthroughs, heralding advancements that could redefine the landscape of AI capabilities (Fig. 5). A. Enhanced General Intelligence A. Enhanced General Intelligence Q*’s development in the arena of general intelligence represents a paradigm shift from specialized to holistic AI, indicating a broadening of the model’s cognitive abilities akin to human intelligence. This advanced form of general intelligence involves integrating diverse neural network architectures and machine learning techniques, enabling the AI to process and synthesize multifaceted information seamlessly. The universal adapter approach, mirroring models like T0, could endow Q* with the capability to rapidly assimilate knowledge from various domains. This method allows Q* to learn adaptable module plugins, enhancing its ability to tackle new data types while preserving existing skills, leading to an AI model that combines narrow specializations into a comprehensive, adaptive, and versatile reasoning system. The corresponding quasi-mathematical formulation can be expressed as: Such advancements in AI suggest the emergence of an intelligence that not only parallels but potentially exceeds human cognitive flexibility, with far-reaching implications in facilitating cross-disciplinary innovations and complex problem-solving. The speculated capabilities of Q* bring forth complex ethical implications and governance challenges. As AI systems approach higher levels of autonomy and decision-making, it is crucial to establish robust ethical frameworks and governance structures to ensure responsible and transparent AI development. This involves mitigating potential risks associated with advanced AI capabilities, emphasizing the need for comprehensive and dynamic ethical guidelines that evolve in tandem with AI advancements. B. Advanced Self-Learning and Exploration B. Advanced Self-Learning and Exploration In the realm of advanced AI development, Q* is anticipated to represent a significant evolution in self-learning and exploration capabilities. It is speculated to utilize sophisticated Policy Neural Networks (NNs), similar to those in AlphaGo, but with substantial enhancements to handle the complexities of language and reasoning tasks. These networks are expected to employ advanced reinforcement learning techniques like Proximal Policy Optimization (PPO), which stabilizes policy updates and improves sample efficiency, a crucial factor in autonomous learning. The integration of these NNs with cutting-edge search algorithms, potentially including novel iterations of Tree or Graph of Thought, is predicted to enable Q* to autonomously navigate and assimilate complex information. This approach might be augmented with graph neural networks to bolster meta-learning capacities, allowing Q* to rapidly adapt to new tasks and environments while retaining previously acquired knowledge. The corresponding quasi-mathematical formulation can be represented as: ASLE(Q∗) = RL(P NN, SA) × GNN Where: • ASLE : “Advanced Self-Learning and Exploration” ASLE • RL : to reinforcement learning algorithms, particularly Proximal Policy Optimization (PPO). RL • PNN : Policy Neural Networks, adapted for language and reasoning tasks. • PNN • SA : sophisticated search algorithms, like Tree or Graph of Thought. • SA • GNN : the incorporation of Graph Neural Networks for meta-learning. • GNN • ×: the cross-functional enhancement of RL with GNN. Such capabilities indicate a model not limited to understanding existing data but equipped to actively seek and synthesize new knowledge, effectively adapting to evolving scenarios without the need for frequent retraining. This signifies a leap beyond current AI models, embedding a level of autonomy and efficiency previously unattained. C. Superior Human-Level Understanding C. Superior Human-Level Understanding Q*’s aspiration to achieve superior human-level understanding is speculated to hinge on an advanced integration of multiple neural networks, including a Value Neural Network (VNN), paralleling the evaluative components found in systems like AlphaGo. This network would extend beyond assessing accuracy and relevance in language and reasoning processes, delving into the subtleties of human communication. The model’s deep comprehension capabilities may be enhanced by advanced natural language processing algorithms and techniques, such as those found in transformer architectures like DeBERTa. These algorithms would empower Q* to interpret not just the text but also the nuanced socio-emotional aspects such as intent, emotion, and underlying meanings. Incorporating sentiment analysis and natural language inference, Q* could navigate layers of socio-emotional insights, including empathy, sarcasm, and attitude. The corresponding quasi-mathematical formulation can be expressed as: Where: • SHLU : “Superior Human-Level Understanding”. SHLU • VNN : the Value Neural Network, similar to evaluative components in systems like AlphaGo. VNN • NLP : a set of advanced NLP algorithms. NLP • ⊕: the combination of VNN evaluation with NLP algorithms. • alg : individual algorithms within the NLP set. alg This level of understanding, surpassing current language models, would position Q* to excel in empathetic, context aware interactions, thus enabling a new echelon of personalization and user engagement in AI applications. D. Advanced Common Sense Reasoning D. Advanced Common Sense Reasoning Q*’s anticipated development in advanced common sense reasoning is predicted to integrate sophisticated logic and decision-making algorithms, potentially combining elements of symbolic AI and probabilistic reasoning. This integration aims to endow Q* with an intuitive grasp of everyday logic and an understanding akin to human common sense, thus bridging a significant gap between artificial and natural intelligence. Enhancements in Q*’s reasoning abilities might involve graphstructured world knowledge, incorporating physics and social engines similar to those in models like CogSKR. This approach, grounded in physical reality, is expected to capture and interpret the everyday logic often absent in contemporary AI systems. By leveraging large-scale knowledge bases and semantic networks, Q* could effectively navigate and respond to complex social and practical scenarios, aligning its inferences and decisions more closely with human experiences and expectations. The corresponding quasi-mathematical formulation can be represented as: Where: • ACSR : “Advanced Common Sense Reasoning”. • ACSR • LogicAI and ProbAI : symbolic AI and probabilistic reasoning components, respectively. • LogicAI ProbAI • WorldK : the integration of graph-structured world knowledge. • WorldK • ⊙: the integrated operation of these elements for common sense reasoning. E. Extensive Real-World Knowledge Integration E. Extensive Real-World Knowledge Integration edge is speculated to involve the use of advanced formal verification systems, which would provide a robust basis for validating its logical and factual reasoning. This method, when coupled with sophisticated neural network architectures and dynamic learning algorithms, would enable Q* to engage deeply with the complexities of the real world, transcending conventional AI limitations. Additionally, Q* might employ mathematical theorem proving techniques for validation, ensuring that its reasoning and outputs are not only accurate but also ethically grounded. The incorporation of Ethics classifiers in this process further strengthens its capacity to deliver reliable and responsible understanding and interaction with real-world scenarios. The corresponding quasi-mathematical formulation can be represented as: Where: • ERWKI : “Extensive Real-World Knowledge Integration”. • ERWKI • F V S: Formal Verification Systems. • F V S: • NN : neural network architectures. • NN • LT P : mathematical theorem proving for logical and factual validation. • LT P • EC : the incorporation of Ethics classifiers. • EC • ⊗: the comprehensive integration for knowledge synthesis and ethical alignment. Furthermore, the speculated capabilities of Q* have the potential to significantly reshape the job market and labor dynamics. With its advanced functionalities, Q* could automate complex tasks, leading to a shift in job requirements and the emergence of new skill demands. This necessitates a reevaluation of workforce strategies and educational paradigms, aligning them with the evolving technological landscape and ensuring that the workforce is equipped to interact with and complement these advanced AI systems. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Surveying the Evolution and Future Trajectory of Generative AI - Speculated Capabilities of Q*

Jinesh Kumar Chinnathambi, a Solution Architect at a leading health insurance company, is using his real-world experience in the fields of Machine Learning (ML) and Artificial Intelligence (AI) to make the healthcare sector more patient-centric and improve patient care. People pursuing a career in healthcare IT are often driven by the aspiration to have a significant impact on people's lives. For Jinesh, the best way to do this in 2024 is by utilizing the problem-solving capabilities of technology to improve and streamline the healthcare industry. As he says, "The ever-changing healthcare landscape, with advancements such as Electronic Health Records (EHR), telemedicine, artificial intelligence, and predictive analytics transforming patient care, presents captivating challenges and opportunities for innovation. It provides the satisfaction of knowing that work in this field directly contributes to enhancing the quality, accessibility, and effectiveness of patient care, ultimately making healthcare more patient-centric." Combining the innovations and technologies in this healthcare IT, such as AI-driven diagnostics, predictive analytics, and personalized treatment plans, with the soft skills required in Healthcare roles, including problem-solving, analytical thinking, communication, and understanding of healthcare operations, has proven incredibly beneficial to both patients and healthcare professionals. Introducing Jinesh Kumar Chinnathambi Jinesh Kumar Chinnathambi's education began with a Bachelor's degree in computer science engineering, which gave him a strong foundation for his work in healthcare IT. During his studies, Jinesh focused on subjects at the intersection of technology and healthcare, including databases, data analysis, and healthcare systems. After graduating, he gained practical IT experience through entry-level roles and tech positions in the healthcare industry. He achieved further specialization in healthcare IT through certifications offered by AHIP (America's Health Insurance Plan) and has kept updated on new healthcare regulations, emerging technologies, and industry best practices to stay ahead of the curve. Networking with professionals, attending industry conferences, and keeping abreast of healthcare IT journals have provided valuable insights and helped him garner success in his career. As he says himself, "The journey into Healthcare IT involves a combination of education, practical experience, ongoing learning, and a dedication to enhancing healthcare delivery through technology." Jinesh has published numerous publications and pieces of academic research, including the well-received "Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence," "Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps," “Harnessing Data Analytics and Artificial Intelligence in Healthcare,” and “Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration.” His wide range of professional certifications and important achievements demonstrate his expertise in a highly competitive field. He is the only one of Elevance Health’s approximately 100,000 employees to hold four AWS certifications, including AWS Certified DevOps Professional, AWS Certified Solutions Architect Associate, AWS Certified Developer Associate, and AWS Certified SysOps Administrator Associate. He also holds a Sun Certified Java Professional (SCJP) certification and multiple America's Health Insurance Plans (AHIP) certifications, including Fundamentals of Healthcare Parts A and B and Basics of Managed Care Part A. In addition to all of this, Jinesh has been recognized with a 2024 Global Recognition Award for his notable healthcare and IT industry achievements. In August 2024, he also won the Cloud Innovator of the Year Award at the Business Innovation Awards 2024 in the Health Care/Information Technology category. This award recognizes outstanding achievements in cloud innovation. In addition to being awarded for his work, Jinesh was also named a judge for the Globee® Awards for Leadership in 2024, signifying a formal recognition of his commitment and significant contributions to his field, and received recognition for his role as judge at the Virginia Tech College Hackathon event. The Role of AI, ML, Data Analytics, Data Warehouses & Cloud Migration in Healthcare IT One of the major benefits of implementing technological advancements like AI and ML into healthcare IT is how they can be utilized to analyze vast amounts of healthcare data, accurately predict patient outcomes and improve treatment plans. A pivotal moment in Jinesh's IT career came with the advent of cloud computing, which reshaped the entire technology landscape. It was here, at the precipice of such a monumental technological change, that Jinesh realized just how beneficial such systems could be to the work he was doing. "Before the rise of cloud computing, businesses were required to establish, manage, and uphold their own expensive IT infrastructures,” recalls Jinesh. “Companies like Amazon, Google, and Microsoft began providing platforms that allowed access to databases, servers, software, and analytics via the internet. This led to a substantial reduction in the cost and complexity of IT operations, hastened innovation, and offered the flexibility to scale resources as needed. It empowered businesses to concentrate more on their core operations while leveraging state-of-the-art technology. Furthermore, cloud computing paved the way for a multitude of other technological advancements seen today, such as big data analytics and AI, which I am currently working on. It stands as a milestone that has redefined the possibilities of IT and continues to shape the future of technology." All of this works in tandem with the dedication of people in the healthcare field to craft a more patient-centric approach, providing personalized and timely care with revolutionary speed and precision. Real-World Studies and Applications By implementing these technological advancements into his established healthcare IT system, Jinesh has been able to successfully develop predictive analytics products to enhance patient care. His work has even been able to identify the signs and likelihood of cancer recurrence far earlier than previously thought possible, all thanks to data analytics, AI, and ML. By configuring AI models to run seamlessly on the cloud, Jinesh has contributed to accelerating predictive diagnoses, enhancing treatment plans, automating administrative tasks, and improving patient care. The strategic designs and technical expertise of solution architects are a vital part of shaping the future of AI-enabled, cloud-based healthcare systems. The impact of these projects has improved patient outcomes, reduced costs, improved efficiency, and helped to deliver more personalized care than ever before. Jinesh has worked tirelessly to guide and develop technology and tool solutions that improve data lineage adoption and governance by collaborating with technology partners to create a cloud-based data operation strategy. He oversaw a comprehensive cloud migration and data governance framework at Elevance Health, ensuring alignment with business objectives and HIPAA regulatory requirements, and also established a data council of senior executives across the enterprise to facilitate efficient collaboration on data initiatives. Furthermore, during his time at Elevance, he worked closely with the enterprise infrastructure and technology team to explore a cloud-based lake-house solution. Jinesh led the implementation of a data migration framework, including issue research, resolution processes, root cause analysis, reporting, data classifications and standards, and data quality validation and controls. He also developed a data governance framework to align with business objectives and HIPAA regulatory requirements, which supported the implementation of monolithic microservices for various use cases. He researched and recommended the appropriate Snowflake warehouse sizes after conducting several proofs of concept, resulting in optimal performance for middleware queries and reducing annual costs by 30%. One study estimated the national cost-savings in the United States from early diagnosis to be $26 billion per year. The Future of Healthcare Technology Jinesh's vision for the future of healthcare technology is one full of promise, continued improvement, and the potential to create a more patient-centric healthcare system that benefits everyone. He plans to work at the convergence of healthcare and technology to improve patient care by introducing new and advanced solutions and developing and managing Electronic Health Records (EHRs), health information exchanges, telemedicine services, and other initiatives to enhance healthcare coordination and accessibility, ultimately benefiting local communities. This will contribute to early disease detection, effective monitoring, decreased medical errors, and better communication between healthcare providers and patients. Jinesh Kumar Chinnathambi , a Solution Architect at a leading health insurance company, is using his real-world experience in the fields of Machine Learning (ML) and Artificial Intelligence (AI) to make the healthcare sector more patient-centric and improve patient care. Jinesh Kumar Chinnathambi Jinesh Kumar Chinnathambi People pursuing a career in healthcare IT are often driven by the aspiration to have a significant impact on people's lives. For Jinesh, the best way to do this in 2024 is by utilizing the problem-solving capabilities of technology to improve and streamline the healthcare industry. As he says, "The ever-changing healthcare landscape, with advancements such as Electronic Health Records (EHR), telemedicine, artificial intelligence, and predictive analytics transforming patient care, presents captivating challenges and opportunities for innovation. It provides the satisfaction of knowing that work in this field directly contributes to enhancing the quality, accessibility, and effectiveness of patient care, ultimately making healthcare more patient-centric." Combining the innovations and technologies in this healthcare IT, such as AI-driven diagnostics, predictive analytics, and personalized treatment plans, with the soft skills required in Healthcare roles, including problem-solving, analytical thinking, communication, and understanding of healthcare operations, has proven incredibly beneficial to both patients and healthcare professionals. Introducing Jinesh Kumar Chinnathambi Jinesh Kumar Chinnathambi's education began with a Bachelor's degree in computer science engineering, which gave him a strong foundation for his work in healthcare IT. During his studies, Jinesh focused on subjects at the intersection of technology and healthcare, including databases, data analysis, and healthcare systems. After graduating, he gained practical IT experience through entry-level roles and tech positions in the healthcare industry. He achieved further specialization in healthcare IT through certifications offered by AHIP (America's Health Insurance Plan) and has kept updated on new healthcare regulations, emerging technologies, and industry best practices to stay ahead of the curve. Networking with professionals, attending industry conferences, and keeping abreast of healthcare IT journals have provided valuable insights and helped him garner success in his career. As he says himself, "The journey into Healthcare IT involves a combination of education, practical experience, ongoing learning, and a dedication to enhancing healthcare delivery through technology." Jinesh has published numerous publications and pieces of academic research, including the well-received " Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence ," " Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps ," “ Harnessing Data Analytics and Artificial Intelligence in Healthcare ,” and “ Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration .” Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps Harnessing Data Analytics and Artificial Intelligence in Healthcare Harnessing Data Analytics and Artificial Intelligence in Healthcare Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration His wide range of professional certifications and important achievements demonstrate his expertise in a highly competitive field. He is the only one of Elevance Health’s approximately 100,000 employees to hold four AWS certifications, including AWS Certified DevOps Professional, AWS Certified Solutions Architect Associate, AWS Certified Developer Associate, and AWS Certified SysOps Administrator Associate. He also holds a Sun Certified Java Professional (SCJP) certification and multiple America's Health Insurance Plans (AHIP) certifications, including Fundamentals of Healthcare Parts A and B and Basics of Managed Care Part A. In addition to all of this, Jinesh has been recognized with a 2024 Global Recognition Award for his notable healthcare and IT industry achievements. Global Recognition Award Global Recognition Award In August 2024, he also won the Cloud Innovator of the Year Award at the Business Innovation Awards 2024 in the Health Care/Information Technology category. This award recognizes outstanding achievements in cloud innovation. In addition to being awarded for his work, Jinesh was also named a judge for the Globee® Awards for Leadership in 2024, signifying a formal recognition of his commitment and significant contributions to his field, and received recognition for his role as judge at the Virginia Tech College Hackathon event . Globee® Awards for Leadership Globee® Awards for Leadership Virginia Tech College Hackathon event Virginia Tech College Hackathon event The Role of AI, ML, Data Analytics, Data Warehouses & Cloud Migration in Healthcare IT One of the major benefits of implementing technological advancements like AI and ML into healthcare IT is how they can be utilized to analyze vast amounts of healthcare data, accurately predict patient outcomes and improve treatment plans. A pivotal moment in Jinesh's IT career came with the advent of cloud computing, which reshaped the entire technology landscape. It was here, at the precipice of such a monumental technological change, that Jinesh realized just how beneficial such systems could be to the work he was doing. "Before the rise of cloud computing, businesses were required to establish, manage, and uphold their own expensive IT infrastructures,” recalls Jinesh. “Companies like Amazon, Google, and Microsoft began providing platforms that allowed access to databases, servers, software, and analytics via the internet. This led to a substantial reduction in the cost and complexity of IT operations, hastened innovation, and offered the flexibility to scale resources as needed. It empowered businesses to concentrate more on their core operations while leveraging state-of-the-art technology. Furthermore, cloud computing paved the way for a multitude of other technological advancements seen today, such as big data analytics and AI, which I am currently working on. It stands as a milestone that has redefined the possibilities of IT and continues to shape the future of technology." All of this works in tandem with the dedication of people in the healthcare field to craft a more patient-centric approach, providing personalized and timely care with revolutionary speed and precision. Real-World Studies and Applications By implementing these technological advancements into his established healthcare IT system, Jinesh has been able to successfully develop predictive analytics products to enhance patient care. His work has even been able to identify the signs and likelihood of cancer recurrence far earlier than previously thought possible, all thanks to data analytics, AI, and ML. By configuring AI models to run seamlessly on the cloud, Jinesh has contributed to accelerating predictive diagnoses, enhancing treatment plans, automating administrative tasks, and improving patient care. The strategic designs and technical expertise of solution architects are a vital part of shaping the future of AI-enabled, cloud-based healthcare systems. The impact of these projects has improved patient outcomes, reduced costs, improved efficiency, and helped to deliver more personalized care than ever before. Jinesh has worked tirelessly to guide and develop technology and tool solutions that improve data lineage adoption and governance by collaborating with technology partners to create a cloud-based data operation strategy. He oversaw a comprehensive cloud migration and data governance framework at Elevance Health, ensuring alignment with business objectives and HIPAA regulatory requirements, and also established a data council of senior executives across the enterprise to facilitate efficient collaboration on data initiatives. Furthermore, during his time at Elevance, he worked closely with the enterprise infrastructure and technology team to explore a cloud-based lake-house solution. Jinesh led the implementation of a data migration framework, including issue research, resolution processes, root cause analysis, reporting, data classifications and standards, and data quality validation and controls. He also developed a data governance framework to align with business objectives and HIPAA regulatory requirements, which supported the implementation of monolithic microservices for various use cases. He researched and recommended the appropriate Snowflake warehouse sizes after conducting several proofs of concept, resulting in optimal performance for middleware queries and reducing annual costs by 30%. One study estimated the national cost-savings in the United States from early diagnosis to be $26 billion per year. One study estimated the national cost-savings One study estimated the national cost-savings The Future of Healthcare Technology Jinesh's vision for the future of healthcare technology is one full of promise, continued improvement, and the potential to create a more patient-centric healthcare system that benefits everyone. He plans to work at the convergence of healthcare and technology to improve patient care by introducing new and advanced solutions and developing and managing Electronic Health Records (EHRs), health information exchanges, telemedicine services, and other initiatives to enhance healthcare coordination and accessibility, ultimately benefiting local communities. This will contribute to early disease detection, effective monitoring, decreased medical errors, and better communication between healthcare providers and patients.

How AI and Machine Learning Are Making the Healthcare Sector More Patient-centric

እንዴት AI እና ማሽን መማር የጤና አጠባበቅ ሴክተሩን የበለጠ ታጋሽ ተኮር እያደረጉት ነው።

Süni intellekt və Maşın Öyrənməsi Səhiyyə Sektorunu necə daha xəstə mərkəzli edir

Com la IA i l'aprenentatge automàtic estan fent que el sector sanitari sigui més centrat en el pacient

Jak umělá inteligence a strojové učení dělají sektor zdravotnictví více zaměřený na pacienta

Cómo la IA y el aprendizaje automático están haciendo que el sector sanitario esté más centrado en el paciente

Comment l'IA et l'apprentissage automatique rendent le secteur de la santé plus centré sur le patient

Como a IA e a aprendizaxe automática están facendo que o sector sanitario sexa máis centrado no paciente

कैसे AI और मशीन लर्निंग स्वास्थ्य सेवा क्षेत्र को अधिक रोगी-केंद्रित बना रहे हैं

AIと機械学習が医療分野をより患者中心に変える

AI жана машинаны үйрөнүү кантип саламаттыкты сактоо секторун сабырдуураак кылат

Ndenge nini AI mpe Machine Learning Ezali Kosala ete Secteur ya Soins de Santé ezala mingi na ba malades

Paano Ginagawa ng AI at Machine Learning ang Sektor ng Pangangalagang Pangkalusugan na Mas Nakasentro sa Pasyente

RAG. RAG. RAG. In the race to implement artificial intelligence in business processes and products, there’s been a troubling trend: an obsession with Retrieval-Augmented Generation (RAG). While RAG—a method that blends large language models (LLMs) with external knowledge retrieval—has undeniably opened up new avenues for interacting with knowledge, too many practitioners are struggling with it. It’s time we reframe the conversation around AI implementation, acknowledge the pitfalls of over-reliance on RAG, and explore alternative approaches that may be more suitable, cost-effective, and elegant. The RAG Mania: Overkill for Many Use Cases RAG has become the go-to technique for many AI engineers who want to improve the accuracy of language models by providing external context. The premise is simple enough: by uploading vast amounts of text into vector stores, these AI systems can look up relevant documents, retrieve the data, and combine it with the language model’s generative abilities to produce more precise answers. However, the enthusiasm for RAG has led to an explosion of implementations that overestimate its usefulness. It’s not uncommon to see engineers dumping millions of documents into vector stores, inflating cloud storage and processing costs without understanding if the use case even necessitates such complexity. Many fail to consider whether a simpler solution could suffice or if RAG is even necessary for their specific problem. The Pitfalls of Naive RAG Implementations What’s worse, most engineers approach RAG implementation with a naive mindset, overlooking the long-term costs and maintenance burdens. They believe that uploading every piece of text into a vector store will somehow make the AI smarter. But more often than not, this practice does the opposite. With vector stores brimming with redundant and unnecessary documents, LLMs are overwhelmed with retrieving data that doesn’t add value. This results in slower response times, higher costs, and less effective solutions. RAG works best when it’s used to augment precise and relevant knowledge, not when it’s employed as a catch-all for any document dump available. Overengineering through RAG also leads to underutilization of other key AI capabilities and an overblown focus on retrieval when many problems could be solved with simpler logic and structure. Not Every Problem Needs RAG Here’s the truth: Not all use cases require a RAG setup. If the task is narrow and well-defined—like responding to FAQs, customer support queries, or engaging in structured dialogue—a simple lookup table or knowledge graph may suffice. There’s no need to incur the overhead of running a massive vector store and a multi-million parameter model when the solution can be built using a rule-based system or even an agent framework. The zeal to use RAG stems from the idea that more data equals better performance. But in many cases, quality trumps quantity. A fine-tuned model with targeted knowledge, or even a knowledge-aware chatbot with rule-based capabilities, can perform better without ever touching a RAG pipeline. The decision to implement RAG should be dictated by the complexity of the task, not by its popularity among AI enthusiasts. The Case for Small Agents With Narrow Knowledge The alternative to bloated RAG systems is often more elegant and effective: small, specialized agents with limited but precise knowledge. These agents, when used in tandem, can outperform a single large model burdened by terabytes of text. Each agent can be designed to handle specific parts of a workflow or respond to certain kinds of queries, allowing for modular and flexible AI systems. This not only reduces costs but also makes the entire system easier to maintain and scale. Imagine a scenario where one agent is responsible for scheduling, another for summarization, and a third for performing web searches. Each of these agents can work together, leveraging only the knowledge they need, without the overhead of a monolithic system. By deploying many small models or logic-based agents, businesses can get more precise and faster outputs while significantly cutting down on processing and storage costs. Overusing LLMs: When Simple Logic Will Do Finally, there’s the overuse of LLMs in scenarios where simple logic would do. LLMs are remarkably good at understanding and generating natural language, but that doesn’t mean they should replace all forms of automation. Many tasks—like data validation, form filling, or structured report generation—can be done faster and more reliably with basic scripts, rule engines, or deterministic systems. A prime example is using an LLM for an arithmetic task or a sorting problem. This is inefficient and unnecessary. Not only does it waste computational resources, but it also increases the likelihood of errors in cases where a simple function or algorithm would be more accurate. The eagerness to implement LLMs for everything has turned into an “LLM hammer looking for nails” syndrome. This misuse leads to inflated expectations and an eventual disillusionment when the models don’t perform as expected in tasks they weren’t designed to handle. Rethinking AI Engineering It’s time to rethink AI engineering and move beyond the fads. RAG has its place in the toolkit, but it’s not a panacea. The future lies in deploying the right models for the right tasks—sometimes that means RAG, but often it doesn’t. With a nuanced understanding of AI capabilities, engineers can design systems that are more effective, efficient, and easier to maintain. About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com RAG. RAG. RAG. In the race to implement artificial intelligence in business processes and products, there’s been a troubling trend: an obsession with Retrieval-Augmented Generation (RAG). While RAG—a method that blends large language models (LLMs) with external knowledge retrieval—has undeniably opened up new avenues for interacting with knowledge, too many practitioners are struggling with it. It’s time we reframe the conversation around AI implementation, acknowledge the pitfalls of over-reliance on RAG, and explore alternative approaches that may be more suitable, cost-effective, and elegant. The RAG Mania: Overkill for Many Use Cases The RAG Mania: Overkill for Many Use Cases RAG has become the go-to technique for many AI engineers who want to improve the accuracy of language models by providing external context. The premise is simple enough: by uploading vast amounts of text into vector stores, these AI systems can look up relevant documents, retrieve the data, and combine it with the language model’s generative abilities to produce more precise answers. However, the enthusiasm for RAG has led to an explosion of implementations that overestimate its usefulness. It’s not uncommon to see engineers dumping millions of documents into vector stores, inflating cloud storage and processing costs without understanding if the use case even necessitates such complexity. Many fail to consider whether a simpler solution could suffice or if RAG is even necessary for their specific problem. The Pitfalls of Naive RAG Implementations The Pitfalls of Naive RAG Implementations What’s worse, most engineers approach RAG implementation with a naive mindset, overlooking the long-term costs and maintenance burdens. They believe that uploading every piece of text into a vector store will somehow make the AI smarter. But more often than not, this practice does the opposite. With vector stores brimming with redundant and unnecessary documents, LLMs are overwhelmed with retrieving data that doesn’t add value. This results in slower response times, higher costs, and less effective solutions. RAG works best when it’s used to augment precise and relevant knowledge, not when it’s employed as a catch-all for any document dump available. Overengineering through RAG also leads to underutilization of other key AI capabilities and an overblown focus on retrieval when many problems could be solved with simpler logic and structure. precise relevant Not Every Problem Needs RAG Not Every Problem Needs RAG Here’s the truth: Not all use cases require a RAG setup. If the task is narrow and well-defined—like responding to FAQs, customer support queries, or engaging in structured dialogue—a simple lookup table or knowledge graph may suffice. There’s no need to incur the overhead of running a massive vector store and a multi-million parameter model when the solution can be built using a rule-based system or even an agent framework. The zeal to use RAG stems from the idea that more data equals better performance. But in many cases, quality trumps quantity. A fine-tuned model with targeted knowledge, or even a knowledge-aware chatbot with rule-based capabilities, can perform better without ever touching a RAG pipeline. The decision to implement RAG should be dictated by the complexity of the task, not by its popularity among AI enthusiasts. The Case for Small Agents With Narrow Knowledge The Case for Small Agents With Narrow Knowledge The alternative to bloated RAG systems is often more elegant and effective: small, specialized agents with limited but precise knowledge. These agents, when used in tandem, can outperform a single large model burdened by terabytes of text. Each agent can be designed to handle specific parts of a workflow or respond to certain kinds of queries, allowing for modular and flexible AI systems. This not only reduces costs but also makes the entire system easier to maintain and scale. Imagine a scenario where one agent is responsible for scheduling, another for summarization, and a third for performing web searches. Each of these agents can work together, leveraging only the knowledge they need, without the overhead of a monolithic system. By deploying many small models or logic-based agents, businesses can get more precise and faster outputs while significantly cutting down on processing and storage costs. Overusing LLMs: When Simple Logic Will Do Overusing LLMs: When Simple Logic Will Do Finally, there’s the overuse of LLMs in scenarios where simple logic would do. LLMs are remarkably good at understanding and generating natural language, but that doesn’t mean they should replace all forms of automation. Many tasks—like data validation, form filling, or structured report generation—can be done faster and more reliably with basic scripts, rule engines, or deterministic systems. A prime example is using an LLM for an arithmetic task or a sorting problem. This is inefficient and unnecessary. Not only does it waste computational resources, but it also increases the likelihood of errors in cases where a simple function or algorithm would be more accurate. The eagerness to implement LLMs for everything has turned into an “LLM hammer looking for nails” syndrome. This misuse leads to inflated expectations and an eventual disillusionment when the models don’t perform as expected in tasks they weren’t designed to handle. Rethinking AI Engineering Rethinking AI Engineering It’s time to rethink AI engineering and move beyond the fads. RAG has its place in the toolkit, but it’s not a panacea. The future lies in deploying the right models for the right tasks—sometimes that means RAG, but often it doesn’t. With a nuanced understanding of AI capabilities, engineers can design systems that are more effective, efficient, and easier to maintain. About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com About Me: 20+ year veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com

RAG Revisited

RAG revisitado

RAG Berrikusita

आरएजी पर पुनः विचार

RAG 再訪

RAG қайта қаралды

RAG peržiūrėtas

RAG Dilawati Semula

RAG Revizuit

RAG Imerudiwa

RAG کا دوبارہ جائزہ لیا گیا۔

重温 RAG

Transformers: Age of Attention

ট্রান্সফরমার: মনোযোগের বয়স

Transformers: Zeitalter der Aufmerksamkeit

Transformers: La era de la atención

Transformers : L'Âge de l'Attention

ट्रांसफॉर्मर्स: एज ऑफ अटेंशन

トランスフォーマー：注目の時代

트랜스포머: 주의의 시대

Transformers: Era da Atenção

Трансформеры: Эпоха внимания

Transformers: Dikkat Çağı

Transformers: Thời đại của sự chú ý

变形金刚：注意力时代

Introducing Builder: Your Buddy in Test-Driven Development (TDD)

নির্মাতার সাথে পরিচয় করিয়ে দেওয়া হচ্ছে: টেস্ট-ড্রিভেন ডেভেলপমেন্টে আপনার বন্ধু (TDD)

Wir stellen vor: Builder: Ihr Kumpel für testgetriebene Entwicklung (TDD).

Presentamos Builder: su aliado en el desarrollo basado en pruebas (TDD)

Présentation de Builder : votre compagnon dans le développement piloté par les tests (TDD)

बिल्डर का परिचय: टेस्ट-ड्रिवेन डेवलपमेंट (TDD) में आपका साथी

Builder の紹介: テスト駆動開発 (TDD) の相棒

Builder 소개: 테스트 주도 개발(TDD)의 친구

Apresentando o Builder: seu amigo no desenvolvimento orientado a testes (TDD)

Представляем Builder: ваш помощник в разработке через тестирование (TDD)

Builder'ı Tanıtıyoruz: Test Odaklı Geliştirmede (TDD) Arkadaşınız

Giới thiệu Builder: Người bạn đồng hành của bạn trong Phát triển theo hướng kiểm thử (TDD)

Builder 介绍：测试驱动开发 (TDD) 的好帮手

AI image manipulation tools have come a long way in the last few years. It’s quite easy nowadays to create beautiful, stylistic portraits of people and animals. But AI models are highly unpredictable. So most tools rely on the user (or some human) to weed out bad generations and find the best one. This is the classic “human in the loop” problem that often plagues AI tools. Turns out, with some clever tricks and careful tuning, you can build a pipeline that reliably works for the vast majority of pets. It is extremely resilient to variations in pose, lighting etc. In this post, I’ll dive deeper into how it works and all the neat little tricks that enable this. Here’s some examples of portraits you can generate with this pipeline. Let’s get started! The Key Ingredients IPAdapter The crux of the technique is the IPAdapter. It’s essentially a way to prompt a model using an image instead of text (it literally stands for Image Prompt Adapter). So instead of taking a text embeddings, it uses an image to get the embeddings. This is super powerful because it can accurately capture the style and structure in an image directly, instead of someone having to translate what they want from an image into text. In our ComfyUI IPAdapter node, we have two inputs, one for style and one for composition. We use a watercolor painting image for style, and feed the original image for composition (since we want to keep the same composition, but change the style). ControlNets Now that we have a way to keep the style consistent, we can turn our attention to faithfully representing the pet. IPAdapters bias heavily towards image quality, and image likeness suffers. So we need to do something to be able to keep the output looking like the same object as the input. The answer to that is ControlNet. ControlNets are another cool technique to provide additional constraints to the image generation process. Using a ControlNet, you can specify constraints in the form of edges, depth, human pose etc. A great feature of ControlNets is that they can be stacked. So you can have an edge controlnet that forces the output to have similar edges as the input, and also a depth controlnet that forces the output to have a similar depth profile. And that’s exactly what I do here. It turns out that controlnets are not just stackable with other controlnets, but they can also work in tandem with IPAdapter mentioned above. And so those are the tools we’ll use for this - IPAdapter with a source image to get the style, ControlNet with canny edge detector to constrain based on edges, and controlnet with depth to constrain based on depth profile. That’s really all you need in terms of techniques, but what I’ve learnt from experimenting with machine learning for production is that a lot of the value of these things comes from taking the time to tune all your parameters perfectly. So I want to talk a little bit about that. Finetuning Have you ever found a model with amazing example outputs, tried it on your own images and realized they look terrible? Often, the only reason for that is that the model hasn’t been finetuned for your images. It can feel like a complete blocker sometimes, because where do we even start with finetuning a pre-trained model?! Here’s what I’ve learnt about the topic. This extends beyond this particular pipeline too, so it’s good knowledge to have generally. ControlNets ControlNets are very powerful, so you have to be careful to control their effect on the output. Luckily, the custom nodes in ComfyUI let us reduce the effect of controlnets, and also stop their effects at any point. So we set the edge detector to 75% strength and make it stop influencing the generation at 75% and the depth detector stops at 30%. The reason we stop them at the end rather than just reducing their strength is that this allows the network to “clean up” any artifacts caused by them in the last few steps without being constrained externally. It just makes the image prettier. So it’s using just it’s training data to make things look as nice as possible, ignoring the edges and depth. The other big thing to tune is the KSampler. There’s a lot of little things going on here, but I’ll just briefly touch on some of them: KSampler - Steps First we have the steps. This is literally the number of times the model will repeatedly run. The more it runs, the more stylized your output gets, and the further away it will get from the original image. The effects of this are often not that obvious, so it’s worth playing around with it. KSampler - CFG Then there’s CFG. I honestly don’t fully understand this, but from its name - Classifier Free Guidance - I’m assuming it controls how much the model is allowed to modify the image unconstrained by the prompts in order to make it look better. This also affects the output image significantly, so is worth playing around with. Denoise Another neat little trick I use here is to start the image generation process with the input image  instead of a blank image, and keep the denoising low. This ensures that the output will look similar in terms of colors and textures. Text Prompt One thing you’ll notice I never mentioned is the text prompt until now! Surprising, since that’s usually the only conditioning that you normally provide to diffusion models. But in this case, we have so many other ways of conditioning that the text prompts usually just get in the way. So in this case, the prompt is literally just “a dog”. I do use the text prompt a little more in some of the more stylized portraits, like the chef dog or the one in the bathroom. Adding accessories In essence, this is more or less just an “AI Filter” that converts images into watercolor portraits. But it’s amazing how flexible this can be. For example, to make the portrait of the dog taking a shower, I literally just put images together in an image editing tool and used that as the input! The model takes care of unifying everything and cleaning up the image. Conclusion Now remove the background, add some text and Boom! You have a beautiful portrait that captures all the little details of your pet, and always paints them in the best light! Huge thanks to @cubiq for his work on ComfyUI nodes and his awesome explainer series on youtube! Most of this pipeline was built and explained by him in his videos. If you want a pet portrait without going through all this trouble, consider buying one from here: pawprints.pinenlime.com! AI image manipulation tools have come a long way in the last few years. It’s quite easy nowadays to create beautiful, stylistic portraits of people and animals. But AI models are highly unpredictable. So most tools rely on the user (or some human) to weed out bad generations and find the best one. This is the classic “human in the loop” problem that often plagues AI tools. Turns out, with some clever tricks and careful tuning, you can build a pipeline that reliably works for the vast majority of pets. It is extremely resilient to variations in pose, lighting etc. In this post, I’ll dive deeper into how it works and all the neat little tricks that enable this. Here’s some examples of portraits you can generate with this pipeline. Let’s get started! The Key Ingredients IPAdapter The crux of the technique is the IPAdapter. It’s essentially a way to prompt a model using an image instead of text (it literally stands for Image Prompt Adapter). So instead of taking a text embeddings, it uses an image to get the embeddings. This is super powerful because it can accurately capture the style and structure in an image directly, instead of someone having to translate what they want from an image into text. In our ComfyUI IPAdapter node, we have two inputs, one for style and one for composition. We use a watercolor painting image for style, and feed the original image for composition (since we want to keep the same composition, but change the style). ControlNets Now that we have a way to keep the style consistent, we can turn our attention to faithfully representing the pet. IPAdapters bias heavily towards image quality, and image likeness suffers. So we need to do something to be able to keep the output looking like the same object as the input. The answer to that is ControlNet. ControlNets are another cool technique to provide additional constraints to the image generation process. Using a ControlNet, you can specify constraints in the form of edges, depth, human pose etc. A great feature of ControlNets is that they can be stacked. So you can have an edge controlnet that forces the output to have similar edges as the input, and also a depth controlnet that forces the output to have a similar depth profile. And that’s exactly what I do here. It turns out that controlnets are not just stackable with other controlnets, but they can also work in tandem with IPAdapter mentioned above. And so those are the tools we’ll use for this - IPAdapter with a source image to get the style, ControlNet with canny edge detector to constrain based on edges, and controlnet with depth to constrain based on depth profile. That’s really all you need in terms of techniques, but what I’ve learnt from experimenting with machine learning for production is that a lot of the value of these things comes from taking the time to tune all your parameters perfectly. So I want to talk a little bit about that. Finetuning Have you ever found a model with amazing example outputs, tried it on your own images and realized they look terrible? Often, the only reason for that is that the model hasn’t been finetuned for your images. It can feel like a complete blocker sometimes, because where do we even start with finetuning a pre-trained model?! Here’s what I’ve learnt about the topic. This extends beyond this particular pipeline too, so it’s good knowledge to have generally. ControlNets ControlNets are very powerful, so you have to be careful to control their effect on the output. Luckily, the custom nodes in ComfyUI let us reduce the effect of controlnets, and also stop their effects at any point. So we set the edge detector to 75% strength and make it stop influencing the generation at 75% and the depth detector stops at 30%. The reason we stop them at the end rather than just reducing their strength is that this allows the network to “clean up” any artifacts caused by them in the last few steps without being constrained externally. It just makes the image prettier. So it’s using just it’s training data to make things look as nice as possible, ignoring the edges and depth. The other big thing to tune is the KSampler. There’s a lot of little things going on here, but I’ll just briefly touch on some of them: KSampler - Steps First we have the steps. This is literally the number of times the model will repeatedly run. The more it runs, the more stylized your output gets, and the further away it will get from the original image. The effects of this are often not that obvious, so it’s worth playing around with it. KSampler - CFG Then there’s CFG. I honestly don’t fully understand this, but from its name - Classifier Free Guidance - I’m assuming it controls how much the model is allowed to modify the image unconstrained by the prompts in order to make it look better. This also affects the output image significantly, so is worth playing around with. Denoise Another neat little trick I use here is to start the image generation process with the input image  instead of a blank image, and keep the denoising low. This ensures that the output will look similar in terms of colors and textures. Text Prompt One thing you’ll notice I never mentioned is the text prompt until now! Surprising, since that’s usually the only conditioning that you normally provide to diffusion models. But in this case, we have so many other ways of conditioning that the text prompts usually just get in the way. So in this case, the prompt is literally just “a dog”. I do use the text prompt a little more in some of the more stylized portraits, like the chef dog or the one in the bathroom. Adding accessories In essence, this is more or less just an “AI Filter” that converts images into watercolor portraits. But it’s amazing how flexible this can be. For example, to make the portrait of the dog taking a shower, I literally just put images together in an image editing tool and used that as the input! The model takes care of unifying everything and cleaning up the image. Conclusion Now remove the background, add some text and Boom! You have a beautiful portrait that captures all the little details of your pet, and always paints them in the best light! Huge thanks to @cubiq for his work on ComfyUI nodes and his awesome explainer series on youtube! Most of this pipeline was built and explained by him in his videos. If you want a pet portrait without going through all this trouble, consider buying one from here: pawprints.pinenlime.com!

Make Masterpiece Pet Portraits with ComfyUI

ComfyUI এর সাথে মাস্টারপিস পোট্রেট পোট্রেট তৈরি করুন

Erstellen Sie meisterhafte Tierporträts mit ComfyUI

Crea retratos de mascotas de gran calidad con ComfyUI

Créez des portraits d'animaux de compagnie de qualité avec ComfyUI

ComfyUI के साथ पालतू जानवरों के बेहतरीन चित्र बनाएं

ComfyUI で傑作ペットポートレートを作ろう

ComfyUI로 걸작 애완동물 초상화 만들기

Crie retratos de animais de estimação de obras-primas com ComfyUI

Создавайте шедевральные портреты домашних животных с помощью ComfyUI

ComfyUI ile Başyapıt Evcil Hayvan Portreleri Yapın

Tạo ảnh chân dung thú cưng tuyệt đẹp với ComfyUI

使用 ComfyUI 制作宠物肖像杰作

Stories, News & Mentions about TANDYM

Tandym’s Stories on HackerNoon