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You’re an idiot. What we’re far from is AGI. The current models are fully functional. Punguza bangi.
what the fuck did you call me? do you have any foundation on what an idiot is? complete retarded low tier iq low thinking stone age bonobo
I have a masters in robotics and automation my friend, I have coded neural nets for over 5 years, you want to get academic now sindio kaaa chini nikufunze lesson. There is nothing about AI you can say sijui I am literally on the forefront of research go through my post history I even developed one for villagers to test
ilianza in the early '80s. Back in the 1980s, AI was mostly dominated by the symbolic or “Good Old-Fashioned AI” (GOFAI) approach. Researchers like John McCarthy and Marvin Minsky were proponents of systems based on logical reasoning, where intelligence was seen as a computational process that could be formally encoded into algorithms. The core belief was that, with the right set of rules and a powerful enough processor, machines could replicate human thought processes. It was an era steeped in the optimism that AI could be created by merely mimicking human logic through structured symbolic representations.
Fast forward to today, and we find ourselves enmeshed in the complexities of connectionism, deep learning, and neural networks, which are essentially statistical models that approximate functions from vast datasets. While these models have certainly led to impressive advancements, like GPT, they operate fundamentally differently from the symbolic reasoning of the past. Instead of explicit rule-based systems, modern AI relies on vast amounts of data to train models that exhibit pattern recognition, leading to what some like you might mistakenly call “intelligence.” However, even with all the fanfare around machine learning and neural networks, we haven’t cracked the nut of “true” AI—an entity capable of general intelligence, self-awareness, and understanding that transcends its programming.
And here’s why: true AI requires more than just sophisticated algorithms and massive datasets. The crux of the problem lies in our limited understanding of consciousness and the nature of intelligence itself. Despite all the noise in AI research, we’re still fundamentally dealing with advanced pattern matching and data processing systems, not entities that possess cognition or a sense of self. The quest for general AI is impeded by the fact that intelligence, as we know it in biological entities, is deeply intertwined with embodied experiences, emotional states, and a level of contextual understanding that machines, for all their processing power, simply cannot achieve.
In essence, the notion that we might one day develop true AI—something that could think, reason, and perceive like a human—is, at best, speculative fiction. We might build more sophisticated tools, but to claim we’re on the brink of creating a machine that possesses anything akin to human intelligence is an overreach. Until we have a breakthrough in understanding the fundamental nature of consciousness or develop entirely new paradigms of computation that can simulate or replicate this phenomenon, true AI will remain in the realm of science fiction. The reality is, we don’t even fully grasp what it means to “think,” let alone encode it into a machine. So, as tantalizing as the dream might be, the odds are stacked against us ever seeing true AI in our lifetime, or perhaps, ever.
I just pulled this out of my thesis paper, we will never achieve true AI, what we can and what we have achieved is true automation. To achieve true AI we must re invent the computer.
Nauni heshimu never ever call me an idiot again nikiongea unaangalia chini maffi wewe panguza makamasi mshenzi jinga sana kunguni, your IQ equaled mine when I was 10.
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If you want to remain sane avoid @DukeOfKabeteshire alias @applebee100 na @Gaines . These two think they know everything.
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maliza ua kanyaga shingo kabisa
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Bruh…hizi essays zote 
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Nyeff nyeff nyeff. The professors who approved that thesis are idiots just like you. Nowhere did I mention that AI has achieved human intelligence you idiot. Read what I said before you came to diarrhea on my comments. Bure kabisa.
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enyewe reading comprehension yako ni dog tier, yaani wewe na bosco mkisoma ni the same hamuelewi kitu
Lets track our arguments.
This was my original post
In my original post, I argue that we are still far away from building a fully functional AI (and I emphasize this by putting it in all caps). My post is structured as follows: I start by defining the AI bubble, then provide an update on the current state of AI development. I follow this by introducing the correct term, “master systems,” and conclude with a marketing gimmick example designed to pitch to investors.
your reply
The most bonobo reply ever in this forum. You open with an insult bring in a new term irrelevant to the topic AGI (artificial general intelligence). You state current models are fine what models are you talking about ??? name them I want to hear a fully working AI model, shake the science world I dare you name one right now?? Gpt? sorry bro that a large language model, alpha go? eh thats a reinforcement learning model not an ai self driving cars??? those are known as autonomous sytems. Their is no working ai model in existence(read one word at a time hadi iingie kwa akili). You then close your argument in the most bonobo way possible bangi sasa imetokea wapi???
I then reply
I then reply to your insult and explain to you logically why there is no AI, I even take time to look up my thesis and quote it I wish your even a bit smart I would have gone full nerd on you. I then close by telling you to give me respect the only way your bonobo brain would comprehend
and this what you reply with complete diarrhea
And no where am I comparing ai to human intelligence am arguing on the term artificial “intelligence”. Understand that Intelligence will always be measured on human standards simple as that and I do not want to go deep into this I might break your brain. So here is the kicker AI does not exist!
You are a complete shit show commenting on a post you have zero knowledge own, incapable of logic debate beyond basic bonobo insults, Incapable of being corrected and adjusting your knowledge(this is basics of ‘intelligence’) meaning you are completely un intelligent your self
and that thesis you just dissed what you do not know is that I had to make it easy for you to understand this is the first page take your time, make sure you do not crack your brain. unintelligible blubbering brain less shitty bottom of the barrel uneducated bonobo you can spend the next 50 years of your life trying to read the first page of my thesis you wont grasp anything fala sana.
Title: The Confluence of Computational Theories and Cognitive Architectures: An Examination of Emergent Paradigms in Artificial Intelligence
Abstract: This thesis investigates the intricate interplay between computational theories and cognitive architectures in the development of Artificial Intelligence (AI), emphasizing the epistemological underpinnings and ontological constraints that shape current and emergent paradigms. Through a rigorous analysis of symbolic reasoning, connectionist networks, and hybridized approaches, the research delineates the boundaries of algorithmic cognition and the limitations inherent in substrate-independent models. Furthermore, it explores the implications of Gödelian incompleteness and the Church-Turing thesis on the feasibility of constructing Artificial General Intelligence (AGI), arguing that the entropic dissipation of information within complex systems fundamentally precludes the realization of truly autonomous, self-modifying AI entities. The thesis posits that the pursuit of AGI, as traditionally conceived, may be asymptotically unachievable within the current computational frameworks, necessitating a paradigm shift toward quantum computational substrates or novel non-Turing-complete architectures.
Chapter 1: Introduction to AI and Computational Theories The inception of AI is rooted in the conceptualization of intelligence as a computational process, an idea heavily influenced by the works of Alan Turing and John von Neumann. This chapter provides an exhaustive review of the foundational theories, including the Turing Machine as an abstract model of computation and the implications of the Church-Turing thesis on the boundaries of algorithmic processes. The chapter also explores the philosophical implications of mechanistic materialism in AI, discussing how early symbolic AI paradigms were constrained by the inherent limitations of first-order predicate logic and the combinatorial explosion problem.
Chapter 2: Symbolic AI and the Limits of Formal Logic This chapter delves into the mechanics of symbolic AI, detailing the use of formal languages and rule-based systems to emulate human cognition. It critically examines the limitations of GOFAI (Good Old-Fashioned AI), particularly in the context of Searle’s Chinese Room argument and Dreyfus’ critique of symbol manipulation as a model of human thought. The chapter also investigates the computational intractability of solving NP-complete problems within symbolic systems and the implications of Gödel’s incompleteness theorems on the limits of formal logical systems in AI.
Chapter 3: Connectionism and the Rise of Subsymbolic Paradigms The third chapter transitions to connectionist approaches, emphasizing the role of neural networks and parallel distributed processing in overcoming the limitations of symbolic AI. It provides a detailed exposition of backpropagation, gradient descent, and the vanishing gradient problem within deep learning architectures. The chapter also critiques the notion of subsymbolic processing as a panacea for AI, discussing the limitations of statistical learning in capturing the semantic depth and contextual understanding inherent in human cognition.
Chapter 4: Hybrid Architectures and the Integration of Symbolic and Subsymbolic AI Building on the discussions of the previous chapters, this section explores hybrid AI systems that attempt to bridge the gap between symbolic and subsymbolic paradigms. The chapter examines the integration of logic-based reasoning with neural networks, focusing on architectures such as neuro-symbolic systems and knowledge graphs. It evaluates the efficacy of these hybrid models in tasks requiring both deep learning and symbolic reasoning, such as natural language processing and automated theorem proving.
Chapter 5: The Epistemological and Ontological Constraints on AGI This chapter addresses the theoretical limitations of constructing AGI, scrutinizing the epistemological assumptions underpinning current AI paradigms. It discusses the role of Gödelian incompleteness, the Halting problem, and the no-free-lunch theorem in constraining the development of universally intelligent systems. The chapter also explores the ontological implications of substrate independence, arguing that the dissipation of information entropy within computational systems imposes a fundamental limit on the scalability of intelligence.
Chapter 6: Toward Quantum and Non-Turing-Complete Architectures In light of the limitations discussed, the final chapter proposes a speculative exploration of alternative computational substrates, such as quantum computing and non-Turing-complete architectures. It investigates the potential of quantum entanglement, superposition, and decoherence as mechanisms for achieving scalable intelligence beyond classical computational paradigms. The chapter also explores the implications of non-Turing-complete models, such as hypercomputational theories, for the future of AI.
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Umemaliza? Kula supper ulale unono.
Yaani uko sure kuna mtu atasoma hii yote?
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